From cfd7a469d49863be68a8c2eb85a9f9cb5542d75c Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Thu, 5 Oct 2023 17:02:52 +1000 Subject: [PATCH 01/42] Added initial files before swapping to rangpur --- lesion_detection_larsmoan/README.md | 0 lesion_detection_larsmoan/data_preperation.py | 0 lesion_detection_larsmoan/model.py | 0 lesion_detection_larsmoan/run.py | 0 lesion_detection_larsmoan/slurm.sh | 15 +++++++++++++++ 5 files changed, 15 insertions(+) create mode 100644 lesion_detection_larsmoan/README.md create mode 100644 lesion_detection_larsmoan/data_preperation.py create mode 100644 lesion_detection_larsmoan/model.py create mode 100644 lesion_detection_larsmoan/run.py create mode 100644 lesion_detection_larsmoan/slurm.sh diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md new file mode 100644 index 000000000..e69de29bb diff --git a/lesion_detection_larsmoan/data_preperation.py b/lesion_detection_larsmoan/data_preperation.py new file mode 100644 index 000000000..e69de29bb diff --git a/lesion_detection_larsmoan/model.py b/lesion_detection_larsmoan/model.py new file mode 100644 index 000000000..e69de29bb diff --git a/lesion_detection_larsmoan/run.py b/lesion_detection_larsmoan/run.py new file mode 100644 index 000000000..e69de29bb diff --git a/lesion_detection_larsmoan/slurm.sh b/lesion_detection_larsmoan/slurm.sh new file mode 100644 index 000000000..334a0f5f1 --- /dev/null +++ b/lesion_detection_larsmoan/slurm.sh @@ -0,0 +1,15 @@ +#!/bin/bash +#SBATCH -N 1 +#SBATCH -n 1 +#SBATCH --job-name=RESNET_18_step +#SBATCH --cpus-per-task 1 +#SBATCH --mail-type=All +#SBATCH --mail-user=l.moan@uqconnect.edu.au +#SBATCH -o out.txt +#SBATCH -e err.txt +#SBATCH --partition=test +#SBATCH --gres=gpu:1 + +conda activate comp3710-env + +python run.py \ No newline at end of file From 9bec4a7722243f293931a59fca42e75dc69964bd Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Thu, 5 Oct 2023 17:05:06 +1000 Subject: [PATCH 02/42] skeleton for readme --- lesion_detection_larsmoan/README.md | 80 +++++++++++++++++++++++++++++ 1 file changed, 80 insertions(+) diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index e69de29bb..a330dc128 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -0,0 +1,80 @@ +Certainly! A README file for a Python machine learning project should be clear, concise, and provide all the necessary information for someone new to the project. Here's a basic skeleton: + +--- + +## Project Title + +Brief description of the project, its objectives, and its significance. + +### Table of Contents +- [Project Title](#project-title) + - [Table of Contents](#table-of-contents) + - [Installation](#installation) + - [Dataset](#dataset) + - [Usage](#usage) + - [Model Architecture](#model-architecture) + - [Training](#training) + - [Results](#results) + - [Contributing](#contributing) + - [License](#license) + - [Acknowledgements](#acknowledgements) + +### Installation + +- Prerequisites: List any pre-required libraries or software. +- Installation steps: + ```bash + pip install -r requirements.txt + ``` + +### Dataset + +- Description of the dataset. +- Source of the dataset (if it's publicly available). +- Preprocessing steps (if any). + +### Usage + +- How to use the code/model. +- Example commands: + ```bash + python your_script_name.py --arg1 value1 --arg2 value2 + ``` + +### Model Architecture + +- Description of the model(s) used. +- Reasoning behind choosing this model. +- Any reference papers or sources. + +### Training + +- Details about the training process. +- Hyperparameters used. +- Training time, hardware details (if relevant). + +### Results + +- Summarize the results obtained. +- Any metrics used (e.g., accuracy, F1-score). +- Visual results (e.g., plots, graphs) if applicable. + +### Contributing + +- Details on how others can contribute to this project. +- Any specific guidelines for contributing. + +### License + +- Licensing information. For example: + ``` + This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details. + ``` + +### Acknowledgements + +- Any credits or acknowledgments for third-party resources or researchers. + +--- + +Remember, the above is just a basic skeleton. Depending on the project's complexity and breadth, you might want to add more sections or elaborate on the existing ones. \ No newline at end of file From 17090b1ae4b3de59f5afeb1aad8524a26bd9cc98 Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Thu, 5 Oct 2023 18:13:46 +1000 Subject: [PATCH 03/42] Added dataset and fixed gitignore --- .DS_Store | Bin 0 -> 6148 bytes lesion_detection_larsmoan/model.py | 24 ++++++++++++++++++++++++ lesion_detection_larsmoan/utils.py | 18 ++++++++++++++++++ 3 files changed, 42 insertions(+) create mode 100644 .DS_Store create mode 100644 lesion_detection_larsmoan/utils.py diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..16d4b0ed67635ed56e6ac2c0e29b0a671565442d GIT binary patch literal 6148 zcmeHK%}&BV5S|5;5@NzZgU2RbAr?Y{iI)cO;MEd6sKGW6Y^+@aMGl0dSA8Mh#OHBl zcWWf*!GlK3Ofvh;&d%(1zisvh07PdtYyeaMz(OU=m9Y3ksGW38a>hd_R3CL{f&&d` z!(c6%9e&~)fFB+{D+DGl?a#^zW4i1mc26xeYET1&f0)I*+ zI|eg&M&p~4e)J}BB;zq=X1!*;F){LG@O6VF) zHL9%x3%Wj1dx4MyZF)-(+6G;NsYYx;5jqu7rwa4L5IP Date: Wed, 11 Oct 2023 15:58:00 +1000 Subject: [PATCH 04/42] Almost finished preprocessing the ISIC dataset so that it can be used with yolo architecture, just missing the testset --- .DS_Store | Bin 6148 -> 6148 bytes lesion_detection_larsmoan/dataset.py | 84 +++++++ lesion_detection_larsmoan/dataset_utils.ipynb | 218 ++++++++++++++++++ .../{model.py => modules.py} | 0 .../{data_preperation.py => predict.py} | 0 lesion_detection_larsmoan/run.py | 0 lesion_detection_larsmoan/train.py | 1 + lesion_detection_larsmoan/utils.py | 6 +- 8 files changed, 308 insertions(+), 1 deletion(-) create mode 100644 lesion_detection_larsmoan/dataset.py create mode 100644 lesion_detection_larsmoan/dataset_utils.ipynb rename lesion_detection_larsmoan/{model.py => modules.py} (100%) rename lesion_detection_larsmoan/{data_preperation.py => predict.py} (100%) delete mode 100644 lesion_detection_larsmoan/run.py create mode 100644 lesion_detection_larsmoan/train.py diff --git a/.DS_Store b/.DS_Store index 16d4b0ed67635ed56e6ac2c0e29b0a671565442d..86ea74f11ee52a6a9f7a4cb836bafae62296d353 100644 GIT binary patch delta 336 zcmZoMXfc@JFUrBdz`)4BAi%&7&tSx0z+lK=o>acsk$E|zI7o_CB2xeW diff --git a/lesion_detection_larsmoan/dataset.py b/lesion_detection_larsmoan/dataset.py new file mode 100644 index 000000000..5323b95ed --- /dev/null +++ b/lesion_detection_larsmoan/dataset.py @@ -0,0 +1,84 @@ +import os +import cv2 + +def draw_yolo_bboxes_on_image(image_path, txt_path): + """ + Draw YOLO bounding boxes from a .txt file on an image, display labels, and show the image in a size of 240x240 pixels. + """ + # Load the image + image = cv2.imread(image_path) + + # Define color mapping based on class_id + class_colors = { + 0: (0, 255, 0), # Green for Melanoma + 1: (0, 0, 255) # Red for Seborrheic Keratosis + } + + # Compute the resizing factors + resize_factor_x = 240 / image.shape[1] + resize_factor_y = 240 / image.shape[0] + + # Resize the image + resized_image = cv2.resize(image, (240, 240)) + + # Read bounding box data from the txt file + with open(txt_path, 'r') as file: + lines = file.readlines() + for line in lines: + class_id, x_center, y_center, width, height = map(float, line.strip().split()) + + # Denormalize the coordinates + x_center *= image.shape[1] + y_center *= image.shape[0] + width *= image.shape[1] + height *= image.shape[0] + + # Adjust the bounding box coordinates based on the resizing factors + x_center *= resize_factor_x + y_center *= resize_factor_y + width *= resize_factor_x + height *= resize_factor_y + + # Convert to top-left x, y coordinates + x_min = int(x_center - width / 2) + y_min = int(y_center - height / 2) + x_max = int(x_center + width / 2) + y_max = int(y_center + height / 2) + + # Define the label and color based on class_id + label = "Melanoma" if class_id == 0 else "Seborrheic Keratosis" if class_id == 1 else "Unknown" + color = class_colors.get(class_id, (255, 255, 255)) # Default color is white + + # Draw the bounding box on the resized image with the defined color + cv2.rectangle(resized_image, (x_min, y_min), (x_max, y_max), color, 2) + + # Overlay the label on the image with the same color + cv2.putText(resized_image, label, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) + + # Display the resized image with bounding boxes and labels + cv2.imshow('Image with Bounding Boxes and Labels', resized_image) + cv2.waitKey(0) + cv2.destroyAllWindows() + + + + + +def main(txt_folder_path, img_folder_path): + """ + Iterate through the .txt files in the folder and draw YOLO bounding boxes on the corresponding images. + """ + for filename in os.listdir(txt_folder_path): + if filename.endswith(".txt"): + # Construct paths to the txt file and its corresponding image + txt_path = os.path.join(txt_folder_path, filename) + image_path = os.path.join(img_folder_path, filename.replace(".txt", ".jpg")) # assuming .jpg extension for images + + # Check if the corresponding image exists + if os.path.exists(image_path): + draw_yolo_bboxes_on_image(image_path, txt_path) + +if __name__ == "__main__": + TXT_FOLDER_PATH = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/yolo_labels" + IMG_FOLDER_PATH = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/imgs" + main(TXT_FOLDER_PATH, IMG_FOLDER_PATH) diff --git a/lesion_detection_larsmoan/dataset_utils.ipynb b/lesion_detection_larsmoan/dataset_utils.ipynb new file mode 100644 index 000000000..78893f6be --- /dev/null +++ b/lesion_detection_larsmoan/dataset_utils.ipynb @@ -0,0 +1,218 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "import os\n", + "\n", + "def draw_bbox_on_image(image_path, txt_path):\n", + " \"\"\"\n", + " Draw YOLO bounding boxes from a .txt file on an image.\n", + " \"\"\"\n", + " # Load the image\n", + " image = cv2.imread(image_path)\n", + "\n", + " # Read bounding box data from the txt file\n", + " with open(txt_path, 'r') as file:\n", + " lines = file.readlines()\n", + " for line in lines:\n", + " _, x_center, y_center, width, height = map(float, line.strip().split())\n", + "\n", + " # Denormalize the coordinates\n", + " x_center *= image.shape[1]\n", + " y_center *= image.shape[0]\n", + " width *= image.shape[1]\n", + " height *= image.shape[0]\n", + "\n", + " # Convert to top-left x, y coordinates\n", + " x_min = int(x_center - width / 2)\n", + " y_min = int(y_center - height / 2)\n", + " x_max = int(x_center + width / 2)\n", + " y_max = int(y_center + height / 2)\n", + "\n", + " # Draw the bounding box on the image\n", + " cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2) # Green color for the bounding box\n", + "\n", + " # Display the image\n", + " cv2.imshow('Image with Bounding Boxes', image)\n", + " cv2.waitKey(0)\n", + " cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "def remove_superpixels(folder):\n", + " \"\"\"\n", + " Remove all superpixel files from a folder.\n", + " \"\"\"\n", + " files = [f for f in os.listdir(folder) if os.path.isfile(os.path.join(folder, f))]\n", + " for file in files:\n", + " if file.endswith('superpixels.png'):\n", + " os.remove(os.path.join(folder, file))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import cv2\n", + "import numpy as np\n", + "\n", + "def compute_yolo_bbox(binary_mask):\n", + " \"\"\"\n", + " Compute the YOLO bounding box for a binary mask.\n", + " \"\"\"\n", + " # Find the coordinates of non-zero pixels\n", + " rows, cols = np.where(binary_mask == 255)\n", + "\n", + " # Get the minimum and maximum coordinates\n", + " x_min, x_max = np.min(cols), np.max(cols)\n", + " y_min, y_max = np.min(rows), np.max(rows)\n", + "\n", + " # Compute the center, width, and height\n", + " x_center = (x_min + x_max) / 2\n", + " y_center = (y_min + y_max) / 2\n", + " width = x_max - x_min\n", + " height = y_max - y_min\n", + "\n", + " return x_center, y_center, width, height\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import cv2\n", + "import pandas as pd\n", + "\n", + "def create_yolo_labels(seg_folder_path, cls_folder_path, dst_folder_path):\n", + " \"\"\"\n", + " Iterate through the binary images in the folder, generate YOLO bounding boxes, \n", + " and determine the class from the dataframe.\n", + " \"\"\"\n", + " labels = pd.read_csv(cls_folder_path, index_col=0)\n", + "\n", + " for filename in os.listdir(seg_folder_path):\n", + " if filename.endswith(\".png\"):\n", + " # Extract image_id from filename\n", + " image_id = filename[:12]\n", + "\n", + " # Determine the class based on the dataframe\n", + " class_row = labels.loc[image_id]\n", + " if class_row['melanoma'] == 1.0:\n", + " class_id = 0\n", + " elif class_row['seborrheic_keratosis'] == 1.0:\n", + " class_id = 1\n", + " else:\n", + " class_id = 2 # Assuming a third class for 'other' or 'nevi' based on the given example\n", + "\n", + " # Load the binary image\n", + " image_path = os.path.join(seg_folder_path, filename)\n", + " image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n", + "\n", + " # Compute the YOLO bounding box\n", + " x_center, y_center, width, height = compute_yolo_bbox(image)\n", + " \n", + " # Normalize the coordinates\n", + " x_center /= image.shape[1]\n", + " y_center /= image.shape[0]\n", + " width /= image.shape[1]\n", + " height /= image.shape[0]\n", + "\n", + " # Write the bounding box to a .txt file\n", + " output_path = os.path.join(dst_folder_path, filename.replace(\".png\", \".txt\"))\n", + " with open(output_path, \"w\") as file:\n", + " file.write(f\"{class_id} {x_center} {y_center} {width} {height}\\n\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "cls_folder_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/ISIC_labels.csv\"\n", + "seg_folder_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/segmentation\"\n", + "dst_folder_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/yolo_labels\"\n", + "\n", + "create_yolo_labels(seg_folder_path, cls_folder_path, dst_folder_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "\n", + "def move_contents_to_folder(source_folder, destination_folder):\n", + " \"\"\"\n", + " Move the contents of the source folder into the destination folder.\n", + " \n", + " Parameters:\n", + " - source_folder: Path to the source folder whose contents will be moved.\n", + " - destination_folder: Path to the destination folder where the content will be moved to.\n", + " \"\"\"\n", + " \n", + " # Ensure the destination folder exists\n", + " os.makedirs(destination_folder, exist_ok=True)\n", + " \n", + " # Move content from source_folder to destination_folder\n", + " for item in os.listdir(source_folder):\n", + " source_item = os.path.join(source_folder, item)\n", + " destination_item = os.path.join(destination_folder, item)\n", + " \n", + " # Move the item to the destination\n", + " shutil.move(source_item, destination_item)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "move_contents_to_folder(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/val\", \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/train\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "comp3710-env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lesion_detection_larsmoan/model.py b/lesion_detection_larsmoan/modules.py similarity index 100% rename from lesion_detection_larsmoan/model.py rename to lesion_detection_larsmoan/modules.py diff --git a/lesion_detection_larsmoan/data_preperation.py b/lesion_detection_larsmoan/predict.py similarity index 100% rename from lesion_detection_larsmoan/data_preperation.py rename to lesion_detection_larsmoan/predict.py diff --git a/lesion_detection_larsmoan/run.py b/lesion_detection_larsmoan/run.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/lesion_detection_larsmoan/train.py b/lesion_detection_larsmoan/train.py new file mode 100644 index 000000000..5f9399c53 --- /dev/null +++ b/lesion_detection_larsmoan/train.py @@ -0,0 +1 @@ +fi \ No newline at end of file diff --git a/lesion_detection_larsmoan/utils.py b/lesion_detection_larsmoan/utils.py index 91215f26c..1ac523b62 100644 --- a/lesion_detection_larsmoan/utils.py +++ b/lesion_detection_larsmoan/utils.py @@ -15,4 +15,8 @@ def get_data_dir(): "No data directory found in the .env file or missing a .env file in the repo's root" ) p = Path(path) - return p \ No newline at end of file + return p + + +if __name__ == "__main__": + print(get_data_dir()) From 148231b8cf2895f25c374de091c924c2b086717a Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Wed, 11 Oct 2023 18:48:51 +1000 Subject: [PATCH 05/42] Added yolov7 as a submodule --- .gitmodules | 3 + lesion_detection_larsmoan/dataset_utils.ipynb | 88 +- lesion_detection_larsmoan/yolov7 | 1 + poetry-demo/README.md | 0 poetry-demo/poetry_demo/__init__.py | 0 poetry-demo/pyproject.toml | 15 + poetry-demo/tests/__init__.py | 0 poetry.lock | 789 ++++++++++++++++++ pyproject.toml | 20 + 9 files changed, 908 insertions(+), 8 deletions(-) create mode 100644 .gitmodules create mode 160000 lesion_detection_larsmoan/yolov7 create mode 100644 poetry-demo/README.md create mode 100644 poetry-demo/poetry_demo/__init__.py create mode 100644 poetry-demo/pyproject.toml create mode 100644 poetry-demo/tests/__init__.py create mode 100644 poetry.lock create mode 100644 pyproject.toml diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 000000000..a574ccd76 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "lesion_detection_larsmoan/yolov7"] + path = lesion_detection_larsmoan/yolov7 + url = https://github.com/WongKinYiu/yolov7 diff --git a/lesion_detection_larsmoan/dataset_utils.ipynb b/lesion_detection_larsmoan/dataset_utils.ipynb index 78893f6be..3fbc32c91 100644 --- a/lesion_detection_larsmoan/dataset_utils.ipynb +++ b/lesion_detection_larsmoan/dataset_utils.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -156,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -186,12 +186,84 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "move_contents_to_folder(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/val\", \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/train\")" ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(767, 1022, 3)\n", + "(2592, 3872, 3)\n", + "(1129, 1504, 3)\n", + "(768, 1024, 3)\n", + "(1129, 1504, 3)\n", + "(768, 1024, 3)\n", + "(2000, 3008, 3)\n", + "(2848, 4288, 3)\n", + "(4402, 6602, 3)\n", + "(2016, 3024, 3)\n", + "(2848, 4288, 3)\n", + "(2592, 3872, 3)\n", + "(1129, 1504, 3)\n", + "(768, 1024, 3)\n", + "(4419, 6668, 3)\n", + "(2592, 3872, 3)\n", + "(2848, 4288, 3)\n", + "(768, 1024, 3)\n", + "(2016, 3024, 3)\n", + "(2848, 4288, 3)\n", + "(768, 1024, 3)\n", + "(768, 1024, 3)\n", + "(2000, 3008, 3)\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/dataset_utils.ipynb Cell 8\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 19\u001b[0m \u001b[39mreturn\u001b[39;00m image\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m], image\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m], img\u001b[39m.\u001b[39mshape[\u001b[39m2\u001b[39m]\n\u001b[1;32m 21\u001b[0m \u001b[39mfor\u001b[39;00m file \u001b[39min\u001b[39;00m os\u001b[39m.\u001b[39mlistdir(\u001b[39m\"\u001b[39m\u001b[39m/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/imgs\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m---> 22\u001b[0m \u001b[39mprint\u001b[39m(get_image_size(os\u001b[39m.\u001b[39;49mpath\u001b[39m.\u001b[39;49mjoin(\u001b[39m\"\u001b[39;49m\u001b[39m/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/imgs\u001b[39;49m\u001b[39m\"\u001b[39;49m, file)))\n", + "\u001b[1;32m/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/dataset_utils.ipynb Cell 8\u001b[0m line \u001b[0;36m1\n\u001b[1;32m 12\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[39mGet the size of an image.\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[39m# Load the image\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m image \u001b[39m=\u001b[39m cv2\u001b[39m.\u001b[39;49mimread(image_path)\n\u001b[1;32m 18\u001b[0m \u001b[39m# Return the image size\u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[39mreturn\u001b[39;00m image\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m], image\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m], img\u001b[39m.\u001b[39mshape[\u001b[39m2\u001b[39m]\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "img_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/imgs/ISIC_0000000.jpg\"\n", + "\n", + "#Open the image and check the size\n", + "import cv2\n", + "img = cv2.imread(img_path)\n", + "\n", + "print(img.shape)\n", + "\n", + "import os\n", + "\n", + "def get_image_size(image_path):\n", + " \"\"\"\n", + " Get the size of an image.\n", + " \"\"\"\n", + " # Load the image\n", + " image = cv2.imread(image_path)\n", + "\n", + " # Return the image size\n", + " return image.shape[0], image.shape[1], img.shape[2]\n", + "\n", + "for file in os.listdir(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/imgs\"):\n", + " print(get_image_size(os.path.join(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/imgs\", file)))" + ] } ], "metadata": { @@ -210,7 +282,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/lesion_detection_larsmoan/yolov7 b/lesion_detection_larsmoan/yolov7 new file mode 160000 index 000000000..84932d70f --- /dev/null +++ b/lesion_detection_larsmoan/yolov7 @@ -0,0 +1 @@ +Subproject commit 84932d70fb9e2932d0a70e4a1f02a1d6dd1dd6ca diff --git a/poetry-demo/README.md b/poetry-demo/README.md new file mode 100644 index 000000000..e69de29bb diff --git a/poetry-demo/poetry_demo/__init__.py b/poetry-demo/poetry_demo/__init__.py new file mode 100644 index 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"^1.26.0" +opencv-python = "^4.8.1.78" + + +[tool.poetry.group.dev.dependencies] +ipykernel = "^6.25.2" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" From 0f7f0a3adbcb098890d09cfba31ede611e02bf7b Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Wed, 11 Oct 2023 18:49:48 +1000 Subject: [PATCH 06/42] Removed unnecessary poetry-dem --- poetry-demo/README.md | 0 poetry-demo/poetry_demo/__init__.py | 0 poetry-demo/pyproject.toml | 15 --------------- poetry-demo/tests/__init__.py | 0 4 files changed, 15 deletions(-) delete mode 100644 poetry-demo/README.md delete mode 100644 poetry-demo/poetry_demo/__init__.py delete mode 100644 poetry-demo/pyproject.toml delete mode 100644 poetry-demo/tests/__init__.py diff --git a/poetry-demo/README.md b/poetry-demo/README.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/poetry-demo/poetry_demo/__init__.py b/poetry-demo/poetry_demo/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/poetry-demo/pyproject.toml b/poetry-demo/pyproject.toml deleted file mode 100644 index c34deffe0..000000000 --- a/poetry-demo/pyproject.toml +++ /dev/null @@ -1,15 +0,0 @@ -[tool.poetry] -name = "poetry-demo" -version = "0.1.0" -description = "" -authors = ["Lars Moan "] -readme = "README.md" -packages = [{include = "poetry_demo"}] - -[tool.poetry.dependencies] -python = "^3.11" - - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" diff --git a/poetry-demo/tests/__init__.py b/poetry-demo/tests/__init__.py deleted file mode 100644 index e69de29bb..000000000 From 60dc40aff58360136bae2214b4f74ec39515c8ca Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Thu, 12 Oct 2023 17:38:21 +1000 Subject: [PATCH 07/42] Working cfg and data.yaml files for custom training with ISIC2017 dataset --- lesion_detection_larsmoan/README.md | 21 +- lesion_detection_larsmoan/dataset_utils.ipynb | 970 +++++++++++++++--- lesion_detection_larsmoan/train.py | 20 +- .../yolov7_isic_cfg.yaml | 140 +++ 4 files changed, 1017 insertions(+), 134 deletions(-) create mode 100644 lesion_detection_larsmoan/yolov7_isic_cfg.yaml diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index a330dc128..8cf44d6ea 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -1,14 +1,14 @@ -Certainly! A README file for a Python machine learning project should be clear, concise, and provide all the necessary information for someone new to the project. Here's a basic skeleton: - ---- - ## Project Title Brief description of the project, its objectives, and its significance. + + + ### Table of Contents - [Project Title](#project-title) - [Table of Contents](#table-of-contents) + - [Todo](#todo) - [Installation](#installation) - [Dataset](#dataset) - [Usage](#usage) @@ -19,6 +19,13 @@ Brief description of the project, its objectives, and its significance. - [License](#license) - [Acknowledgements](#acknowledgements) +### Todo +- [ ] Create my own training script that works on top of yolov7/train.py +- [ ] Manage a way of logging the runs to wandb +- [ ] Zip and upload dataset to gdrive, and make functions for fetching the dataset from the cluster. +- [ ] Make training scripts for use on the rangpur cluster. + + ### Installation - Prerequisites: List any pre-required libraries or software. @@ -42,10 +49,10 @@ Brief description of the project, its objectives, and its significance. ``` ### Model Architecture +The model and architecture used in this project is the open source yolov7 model: + +https://github.com/WongKinYiu/yolov7 -- Description of the model(s) used. -- Reasoning behind choosing this model. -- Any reference papers or sources. ### Training diff --git a/lesion_detection_larsmoan/dataset_utils.ipynb b/lesion_detection_larsmoan/dataset_utils.ipynb index 3fbc32c91..f781b968f 100644 --- a/lesion_detection_larsmoan/dataset_utils.ipynb +++ b/lesion_detection_larsmoan/dataset_utils.ipynb @@ -2,71 +2,36 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "import cv2\n", "import os\n", - "\n", - "def draw_bbox_on_image(image_path, txt_path):\n", + "def remove_superpixels(folder):\n", " \"\"\"\n", - " Draw YOLO bounding boxes from a .txt file on an image.\n", + " Remove all superpixel files from a folder.\n", " \"\"\"\n", - " # Load the image\n", - " image = cv2.imread(image_path)\n", - "\n", - " # Read bounding box data from the txt file\n", - " with open(txt_path, 'r') as file:\n", - " lines = file.readlines()\n", - " for line in lines:\n", - " _, x_center, y_center, width, height = map(float, line.strip().split())\n", - "\n", - " # Denormalize the coordinates\n", - " x_center *= image.shape[1]\n", - " y_center *= image.shape[0]\n", - " width *= image.shape[1]\n", - " height *= image.shape[0]\n", - "\n", - " # Convert to top-left x, y coordinates\n", - " x_min = int(x_center - width / 2)\n", - " y_min = int(y_center - height / 2)\n", - " x_max = int(x_center + width / 2)\n", - " y_max = int(y_center + height / 2)\n", - "\n", - " # Draw the bounding box on the image\n", - " cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2) # Green color for the bounding box\n", - "\n", - " # Display the image\n", - " cv2.imshow('Image with Bounding Boxes', image)\n", - " cv2.waitKey(0)\n", - " cv2.destroyAllWindows()" + " files = [f for f in os.listdir(folder) if os.path.isfile(os.path.join(folder, f))]\n", + " for file in files:\n", + " if file.endswith('superpixels.png'):\n", + " os.remove(os.path.join(folder, file))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "def remove_superpixels(folder):\n", - " \"\"\"\n", - " Remove all superpixel files from a folder.\n", - " \"\"\"\n", - " files = [f for f in os.listdir(folder) if os.path.isfile(os.path.join(folder, f))]\n", - " for file in files:\n", - " if file.endswith('superpixels.png'):\n", - " os.remove(os.path.join(folder, file))" + "remove_superpixels(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC-2017_Test_v2_Data/\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ - "import os\n", "import cv2\n", "import numpy as np\n", "\n", @@ -87,12 +52,12 @@ " width = x_max - x_min\n", " height = y_max - y_min\n", "\n", - " return x_center, y_center, width, height\n" + " return x_center, y_center, width, height" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -100,6 +65,7 @@ "import cv2\n", "import pandas as pd\n", "\n", + "\n", "def create_yolo_labels(seg_folder_path, cls_folder_path, dst_folder_path):\n", " \"\"\"\n", " Iterate through the binary images in the folder, generate YOLO bounding boxes, \n", @@ -143,126 +109,878 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "cls_folder_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/ISIC_labels.csv\"\n", - "seg_folder_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/segmentation\"\n", - "dst_folder_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/yolo_labels\"\n", + "cls_folder_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC-2017_Test_v2_Data/ISIC-2017_Test_v2_Part3_GroundTruth.csv\"\n", + "seg_folder_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC-2017_Test_v2_Part1_GroundTruth/\"\n", + "dst_folder_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC-2017_Test_v2_Data\"\n", "\n", "create_yolo_labels(seg_folder_path, cls_folder_path, dst_folder_path)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "import os\n", - "import shutil\n", "\n", - "def move_contents_to_folder(source_folder, destination_folder):\n", - " \"\"\"\n", - " Move the contents of the source folder into the destination folder.\n", - " \n", - " Parameters:\n", - " - source_folder: Path to the source folder whose contents will be moved.\n", - " - destination_folder: Path to the destination folder where the content will be moved to.\n", - " \"\"\"\n", - " \n", - " # Ensure the destination folder exists\n", - " os.makedirs(destination_folder, exist_ok=True)\n", + "def remove_segmentation_suffix(folder_path):\n", + " # List all files in the folder\n", + " all_files = os.listdir(folder_path)\n", " \n", - " # Move content from source_folder to destination_folder\n", - " for item in os.listdir(source_folder):\n", - " source_item = os.path.join(source_folder, item)\n", - " destination_item = os.path.join(destination_folder, item)\n", + " # Process each file\n", + " for filename in all_files:\n", + " file_path = os.path.join(folder_path, filename)\n", " \n", - " # Move the item to the destination\n", - " shutil.move(source_item, destination_item)\n" + " # Check if it's a file (not a subfolder)\n", + " if os.path.isfile(file_path):\n", + " # Check if filename ends with \"segmentation\"\n", + " if filename.endswith(\"_.txt\"):\n", + " # Determine the new filename without the \"segmentation\" suffix\n", + " new_filename = filename.replace(\"_\", \"\")\n", + " new_file_path = os.path.join(folder_path, new_filename)\n", + " \n", + " # Rename the file\n", + " os.rename(file_path, new_file_path)\n", + "\n", + "# Example usage:\n", + "# remove_segmentation_suffix('./some_folder_path')\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "move_contents_to_folder(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/val\", \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/train\")" + "remove_segmentation_suffix(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/data\")" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "def check_missing_txt_files(folder_path):\n", + " # List all files in the folder\n", + " all_files = os.listdir(folder_path)\n", + " \n", + " # Separate images and txt files\n", + " image_files = [f for f in all_files if f.endswith(('.jpg', '.jpeg', '.png'))]\n", + " txt_files = [f for f in all_files if f.endswith('.txt')]\n", + " i = 0\n", + " # Check for missing txt files\n", + " for img_file in image_files:\n", + " corresponding_txt_file = os.path.splitext(img_file)[0] + '.txt'\n", + " if corresponding_txt_file not in txt_files:\n", + " i += 1\n", + " print(f\"Missing txt file for: {img_file}\")\n", + " print(\"Number of missing txt files: \", i)\n", + "# Example usage:\n", + "# check_missing_txt_files('./some_folder_path')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(767, 1022, 3)\n", - "(2592, 3872, 3)\n", - "(1129, 1504, 3)\n", - "(768, 1024, 3)\n", - "(1129, 1504, 3)\n", - "(768, 1024, 3)\n", - "(2000, 3008, 3)\n", - "(2848, 4288, 3)\n", - "(4402, 6602, 3)\n", - "(2016, 3024, 3)\n", - "(2848, 4288, 3)\n", - "(2592, 3872, 3)\n", - "(1129, 1504, 3)\n", - "(768, 1024, 3)\n", - "(4419, 6668, 3)\n", - "(2592, 3872, 3)\n", - "(2848, 4288, 3)\n", - "(768, 1024, 3)\n", - "(2016, 3024, 3)\n", - "(2848, 4288, 3)\n", - "(768, 1024, 3)\n", - "(768, 1024, 3)\n", - "(2000, 3008, 3)\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/dataset_utils.ipynb Cell 8\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 19\u001b[0m \u001b[39mreturn\u001b[39;00m image\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m], image\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m], img\u001b[39m.\u001b[39mshape[\u001b[39m2\u001b[39m]\n\u001b[1;32m 21\u001b[0m \u001b[39mfor\u001b[39;00m file \u001b[39min\u001b[39;00m os\u001b[39m.\u001b[39mlistdir(\u001b[39m\"\u001b[39m\u001b[39m/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/imgs\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m---> 22\u001b[0m \u001b[39mprint\u001b[39m(get_image_size(os\u001b[39m.\u001b[39;49mpath\u001b[39m.\u001b[39;49mjoin(\u001b[39m\"\u001b[39;49m\u001b[39m/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/imgs\u001b[39;49m\u001b[39m\"\u001b[39;49m, file)))\n", - "\u001b[1;32m/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/dataset_utils.ipynb Cell 8\u001b[0m line \u001b[0;36m1\n\u001b[1;32m 12\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[39mGet the size of an image.\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[39m# Load the image\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m image \u001b[39m=\u001b[39m cv2\u001b[39m.\u001b[39;49mimread(image_path)\n\u001b[1;32m 18\u001b[0m \u001b[39m# Return the image size\u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[39mreturn\u001b[39;00m image\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m], image\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m], img\u001b[39m.\u001b[39mshape[\u001b[39m2\u001b[39m]\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "Missing txt file for: ISIC_0015372.jpg\n", + "Missing txt file for: ISIC_0012151.jpg\n", + "Missing txt file for: ISIC_0013529.jpg\n", + "Missing txt file for: ISIC_0013501.jpg\n", + "Missing txt file for: ISIC_0012757.jpg\n", + "Missing txt file for: ISIC_0015986.jpg\n", + "Missing txt file for: ISIC_0014454.jpg\n", + "Missing txt file for: ISIC_0015992.jpg\n", + "Missing txt file for: ISIC_0015004.jpg\n", + "Missing txt file for: ISIC_0015945.jpg\n", + "Missing txt file for: ISIC_0015951.jpg\n", + "Missing txt file for: ISIC_0015979.jpg\n", + "Missing txt file for: ISIC_0016055.jpg\n", + "Missing txt file for: ISIC_0004337.jpg\n", + "Missing txt file for: ISIC_0016041.jpg\n", + "Missing txt file for: ISIC_0016069.jpg\n", + "Missing txt file for: ISIC_0015206.jpg\n", + "Missing txt file for: ISIC_0013663.jpg\n", + "Missing txt file for: ISIC_0015212.jpg\n", + "Missing txt file for: ISIC_0014695.jpg\n", + "Missing txt file for: ISIC_0008524.jpg\n", + "Missing txt file for: ISIC_0013104.jpg\n", + "Missing txt file for: ISIC_0016068.jpg\n", + "Missing txt file for: ISIC_0015207.jpg\n", + "Missing txt file for: ISIC_0016040.jpg\n", + "Missing txt file for: ISIC_0014643.jpg\n", + "Missing txt file for: ISIC_0016054.jpg\n", + "Missing txt file for: ISIC_0012965.jpg\n", + "Missing txt file for: ISIC_0015978.jpg\n", + "Missing txt file for: ISIC_0015950.jpg\n", + "Missing txt file for: ISIC_0012959.jpg\n", + "Missing txt file for: ISIC_0015944.jpg\n", + "Missing txt file for: ISIC_0015011.jpg\n", + "Missing txt file for: ISIC_0015993.jpg\n", + "Missing txt file for: ISIC_0015987.jpg\n", + "Missing txt file for: ISIC_0014284.jpg\n", + "Missing txt file for: ISIC_0015603.jpg\n", + "Missing txt file for: ISIC_0015171.jpg\n", + "Missing txt file for: ISIC_0012178.jpg\n", + "Missing txt file for: ISIC_0015617.jpg\n", + "Missing txt file for: ISIC_0014910.jpg\n", + "Missing txt file for: ISIC_0014938.jpg\n", + "Missing txt file for: ISIC_0013925.jpg\n", + "Missing txt file for: ISIC_0015401.jpg\n", + "Missing txt file for: ISIC_0013070.jpg\n", + "Missing txt file for: ISIC_0013702.jpg\n", + "Missing txt file for: ISIC_0003539.jpg\n", + "Missing txt file for: ISIC_0012434.jpg\n", + "Missing txt file for: ISIC_0013072.jpg\n", 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ISIC_0014868.jpg\n", + "Missing txt file for: ISIC_0014854.jpg\n", + "Missing txt file for: ISIC_0014698.jpg\n", + "Missing txt file for: ISIC_0016064.jpg\n", + "Missing txt file for: ISIC_0016070.jpg\n", + "Missing txt file for: ISIC_0012216.jpg\n", + "Missing txt file for: ISIC_0016058.jpg\n", + "Missing txt file for: ISIC_0015237.jpg\n", + "Missing txt file for: ISIC_0014129.jpg\n", + "Missing txt file for: ISIC_0015223.jpg\n", + "Missing txt file for: ISIC_0014883.jpg\n", + "Missing txt file for: ISIC_0007141.jpg\n", + "Missing txt file for: ISIC_0015974.jpg\n", + "Missing txt file for: ISIC_0015960.jpg\n", + "Missing txt file for: ISIC_0012941.jpg\n", + "Missing txt file for: ISIC_0015948.jpg\n", + "Missing txt file for: ISIC_0012955.jpg\n", + "Missing txt file for: ISIC_0015009.jpg\n", + "Missing txt file for: ISIC_0003657.jpg\n", + "Missing txt file for: ISIC_0015035.jpg\n", + "Missing txt file for: ISIC_0015021.jpg\n", + "Missing txt file for: ISIC_0013281.jpg\n", + "Missing txt file for: ISIC_0014288.jpg\n", + "Missing txt file for: ISIC_0012160.jpg\n", + "Missing txt file for: ISIC_0013518.jpg\n", + "Missing txt file for: ISIC_0015627.jpg\n", + "Missing txt file for: ISIC_0013242.jpg\n", + "Missing txt file for: ISIC_0015155.jpg\n", + "Missing txt file for: ISIC_0015419.jpg\n", + "Missing txt file for: ISIC_0015357.jpg\n", + "Number of missing txt files: 750\n" ] } ], "source": [ - "img_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/imgs/ISIC_0000000.jpg\"\n", - "\n", - "#Open the image and check the size\n", - "import cv2\n", - "img = cv2.imread(img_path)\n", - "\n", - "print(img.shape)\n", - "\n", + "check_missing_txt_files(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [], + "source": [ "import os\n", + "import pandas as pd\n", + "import shutil\n", "\n", - "def get_image_size(image_path):\n", - " \"\"\"\n", - " Get the size of an image.\n", - " \"\"\"\n", - " # Load the image\n", - " image = cv2.imread(image_path)\n", + "# Create the destination directory if it doesn't exist\n", + "def ensure_directory_exists(directory_path):\n", + " if not os.path.exists(directory_path):\n", + " os.makedirs(directory_path)\n", + "\n", + "df = pd.read_csv(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/validation_split.csv\")\n", + "files = df.image_id.values\n", "\n", - " # Return the image size\n", - " return image.shape[0], image.shape[1], img.shape[2]\n", + "# Ensure that the train directory exists\n", + "ensure_directory_exists(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/val\")\n", "\n", - "for file in os.listdir(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/imgs\"):\n", - " print(get_image_size(os.path.join(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/imgs\", file)))" + "for file in files:\n", + " # Check if it exists\n", + " if os.path.exists(f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/data/{file}.jpg\"):\n", + " # Move the file to the new folder\n", + " shutil.move(f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/data/{file}.jpg\", f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/val/{file}.jpg\")\n", + " shutil.move(f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/data/{file}.txt\", f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/val/{file}.txt\")\n" ] } ], diff --git a/lesion_detection_larsmoan/train.py b/lesion_detection_larsmoan/train.py index 5f9399c53..24acdcf25 100644 --- a/lesion_detection_larsmoan/train.py +++ b/lesion_detection_larsmoan/train.py @@ -1 +1,19 @@ -fi \ No newline at end of file +from yolov7.train import train + +opt = { + 'epochs': 300, + 'batch_size': 32, + 'cfg': 'cfg/training/yolov7.yaml', + 'data': 'data/coco.yaml', + 'weights': '', + 'device': '0', + 'name': 'yolov7', + 'img_size': [640, 640], + 'project': 'runs/train', + 'entity': None, + 'entity_name': None, + 'bucket': None, + 'cache_images': False, +} + +train(hyp, opt, device=opt['device']) \ No newline at end of file diff --git a/lesion_detection_larsmoan/yolov7_isic_cfg.yaml b/lesion_detection_larsmoan/yolov7_isic_cfg.yaml new file mode 100644 index 000000000..f631e92a6 --- /dev/null +++ b/lesion_detection_larsmoan/yolov7_isic_cfg.yaml @@ -0,0 +1,140 @@ +# parameters +nc: 3 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# yolov7 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Conv, [64, 3, 1]], + + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 11 + + [-1, 1, MP, []], + [-1, 1, Conv, [128, 1, 1]], + [-3, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 16-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 24 + + [-1, 1, MP, []], + [-1, 1, Conv, [256, 1, 1]], + [-3, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 29-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 37 + + [-1, 1, MP, []], + [-1, 1, Conv, [512, 1, 1]], + [-3, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 42-P5/32 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 50 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [512]], # 51 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [37, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 63 + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [24, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 75 + + [-1, 1, MP, []], + [-1, 1, Conv, [128, 1, 1]], + [-3, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 2]], + [[-1, -3, 63], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 88 + + [-1, 1, MP, []], + [-1, 1, Conv, [256, 1, 1]], + [-3, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 2]], + [[-1, -3, 51], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 101 + + [75, 1, RepConv, [256, 3, 1]], + [88, 1, RepConv, [512, 3, 1]], + [101, 1, RepConv, [1024, 3, 1]], + + [[102,103,104], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] \ No newline at end of file From 9d079cead1dd15f3ca4c8b06e315193545dceef9 Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Thu, 12 Oct 2023 17:41:36 +1000 Subject: [PATCH 08/42] Removing Ds store --- poetry.lock | 1550 ++++++++++++++++++++++++++++++++++++++++++++++-- pyproject.toml | 16 +- 2 files changed, 1528 insertions(+), 38 deletions(-) diff --git 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+matplotlib = ">=3.2.2" +pillow = ">=7.1.2" +requests = ">=2.23.0" +scipy = ">=1.4.1" +torchvision = ">=0.8.1,<0.13.0 || >0.13.0" +tqdm = ">=4.41.0" +protobuf = "<4.21.3" +pandas = ">=1.1.4" +seaborn = ">=0.11.0" [tool.poetry.group.dev.dependencies] From 61185e5e1ceb1eb9e9a00109d9fc307bbde04ff7 Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Thu, 12 Oct 2023 17:42:15 +1000 Subject: [PATCH 09/42] Removing Ds store --- .DS_Store | Bin 6148 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 .DS_Store diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 86ea74f11ee52a6a9f7a4cb836bafae62296d353..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHK&1%~~5T141W`#-dp(TM_1U&^3Du;v=dQlwb&}&tiLt9+SGKq++6_#vMj6vj< zyilK@uMqM$o!MQN1}orG3CRr1exv!>^?nk&9sm&Sanu560)Rv%tT{MT2*pX4q-H%7 z6nc&>?1Kj_I0~mr+3a|X4A8rC;3EX6C-Bev%d;?1Ax3|NSrX@k=iQ3hTK&b#b-6AZ z@_TThra{pw#(BFp`btlql}f^e-3!0OgK4+<`b4EgFHQ#|T@XhDO!;;er;(br)i{lE zT^pEybfnX5Zq8)-(G_eY2Gxg+1aefQy0_c9r#>e{d=@Wa!xVQ~RB zXjE)0%KJ%(Ka|oc!pV6n=&ND2n58;vs5%|Xj@*8AceQi*`}m>EXvNIzVwIIA!6;2s zdXB5n#w&I($%~};nGNE8I>8JUW`G%B2C5jaN2hF51=+_8Fayt;0XiRCR6^fkX;2>> zIOrAtv5synXj3miHLgY9VrdW~D8iH?no?m~3}MRA@7g@yVrkHngRsqquqzAOp$NS? z&hP4U5WYbknE__tJ_Bp!u}Sy;>BIg1{UUy12AF{-#ek@Hf=(N^Wbf9c&Cy-!qW(Z7 qp}f-I-x4(RRgAfG6>p(xLBC4|qHnP@h#nOFBcN&Eff@L(4EzCM9dd;L From e41ad7ea4a436e1db3677f7039ffd71eb8e8ec46 Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Fri, 13 Oct 2023 13:01:19 +1000 Subject: [PATCH 10/42] Added bash training script and ability to download downsampled dataset from gdrive --- lesion_detection_larsmoan/README.md | 1 + lesion_detection_larsmoan/dataset.py | 40 +++++++++++- lesion_detection_larsmoan/dataset_utils.ipynb | 38 +++++++++++ lesion_detection_larsmoan/downsample.py | 64 +++++++++++++++++++ lesion_detection_larsmoan/hyp.scratch.p5.yaml | 31 +++++++++ lesion_detection_larsmoan/hyp.scratch.p6.yaml | 31 +++++++++ lesion_detection_larsmoan/run_custom_train.sh | 6 ++ lesion_detection_larsmoan/train.py | 19 ------ 8 files changed, 210 insertions(+), 20 deletions(-) create mode 100644 lesion_detection_larsmoan/downsample.py create mode 100644 lesion_detection_larsmoan/hyp.scratch.p5.yaml create mode 100644 lesion_detection_larsmoan/hyp.scratch.p6.yaml create mode 100755 lesion_detection_larsmoan/run_custom_train.sh delete mode 100644 lesion_detection_larsmoan/train.py diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index 8cf44d6ea..6684ce0bf 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -21,6 +21,7 @@ Brief description of the project, its objectives, and its significance. ### Todo - [ ] Create my own training script that works on top of yolov7/train.py +- [ ] Make a downsampled version of the dataset. - [ ] Manage a way of logging the runs to wandb - [ ] Zip and upload dataset to gdrive, and make functions for fetching the dataset from the cluster. - [ ] Make training scripts for use on the rangpur cluster. diff --git a/lesion_detection_larsmoan/dataset.py b/lesion_detection_larsmoan/dataset.py index 5323b95ed..4a8f24ee4 100644 --- a/lesion_detection_larsmoan/dataset.py +++ b/lesion_detection_larsmoan/dataset.py @@ -1,5 +1,7 @@ import os import cv2 +import zipfile +import gdown def draw_yolo_bboxes_on_image(image_path, txt_path): """ @@ -62,6 +64,36 @@ def draw_yolo_bboxes_on_image(image_path, txt_path): +def download_and_unzip(folder_path, download_url): + """ + Checks if a folder exists at the specified path. + If not, downloads a zip file from the provided URL using gdown and unzips it. + + Parameters: + - folder_path: Path to the folder you want to check. + - download_url: The gdown URL of the zip file to download if the folder doesn't exist. + """ + + # Check if folder exists + if not os.path.exists(folder_path): + print(f"Folder {folder_path} does not exist. Downloading the zip file...") + + # Download zip file using gdown + output = "temp_download.zip" + gdown.download(download_url, output, quiet=False) + + # Unzip the downloaded file + print("Unzipping the downloaded file...") + with zipfile.ZipFile(output, 'r') as zip_ref: + zip_ref.extractall(folder_path) + + # Delete the downloaded zip file + os.remove(output) + print(f"Files have been extracted to {folder_path}") + else: + print(f"Folder {folder_path} already exists.") + + def main(txt_folder_path, img_folder_path): @@ -81,4 +113,10 @@ def main(txt_folder_path, img_folder_path): if __name__ == "__main__": TXT_FOLDER_PATH = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/yolo_labels" IMG_FOLDER_PATH = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/imgs" - main(TXT_FOLDER_PATH, IMG_FOLDER_PATH) + #main(TXT_FOLDER_PATH, IMG_FOLDER_PATH) + + + dataset_folder = "path/to/check/folder" + url = "https://drive.google.com/19HcgRBuXyhzxsukE2jHPnBkC0hEk_aMJ" + download_and_unzip(dataset_folder, url) + diff --git a/lesion_detection_larsmoan/dataset_utils.ipynb b/lesion_detection_larsmoan/dataset_utils.ipynb index f781b968f..a622f4aa1 100644 --- a/lesion_detection_larsmoan/dataset_utils.ipynb +++ b/lesion_detection_larsmoan/dataset_utils.ipynb @@ -982,6 +982,44 @@ " shutil.move(f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/data/{file}.jpg\", f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/val/{file}.jpg\")\n", " shutil.move(f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/data/{file}.txt\", f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/val/{file}.txt\")\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from PIL import Image\n", + "\n", + "def downsample_images(folder_path, output_folder):\n", + " # Create the output folder if it doesn't exist\n", + " if not os.path.exists(output_folder):\n", + " os.makedirs(output_folder)\n", + "\n", + " # Iterate over all files in the folder\n", + " for filename in os.listdir(folder_path):\n", + " filepath = os.path.join(folder_path, filename)\n", + " \n", + " # Check if the file is an image\n", + " try:\n", + " with Image.open(filepath) as img:\n", + " # Downsample by a factor of 2x\n", + " new_size = (img.width // 2, img.height // 2)\n", + " resized_img = img.resize(new_size, Image.ANTIALIAS)\n", + " \n", + " # Save the downsampled image to the output folder\n", + " resized_img.save(os.path.join(output_folder, filename))\n", + " print(f\"Downsampled {filename}\")\n", + " \n", + " except Exception as e:\n", + " print(f\"Error processing {filename}: {e}\")\n", + "\n", + "# Provide your folder path and output folder path here\n", + "folder_path = \"/path/to/your/folder\"\n", + "output_folder = os.path.join(folder_path, \"downsized\")\n", + "downsample_images(folder_path, output_folder)\n" + ] } ], "metadata": { diff --git a/lesion_detection_larsmoan/downsample.py b/lesion_detection_larsmoan/downsample.py new file mode 100644 index 000000000..b477fd232 --- /dev/null +++ b/lesion_detection_larsmoan/downsample.py @@ -0,0 +1,64 @@ +import os +from PIL import Image +import os +import shutil +from tqdm import tqdm + +def downsample_images(source_folder, target_folder, n): + # Ensure the target directory exists + if not os.path.exists(target_folder): + os.makedirs(target_folder) + + # Get all files from the source folder + files = [f for f in os.listdir(source_folder) if os.path.isfile(os.path.join(source_folder, f)) and f.endswith('.jpg')] + + for file in tqdm(files, desc="Processing images"): + file_path = os.path.join(source_folder, file) + + # Try to open the file with PIL + try: + with Image.open(file_path) as img: + # Get new dimensions + width, height = img.size + new_dimensions = (width//n, height//n) + + # Resize and save + resized_img = img.resize(new_dimensions) + resized_img.save(os.path.join(target_folder, file)) + + except Exception as e: + print(f"Failed to process {file}. Error: {e}") + + + + +def copy_txt_files(source_folder, target_folder): + """ + Copy all .txt files from source_folder to target_folder. + + Parameters: + - source_folder: Path to the source directory + - target_folder: Path to the target directory + """ + + # Ensure the target directory exists + if not os.path.exists(target_folder): + os.makedirs(target_folder) + + # Iterate through all files in the source folder + for file_name in os.listdir(source_folder): + if file_name.endswith('.txt'): + source_file_path = os.path.join(source_folder, file_name) + target_file_path = os.path.join(target_folder, file_name) + + shutil.copy2(source_file_path, target_file_path) + #print(f"Copied {file_name} to {target_folder}") + + + +if __name__ == "__main__": + source = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/val" + target = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/val_downsampled" + n = 6 # Downsample by factor of 2 + downsample_images(source, target, n) + copy_txt_files(source_folder=source, target_folder=target) diff --git a/lesion_detection_larsmoan/hyp.scratch.p5.yaml b/lesion_detection_larsmoan/hyp.scratch.p5.yaml new file mode 100644 index 000000000..a409bac3d --- /dev/null +++ b/lesion_detection_larsmoan/hyp.scratch.p5.yaml @@ -0,0 +1,31 @@ +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.2 # image translation (+/- fraction) +scale: 0.9 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.15 # image mixup (probability) +copy_paste: 0.0 # image copy paste (probability) +paste_in: 0.15 # image copy paste (probability), use 0 for faster training +loss_ota: 1 # use ComputeLossOTA, use 0 for faster training \ No newline at end of file diff --git a/lesion_detection_larsmoan/hyp.scratch.p6.yaml b/lesion_detection_larsmoan/hyp.scratch.p6.yaml new file mode 100644 index 000000000..192d0d5dd --- /dev/null +++ b/lesion_detection_larsmoan/hyp.scratch.p6.yaml @@ -0,0 +1,31 @@ +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.2 # image translation (+/- fraction) +scale: 0.9 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.15 # image mixup (probability) +copy_paste: 0.0 # image copy paste (probability) +paste_in: 0.15 # image copy paste (probability), use 0 for faster training +loss_ota: 1 # use ComputeLossOTA, use 0 for faster training \ No newline at end of file diff --git a/lesion_detection_larsmoan/run_custom_train.sh b/lesion_detection_larsmoan/run_custom_train.sh new file mode 100755 index 000000000..1f427cbfc --- /dev/null +++ b/lesion_detection_larsmoan/run_custom_train.sh @@ -0,0 +1,6 @@ +#!/bin/bash + +# Activate Python environment (optional) +source /Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/.venv/bin/activate + +python3 yolov7/train.py --batch-size 2 --data data/ISIC_2017/isic.yaml --img 32 32 --cfg yolov7_isic_cfg.yaml --weights '' --name yolov7 --hyp hyp.scratch.p5.yaml \ No newline at end of file diff --git a/lesion_detection_larsmoan/train.py b/lesion_detection_larsmoan/train.py deleted file mode 100644 index 24acdcf25..000000000 --- a/lesion_detection_larsmoan/train.py +++ /dev/null @@ -1,19 +0,0 @@ -from yolov7.train import train - -opt = { - 'epochs': 300, - 'batch_size': 32, - 'cfg': 'cfg/training/yolov7.yaml', - 'data': 'data/coco.yaml', - 'weights': '', - 'device': '0', - 'name': 'yolov7', - 'img_size': [640, 640], - 'project': 'runs/train', - 'entity': None, - 'entity_name': None, - 'bucket': None, - 'cache_images': False, -} - -train(hyp, opt, device=opt['device']) \ No newline at end of file From 2c5318f9c588019035bdc883be55fd5818e74d44 Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Mon, 16 Oct 2023 14:56:25 +1000 Subject: [PATCH 11/42] Added stat function for downsampled dataset and easier visualizing of the yolo labels --- lesion_detection_larsmoan/dataset.py | 70 +++++- poetry.lock | 336 ++++++++++++++++++++++++++- pyproject.toml | 4 +- 3 files changed, 398 insertions(+), 12 deletions(-) diff --git a/lesion_detection_larsmoan/dataset.py b/lesion_detection_larsmoan/dataset.py index 4a8f24ee4..e03c30e68 100644 --- a/lesion_detection_larsmoan/dataset.py +++ b/lesion_detection_larsmoan/dataset.py @@ -96,27 +96,77 @@ def download_and_unzip(folder_path, download_url): -def main(txt_folder_path, img_folder_path): +def visualize_labels(dataset_folder): """ Iterate through the .txt files in the folder and draw YOLO bounding boxes on the corresponding images. """ - for filename in os.listdir(txt_folder_path): + for filename in os.listdir(dataset_folder): if filename.endswith(".txt"): # Construct paths to the txt file and its corresponding image - txt_path = os.path.join(txt_folder_path, filename) - image_path = os.path.join(img_folder_path, filename.replace(".txt", ".jpg")) # assuming .jpg extension for images + txt_path = os.path.join(dataset_folder, filename) + image_path = os.path.join(dataset_folder, filename.replace(".txt", ".jpg")) # assuming .jpg extension for images # Check if the corresponding image exists if os.path.exists(image_path): draw_yolo_bboxes_on_image(image_path, txt_path) +import os +from PIL import Image + +def get_image_dimensions(image_path): + """Return the dimensions of the image.""" + with Image.open(image_path) as img: + return img.size + +def get_all_image_dimensions_in_folder(folder_path, valid_extensions={"jpg", "jpeg", "png"}): + """Return a list of dimensions of all images in the folder.""" + + all_files = [f for f in os.listdir(folder_path) if f.lower().endswith(tuple(valid_extensions))] + + all_dimensions = [] + + for file_name in all_files: + file_path = os.path.join(folder_path, file_name) + try: + dimensions = get_image_dimensions(file_path) + all_dimensions.append(dimensions) + except Exception as e: + # This means the file is probably not an image or there's an issue reading it + print(f"Could not process {file_name}. Error: {e}") + + return all_dimensions + +def analyze_image_dimensions(folder_path): + dimensions = get_all_image_dimensions_in_folder(folder_path) + + if not dimensions: + print("No valid images found in the specified folder.") + return + + widths, heights = zip(*dimensions) + + # Calculating statistics + max_width, max_height = max(widths), max(heights) + min_width, min_height = min(widths), min(heights) + mean_width, mean_height = sum(widths) / len(widths), sum(heights) / len(heights) + + # Since mean and average are the same, we'll just display the mean + print(f"Max Width: {max_width}, Max Height: {max_height}") + print(f"Min Width: {min_width}, Min Height: {min_height}") + print(f"Mean/Average Width: {mean_width:.2f}, Mean/Average Height: {mean_height:.2f}") + + + + if __name__ == "__main__": - TXT_FOLDER_PATH = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/yolo_labels" - IMG_FOLDER_PATH = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/imgs" - #main(TXT_FOLDER_PATH, IMG_FOLDER_PATH) + folder = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017_downsampled/train_downsampled" + #visualize_labels(folder) + + analyze_image_dimensions(folder) - dataset_folder = "path/to/check/folder" - url = "https://drive.google.com/19HcgRBuXyhzxsukE2jHPnBkC0hEk_aMJ" - download_and_unzip(dataset_folder, url) + """ dataset_folder = "path/to/check/folder" + url = "https://drive.google.com/uc?id=19HcgRBuXyhzxsukE2jHPnBkC0hEk_aMJ" """ + + #download_and_unzip(dataset_folder, url) diff --git a/poetry.lock b/poetry.lock index 71ebce39e..a65ea4d1c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -11,6 +11,17 @@ files = [ {file = "absl_py-2.0.0-py3-none-any.whl", hash = "sha256:9a28abb62774ae4e8edbe2dd4c49ffcd45a6a848952a5eccc6a49f3f0fc1e2f3"}, ] +[[package]] +name = "appdirs" +version = "1.4.4" +description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." +optional = false +python-versions = "*" +files = [ + {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"}, + {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"}, +] + [[package]] name = "appnope" version = "0.1.3" @@ -50,6 +61,24 @@ files = [ {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"}, ] +[[package]] +name = "beautifulsoup4" +version = "4.12.2" +description = "Screen-scraping library" +optional = false +python-versions = ">=3.6.0" +files = [ + {file = "beautifulsoup4-4.12.2-py3-none-any.whl", hash = "sha256:bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a"}, + {file = "beautifulsoup4-4.12.2.tar.gz", hash = "sha256:492bbc69dca35d12daac71c4db1bfff0c876c00ef4a2ffacce226d4638eb72da"}, +] + +[package.dependencies] +soupsieve = ">1.2" + +[package.extras] +html5lib = ["html5lib"] +lxml = ["lxml"] + [[package]] name = "cachetools" version = "5.3.1" @@ -235,6 +264,20 @@ files = [ {file = "charset_normalizer-3.3.0-py3-none-any.whl", hash = "sha256:e46cd37076971c1040fc8c41273a8b3e2c624ce4f2be3f5dfcb7a430c1d3acc2"}, ] +[[package]] +name = "click" +version = "8.1.7" +description = "Composable command line interface toolkit" +optional = false +python-versions = ">=3.7" +files = [ + {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"}, + {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"}, +] + +[package.dependencies] +colorama = {version = "*", markers = "platform_system == \"Windows\""} + [[package]] name = "colorama" version = "0.4.6" @@ -447,6 +490,20 @@ files = [ {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"}, ] +[[package]] +name = "docker-pycreds" +version = "0.4.0" +description = "Python bindings for the docker credentials store API" +optional = false +python-versions = "*" +files = [ + {file = "docker-pycreds-0.4.0.tar.gz", hash = "sha256:6ce3270bcaf404cc4c3e27e4b6c70d3521deae82fb508767870fdbf772d584d4"}, + {file = "docker_pycreds-0.4.0-py2.py3-none-any.whl", hash = "sha256:7266112468627868005106ec19cd0d722702d2b7d5912a28e19b826c3d37af49"}, +] + +[package.dependencies] +six = ">=1.4.0" + [[package]] name = "executing" version = "2.0.0" @@ -577,6 +634,55 @@ smb = ["smbprotocol"] ssh = ["paramiko"] tqdm = ["tqdm"] +[[package]] +name = "gdown" +version = "4.7.1" +description = "Google Drive direct download of big files." +optional = false +python-versions = "*" +files = [ + {file = "gdown-4.7.1-py3-none-any.whl", hash = "sha256:65d495699e7c2c61af0d0e9c32748fb4f79abaf80d747a87456c7be14aac2560"}, + {file = "gdown-4.7.1.tar.gz", hash = "sha256:347f23769679aaf7efa73e5655270fcda8ca56be65eb84a4a21d143989541045"}, +] + +[package.dependencies] +beautifulsoup4 = "*" +filelock = "*" +requests = {version = "*", extras = ["socks"]} +six = "*" +tqdm = "*" + +[[package]] +name = "gitdb" +version = "4.0.10" +description = "Git Object Database" +optional = false +python-versions = ">=3.7" +files = [ + {file = "gitdb-4.0.10-py3-none-any.whl", hash = "sha256:c286cf298426064079ed96a9e4a9d39e7f3e9bf15ba60701e95f5492f28415c7"}, + {file = "gitdb-4.0.10.tar.gz", hash = "sha256:6eb990b69df4e15bad899ea868dc46572c3f75339735663b81de79b06f17eb9a"}, +] + +[package.dependencies] +smmap = ">=3.0.1,<6" + +[[package]] +name = "gitpython" +version = "3.1.37" +description = "GitPython is a Python library used to interact with Git repositories" +optional = false +python-versions = ">=3.7" +files = [ + {file = 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the Weights & Biases API." +optional = false +python-versions = ">=3.6" +files = [ + {file = "wandb-0.15.12-py3-none-any.whl", hash = "sha256:75c57b5bb8ddae21d45a02f644628585bdd112fea686de3177099a0996f1c41c"}, + {file = "wandb-0.15.12.tar.gz", hash = "sha256:c344d92fb8044b072a6138afd9adc5d3801ad050cf11378fe2af2fe899dcca84"}, +] + +[package.dependencies] +appdirs = ">=1.4.3" +Click = ">=7.1,<8.0.0 || >8.0.0" +docker-pycreds = ">=0.4.0" +GitPython = ">=1.0.0,<3.1.29 || >3.1.29" +pathtools = "*" +protobuf = {version = ">=3.19.0,<4.21.0 || >4.21.0,<5", markers = "python_version > \"3.9\" or sys_platform != \"linux\""} +psutil = ">=5.0.0" +PyYAML = "*" +requests = ">=2.0.0,<3" +sentry-sdk = ">=1.0.0" +setproctitle = "*" +setuptools = "*" + +[package.extras] +async = ["httpx (>=0.22.0)"] +aws = ["boto3"] +azure = ["azure-identity", "azure-storage-blob"] +gcp = ["google-cloud-storage"] +kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"] +launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "nbconvert", "nbformat", "optuna", "typing-extensions"] +media = ["bokeh", "moviepy", "numpy", "pillow", "plotly", "rdkit-pypi", "soundfile"] +models = ["cloudpickle"] +nexus = ["wandb-core (>=0.16.0b1)"] +perf = ["orjson"] +sweeps = ["sweeps (>=0.2.0)"] + [[package]] name = "wcwidth" version = "0.2.8" @@ -2264,4 +2598,4 @@ watchdog = ["watchdog (>=2.3)"] [metadata] lock-version = "2.0" python-versions = ">=3.11,<3.13" -content-hash = "a17c3b5d3b97f77fc2466b2193139d777acf73372d48d0e7670cecc6626920ca" +content-hash = "a257f0d27f8b02edf7a46d5686a64080b9bbc260f520dc234cdc244dc2b74b56" diff --git a/pyproject.toml b/pyproject.toml index 22b408f6f..6047750c3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,6 @@ torch = ">=1.7.0,<1.12.0 || >1.12.0" pyyaml = ">=5.3.1" tensorboard = ">=2.4.1" matplotlib = ">=3.2.2" -pillow = ">=7.1.2" requests = ">=2.23.0" scipy = ">=1.4.1" torchvision = ">=0.8.1,<0.13.0 || >0.13.0" @@ -22,6 +21,9 @@ tqdm = ">=4.41.0" protobuf = "<4.21.3" pandas = ">=1.1.4" seaborn = ">=0.11.0" +wandb = "^0.15.12" +pillow = "^10.0.1" +gdown = "^4.7.1" [tool.poetry.group.dev.dependencies] From 5c713bd1ef20af35f239fac9fd3347ddb0f159b9 Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Mon, 16 Oct 2023 15:05:23 +1000 Subject: [PATCH 12/42] Added slurm script for cluster --- lesion_detection_larsmoan/.gitignore | 4 ++++ lesion_detection_larsmoan/README.md | 11 ++++++----- lesion_detection_larsmoan/check_torch.py | 8 ++++++++ lesion_detection_larsmoan/downsample.py | 2 +- lesion_detection_larsmoan/install_dep.sh | 18 ++++++++++++++++++ lesion_detection_larsmoan/run_custom_train.sh | 14 ++++++++++++-- lesion_detection_larsmoan/slurm.sh | 6 +++--- 7 files changed, 52 insertions(+), 11 deletions(-) create mode 100644 lesion_detection_larsmoan/.gitignore create mode 100644 lesion_detection_larsmoan/check_torch.py create mode 100755 lesion_detection_larsmoan/install_dep.sh diff --git a/lesion_detection_larsmoan/.gitignore b/lesion_detection_larsmoan/.gitignore new file mode 100644 index 000000000..67ff3303e --- /dev/null +++ b/lesion_detection_larsmoan/.gitignore @@ -0,0 +1,4 @@ +data/ + +out.txt +err.txt \ No newline at end of file diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index 6684ce0bf..21d7b3b96 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -20,11 +20,12 @@ Brief description of the project, its objectives, and its significance. - [Acknowledgements](#acknowledgements) ### Todo -- [ ] Create my own training script that works on top of yolov7/train.py -- [ ] Make a downsampled version of the dataset. -- [ ] Manage a way of logging the runs to wandb -- [ ] Zip and upload dataset to gdrive, and make functions for fetching the dataset from the cluster. -- [ ] Make training scripts for use on the rangpur cluster. +- [x] Create my own training script that works on top of yolov7/train.py +- [x] Make a downsampled version of the dataset. +- [x] Manage a way of logging the runs to wandb +- [x] Zip and upload dataset to gdrive, and make functions for fetching the dataset from the cluster. +- [x] Make training scripts for use on the rangpur cluster. +- [ ] Manage to train on the downsampled dataset using the cluster. Need gpu version of CUDA for this to work. ### Installation diff --git a/lesion_detection_larsmoan/check_torch.py b/lesion_detection_larsmoan/check_torch.py new file mode 100644 index 000000000..b0f4bd948 --- /dev/null +++ b/lesion_detection_larsmoan/check_torch.py @@ -0,0 +1,8 @@ +print("Checking if torch is installed correctl with GPU compatitbility: ") +import torch + +print(torch.__version__) + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +print(device) +print("Hello world") \ No newline at end of file diff --git a/lesion_detection_larsmoan/downsample.py b/lesion_detection_larsmoan/downsample.py index b477fd232..928f12d61 100644 --- a/lesion_detection_larsmoan/downsample.py +++ b/lesion_detection_larsmoan/downsample.py @@ -59,6 +59,6 @@ def copy_txt_files(source_folder, target_folder): if __name__ == "__main__": source = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/val" target = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/val_downsampled" - n = 6 # Downsample by factor of 2 + n = 2 # Downsample by factor of 2 downsample_images(source, target, n) copy_txt_files(source_folder=source, target_folder=target) diff --git a/lesion_detection_larsmoan/install_dep.sh b/lesion_detection_larsmoan/install_dep.sh new file mode 100755 index 000000000..27beef1cb --- /dev/null +++ b/lesion_detection_larsmoan/install_dep.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +# Check if conda is installed +if ! command -v conda &> /dev/null; then + echo "Conda is not installed. Please install Conda first." + exit 1 +fi + +conda activate torch-gpu + +# Read the requirements.txt file and try to install packages using conda +while read package; do + if [[ ! -z "$package" && ! "$package" =~ ^\# ]]; then + conda install --yes "$package" || echo "Failed to install $package with conda. Consider installing manually." + fi +done < yolov7/requirements.txt + +echo "Installation finished." diff --git a/lesion_detection_larsmoan/run_custom_train.sh b/lesion_detection_larsmoan/run_custom_train.sh index 1f427cbfc..960113a9a 100755 --- a/lesion_detection_larsmoan/run_custom_train.sh +++ b/lesion_detection_larsmoan/run_custom_train.sh @@ -1,6 +1,16 @@ #!/bin/bash +#SBATCH -N 1 +#SBATCH -n 1 +#SBATCH --job-name=yolov7_ISIC2017 +#SBATCH --cpus-per-task 1 +#SBATCH --mail-type=All +#SBATCH --mail-user=l.moan@uqconnect.edu.au +#SBATCH -o out.txt +#SBATCH -e err.txt +#SBATCH --partition=test +#SBATCH --gres=gpu:1 # Activate Python environment (optional) -source /Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/.venv/bin/activate +conda activate torch-gpu -python3 yolov7/train.py --batch-size 2 --data data/ISIC_2017/isic.yaml --img 32 32 --cfg yolov7_isic_cfg.yaml --weights '' --name yolov7 --hyp hyp.scratch.p5.yaml \ No newline at end of file +python3 yolov7/train.py --workers 8 --device 0 --batch-size 2 --data data/ISIC_2017_downsampled/isic.yaml --img 32 32 --cfg yolov7_isic_cfg.yaml --weights '' --name yolov7 --hyp hyp.scratch.p5.yaml \ No newline at end of file diff --git a/lesion_detection_larsmoan/slurm.sh b/lesion_detection_larsmoan/slurm.sh index 334a0f5f1..54cec4270 100644 --- a/lesion_detection_larsmoan/slurm.sh +++ b/lesion_detection_larsmoan/slurm.sh @@ -1,7 +1,7 @@ #!/bin/bash #SBATCH -N 1 #SBATCH -n 1 -#SBATCH --job-name=RESNET_18_step +#SBATCH --job-name=torch-gpu #SBATCH --cpus-per-task 1 #SBATCH --mail-type=All #SBATCH --mail-user=l.moan@uqconnect.edu.au @@ -10,6 +10,6 @@ #SBATCH --partition=test #SBATCH --gres=gpu:1 -conda activate comp3710-env +conda activate torch-gpu-exp -python run.py \ No newline at end of file +python check_torch.py \ No newline at end of file From 94d8464855831a10b4f5ed8318e89196b411cef6 Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Mon, 16 Oct 2023 15:08:34 +1000 Subject: [PATCH 13/42] Updated gitignore --- lesion_detection_larsmoan/.gitignore | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/lesion_detection_larsmoan/.gitignore b/lesion_detection_larsmoan/.gitignore index 67ff3303e..eb7954ef8 100644 --- a/lesion_detection_larsmoan/.gitignore +++ b/lesion_detection_larsmoan/.gitignore @@ -1,4 +1,11 @@ data/ out.txt -err.txt \ No newline at end of file +err.txt + +runs/ +wandb + +.DS_Store +.venv +.env \ No newline at end of file From 1e48599e939911903474abcff13c922d8a7e769b Mon Sep 17 00:00:00 2001 From: Lars Moan <75135678+larsmoan@users.noreply.github.com> Date: Mon, 16 Oct 2023 15:13:45 +1000 Subject: [PATCH 14/42] Created using Colaboratory --- lesion_detection_larsmoan/isic_train.ipynb | 1680 ++++++++++++++++++++ 1 file changed, 1680 insertions(+) create mode 100644 lesion_detection_larsmoan/isic_train.ipynb diff --git a/lesion_detection_larsmoan/isic_train.ipynb b/lesion_detection_larsmoan/isic_train.ipynb new file mode 100644 index 000000000..ae88ab140 --- /dev/null +++ b/lesion_detection_larsmoan/isic_train.ipynb @@ -0,0 +1,1680 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4", + "authorship_tag": "ABX9TyPeFJdJGSone4v6j83wW1tV", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JLki5YXMdF6Y", + "outputId": "c1250389-170e-4762-ce31-d4edb4c6f0eb" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'PatternAnalysis-2023'...\n", + "remote: Enumerating objects: 65, done.\u001b[K\n", + "remote: Counting objects: 100% (65/65), done.\u001b[K\n", + "remote: Compressing objects: 100% (38/38), done.\u001b[K\n", + "remote: Total 65 (delta 24), reused 63 (delta 24), pack-reused 0\u001b[K\n", + "Receiving objects: 100% (65/65), 83.76 KiB | 3.81 MiB/s, done.\n", + "Resolving deltas: 100% (24/24), done.\n" + ] + } + ], + "source": [ + "!git clone \"https://github.com/larsmoan/PatternAnalysis-2023.git\"\n", + "!cd PatternAnalysis-2023" + ] + }, + { + "cell_type": "code", + "source": [ + "!cd PatternAnalysis-2023/ && git submodule update --init" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wgWX0QHsfnV6", + "outputId": "e550fdf9-6178-445b-90ba-a4c03ed6cfb8" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Submodule 'lesion_detection_larsmoan/yolov7' (https://github.com/WongKinYiu/yolov7) registered for path 'lesion_detection_larsmoan/yolov7'\n", + "Cloning into '/content/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7'...\n", + "Submodule path 'lesion_detection_larsmoan/yolov7': checked out '84932d70fb9e2932d0a70e4a1f02a1d6dd1dd6ca'\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "os.chdir('/content/PatternAnalysis-2023/lesion_detection_larsmoan/')\n", + "!ls" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ss3upKMvk_9Z", + "outputId": "64a05888-36b3-4e76-d177-7f1764ef8142" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "dataset.py\t hyp.scratch.p5.yaml predict.py\t slurm.sh yolov7_isic_cfg.yaml\n", + "dataset_utils.ipynb hyp.scratch.p6.yaml README.md\t utils.py\n", + "downsample.py\t modules.py\t\t run_custom_train.sh yolov7\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import gdown\n", + "import os\n", + "import zipfile\n", + "def download_and_unzip(folder_path, download_url):\n", + " \"\"\"\n", + " Checks if a folder exists at the specified path.\n", + " If not, downloads a zip file from the provided URL using gdown and unzips it.\n", + "\n", + " Parameters:\n", + " - folder_path: Path to the folder you want to check.\n", + " - download_url: The gdown URL of the zip file to download if the folder doesn't exist.\n", + " \"\"\"\n", + "\n", + " # Check if folder exists\n", + " if not os.path.exists(folder_path):\n", + " print(f\"Folder {folder_path} does not exist. Downloading the zip file...\")\n", + "\n", + " # Download zip file using gdown\n", + " output = \"temp_download.zip\"\n", + " gdown.download(download_url, output, quiet=False)\n", + "\n", + "\n", + " # Unzip the downloaded file\n", + " print(\"Unzipping the downloaded file...\")\n", + " with zipfile.ZipFile(output, 'r') as zip_ref:\n", + " zip_ref.extractall(folder_path)\n", + "\n", + " # Delete the downloaded zip file\n", + " os.remove(output)\n", + " print(f\"Files have been extracted to {folder_path}\")\n", + " else:\n", + " print(f\"Folder {folder_path} already exists.\")" + ], + "metadata": { + "id": "mErp_yNneMej" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "dataset_folder = \"data\"\n", + "url = \"https://drive.google.com/uc?id=19HcgRBuXyhzxsukE2jHPnBkC0hEk_aMJ\"\n", + "download_and_unzip(dataset_folder, url)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GAROAZdheOBE", + "outputId": "c94c1a0e-1de9-4bd3-c51f-b8a9f851fa93" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Folder data does not exist. Downloading the zip file...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Downloading...\n", + "From: https://drive.google.com/uc?id=19HcgRBuXyhzxsukE2jHPnBkC0hEk_aMJ\n", + "To: /content/PatternAnalysis-2023/lesion_detection_larsmoan/temp_download.zip\n", + "100%|██████████| 71.8M/71.8M [00:00<00:00, 76.7MB/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Unzipping the downloaded file...\n", + "Files have been extracted to data\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!python3 yolov7/train.py --epochs 100 --workers 32 --device 0 --batch-size 16 --data data/ISIC_2017_downsampled/isic.yaml --img 256 256 --cfg yolov7_isic_cfg.yaml --weights '' --name yolov7_isic --hyp hyp.scratch.p5.yaml" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K71MTSz2eykw", + "outputId": "c7846616-6844-49e3-c9f3-85815367f286" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2023-10-16 04:52:10.996744: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-10-16 04:52:11.936076: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "YOLOR 🚀 v0.1-126-g84932d7 torch 2.0.1+cu118 CUDA:0 (Tesla T4, 15101.8125MB)\n", + "\n", + "Namespace(weights='', cfg='yolov7_isic_cfg.yaml', data='data/ISIC_2017_downsampled/isic.yaml', hyp='hyp.scratch.p5.yaml', epochs=100, batch_size=16, img_size=[256, 256], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=32, project='runs/train', entity=None, name='yolov7_isic', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs/train/yolov7_isic9', total_batch_size=16)\n", + "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n", + "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15, loss_ota=1\n", + "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 928 models.common.Conv [3, 32, 3, 1] \n", + " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", + " 2 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", + " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", + " 4 -1 1 8320 models.common.Conv [128, 64, 1, 1] \n", + " 5 -2 1 8320 models.common.Conv [128, 64, 1, 1] \n", + " 6 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", + " 7 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", + " 8 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", + " 9 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", + " 10 [-1, -3, -5, -6] 1 0 models.common.Concat [1] \n", + " 11 -1 1 66048 models.common.Conv [256, 256, 1, 1] \n", + " 12 -1 1 0 models.common.MP [] \n", + " 13 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 14 -3 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 15 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", + " 16 [-1, -3] 1 0 models.common.Concat [1] \n", + " 17 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 18 -2 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 19 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", + " 20 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", + " 21 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", + " 22 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", + " 23 [-1, -3, -5, -6] 1 0 models.common.Concat [1] \n", + " 24 -1 1 263168 models.common.Conv [512, 512, 1, 1] \n", + " 25 -1 1 0 models.common.MP [] \n", + " 26 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 27 -3 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 28 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", + " 29 [-1, -3] 1 0 models.common.Concat [1] \n", + " 30 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 31 -2 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 32 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", + " 33 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", + " 34 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", + " 35 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", + " 36 [-1, -3, -5, -6] 1 0 models.common.Concat [1] \n", + " 37 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] \n", + " 38 -1 1 0 models.common.MP [] \n", + " 39 -1 1 525312 models.common.Conv [1024, 512, 1, 1] \n", + " 40 -3 1 525312 models.common.Conv [1024, 512, 1, 1] \n", + " 41 -1 1 2360320 models.common.Conv [512, 512, 3, 2] \n", + " 42 [-1, -3] 1 0 models.common.Concat [1] \n", + " 43 -1 1 262656 models.common.Conv [1024, 256, 1, 1] \n", + " 44 -2 1 262656 models.common.Conv [1024, 256, 1, 1] \n", + " 45 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", + " 46 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", + " 47 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", + " 48 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", + " 49 [-1, -3, -5, -6] 1 0 models.common.Concat [1] \n", + " 50 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] \n", + " 51 -1 1 7609344 models.common.SPPCSPC [1024, 512, 1] \n", + " 52 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 53 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 54 37 1 262656 models.common.Conv [1024, 256, 1, 1] \n", + " 55 [-1, -2] 1 0 models.common.Concat [1] \n", + " 56 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 57 -2 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 58 -1 1 295168 models.common.Conv [256, 128, 3, 1] \n", + " 59 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", + " 60 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", + " 61 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", + " 62[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] \n", + " 63 -1 1 262656 models.common.Conv [1024, 256, 1, 1] \n", + " 64 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 65 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 66 24 1 65792 models.common.Conv [512, 128, 1, 1] \n", + " 67 [-1, -2] 1 0 models.common.Concat [1] \n", + " 68 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 69 -2 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 70 -1 1 73856 models.common.Conv [128, 64, 3, 1] \n", + " 71 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", + " 72 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", + " 73 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", + " 74[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] \n", + " 75 -1 1 65792 models.common.Conv [512, 128, 1, 1] \n", + " 76 -1 1 0 models.common.MP [] \n", + " 77 -1 1 16640 models.common.Conv [128, 128, 1, 1] \n", + " 78 -3 1 16640 models.common.Conv [128, 128, 1, 1] \n", + " 79 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", + " 80 [-1, -3, 63] 1 0 models.common.Concat [1] \n", + " 81 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 82 -2 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 83 -1 1 295168 models.common.Conv [256, 128, 3, 1] \n", + " 84 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", + " 85 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", + " 86 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", + " 87[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] \n", + " 88 -1 1 262656 models.common.Conv [1024, 256, 1, 1] \n", + " 89 -1 1 0 models.common.MP [] \n", + " 90 -1 1 66048 models.common.Conv [256, 256, 1, 1] \n", + " 91 -3 1 66048 models.common.Conv [256, 256, 1, 1] \n", + " 92 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", + " 93 [-1, -3, 51] 1 0 models.common.Concat [1] \n", + " 94 -1 1 525312 models.common.Conv [1024, 512, 1, 1] \n", + " 95 -2 1 525312 models.common.Conv [1024, 512, 1, 1] \n", + " 96 -1 1 1180160 models.common.Conv [512, 256, 3, 1] \n", + " 97 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", + " 98 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", + " 99 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", + "100[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] \n", + "101 -1 1 1049600 models.common.Conv [2048, 512, 1, 1] \n", + "102 75 1 328704 models.common.RepConv [128, 256, 3, 1] \n", + "103 88 1 1312768 models.common.RepConv [256, 512, 3, 1] \n", + "104 101 1 5246976 models.common.RepConv [512, 1024, 3, 1] \n", + "105 [102, 103, 104] 1 44944 models.yolo.IDetect [3, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]]\n", + "Model Summary: 415 layers, 37207344 parameters, 37207344 gradients\n", + "\n", + "Scaled weight_decay = 0.0005\n", + "Optimizer groups: 95 .bias, 95 conv.weight, 98 other\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning 'data/ISIC_2017_downsampled/train_downsampled.cache' images and labels... 2000 found, 0 missing, 0 empty, 0 corrupted: 100% 2000/2000 [00:00\n", + " train(hyp, opt, device, tb_writer)\n", + " File \"/content/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/train.py\", line 363, in train\n", + " loss, loss_items = compute_loss_ota(pred, targets.to(device), imgs) # loss scaled by batch_size\n", + " File \"/content/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/utils/loss.py\", line 585, in __call__\n", + " bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs)\n", + " File \"/content/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/utils/loss.py\", line 695, in build_targets\n", + " pxywh = torch.cat([pxy, pwh], dim=-1)\n", + "KeyboardInterrupt\n", + "^C\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!python yolov7/detect.py --weights runs/train/yolov7_isic9/weights/best.pt --conf 0.2 --img-size 640 --source data/ISIC_2017_downsampled/test_downsampled" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OOFoLaI9uCof", + "outputId": "8151d7a6-ddaa-4c70-e0f1-dac59f440d8c" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Namespace(weights=['runs/train/yolov7_isic9/weights/best.pt'], source='data/ISIC_2017_downsampled/test_downsampled', img_size=640, conf_thres=0.2, iou_thres=0.45, device='', view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', exist_ok=False, no_trace=False)\n", + "YOLOR 🚀 v0.1-126-g84932d7 torch 2.0.1+cu118 CUDA:0 (Tesla T4, 15101.8125MB)\n", + "\n", + "Fusing layers... \n", + "RepConv.fuse_repvgg_block\n", + "RepConv.fuse_repvgg_block\n", + "RepConv.fuse_repvgg_block\n", + "IDetect.fuse\n", + "Model Summary: 314 layers, 36492560 parameters, 6194944 gradients\n", + " Convert model to Traced-model... \n", + " traced_script_module saved! \n", + " model is traced! \n", + "\n", + "/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n", + " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n", + "Done. (16.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012086.jpg\n", + "5 unknowns, Done. (16.4ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012092.jpg\n", + "1 unknown, Done. (16.4ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012095.jpg\n", + "1 unknown, Done. (16.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012134.jpg\n", + "2 unknowns, Done. (16.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012136.jpg\n", + "Done. (16.4ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012147.jpg\n", + "Done. (16.4ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012149.jpg\n", + "1 unknown, Done. (16.4ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012152.jpg\n", + "1 unknown, Done. (16.4ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012178.jpg\n", + "1 unknown, Done. (11.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012199.jpg\n", + "Done. (11.4ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012207.jpg\n", + "3 unknowns, Done. (11.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012215.jpg\n", + "6 unknowns, Done. (11.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012216.jpg\n", + "Done. (11.4ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012223.jpg\n", + "Done. (11.3ms) Inference, (0.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012240.jpg\n", + "Done. (11.2ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012248.jpg\n", + "8 unknowns, Done. (11.3ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012258.jpg\n", + "2 unknowns, Done. (11.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012265.jpg\n", + "1 unknown, Done. (9.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012266.jpg\n", + "1 unknown, Done. (9.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012272.jpg\n", + "1 unknown, Done. (9.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012273.jpg\n", + "Done. (9.5ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012314.jpg\n", + "2 unknowns, Done. (9.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012323.jpg\n", + "1 unknown, Done. (9.4ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012330.jpg\n", + "Done. (9.3ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012356.jpg\n", + "2 unknowns, Done. (9.3ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012357.jpg\n", + "11 unknowns, Done. (9.3ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012358.jpg\n", + "Done. (9.4ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012364.jpg\n", + "4 unknowns, Done. (8.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012369.jpg\n", + "3 unknowns, Done. (8.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012372.jpg\n", + "2 unknowns, Done. (8.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012375.jpg\n", + "1 unknown, Done. (8.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012387.jpg\n", + "1 unknown, Done. (8.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012388.jpg\n", + "1 unknown, Done. (8.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012395.jpg\n", + "Done. (8.8ms) Inference, (0.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012414.jpg\n", + "2 unknowns, Done. (8.8ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012425.jpg\n", + "2 unknowns, Done. (8.7ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012428.jpg\n", + "Done. (8.6ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012432.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012447.jpg\n", + "2 unknowns, Done. (8.8ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012448.jpg\n", + "Done. (8.8ms) Inference, (0.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012484.jpg\n", + "2 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012493.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012510.jpg\n", + "1 unknown, Done. (8.3ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012522.jpg\n", + "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012537.jpg\n", + "1 unknown, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012548.jpg\n", + "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012551.jpg\n", + "3 unknowns, Done. (8.2ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012654.jpg\n", + "6 unknowns, Done. (8.4ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012656.jpg\n", + "1 unknown, Done. (8.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012705.jpg\n", + "6 unknowns, Done. (8.8ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012708.jpg\n", + "1 unknown, Done. (8.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012722.jpg\n", + "1 unknown, Done. (8.9ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012757.jpg\n", + "1 unknown, Done. (11.3ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012758.jpg\n", + "4 unknowns, Done. (10.0ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012786.jpg\n", + "5 unknowns, Done. (9.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012803.jpg\n", + "1 unknown, Done. (8.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012836.jpg\n", + "2 unknowns, Done. (8.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012837.jpg\n", + "3 unknowns, Done. (8.3ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012848.jpg\n", + "1 unknown, Done. (8.2ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012852.jpg\n", + "2 unknowns, Done. (8.1ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012903.jpg\n", + "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012904.jpg\n", + "1 unknown, Done. (8.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012928.jpg\n", + "Done. (8.2ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012941.jpg\n", + "1 unknown, Done. (8.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012955.jpg\n", + "Done. (8.1ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012967.jpg\n", + "1 unknown, Done. (8.8ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012974.jpg\n", + "1 unknown, Done. (8.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0012989.jpg\n", + "Done. (8.1ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013030.jpg\n", + "Done. (8.1ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013035.jpg\n", + "5 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013045.jpg\n", + "4 unknowns, Done. (8.6ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013070.jpg\n", + "4 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013072.jpg\n", + "6 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013073.jpg\n", + "2 unknowns, Done. (8.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013085.jpg\n", + "5 unknowns, Done. (8.8ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013109.jpg\n", + "4 unknowns, Done. (8.6ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013159.jpg\n", + "5 unknowns, Done. (8.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013164.jpg\n", + "2 unknowns, Done. (8.3ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013169.jpg\n", + "Done. (8.2ms) Inference, (0.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013170.jpg\n", + "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013176.jpg\n", + "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013191.jpg\n", + "5 unknowns, Done. (8.6ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013203.jpg\n", + "3 unknowns, Done. (8.9ms) Inference, (1.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013216.jpg\n", + "1 unknown, Done. (8.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013226.jpg\n", + "2 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013230.jpg\n", + "3 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013242.jpg\n", + "7 unknowns, Done. (8.6ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013269.jpg\n", + "10 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013270.jpg\n", + "1 unknown, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013271.jpg\n", + "5 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013277.jpg\n", + "Done. (8.3ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013281.jpg\n", + "5 unknowns, Done. (8.2ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013291.jpg\n", + "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013319.jpg\n", + "1 unknown, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013321.jpg\n", + "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013325.jpg\n", + "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013374.jpg\n", + "4 unknowns, Done. (8.2ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013393.jpg\n", + "1 unknown, Done. (8.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013399.jpg\n", + "Done. (8.5ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013411.jpg\n", + "5 unknowns, Done. (8.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013414.jpg\n", + "2 unknowns, Done. (8.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013416.jpg\n", + "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013455.jpg\n", + "1 unknown, Done. (8.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013457.jpg\n", + "15 unknowns, Done. (8.2ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013459.jpg\n", + "6 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013465.jpg\n", + "1 unknown, Done. (8.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013472.jpg\n", + "1 unknown, Done. (8.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013473.jpg\n", + "2 unknowns, Done. (8.7ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013511.jpg\n", + "5 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013512.jpg\n", + "1 unknown, Done. (8.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013529.jpg\n", + "Done. (8.9ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013565.jpg\n", + "2 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013577.jpg\n", + "4 unknowns, Done. (8.3ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013588.jpg\n", + "3 unknowns, Done. (8.2ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013600.jpg\n", + "5 unknowns, Done. (8.2ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013602.jpg\n", + "2 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013615.jpg\n", + "2 unknowns, Done. (8.3ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013617.jpg\n", + "1 unknown, Done. (8.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013636.jpg\n", + "1 unknown, Done. (8.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013673.jpg\n", + "5 unknowns, Done. (8.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013678.jpg\n", + "2 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013696.jpg\n", + "1 unknown, Done. (8.3ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013708.jpg\n", + "3 unknowns, Done. (8.3ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013733.jpg\n", + "2 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013738.jpg\n", + "4 unknowns, Done. (8.4ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013739.jpg\n", + "3 unknowns, Done. (8.3ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013764.jpg\n", + "5 unknowns, Done. (8.3ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013766.jpg\n", + "Done. (8.5ms) Inference, (0.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013767.jpg\n", + "1 unknown, Done. (8.7ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013794.jpg\n", + "7 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013809.jpg\n", + "Done. (8.5ms) Inference, (0.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013813.jpg\n", + "Done. (8.6ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013814.jpg\n", + "Done. (8.7ms) Inference, (0.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013833.jpg\n", + "2 unknowns, Done. (8.7ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013842.jpg\n", + "3 unknowns, Done. (8.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013867.jpg\n", + "2 unknowns, Done. (8.8ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013891.jpg\n", + "5 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013897.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013908.jpg\n", + "2 unknowns, Done. (8.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013911.jpg\n", + "1 unknown, Done. (8.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013917.jpg\n", + "6 unknowns, Done. (8.8ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013925.jpg\n", + "1 unknown, Done. (8.8ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013948.jpg\n", + "Done. (8.7ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013953.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013966.jpg\n", + "2 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013977.jpg\n", + "3 unknowns, Done. (8.3ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013987.jpg\n", + "2 unknowns, Done. (8.2ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013988.jpg\n", + "4 unknowns, Done. (8.3ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0013998.jpg\n", + "Done. (8.5ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014006.jpg\n", + "Done. (8.3ms) Inference, (0.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014027.jpg\n", + "1 unknown, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014059.jpg\n", + "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014077.jpg\n", + "1 unknown, Done. (8.1ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014090.jpg\n", + "4 unknowns, Done. (8.1ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014103.jpg\n", + "3 unknowns, Done. (8.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014110.jpg\n", + "3 unknowns, Done. (8.1ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014117.jpg\n", + "Done. (8.1ms) Inference, (0.8ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014129.jpg\n", + "1 unknown, Done. (8.2ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014148.jpg\n", + "3 unknowns, Done. (8.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014160.jpg\n", + "Done. (8.6ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014177.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014181.jpg\n", + "6 unknowns, Done. (8.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014186.jpg\n", + "3 unknowns, Done. (8.3ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014219.jpg\n", + "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014221.jpg\n", + "3 unknowns, Done. (9.8ms) Inference, (1.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014233.jpg\n", + "Done. (8.2ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014251.jpg\n", + "2 unknowns, Done. (8.2ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014255.jpg\n", + "5 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014270.jpg\n", + "3 unknowns, Done. (8.6ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014278.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014284.jpg\n", + "2 unknowns, Done. (8.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014288.jpg\n", + "5 unknowns, Done. (8.3ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014319.jpg\n", + "Done. (8.3ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014336.jpg\n", + "1 unknown, Done. (8.2ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014349.jpg\n", + "1 unknown, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014369.jpg\n", + "Done. (8.1ms) Inference, (0.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014386.jpg\n", + "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014392.jpg\n", + "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014409.jpg\n", + "Done. (8.1ms) Inference, (0.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014419.jpg\n", + "3 unknowns, Done. (8.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014423.jpg\n", + "Done. (8.1ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014434.jpg\n", + "2 unknowns, Done. (8.3ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014454.jpg\n", + "6 unknowns, Done. (8.7ms) Inference, (2.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014457.jpg\n", + "3 unknowns, Done. (10.4ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014470.jpg\n", + "2 unknowns, Done. (10.8ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014474.jpg\n", + "3 unknowns, Done. (8.3ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014478.jpg\n", + "1 unknown, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014489.jpg\n", + "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014500.jpg\n", + "Done. (8.2ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014503.jpg\n", + "Done. (8.3ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014506.jpg\n", + "2 unknowns, Done. (8.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014513.jpg\n", + "5 unknowns, Done. (8.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014541.jpg\n", + "Done. (8.7ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014542.jpg\n", + "Done. (8.6ms) Inference, (0.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014546.jpg\n", + "2 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014548.jpg\n", + "Done. (8.5ms) Inference, (0.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014559.jpg\n", + "5 unknowns, Done. (8.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014567.jpg\n", + "2 unknowns, Done. (9.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014574.jpg\n", + "2 unknowns, Done. (9.8ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014575.jpg\n", + "2 unknowns, Done. (9.6ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014586.jpg\n", + "5 unknowns, Done. (9.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014587.jpg\n", + "4 unknowns, Done. (9.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014588.jpg\n", + "2 unknowns, Done. (9.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014590.jpg\n", + "3 unknowns, Done. (9.3ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014600.jpg\n", + "2 unknowns, Done. (9.3ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014619.jpg\n", + "1 unknown, Done. (9.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014626.jpg\n", + "1 unknown, Done. (9.3ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014627.jpg\n", + "3 unknowns, Done. (9.1ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014629.jpg\n", + "1 unknown, Done. (9.0ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014631.jpg\n", + "2 unknowns, Done. (8.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014634.jpg\n", + "1 unknown, Done. (9.0ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014643.jpg\n", + "6 unknowns, Done. (9.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014644.jpg\n", + "1 unknown, Done. (9.0ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014645.jpg\n", + "1 unknown, Done. (9.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014647.jpg\n", + "3 unknowns, Done. (10.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014648.jpg\n", + "4 unknowns, Done. (10.3ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014649.jpg\n", + "2 unknowns, Done. (9.6ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014652.jpg\n", + "Done. (9.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014653.jpg\n", + "Done. (8.7ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014663.jpg\n", + "1 unknown, Done. (8.9ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014666.jpg\n", + "5 unknowns, Done. (8.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014675.jpg\n", + "4 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014677.jpg\n", + "4 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014687.jpg\n", + "2 unknowns, Done. (8.6ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014693.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014695.jpg\n", + "2 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014697.jpg\n", + "3 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014698.jpg\n", + "3 unknowns, Done. (8.7ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014703.jpg\n", + "4 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014720.jpg\n", + "4 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014725.jpg\n", + "2 unknowns, Done. (9.2ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014727.jpg\n", + "1 unknown, Done. (10.8ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014728.jpg\n", + "1 unknown, Done. (8.7ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014729.jpg\n", + "1 unknown, Done. (11.0ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014740.jpg\n", + "6 unknowns, Done. (10.9ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014743.jpg\n", + "3 unknowns, Done. (8.7ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014746.jpg\n", + "6 unknowns, Done. (8.7ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014749.jpg\n", + "3 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014753.jpg\n", + "6 unknowns, Done. (8.7ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014755.jpg\n", + "4 unknowns, Done. (9.4ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014765.jpg\n", + "4 unknowns, Done. (9.3ms) Inference, (1.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014766.jpg\n", + "5 unknowns, Done. (8.8ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014768.jpg\n", + "5 unknowns, Done. (8.7ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014772.jpg\n", + "3 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014773.jpg\n", + "8 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014780.jpg\n", + "5 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014784.jpg\n", + "2 unknowns, Done. (8.8ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014786.jpg\n", + "6 unknowns, Done. (8.8ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014787.jpg\n", + "Done. (8.7ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014790.jpg\n", + "1 unknown, Done. (8.7ms) Inference, (2.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014792.jpg\n", + "4 unknowns, Done. (9.4ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014796.jpg\n", + "Done. (9.3ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014798.jpg\n", + "4 unknowns, Done. (8.8ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014800.jpg\n", + "2 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014807.jpg\n", + "5 unknowns, Done. (8.7ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014814.jpg\n", + "5 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014815.jpg\n", + "6 unknowns, Done. (13.1ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014820.jpg\n", + "1 unknown, Done. (13.4ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014822.jpg\n", + "Done. (13.3ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014826.jpg\n", + "Done. (13.3ms) Inference, (0.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014833.jpg\n", + "5 unknowns, Done. (13.6ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014835.jpg\n", + "4 unknowns, Done. (13.5ms) Inference, (1.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014844.jpg\n", + "5 unknowns, Done. (13.6ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014853.jpg\n", + "6 unknowns, Done. (13.9ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014854.jpg\n", + "1 unknown, Done. (13.9ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014862.jpg\n", + "5 unknowns, Done. (14.0ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014863.jpg\n", + "4 unknowns, Done. (14.3ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014867.jpg\n", + "1 unknown, Done. (14.4ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014868.jpg\n", + "Done. (14.3ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014872.jpg\n", + "3 unknowns, Done. (14.4ms) Inference, (1.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014876.jpg\n", + "2 unknowns, Done. (14.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014879.jpg\n", + "Done. (14.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014883.jpg\n", + "4 unknowns, Done. (14.6ms) Inference, (1.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014901.jpg\n", + "3 unknowns, Done. (15.1ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014907.jpg\n", + "3 unknowns, Done. (15.0ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014910.jpg\n", + "3 unknowns, Done. (15.0ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014912.jpg\n", + "4 unknowns, Done. (15.0ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014921.jpg\n", + "2 unknowns, Done. (15.0ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014927.jpg\n", + "3 unknowns, Done. (15.0ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014928.jpg\n", + "4 unknowns, Done. (15.0ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014932.jpg\n", + "11 unknowns, Done. (15.0ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014936.jpg\n", + "10 unknowns, Done. (14.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014938.jpg\n", + "5 unknowns, Done. (15.3ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014940.jpg\n", + "1 unknown, Done. (15.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014941.jpg\n", + "5 unknowns, Done. (15.3ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014942.jpg\n", + "6 unknowns, Done. (15.3ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014943.jpg\n", + "5 unknowns, Done. (15.3ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014944.jpg\n", + "9 unknowns, Done. (15.3ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014947.jpg\n", + "1 unknown, Done. (15.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014948.jpg\n", + "4 unknowns, Done. (15.3ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014949.jpg\n", + "3 unknowns, Done. (15.3ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014952.jpg\n", + "16 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014955.jpg\n", + "4 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014956.jpg\n", + "1 unknown, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014957.jpg\n", + "10 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014958.jpg\n", + "6 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014959.jpg\n", + "2 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014961.jpg\n", + "3 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014962.jpg\n", + "4 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014963.jpg\n", + "1 unknown, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014964.jpg\n", + "5 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014966.jpg\n", + "3 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014968.jpg\n", + "5 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014969.jpg\n", + "5 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014973.jpg\n", + "7 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014974.jpg\n", + "5 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014977.jpg\n", + "4 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014982.jpg\n", + "4 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014992.jpg\n", + "3 unknowns, Done. (15.9ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014994.jpg\n", + "5 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0014998.jpg\n", + "Done. (15.8ms) Inference, (0.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015002.jpg\n", + "4 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015003.jpg\n", + "Done. (15.9ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015004.jpg\n", + "3 unknowns, Done. (15.8ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015007.jpg\n", + "Done. (15.8ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015008.jpg\n", + "1 unknown, Done. (15.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015009.jpg\n", + "Done. (15.8ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015011.jpg\n", + "Done. (15.8ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015013.jpg\n", + "5 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015015.jpg\n", + "5 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015016.jpg\n", + "4 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015018.jpg\n", + "3 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015019.jpg\n", + "2 unknowns, Done. (15.8ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015020.jpg\n", + "3 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015021.jpg\n", + "6 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015023.jpg\n", + "1 unknown, Done. (15.8ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015026.jpg\n", + "6 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015030.jpg\n", + "Done. (16.1ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015031.jpg\n", + "4 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015034.jpg\n", + "2 unknowns, Done. (16.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015035.jpg\n", + "2 unknowns, Done. (16.1ms) Inference, (1.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015037.jpg\n", + "5 unknowns, Done. (16.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015040.jpg\n", + "5 unknowns, Done. (16.2ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015041.jpg\n", + "6 unknowns, Done. (16.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015046.jpg\n", + "Done. (16.1ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015050.jpg\n", + "4 unknowns, Done. (16.2ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015051.jpg\n", + "6 unknowns, Done. (16.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015056.jpg\n", + "3 unknowns, Done. (16.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015057.jpg\n", + "2 unknowns, Done. (16.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015060.jpg\n", + "3 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015064.jpg\n", + "7 unknowns, Done. (16.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015071.jpg\n", + "Done. (16.1ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015078.jpg\n", + "Done. (16.1ms) Inference, (0.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015089.jpg\n", + "1 unknown, Done. (16.1ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015102.jpg\n", + "2 unknowns, Done. (16.3ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015115.jpg\n", + "Done. (16.5ms) Inference, (0.8ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015118.jpg\n", + "3 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015119.jpg\n", + "6 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015125.jpg\n", + "2 unknowns, Done. (16.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015127.jpg\n", + "4 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015129.jpg\n", + "Done. (16.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015130.jpg\n", + "2 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015132.jpg\n", + "6 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015133.jpg\n", + "3 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015136.jpg\n", + "14 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015139.jpg\n", + "1 unknown, Done. (16.4ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015140.jpg\n", + "2 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015142.jpg\n", + "11 unknowns, Done. (16.6ms) Inference, (1.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015146.jpg\n", + "5 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015149.jpg\n", + "4 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015150.jpg\n", + "Done. (16.6ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015152.jpg\n", + "Done. (16.5ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015155.jpg\n", + "3 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015156.jpg\n", + "5 unknowns, Done. (16.5ms) Inference, (1.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015157.jpg\n", + "Done. (16.5ms) Inference, (0.8ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015160.jpg\n", + "2 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015161.jpg\n", + "2 unknowns, Done. (16.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015163.jpg\n", + "4 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015167.jpg\n", + "Done. (16.5ms) Inference, (0.8ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015171.jpg\n", + "Done. (16.5ms) Inference, (0.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015173.jpg\n", + "5 unknowns, Done. (16.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015174.jpg\n", + "1 unknown, Done. (15.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015175.jpg\n", + "Done. (15.8ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015176.jpg\n", + "3 unknowns, Done. (15.8ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015179.jpg\n", + "4 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015180.jpg\n", + "4 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015184.jpg\n", + "Done. (15.8ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015185.jpg\n", + "2 unknowns, Done. (15.8ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015193.jpg\n", + "4 unknowns, Done. (15.8ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015201.jpg\n", + "2 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015202.jpg\n", + "Done. (15.5ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015203.jpg\n", + "11 unknowns, Done. (15.6ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015206.jpg\n", + "6 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015207.jpg\n", + "3 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015208.jpg\n", + "4 unknowns, Done. (15.7ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015212.jpg\n", + "1 unknown, Done. (15.5ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015215.jpg\n", + "Done. (15.5ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015216.jpg\n", + "5 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015217.jpg\n", + "5 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015218.jpg\n", + "6 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015223.jpg\n", + "1 unknown, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015224.jpg\n", + "1 unknown, Done. (15.6ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015226.jpg\n", + "1 unknown, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015229.jpg\n", + "2 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015232.jpg\n", + "1 unknown, Done. (15.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015237.jpg\n", + "4 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015241.jpg\n", + "2 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015244.jpg\n", + "4 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015245.jpg\n", + "4 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015250.jpg\n", + "4 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015251.jpg\n", + "4 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015254.jpg\n", + "Done. (15.6ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015255.jpg\n", + "6 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015258.jpg\n", + "5 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015264.jpg\n", + "7 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015270.jpg\n", + "5 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015273.jpg\n", + "4 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015274.jpg\n", + "6 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015276.jpg\n", + "4 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015279.jpg\n", + "1 unknown, Done. (15.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015283.jpg\n", + "5 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015291.jpg\n", + "2 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015293.jpg\n", + "5 unknowns, Done. (15.6ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015298.jpg\n", + "6 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015309.jpg\n", + "1 unknown, Done. (15.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015310.jpg\n", + "Done. (15.6ms) Inference, (0.8ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015311.jpg\n", + "4 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015312.jpg\n", + "4 unknowns, Done. (15.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015330.jpg\n", + "6 unknowns, Done. (15.6ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015331.jpg\n", + "3 unknowns, Done. (15.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015347.jpg\n", + "1 unknown, Done. (15.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015353.jpg\n", + "Done. (15.5ms) Inference, (0.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015355.jpg\n", + "4 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015357.jpg\n", + "3 unknowns, Done. (15.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015360.jpg\n", + "14 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015363.jpg\n", + "3 unknowns, Done. (15.8ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015364.jpg\n", + "Done. (15.8ms) Inference, (0.8ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015368.jpg\n", + "4 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015369.jpg\n", + "3 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015383.jpg\n", + "2 unknowns, Done. (15.8ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015386.jpg\n", + "2 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015390.jpg\n", + "Done. (15.8ms) Inference, (0.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015395.jpg\n", + "6 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015403.jpg\n", + "Done. (15.8ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015404.jpg\n", + "5 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015411.jpg\n", + "2 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015412.jpg\n", + "Done. (15.8ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015416.jpg\n", + "1 unknown, Done. (15.9ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015417.jpg\n", + "5 unknowns, Done. (16.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015418.jpg\n", + "6 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015419.jpg\n", + "5 unknowns, Done. (16.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015436.jpg\n", + "4 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015440.jpg\n", + "Done. (16.1ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015447.jpg\n", + "1 unknown, Done. (16.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015455.jpg\n", + "3 unknowns, Done. (16.1ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015464.jpg\n", + "Done. (16.1ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015466.jpg\n", + "5 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015468.jpg\n", + "3 unknowns, Done. (16.1ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015476.jpg\n", + "5 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015481.jpg\n", + "Done. (16.1ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015482.jpg\n", + "11 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015485.jpg\n", + "1 unknown, Done. (16.1ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015510.jpg\n", + "5 unknowns, Done. (16.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015526.jpg\n", + "4 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015537.jpg\n", + "2 unknowns, Done. (16.2ms) Inference, (1.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015544.jpg\n", + "5 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015559.jpg\n", + "4 unknowns, Done. (16.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015563.jpg\n", + "2 unknowns, Done. (16.4ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015566.jpg\n", + "2 unknowns, Done. (16.5ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015568.jpg\n", + "5 unknowns, Done. (16.5ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015582.jpg\n", + "Done. (16.5ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015593.jpg\n", + "7 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015603.jpg\n", + "1 unknown, Done. (16.4ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015607.jpg\n", + "4 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015614.jpg\n", + "2 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015617.jpg\n", + "6 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015625.jpg\n", + "Done. (16.5ms) Inference, (0.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015631.jpg\n", + "Done. (16.4ms) Inference, (0.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015636.jpg\n", + "4 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015638.jpg\n", + "5 unknowns, Done. (16.6ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015641.jpg\n", + "2 unknowns, Done. (16.6ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015645.jpg\n", + "2 unknowns, Done. (16.5ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015936.jpg\n", + "1 unknown, Done. (16.4ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015937.jpg\n", + "3 unknowns, Done. (16.4ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015938.jpg\n", + "5 unknowns, Done. (14.3ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015939.jpg\n", + "1 unknown, Done. (14.3ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015940.jpg\n", + "4 unknowns, Done. (14.3ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015941.jpg\n", + "1 unknown, Done. (14.3ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015942.jpg\n", + "1 unknown, Done. (14.3ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015943.jpg\n", + "2 unknowns, Done. (14.3ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015944.jpg\n", + "2 unknowns, Done. (14.3ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015945.jpg\n", + "2 unknowns, Done. (14.3ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015946.jpg\n", + "3 unknowns, Done. (10.8ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015947.jpg\n", + "2 unknowns, Done. (10.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015948.jpg\n", + "3 unknowns, Done. (10.8ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015949.jpg\n", + "1 unknown, Done. (10.8ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015950.jpg\n", + "2 unknowns, Done. (10.8ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015951.jpg\n", + "1 unknown, Done. (10.7ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015952.jpg\n", + "1 unknown, Done. (10.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015953.jpg\n", + "1 unknown, Done. (10.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015954.jpg\n", + "4 unknowns, Done. (10.6ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015955.jpg\n", + "3 unknowns, Done. (10.7ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015956.jpg\n", + "3 unknowns, Done. (9.2ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015957.jpg\n", + "1 unknown, Done. (9.0ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015958.jpg\n", + "10 unknowns, Done. (8.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015959.jpg\n", + "16 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015960.jpg\n", + "27 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015961.jpg\n", + "3 unknowns, Done. (8.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015962.jpg\n", + "1 unknown, Done. (8.7ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015963.jpg\n", + "5 unknowns, Done. (8.8ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015964.jpg\n", + "2 unknowns, Done. (8.9ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015965.jpg\n", + "4 unknowns, Done. (8.8ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015966.jpg\n", + "2 unknowns, Done. (8.8ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015967.jpg\n", + "Done. (8.6ms) Inference, (0.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015968.jpg\n", + "5 unknowns, Done. (8.4ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015969.jpg\n", + "3 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015971.jpg\n", + "1 unknown, Done. (8.3ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015972.jpg\n", + "1 unknown, Done. (10.0ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015973.jpg\n", + "1 unknown, Done. (10.2ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015974.jpg\n", + "2 unknowns, Done. (10.0ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015975.jpg\n", + "3 unknowns, Done. (9.6ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015976.jpg\n", + "1 unknown, Done. (8.9ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015978.jpg\n", + "1 unknown, Done. (8.5ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015979.jpg\n", + "4 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015980.jpg\n", + "10 unknowns, Done. (8.4ms) Inference, (1.1ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015981.jpg\n", + "3 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015982.jpg\n", + "5 unknowns, Done. (9.3ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015983.jpg\n", + "10 unknowns, Done. (8.5ms) Inference, (1.0ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015984.jpg\n", + "4 unknowns, Done. (9.8ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015985.jpg\n", + "2 unknowns, Done. (9.4ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015986.jpg\n", + "2 unknowns, Done. (9.4ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015987.jpg\n", + "6 unknowns, Done. (9.0ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015988.jpg\n", + "4 unknowns, Done. (8.8ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015989.jpg\n", + "5 unknowns, Done. (9.0ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015990.jpg\n", + "3 unknowns, Done. (10.6ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015991.jpg\n", + "2 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015992.jpg\n", + "5 unknowns, Done. (8.5ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015993.jpg\n", + "1 unknown, Done. (9.5ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015994.jpg\n", + "3 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015995.jpg\n", + "2 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015996.jpg\n", + "2 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015997.jpg\n", + "2 unknowns, Done. (10.2ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015998.jpg\n", + "1 unknown, Done. (10.6ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0015999.jpg\n", + "3 unknowns, Done. (8.6ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016000.jpg\n", + "6 unknowns, Done. (8.6ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016001.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016002.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016003.jpg\n", + "4 unknowns, Done. (8.5ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016004.jpg\n", + "3 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016005.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016006.jpg\n", + "2 unknowns, Done. (8.5ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016007.jpg\n", + "3 unknowns, Done. (8.5ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016008.jpg\n", + "1 unknown, Done. (9.8ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016009.jpg\n", + "3 unknowns, Done. (8.5ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016011.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016012.jpg\n", + "2 unknowns, Done. (8.6ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016013.jpg\n", + "1 unknown, Done. (8.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016014.jpg\n", + "1 unknown, Done. (8.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016015.jpg\n", + "1 unknown, Done. (8.5ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016016.jpg\n", + "1 unknown, Done. (8.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016017.jpg\n", + "4 unknowns, Done. (8.5ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016018.jpg\n", + "Done. (8.5ms) Inference, (0.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016019.jpg\n", + "6 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016022.jpg\n", + "4 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016023.jpg\n", + "2 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016024.jpg\n", + "7 unknowns, Done. (8.6ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016025.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016026.jpg\n", + "1 unknown, Done. (8.5ms) Inference, (2.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016027.jpg\n", + "2 unknowns, Done. (8.6ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016028.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016029.jpg\n", + "2 unknowns, Done. (8.8ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016030.jpg\n", + "3 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016031.jpg\n", + "5 unknowns, Done. (8.5ms) Inference, (1.2ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016033.jpg\n", + "3 unknowns, Done. (10.8ms) Inference, (1.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016034.jpg\n", + "3 unknowns, Done. (11.3ms) Inference, (1.7ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016035.jpg\n", + "4 unknowns, Done. (9.6ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016036.jpg\n", + "3 unknowns, Done. (8.6ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016037.jpg\n", + "6 unknowns, Done. (8.6ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016038.jpg\n", + "2 unknowns, Done. (8.6ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016040.jpg\n", + "5 unknowns, Done. (10.9ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016041.jpg\n", + "2 unknowns, Done. (10.9ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016042.jpg\n", + "4 unknowns, Done. (8.6ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016043.jpg\n", + "5 unknowns, Done. (8.6ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016044.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016045.jpg\n", + "1 unknown, Done. (8.9ms) Inference, (1.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016046.jpg\n", + "1 unknown, Done. (8.6ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016048.jpg\n", + "4 unknowns, Done. (9.4ms) Inference, (1.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016049.jpg\n", + "5 unknowns, Done. (9.3ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016050.jpg\n", + "2 unknowns, Done. (9.3ms) Inference, (1.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016051.jpg\n", + "7 unknowns, Done. (9.1ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016052.jpg\n", + "4 unknowns, Done. (12.3ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016053.jpg\n", + "5 unknowns, Done. (8.6ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016054.jpg\n", + "2 unknowns, Done. (11.3ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016055.jpg\n", + "3 unknowns, Done. (10.0ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016056.jpg\n", + "5 unknowns, Done. (9.9ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016057.jpg\n", + "Done. (9.9ms) Inference, (0.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016058.jpg\n", + "1 unknown, Done. (9.9ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016059.jpg\n", + "4 unknowns, Done. (9.9ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016060.jpg\n", + "5 unknowns, Done. (9.9ms) Inference, (2.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016061.jpg\n", + "10 unknowns, Done. (10.2ms) Inference, (1.9ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016062.jpg\n", + "4 unknowns, Done. (10.1ms) Inference, (1.5ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016063.jpg\n", + "2 unknowns, Done. (10.1ms) Inference, (1.6ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016064.jpg\n", + "3 unknowns, Done. (10.0ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016065.jpg\n", + "1 unknown, Done. (10.2ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016066.jpg\n", + "3 unknowns, Done. (10.0ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016068.jpg\n", + "4 unknowns, Done. (10.1ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016069.jpg\n", + "2 unknowns, Done. (10.4ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016070.jpg\n", + "7 unknowns, Done. (10.2ms) Inference, (1.3ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016071.jpg\n", + "2 unknowns, Done. (11.2ms) Inference, (1.4ms) NMS\n", + " The image with the result is saved in: runs/detect/exp5/ISIC_0016072.jpg\n", + "Done. (20.704s)\n" + ] + } + ] + } + ] +} \ No newline at end of file From 642ff41b6b6e63b2708049ba8b7d32598872ed16 Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Tue, 17 Oct 2023 15:22:30 +1000 Subject: [PATCH 15/42] Started working on description of dataset for the reeport --- err.txt | 6 ++ lesion_detection_larsmoan/README.md | 56 ++++++++++++++++--- lesion_detection_larsmoan/check_torch.py | 2 +- lesion_detection_larsmoan/run_custom_train.sh | 6 +- lesion_detection_larsmoan/slurm.sh | 2 +- out.txt | 0 6 files changed, 58 insertions(+), 14 deletions(-) create mode 100644 err.txt create mode 100644 out.txt diff --git a/err.txt b/err.txt new file mode 100644 index 000000000..183dcbd36 --- /dev/null +++ b/err.txt @@ -0,0 +1,6 @@ + +EnvironmentNameNotFound: Could not find conda environment: pytorch +You can list all discoverable environments with `conda info --envs`. + + +python3: can't open file '/home/Student/s4827064/PatternAnalysis-2023/yolov7/train.py': [Errno 2] No such file or directory diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index 21d7b3b96..c31747604 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -1,9 +1,4 @@ -## Project Title - -Brief description of the project, its objectives, and its significance. - - - +## Lesion detection and classification using YOLOV7 on the ISIC2017 dataset ### Table of Contents - [Project Title](#project-title) @@ -37,10 +32,48 @@ Brief description of the project, its objectives, and its significance. ``` ### Dataset - +https://challenge.isic-archive.com/data/#2017 - Description of the dataset. -- Source of the dataset (if it's publicly available). +The original dataset consist of 2000 training images of skin lesions with corresponding labels and segmentation files for the specific part of the picture that is the lesion. +The validation set consist of 600 images with corresponding labels and the test set consists of 150 images with labels. + + +The different classes that are present in the dataset is: Melanoma, Nevi, and Seborrheic Keratosis. Where Nevi (Unknown) is the technical term for a benign skin lesion, often referred to as a mole. + - Preprocessing steps (if any). +Since the dataset comes with segmentation files preprocessing is needed to convert these lables into yolo bbox labels with the corresponding class. + +This consist of mainly finding the max and min pixels of the segmentation area and fitting a bounding box to this area. The class is then determined by the label given in the csv file. + +The dataset itself also needs to be refactored a bit to work with yolov7, therefore the structure is changed to the following: +``` +dataset/ +│ +├── train/ +│ ├── img_1.jpg +│ ├── ... +│ ├── img_n.jpg +│ ├── img_1.txt +│ ├── ... +│ └── img_n.txt +│ +├── val/ +│ ├── img_1.jpg +│ ├── ... +│ ├── img_n.jpg +│ ├── img_1.txt +│ ├── ... +│ └── img_n.txt +│ +└── test/ + ├── img_1.jpg + ├── ... + ├── img_n.jpg + ├── img_1.txt + ├── ... + └── img_n.txt +``` + ### Usage @@ -52,11 +85,16 @@ Brief description of the project, its objectives, and its significance. ### Model Architecture The model and architecture used in this project is the open source yolov7 model: - https://github.com/WongKinYiu/yolov7 ### Training +Training the yolov7 model on the dataset can be done in two different ways: +1. Using the isic_train.ipynb script +- This utilizes the free GPU provided by google-colab for training. + +2. Using the rangpur cluster. +- Training can then be done using the run_custom_train.sh script. Here you can specify the number of epochs, batch size etc etc. Statistics from the training will be logged to the runs/ folder and wandb if that is enabled beforehand. - Details about the training process. - Hyperparameters used. diff --git a/lesion_detection_larsmoan/check_torch.py b/lesion_detection_larsmoan/check_torch.py index b0f4bd948..93d997d46 100644 --- a/lesion_detection_larsmoan/check_torch.py +++ b/lesion_detection_larsmoan/check_torch.py @@ -1,4 +1,4 @@ -print("Checking if torch is installed correctl with GPU compatitbility: ") +print("Checking if torch is installed correctly with GPU compatitbility: ") import torch print(torch.__version__) diff --git a/lesion_detection_larsmoan/run_custom_train.sh b/lesion_detection_larsmoan/run_custom_train.sh index 960113a9a..94cdc1593 100755 --- a/lesion_detection_larsmoan/run_custom_train.sh +++ b/lesion_detection_larsmoan/run_custom_train.sh @@ -7,10 +7,10 @@ #SBATCH --mail-user=l.moan@uqconnect.edu.au #SBATCH -o out.txt #SBATCH -e err.txt -#SBATCH --partition=test +#SBATCH --partition=vgpu #SBATCH --gres=gpu:1 # Activate Python environment (optional) -conda activate torch-gpu +conda activate gpu_fix -python3 yolov7/train.py --workers 8 --device 0 --batch-size 2 --data data/ISIC_2017_downsampled/isic.yaml --img 32 32 --cfg yolov7_isic_cfg.yaml --weights '' --name yolov7 --hyp hyp.scratch.p5.yaml \ No newline at end of file +python3 yolov7/train.py --device 0 --workers 8 --batch-size 2 --data data/ISIC_2017_downsampled/isic.yaml --img 256 256 --cfg yolov7_isic_cfg.yaml --weights '' --name yolov7 --hyp hyp.scratch.p5.yaml \ No newline at end of file diff --git a/lesion_detection_larsmoan/slurm.sh b/lesion_detection_larsmoan/slurm.sh index 54cec4270..66bebab85 100644 --- a/lesion_detection_larsmoan/slurm.sh +++ b/lesion_detection_larsmoan/slurm.sh @@ -10,6 +10,6 @@ #SBATCH --partition=test #SBATCH --gres=gpu:1 -conda activate torch-gpu-exp +conda activate gpu_fix python check_torch.py \ No newline at end of file diff --git a/out.txt b/out.txt new file mode 100644 index 000000000..e69de29bb From c19b1f92dc129f3f68c233b5b7def07c9a82a989 Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Tue, 17 Oct 2023 20:51:29 +1000 Subject: [PATCH 16/42] Refactored the dataset utils needed to create YOLO version of the dataset --- lesion_detection_larsmoan/.gitignore | 2 +- lesion_detection_larsmoan/check_torch.py | 5 +- lesion_detection_larsmoan/dataset.py | 172 --- lesion_detection_larsmoan/dataset_utils.ipynb | 1046 ----------------- lesion_detection_larsmoan/dataset_utils.py | 217 ++++ lesion_detection_larsmoan/downsample.py | 2 +- lesion_detection_larsmoan/modules.py | 24 - lesion_detection_larsmoan/predict.py | 0 .../preprocess_dataset.ipynb | 97 ++ poetry.lock | 16 +- pyproject.toml | 1 + 11 files changed, 333 insertions(+), 1249 deletions(-) delete mode 100644 lesion_detection_larsmoan/dataset.py delete mode 100644 lesion_detection_larsmoan/dataset_utils.ipynb create mode 100644 lesion_detection_larsmoan/dataset_utils.py delete mode 100644 lesion_detection_larsmoan/modules.py delete mode 100644 lesion_detection_larsmoan/predict.py create mode 100644 lesion_detection_larsmoan/preprocess_dataset.ipynb diff --git a/lesion_detection_larsmoan/.gitignore b/lesion_detection_larsmoan/.gitignore index eb7954ef8..f08965c56 100644 --- a/lesion_detection_larsmoan/.gitignore +++ b/lesion_detection_larsmoan/.gitignore @@ -1,5 +1,5 @@ data/ - +__pycache__ out.txt err.txt diff --git a/lesion_detection_larsmoan/check_torch.py b/lesion_detection_larsmoan/check_torch.py index b0f4bd948..3925c9e3e 100644 --- a/lesion_detection_larsmoan/check_torch.py +++ b/lesion_detection_larsmoan/check_torch.py @@ -1,8 +1,5 @@ -print("Checking if torch is installed correctl with GPU compatitbility: ") +print("Checking if torch is installed correctly with GPU compatitbility: ") import torch - print(torch.__version__) - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print(device) -print("Hello world") \ No newline at end of file diff --git a/lesion_detection_larsmoan/dataset.py b/lesion_detection_larsmoan/dataset.py deleted file mode 100644 index e03c30e68..000000000 --- a/lesion_detection_larsmoan/dataset.py +++ /dev/null @@ -1,172 +0,0 @@ -import os -import cv2 -import zipfile -import gdown - -def draw_yolo_bboxes_on_image(image_path, txt_path): - """ - Draw YOLO bounding boxes from a .txt file on an image, display labels, and show the image in a size of 240x240 pixels. - """ - # Load the image - image = cv2.imread(image_path) - - # Define color mapping based on class_id - class_colors = { - 0: (0, 255, 0), # Green for Melanoma - 1: (0, 0, 255) # Red for Seborrheic Keratosis - } - - # Compute the resizing factors - resize_factor_x = 240 / image.shape[1] - resize_factor_y = 240 / image.shape[0] - - # Resize the image - resized_image = cv2.resize(image, (240, 240)) - - # Read bounding box data from the txt file - with open(txt_path, 'r') as file: - lines = file.readlines() - for line in lines: - class_id, x_center, y_center, width, height = map(float, line.strip().split()) - - # Denormalize the coordinates - x_center *= image.shape[1] - y_center *= image.shape[0] - width *= image.shape[1] - height *= image.shape[0] - - # Adjust the bounding box coordinates based on the resizing factors - x_center *= resize_factor_x - y_center *= resize_factor_y - width *= resize_factor_x - height *= resize_factor_y - - # Convert to top-left x, y coordinates - x_min = int(x_center - width / 2) - y_min = int(y_center - height / 2) - x_max = int(x_center + width / 2) - y_max = int(y_center + height / 2) - - # Define the label and color based on class_id - label = "Melanoma" if class_id == 0 else "Seborrheic Keratosis" if class_id == 1 else "Unknown" - color = class_colors.get(class_id, (255, 255, 255)) # Default color is white - - # Draw the bounding box on the resized image with the defined color - cv2.rectangle(resized_image, (x_min, y_min), (x_max, y_max), color, 2) - - # Overlay the label on the image with the same color - cv2.putText(resized_image, label, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) - - # Display the resized image with bounding boxes and labels - cv2.imshow('Image with Bounding Boxes and Labels', resized_image) - cv2.waitKey(0) - cv2.destroyAllWindows() - - - -def download_and_unzip(folder_path, download_url): - """ - Checks if a folder exists at the specified path. - If not, downloads a zip file from the provided URL using gdown and unzips it. - - Parameters: - - folder_path: Path to the folder you want to check. - - download_url: The gdown URL of the zip file to download if the folder doesn't exist. - """ - - # Check if folder exists - if not os.path.exists(folder_path): - print(f"Folder {folder_path} does not exist. Downloading the zip file...") - - # Download zip file using gdown - output = "temp_download.zip" - gdown.download(download_url, output, quiet=False) - - # Unzip the downloaded file - print("Unzipping the downloaded file...") - with zipfile.ZipFile(output, 'r') as zip_ref: - zip_ref.extractall(folder_path) - - # Delete the downloaded zip file - os.remove(output) - print(f"Files have been extracted to {folder_path}") - else: - print(f"Folder {folder_path} already exists.") - - - - -def visualize_labels(dataset_folder): - """ - Iterate through the .txt files in the folder and draw YOLO bounding boxes on the corresponding images. - """ - for filename in os.listdir(dataset_folder): - if filename.endswith(".txt"): - # Construct paths to the txt file and its corresponding image - txt_path = os.path.join(dataset_folder, filename) - image_path = os.path.join(dataset_folder, filename.replace(".txt", ".jpg")) # assuming .jpg extension for images - - # Check if the corresponding image exists - if os.path.exists(image_path): - draw_yolo_bboxes_on_image(image_path, txt_path) - -import os -from PIL import Image - -def get_image_dimensions(image_path): - """Return the dimensions of the image.""" - with Image.open(image_path) as img: - return img.size - -def get_all_image_dimensions_in_folder(folder_path, valid_extensions={"jpg", "jpeg", "png"}): - """Return a list of dimensions of all images in the folder.""" - - all_files = [f for f in os.listdir(folder_path) if f.lower().endswith(tuple(valid_extensions))] - - all_dimensions = [] - - for file_name in all_files: - file_path = os.path.join(folder_path, file_name) - try: - dimensions = get_image_dimensions(file_path) - all_dimensions.append(dimensions) - except Exception as e: - # This means the file is probably not an image or there's an issue reading it - print(f"Could not process {file_name}. Error: {e}") - - return all_dimensions - -def analyze_image_dimensions(folder_path): - dimensions = get_all_image_dimensions_in_folder(folder_path) - - if not dimensions: - print("No valid images found in the specified folder.") - return - - widths, heights = zip(*dimensions) - - # Calculating statistics - max_width, max_height = max(widths), max(heights) - min_width, min_height = min(widths), min(heights) - mean_width, mean_height = sum(widths) / len(widths), sum(heights) / len(heights) - - # Since mean and average are the same, we'll just display the mean - print(f"Max Width: {max_width}, Max Height: {max_height}") - print(f"Min Width: {min_width}, Min Height: {min_height}") - print(f"Mean/Average Width: {mean_width:.2f}, Mean/Average Height: {mean_height:.2f}") - - - - -if __name__ == "__main__": - folder = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017_downsampled/train_downsampled" - #visualize_labels(folder) - - analyze_image_dimensions(folder) - - - """ dataset_folder = "path/to/check/folder" - url = "https://drive.google.com/uc?id=19HcgRBuXyhzxsukE2jHPnBkC0hEk_aMJ" """ - - #download_and_unzip(dataset_folder, url) - diff --git a/lesion_detection_larsmoan/dataset_utils.ipynb b/lesion_detection_larsmoan/dataset_utils.ipynb deleted file mode 100644 index a622f4aa1..000000000 --- a/lesion_detection_larsmoan/dataset_utils.ipynb +++ /dev/null @@ -1,1046 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "def remove_superpixels(folder):\n", - " \"\"\"\n", - " Remove all superpixel files from a folder.\n", - " \"\"\"\n", - " files = [f for f in os.listdir(folder) if os.path.isfile(os.path.join(folder, f))]\n", - " for file in files:\n", - " if file.endswith('superpixels.png'):\n", - " os.remove(os.path.join(folder, file))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "remove_superpixels(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC-2017_Test_v2_Data/\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "import cv2\n", - "import numpy as np\n", - "\n", - "def compute_yolo_bbox(binary_mask):\n", - " \"\"\"\n", - " Compute the YOLO bounding box for a binary mask.\n", - " \"\"\"\n", - " # Find the coordinates of non-zero pixels\n", - " rows, cols = np.where(binary_mask == 255)\n", - "\n", - " # Get the minimum and maximum coordinates\n", - " x_min, x_max = np.min(cols), np.max(cols)\n", - " y_min, y_max = np.min(rows), np.max(rows)\n", - "\n", - " # Compute the center, width, and height\n", - " x_center = (x_min + x_max) / 2\n", - " y_center = (y_min + y_max) / 2\n", - " width = x_max - x_min\n", - " height = y_max - y_min\n", - "\n", - " return x_center, y_center, width, height" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import cv2\n", - "import pandas as pd\n", - "\n", - "\n", - "def create_yolo_labels(seg_folder_path, cls_folder_path, dst_folder_path):\n", - " \"\"\"\n", - " Iterate through the binary images in the folder, generate YOLO bounding boxes, \n", - " and determine the class from the dataframe.\n", - " \"\"\"\n", - " labels = pd.read_csv(cls_folder_path, index_col=0)\n", - "\n", - " for filename in os.listdir(seg_folder_path):\n", - " if filename.endswith(\".png\"):\n", - " # Extract image_id from filename\n", - " image_id = filename[:12]\n", - "\n", - " # Determine the class based on the dataframe\n", - " class_row = labels.loc[image_id]\n", - " if class_row['melanoma'] == 1.0:\n", - " class_id = 0\n", - " elif class_row['seborrheic_keratosis'] == 1.0:\n", - " class_id = 1\n", - " else:\n", - " class_id = 2 # Assuming a third class for 'other' or 'nevi' based on the given example\n", - "\n", - " # Load the binary image\n", - " image_path = os.path.join(seg_folder_path, filename)\n", - " image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n", - "\n", - " # Compute the YOLO bounding box\n", - " x_center, y_center, width, height = compute_yolo_bbox(image)\n", - " \n", - " # Normalize the coordinates\n", - " x_center /= image.shape[1]\n", - " y_center /= image.shape[0]\n", - " width /= image.shape[1]\n", - " height /= image.shape[0]\n", - "\n", - " # Write the bounding box to a .txt file\n", - " output_path = os.path.join(dst_folder_path, filename.replace(\".png\", \".txt\"))\n", - " with open(output_path, \"w\") as file:\n", - " file.write(f\"{class_id} {x_center} {y_center} {width} {height}\\n\")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "cls_folder_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC-2017_Test_v2_Data/ISIC-2017_Test_v2_Part3_GroundTruth.csv\"\n", - "seg_folder_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC-2017_Test_v2_Part1_GroundTruth/\"\n", - "dst_folder_path = \"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC-2017_Test_v2_Data\"\n", - "\n", - "create_yolo_labels(seg_folder_path, cls_folder_path, dst_folder_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "def remove_segmentation_suffix(folder_path):\n", - " # List all files in the folder\n", - " all_files = os.listdir(folder_path)\n", - " \n", - " # Process each file\n", - " for filename in all_files:\n", - " file_path = os.path.join(folder_path, filename)\n", - " \n", - " # Check if it's a file (not a subfolder)\n", - " if os.path.isfile(file_path):\n", - " # Check if filename ends with \"segmentation\"\n", - " if filename.endswith(\"_.txt\"):\n", - " # Determine the new filename without the \"segmentation\" suffix\n", - " new_filename = filename.replace(\"_\", \"\")\n", - " new_file_path = os.path.join(folder_path, new_filename)\n", - " \n", - " # Rename the file\n", - " os.rename(file_path, new_file_path)\n", - "\n", - "# Example usage:\n", - "# remove_segmentation_suffix('./some_folder_path')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "remove_segmentation_suffix(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/data\")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "def check_missing_txt_files(folder_path):\n", - " # List all files in the folder\n", - " all_files = os.listdir(folder_path)\n", - " \n", - " # Separate images and txt files\n", - " image_files = [f for f in all_files if f.endswith(('.jpg', '.jpeg', '.png'))]\n", - " txt_files = [f for f in all_files if f.endswith('.txt')]\n", - " i = 0\n", - " # Check for missing txt files\n", - " for img_file in image_files:\n", - " corresponding_txt_file = os.path.splitext(img_file)[0] + '.txt'\n", - " if corresponding_txt_file not in txt_files:\n", - " i += 1\n", - " print(f\"Missing txt file for: {img_file}\")\n", - " print(\"Number of missing txt files: \", i)\n", - "# Example usage:\n", - "# check_missing_txt_files('./some_folder_path')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Missing txt file for: ISIC_0015372.jpg\n", - "Missing txt file for: ISIC_0012151.jpg\n", - "Missing txt file for: ISIC_0013529.jpg\n", - "Missing txt file for: ISIC_0013501.jpg\n", - "Missing txt file for: ISIC_0012757.jpg\n", - "Missing txt file for: ISIC_0015986.jpg\n", - "Missing txt file for: ISIC_0014454.jpg\n", - "Missing txt file for: ISIC_0015992.jpg\n", - "Missing txt file for: ISIC_0015004.jpg\n", - "Missing txt file for: ISIC_0015945.jpg\n", - "Missing txt file for: ISIC_0015951.jpg\n", - "Missing txt file for: ISIC_0015979.jpg\n", - "Missing txt file for: ISIC_0016055.jpg\n", - "Missing txt file for: ISIC_0004337.jpg\n", - "Missing txt file for: ISIC_0016041.jpg\n", - "Missing txt file for: ISIC_0016069.jpg\n", - "Missing txt file for: ISIC_0015206.jpg\n", - 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"Missing txt file for: ISIC_0013319.jpg\n", - "Missing txt file for: ISIC_0013325.jpg\n", - "Missing txt file for: ISIC_0015998.jpg\n", - "Missing txt file for: ISIC_0007344.jpg\n", - "Missing txt file for: ISIC_0015026.jpg\n", - "Missing txt file for: ISIC_0013457.jpg\n", - "Missing txt file for: ISIC_0015973.jpg\n", - "Missing txt file for: ISIC_0015967.jpg\n", - "Missing txt file for: ISIC_0014489.jpg\n", - "Missing txt file for: ISIC_0016063.jpg\n", - "Missing txt file for: ISIC_0015218.jpg\n", - "Missing txt file for: ISIC_0013127.jpg\n", - "Missing txt file for: ISIC_0014648.jpg\n", - "Missing txt file for: ISIC_0015224.jpg\n", - "Missing txt file for: ISIC_0014853.jpg\n", - "Missing txt file for: ISIC_0013696.jpg\n", - "Missing txt file for: ISIC_0013867.jpg\n", - "Missing txt file for: ISIC_0013132.jpg\n", - "Missing txt file for: ISIC_0014649.jpg\n", - "Missing txt file for: ISIC_0013898.jpg\n", - "Missing txt file for: ISIC_0012210.jpg\n", - "Missing txt file for: ISIC_0014675.jpg\n", - "Missing txt file for: ISIC_0012204.jpg\n", - "Missing txt file for: ISIC_0016062.jpg\n", - "Missing txt file for: ISIC_0015966.jpg\n", - "Missing txt file for: ISIC_0015972.jpg\n", - "Missing txt file for: ISIC_0015999.jpg\n", - "Missing txt file for: ISIC_0012199.jpg\n", - "Missing txt file for: ISIC_0015184.jpg\n", - "Missing txt file for: ISIC_0014503.jpg\n", - "Missing txt file for: ISIC_0007796.jpg\n", - "Missing txt file for: ISIC_0013085.jpg\n", - "Missing txt file for: ISIC_0014932.jpg\n", - "Missing txt file for: ISIC_0015386.jpg\n", - "Missing txt file for: ISIC_0012358.jpg\n", - "Missing txt file for: ISIC_0014729.jpg\n", - "Missing txt file for: ISIC_0012364.jpg\n", - "Missing txt file for: ISIC_0013708.jpg\n", - "Missing txt file for: ISIC_0015355.jpg\n", - "Missing txt file for: ISIC_0015369.jpg\n", - "Missing txt file for: ISIC_0014077.jpg\n", - "Missing txt file for: ISIC_0014936.jpg\n", - "Missing txt file for: ISIC_0013917.jpg\n", - "Missing txt file for: ISIC_0015625.jpg\n", - "Missing txt file for: ISIC_0015157.jpg\n", - "Missing txt file for: ISIC_0015631.jpg\n", - "Missing txt file for: ISIC_0007235.jpg\n", - "Missing txt file for: ISIC_0006671.jpg\n", - "Missing txt file for: ISIC_0014513.jpg\n", - "Missing txt file for: ISIC_0015180.jpg\n", - "Missing txt file for: ISIC_0012837.jpg\n", - "Missing txt file for: ISIC_0015989.jpg\n", - "Missing txt file for: ISIC_0012758.jpg\n", - "Missing txt file for: ISIC_0015037.jpg\n", - "Missing txt file for: ISIC_0015023.jpg\n", - "Missing txt file for: ISIC_0013491.jpg\n", - "Missing txt file for: ISIC_0015976.jpg\n", - "Missing txt file for: ISIC_0015962.jpg\n", - "Missing txt file for: ISIC_0013644.jpg\n", - "Missing txt file for: ISIC_0013678.jpg\n", - "Missing txt file for: ISIC_0014117.jpg\n", - "Missing txt file for: ISIC_0016066.jpg\n", - "Missing txt file for: ISIC_0016072.jpg\n", - "Missing txt file for: ISIC_0014103.jpg\n", - "Missing txt file for: ISIC_0013863.jpg\n", - "Missing txt file for: ISIC_0014857.jpg\n", - "Missing txt file for: ISIC_0012215.jpg\n", - "Missing txt file for: ISIC_0015208.jpg\n", - "Missing txt file for: ISIC_0012201.jpg\n", - "Missing txt file for: ISIC_0013651.jpg\n", - "Missing txt file for: ISIC_0007156.jpg\n", - "Missing txt file for: ISIC_0015963.jpg\n", - "Missing txt file for: ISIC_0012956.jpg\n", - "Missing txt file for: ISIC_0015988.jpg\n", - "Missing txt file for: ISIC_0013321.jpg\n", - "Missing txt file for: ISIC_0012836.jpg\n", - "Missing txt file for: ISIC_0013269.jpg\n", - "Missing txt file for: ISIC_0014506.jpg\n", - "Missing txt file for: ISIC_0015156.jpg\n", - "Missing txt file for: ISIC_0013527.jpg\n", - "Missing txt file for: ISIC_0015142.jpg\n", - "Missing txt file for: ISIC_0014937.jpg\n", - "Missing txt file for: ISIC_0015383.jpg\n", - "Missing txt file for: ISIC_0015368.jpg\n", - "Missing txt file for: ISIC_0012375.jpg\n", - "Missing txt file for: ISIC_0013733.jpg\n", - "Missing txt file for: ISIC_0014712.jpg\n", - "Missing txt file for: ISIC_0015418.jpg\n", - "Missing txt file for: ISIC_0015395.jpg\n", - "Missing txt file for: ISIC_0013082.jpg\n", - "Missing txt file for: ISIC_0012388.jpg\n", - "Missing txt file for: ISIC_0014921.jpg\n", - "Missing txt file for: ISIC_0015140.jpg\n", - "Missing txt file for: ISIC_0012149.jpg\n", - "Missing txt file for: ISIC_0015020.jpg\n", - "Missing txt file for: ISIC_0015034.jpg\n", - "Missing txt file for: ISIC_0014302.jpg\n", - "Missing txt file for: ISIC_0015008.jpg\n", - "Missing txt file for: ISIC_0014470.jpg\n", - "Missing txt file for: ISIC_0015949.jpg\n", - "Missing txt file for: ISIC_0015961.jpg\n", - "Missing txt file for: ISIC_0015975.jpg\n", - "Missing txt file for: ISIC_0015544.jpg\n", - "Missing txt file for: ISIC_0016059.jpg\n", - "Missing txt file for: ISIC_0014666.jpg\n", - "Missing txt file for: ISIC_0013109.jpg\n", - "Missing txt file for: ISIC_0016071.jpg\n", - "Missing txt file for: ISIC_0016065.jpg\n", - "Missing txt file for: ISIC_0015593.jpg\n", - "Missing txt file for: ISIC_0014868.jpg\n", - "Missing txt file for: ISIC_0014854.jpg\n", - "Missing txt file for: ISIC_0014698.jpg\n", - "Missing txt file for: ISIC_0016064.jpg\n", - "Missing txt file for: ISIC_0016070.jpg\n", - "Missing txt file for: ISIC_0012216.jpg\n", - "Missing txt file for: ISIC_0016058.jpg\n", - "Missing txt file for: ISIC_0015237.jpg\n", - "Missing txt file for: ISIC_0014129.jpg\n", - "Missing txt file for: ISIC_0015223.jpg\n", - "Missing txt file for: ISIC_0014883.jpg\n", - "Missing txt file for: ISIC_0007141.jpg\n", - "Missing txt file for: ISIC_0015974.jpg\n", - "Missing txt file for: ISIC_0015960.jpg\n", - "Missing txt file for: ISIC_0012941.jpg\n", - "Missing txt file for: ISIC_0015948.jpg\n", - "Missing txt file for: ISIC_0012955.jpg\n", - "Missing txt file for: ISIC_0015009.jpg\n", - "Missing txt file for: ISIC_0003657.jpg\n", - "Missing txt file for: ISIC_0015035.jpg\n", - "Missing txt file for: ISIC_0015021.jpg\n", - "Missing txt file for: ISIC_0013281.jpg\n", - "Missing txt file for: ISIC_0014288.jpg\n", - "Missing txt file for: ISIC_0012160.jpg\n", - "Missing txt file for: ISIC_0013518.jpg\n", - "Missing txt file for: ISIC_0015627.jpg\n", - "Missing txt file for: ISIC_0013242.jpg\n", - "Missing txt file for: ISIC_0015155.jpg\n", - "Missing txt file for: ISIC_0015419.jpg\n", - "Missing txt file for: ISIC_0015357.jpg\n", - "Number of missing txt files: 750\n" - ] - } - ], - "source": [ - "check_missing_txt_files(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/data\")" - ] - }, - { - "cell_type": "code", - "execution_count": 205, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import pandas as pd\n", - "import shutil\n", - "\n", - "# Create the destination directory if it doesn't exist\n", - "def ensure_directory_exists(directory_path):\n", - " if not os.path.exists(directory_path):\n", - " os.makedirs(directory_path)\n", - "\n", - "df = pd.read_csv(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/validation_split.csv\")\n", - "files = df.image_id.values\n", - "\n", - "# Ensure that the train directory exists\n", - "ensure_directory_exists(\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/val\")\n", - "\n", - "for file in files:\n", - " # Check if it exists\n", - " if os.path.exists(f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/data/{file}.jpg\"):\n", - " # Move the file to the new folder\n", - " shutil.move(f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/data/{file}.jpg\", f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/val/{file}.jpg\")\n", - " shutil.move(f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/data/{file}.txt\", f\"/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/val/{file}.txt\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from PIL import Image\n", - "\n", - "def downsample_images(folder_path, output_folder):\n", - " # Create the output folder if it doesn't exist\n", - " if not os.path.exists(output_folder):\n", - " os.makedirs(output_folder)\n", - "\n", - " # Iterate over all files in the folder\n", - " for filename in os.listdir(folder_path):\n", - " filepath = os.path.join(folder_path, filename)\n", - " \n", - " # Check if the file is an image\n", - " try:\n", - " with Image.open(filepath) as img:\n", - " # Downsample by a factor of 2x\n", - " new_size = (img.width // 2, img.height // 2)\n", - " resized_img = img.resize(new_size, Image.ANTIALIAS)\n", - " \n", - " # Save the downsampled image to the output folder\n", - " resized_img.save(os.path.join(output_folder, filename))\n", - " print(f\"Downsampled {filename}\")\n", - " \n", - " except Exception as e:\n", - " print(f\"Error processing {filename}: {e}\")\n", - "\n", - "# Provide your folder path and output folder path here\n", - "folder_path = \"/path/to/your/folder\"\n", - "output_folder = os.path.join(folder_path, \"downsized\")\n", - "downsample_images(folder_path, output_folder)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "comp3710-env", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/lesion_detection_larsmoan/dataset_utils.py b/lesion_detection_larsmoan/dataset_utils.py new file mode 100644 index 000000000..cf54ee70a --- /dev/null +++ b/lesion_detection_larsmoan/dataset_utils.py @@ -0,0 +1,217 @@ +import os +import cv2 +import pandas as pd +import numpy as np +from PIL import Image +import zipfile +import gdown +import signal +import sys +from tqdm import tqdm +import shutil + + +def visualize_labels(dataset_folder): + for filename in os.listdir(dataset_folder): + if filename.endswith(".txt"): + txt_path = os.path.join(dataset_folder, filename) + image_path = os.path.join(dataset_folder, filename.replace(".txt", ".jpg")) + if os.path.exists(image_path): + draw_yolo_bboxes_on_image(image_path, txt_path) + else: + print("Could not find the corresponding image for", txt_path) + + +#Removes all superpixel images from a folder +def remove_superpixels(folder): + files = [f for f in os.listdir(folder) if os.path.isfile(os.path.join(folder, f))] + for file in files: + if file.endswith('superpixels.png'): + os.remove(os.path.join(folder, file)) + + +#Computes the bounding box of a binary mask, not normalized +def compute_bbx(binary_mask): + # Find the coordinates of non-zero pixels + rows, cols = np.where(binary_mask == 255) + + # Get the minimum and maximum coordinates + x_min, x_max = np.min(cols), np.max(cols) + y_min, y_max = np.min(rows), np.max(rows) + + # Compute the center, width, and height + x_center = (x_min + x_max) / 2 + y_center = (y_min + y_max) / 2 + width = x_max - x_min + height = y_max - y_min + + return x_center, y_center, width, height + +#Based on the binary / segmentaiton images, create bounding boxes, normalize them to YOLO format and fetch the class from the csv file +def create_yolo_labels(seg_folder_path, class_csv_file, dst_folder_path): + labels = pd.read_csv(class_csv_file, index_col=0) + + for filename in tqdm(os.listdir(seg_folder_path), desc="Processing images"): + if filename.endswith(".png"): + image_id = filename[:12] + + # Determine the class based on the dataframe + class_row = labels.loc[image_id] + if class_row['melanoma'] == 1.0: + class_id = 0 + elif class_row['seborrheic_keratosis'] == 1.0: + class_id = 1 + else: + class_id = 2 # Assuming a third class for 'other' or 'nevi' based on the given example + + # Load the binary image + image_path = os.path.join(seg_folder_path, filename) + image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) + + # Compute the bounding box + x_center, y_center, width, height = compute_bbx(image) + + # Normalize the coordinates -> YOLO format + x_center /= image.shape[1] + y_center /= image.shape[0] + width /= image.shape[1] + height /= image.shape[0] + + # Write the YOLO bbx with class to a .txt file + output_path = os.path.join(dst_folder_path, filename.replace("_segmentation.png", ".txt")) + with open(output_path, "w") as file: + file.write(f"{class_id} {x_center} {y_center} {width} {height}\n") + + + +def draw_yolo_bboxes_on_image(image_path, txt_path): + image = cv2.imread(image_path) + + # Extracting file ID from image_path + file_id = os.path.splitext(os.path.basename(image_path))[0] + + #color mapping based on class_id + class_colors = { + 0: (0, 255, 0), # Green for Melanoma + 1: (0, 0, 255) # Red for Seborrheic Keratosis + } + + #Resizing factors for displaying + resize_factor_x = 480 / image.shape[1] + resize_factor_y = 480 / image.shape[0] + + resized_image = cv2.resize(image, (480, 480)) + + # Display the file ID on top of the image + cv2.putText(resized_image, file_id, (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2) + + # Read bounding box data from the txt file + with open(txt_path, 'r') as file: + lines = file.readlines() + for line in lines: + class_id, x_center, y_center, width, height = map(float, line.strip().split()) + + # Denormalize the coordinates + x_center *= image.shape[1] + y_center *= image.shape[0] + width *= image.shape[1] + height *= image.shape[0] + + # Adjust the bounding box coordinates based on the resizing factors + x_center *= resize_factor_x + y_center *= resize_factor_y + width *= resize_factor_x + height *= resize_factor_y + + # Convert to top-left x, y coordinates + x_min = int(x_center - width / 2) + y_min = int(y_center - height / 2) + x_max = int(x_center + width / 2) + y_max = int(y_center + height / 2) + + # Define the label and color based on class_id + label = "Melanoma" if class_id == 0 else "Seborrheic Keratosis" if class_id == 1 else "Unknown" + color = class_colors.get(class_id, (255, 255, 255)) # Default color is white + + cv2.rectangle(resized_image, (x_min, y_min), (x_max, y_max), color, 2) + cv2.putText(resized_image, label, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) + + cv2.imshow('Image with Bounding Boxes and Labels', resized_image) + cv2.waitKey(0) + cv2.destroyAllWindows() + +def visualize_labels(dataset_folder): + for filename in os.listdir(dataset_folder): + if filename.endswith(".txt"): + txt_path = os.path.join(dataset_folder, filename) + image_path = os.path.join(dataset_folder, filename.replace(".txt", ".jpg")) + if os.path.exists(image_path): + draw_yolo_bboxes_on_image(image_path, txt_path) + else: + print("Could not find the corresponding image for", txt_path) + + +def downsample_images(input_folder_path, output_folder, downsample_factor): + # Create the output folder if it doesn't exist + if not os.path.exists(output_folder): + os.makedirs(output_folder) + + for filename in tqdm(os.listdir(input_folder_path), desc="Downsampling images"): + if filename.endswith(".jpg"): + filepath = os.path.join(input_folder_path, filename) + + try: + # Load the image + img = cv2.imread(filepath) + + # Check if the image is loaded correctly + if img is None: + print(f"Failed to load {filename}. Skipping...") + continue + + # Calculate the new size + new_size = (img.shape[1] // downsample_factor, img.shape[0] // downsample_factor) + + # Resize the image + resized_img = cv2.resize(img, new_size, interpolation=cv2.INTER_LINEAR) + + # Save the downsampled image to the output folder + cv2.imwrite(os.path.join(output_folder, filename), resized_img) + + except Exception as e: + print(f"Error processing {filename}: {e}") + + + +def copy_txt_files(source_folder, destination_folder): + # Ensure destination directory exists; if not, create it + if not os.path.exists(destination_folder): + os.makedirs(destination_folder) + + for filename in tqdm(os.listdir(source_folder), desc="Copying label files"): + if filename.endswith('.txt'): + source_path = os.path.join(source_folder, filename) + destination_path = os.path.join(destination_folder, filename) + + shutil.copy2(source_path, destination_path) + + +#Used to download and unzip compressed dataset from google drive if the dataset folder doesn't exist +def download_and_unzip(dataset_folder, download_url="https://drive.google.com/uc?id=19HcgRBuXyhzxsukE2jHPnBkC0hEk_aMJ"): + if not os.path.exists(dataset_folder): + print(f"Folder {dataset_folder} does not exist. Downloading the zip file...") + + # Download zip file using gdown + output = "temp_download.zip" + gdown.download(download_url, output, quiet=False) + + # Unzip the downloaded file + print("Unzipping the downloaded file...") + with zipfile.ZipFile(output, 'r') as zip_ref: + zip_ref.extractall(dataset_folder) + + # Delete the downloaded zip file + os.remove(output) + print(f"Files have been extracted to {dataset_folder}") + else: + print(f"Folder {dataset_folder} already exists.") diff --git a/lesion_detection_larsmoan/downsample.py b/lesion_detection_larsmoan/downsample.py index 928f12d61..e4de59321 100644 --- a/lesion_detection_larsmoan/downsample.py +++ b/lesion_detection_larsmoan/downsample.py @@ -58,7 +58,7 @@ def copy_txt_files(source_folder, target_folder): if __name__ == "__main__": source = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/val" - target = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/val_downsampled" + target = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017_0.5/val" n = 2 # Downsample by factor of 2 downsample_images(source, target, n) copy_txt_files(source_folder=source, target_folder=target) diff --git a/lesion_detection_larsmoan/modules.py b/lesion_detection_larsmoan/modules.py deleted file mode 100644 index 12a01e0d9..000000000 --- a/lesion_detection_larsmoan/modules.py +++ /dev/null @@ -1,24 +0,0 @@ -import torch -import torchvision -from torchvision.models.detection import MaskRCNN -from torchvision.models.detection.faster_rcnn import FastRCNNPredictor -from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor - -model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True) - -# Get the number of input features for the classifier -in_features = model.roi_heads.box_predictor.cls_score.in_features - -# Replace the pre-trained head with a new one -model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) - -# Now get the number of input features for the mask classifier -in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels - -hidden_layer = 256 # This can be adjusted based on your preference - -# And replace the mask predictor with a new one -model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask, hidden_layer, num_classes) - - - diff --git a/lesion_detection_larsmoan/predict.py b/lesion_detection_larsmoan/predict.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/lesion_detection_larsmoan/preprocess_dataset.ipynb b/lesion_detection_larsmoan/preprocess_dataset.ipynb new file mode 100644 index 000000000..61abd4acb --- /dev/null +++ b/lesion_detection_larsmoan/preprocess_dataset.ipynb @@ -0,0 +1,97 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from dataset_utils import *\n", + "from utils import get_data_dir" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Use this function on the corresponding folders with superpixels, as they are not needed for our yolo model\n", + "# train/\n", + "# val/\n", + "# test/ \n", + "remove_superpixels(get_data_dir() / 'ISIC_2017/test')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Compute the yolo labels and place them in the same folder as the images\n", + "create_yolo_labels(\n", + " get_data_dir() / 'ISIC_2017_orig/train_seg', \n", + " get_data_dir() / 'ISIC_2017_orig/train_labels.csv', \n", + " get_data_dir() / 'ISIC_2017_orig/train_data')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "visualize_labels(get_data_dir() / 'ISIC_2017/test')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downsampling images: 0%| | 0/301 [00:00=1.5" +[[package]] +name = "python-dotenv" +version = "1.0.0" +description = "Read key-value pairs from a .env file and set them as environment variables" +optional = false +python-versions = ">=3.8" +files = [ + {file = "python-dotenv-1.0.0.tar.gz", hash = "sha256:a8df96034aae6d2d50a4ebe8216326c61c3eb64836776504fcca410e5937a3ba"}, + {file = "python_dotenv-1.0.0-py3-none-any.whl", hash = "sha256:f5971a9226b701070a4bf2c38c89e5a3f0d64de8debda981d1db98583009122a"}, +] + +[package.extras] +cli = ["click (>=5.0)"] + [[package]] name = "pytz" version = "2023.3.post1" @@ -2598,4 +2612,4 @@ watchdog = ["watchdog (>=2.3)"] [metadata] lock-version = "2.0" python-versions = ">=3.11,<3.13" -content-hash = "a257f0d27f8b02edf7a46d5686a64080b9bbc260f520dc234cdc244dc2b74b56" +content-hash = "a090b9cc8827cdbc78d22e732d11841b05cc553ca6a6540273c2dbd5b4a60785" diff --git a/pyproject.toml b/pyproject.toml index 6047750c3..f79cd9327 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,6 +24,7 @@ seaborn = ">=0.11.0" wandb = "^0.15.12" pillow = "^10.0.1" gdown = "^4.7.1" +python-dotenv = "^1.0.0" [tool.poetry.group.dev.dependencies] From 1e8ea008958f8d98af410ea356d4cdd49b4bf2bf Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Tue, 17 Oct 2023 21:34:57 +1000 Subject: [PATCH 17/42] Added link to downsampled dataset --- lesion_detection_larsmoan/downsample.py | 64 ----------- lesion_detection_larsmoan/isic_train.ipynb | 121 +++++++++++---------- lesion_detection_larsmoan/slurm.sh | 5 +- 3 files changed, 65 insertions(+), 125 deletions(-) delete mode 100644 lesion_detection_larsmoan/downsample.py diff --git a/lesion_detection_larsmoan/downsample.py b/lesion_detection_larsmoan/downsample.py deleted file mode 100644 index e4de59321..000000000 --- a/lesion_detection_larsmoan/downsample.py +++ /dev/null @@ -1,64 +0,0 @@ -import os -from PIL import Image -import os -import shutil -from tqdm import tqdm - -def downsample_images(source_folder, target_folder, n): - # Ensure the target directory exists - if not os.path.exists(target_folder): - os.makedirs(target_folder) - - # Get all files from the source folder - files = [f for f in os.listdir(source_folder) if os.path.isfile(os.path.join(source_folder, f)) and f.endswith('.jpg')] - - for file in tqdm(files, desc="Processing images"): - file_path = os.path.join(source_folder, file) - - # Try to open the file with PIL - try: - with Image.open(file_path) as img: - # Get new dimensions - width, height = img.size - new_dimensions = (width//n, height//n) - - # Resize and save - resized_img = img.resize(new_dimensions) - resized_img.save(os.path.join(target_folder, file)) - - except Exception as e: - print(f"Failed to process {file}. Error: {e}") - - - - -def copy_txt_files(source_folder, target_folder): - """ - Copy all .txt files from source_folder to target_folder. - - Parameters: - - source_folder: Path to the source directory - - target_folder: Path to the target directory - """ - - # Ensure the target directory exists - if not os.path.exists(target_folder): - os.makedirs(target_folder) - - # Iterate through all files in the source folder - for file_name in os.listdir(source_folder): - if file_name.endswith('.txt'): - source_file_path = os.path.join(source_folder, file_name) - target_file_path = os.path.join(target_folder, file_name) - - shutil.copy2(source_file_path, target_file_path) - #print(f"Copied {file_name} to {target_folder}") - - - -if __name__ == "__main__": - source = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017/val" - target = "/Users/larsmoan/Documents/UQ/COMP3710/PatternAnalysis-2023/lesion_detection_larsmoan/data/ISIC_2017_0.5/val" - n = 2 # Downsample by factor of 2 - downsample_images(source, target, n) - copy_txt_files(source_folder=source, target_folder=target) diff --git a/lesion_detection_larsmoan/isic_train.ipynb b/lesion_detection_larsmoan/isic_train.ipynb index ae88ab140..bed1b9011 100644 --- a/lesion_detection_larsmoan/isic_train.ipynb +++ b/lesion_detection_larsmoan/isic_train.ipynb @@ -1,28 +1,10 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "gpuType": "T4", - "authorship_tag": "ABX9TyPeFJdJGSone4v6j83wW1tV", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" - }, "cells": [ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ "\"Open" @@ -40,8 +22,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'PatternAnalysis-2023'...\n", "remote: Enumerating objects: 65, done.\u001b[K\n", @@ -60,9 +42,7 @@ }, { "cell_type": "code", - "source": [ - "!cd PatternAnalysis-2023/ && git submodule update --init" - ], + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -70,26 +50,24 @@ "id": "wgWX0QHsfnV6", "outputId": "e550fdf9-6178-445b-90ba-a4c03ed6cfb8" }, - "execution_count": 2, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Submodule 'lesion_detection_larsmoan/yolov7' (https://github.com/WongKinYiu/yolov7) registered for path 'lesion_detection_larsmoan/yolov7'\n", "Cloning into '/content/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7'...\n", "Submodule path 'lesion_detection_larsmoan/yolov7': checked out '84932d70fb9e2932d0a70e4a1f02a1d6dd1dd6ca'\n" ] } + ], + "source": [ + "!cd PatternAnalysis-2023/ && git submodule update --init" ] }, { "cell_type": "code", - "source": [ - "import os\n", - "os.chdir('/content/PatternAnalysis-2023/lesion_detection_larsmoan/')\n", - "!ls" - ], + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -97,21 +75,30 @@ "id": "ss3upKMvk_9Z", "outputId": "64a05888-36b3-4e76-d177-7f1764ef8142" }, - "execution_count": 4, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "dataset.py\t hyp.scratch.p5.yaml predict.py\t slurm.sh yolov7_isic_cfg.yaml\n", "dataset_utils.ipynb hyp.scratch.p6.yaml README.md\t utils.py\n", "downsample.py\t modules.py\t\t run_custom_train.sh yolov7\n" ] } + ], + "source": [ + "import os\n", + "os.chdir('/content/PatternAnalysis-2023/lesion_detection_larsmoan/')\n", + "!ls" ] }, { "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "mErp_yNneMej" + }, + "outputs": [], "source": [ "import gdown\n", "import os\n", @@ -145,20 +132,11 @@ " print(f\"Files have been extracted to {folder_path}\")\n", " else:\n", " print(f\"Folder {folder_path} already exists.\")" - ], - "metadata": { - "id": "mErp_yNneMej" - }, - "execution_count": 5, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "dataset_folder = \"data\"\n", - "url = \"https://drive.google.com/uc?id=19HcgRBuXyhzxsukE2jHPnBkC0hEk_aMJ\"\n", - "download_and_unzip(dataset_folder, url)" - ], + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -166,18 +144,17 @@ "id": "GAROAZdheOBE", "outputId": "c94c1a0e-1de9-4bd3-c51f-b8a9f851fa93" }, - "execution_count": 6, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Folder data does not exist. Downloading the zip file...\n" ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "Downloading...\n", "From: https://drive.google.com/uc?id=19HcgRBuXyhzxsukE2jHPnBkC0hEk_aMJ\n", @@ -186,20 +163,24 @@ ] }, { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Unzipping the downloaded file...\n", "Files have been extracted to data\n" ] } + ], + "source": [ + "dataset_folder = \"data\"\n", + "url = \"https://drive.google.com/uc?id=19HcgRBuXyhzxsukE2jHPnBkC0hEk_aMJ\"\n", + "url_05= \"https://drive.google.com/uc?id=1YI3pwanX35i7NCIxKnfXBozXiyQZcGbL\"\n", + "download_and_unzip(dataset_folder, url)" ] }, { "cell_type": "code", - "source": [ - "!python3 yolov7/train.py --epochs 100 --workers 32 --device 0 --batch-size 16 --data data/ISIC_2017_downsampled/isic.yaml --img 256 256 --cfg yolov7_isic_cfg.yaml --weights '' --name yolov7_isic --hyp hyp.scratch.p5.yaml" - ], + "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -207,11 +188,10 @@ "id": "K71MTSz2eykw", "outputId": "c7846616-6844-49e3-c9f3-85815367f286" }, - "execution_count": 17, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "2023-10-16 04:52:10.996744: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", @@ -436,13 +416,14 @@ "^C\n" ] } + ], + "source": [ + "!python3 yolov7/train.py --epochs 100 --workers 32 --device 0 --batch-size 16 --data data/ISIC_2017_downsampled/isic.yaml --img 256 256 --cfg yolov7_isic_cfg.yaml --weights '' --name yolov7_isic --hyp hyp.scratch.p5.yaml" ] }, { "cell_type": "code", - "source": [ - "!python yolov7/detect.py --weights runs/train/yolov7_isic9/weights/best.pt --conf 0.2 --img-size 640 --source data/ISIC_2017_downsampled/test_downsampled" - ], + "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -450,11 +431,10 @@ "id": "OOFoLaI9uCof", "outputId": "8151d7a6-ddaa-4c70-e0f1-dac59f440d8c" }, - "execution_count": 20, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Namespace(weights=['runs/train/yolov7_isic9/weights/best.pt'], source='data/ISIC_2017_downsampled/test_downsampled', img_size=640, conf_thres=0.2, iou_thres=0.45, device='', view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', exist_ok=False, no_trace=False)\n", "YOLOR 🚀 v0.1-126-g84932d7 torch 2.0.1+cu118 CUDA:0 (Tesla T4, 15101.8125MB)\n", @@ -1674,7 +1654,28 @@ "Done. (20.704s)\n" ] } + ], + "source": [ + "!python yolov7/detect.py --weights runs/train/yolov7_isic9/weights/best.pt --conf 0.2 --img-size 640 --source data/ISIC_2017_downsampled/test_downsampled" ] } - ] -} \ No newline at end of file + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "authorship_tag": "ABX9TyPeFJdJGSone4v6j83wW1tV", + "gpuType": "T4", + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/lesion_detection_larsmoan/slurm.sh b/lesion_detection_larsmoan/slurm.sh index 66bebab85..cb3869d59 100644 --- a/lesion_detection_larsmoan/slurm.sh +++ b/lesion_detection_larsmoan/slurm.sh @@ -10,6 +10,9 @@ #SBATCH --partition=test #SBATCH --gres=gpu:1 -conda activate gpu_fix +conda activate gpu-env + +module load cuda +nvidia-smi python check_torch.py \ No newline at end of file From efef3f25812b141361c3926cd19f957d02af847a Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Wed, 18 Oct 2023 16:04:47 +1000 Subject: [PATCH 18/42] Added working rangpur scripts and requirements.txt --- err.txt | 6 -- lesion_detection_larsmoan/.gitignore | 4 +- lesion_detection_larsmoan/README.md | 5 + lesion_detection_larsmoan/check_torch.py | 2 +- lesion_detection_larsmoan/dataset_utils.py | 9 +- lesion_detection_larsmoan/requirements.txt | 96 +++++++++++++++++++ lesion_detection_larsmoan/run_custom_train.sh | 5 +- lesion_detection_larsmoan/slurm.sh | 6 +- out.txt | 0 9 files changed, 120 insertions(+), 13 deletions(-) delete mode 100644 err.txt create mode 100644 lesion_detection_larsmoan/requirements.txt delete mode 100644 out.txt diff --git a/err.txt b/err.txt deleted file mode 100644 index 183dcbd36..000000000 --- a/err.txt +++ /dev/null @@ -1,6 +0,0 @@ - -EnvironmentNameNotFound: Could not find conda environment: pytorch -You can list all discoverable environments with `conda info --envs`. - - -python3: can't open file '/home/Student/s4827064/PatternAnalysis-2023/yolov7/train.py': [Errno 2] No such file or directory diff --git a/lesion_detection_larsmoan/.gitignore b/lesion_detection_larsmoan/.gitignore index f08965c56..402532433 100644 --- a/lesion_detection_larsmoan/.gitignore +++ b/lesion_detection_larsmoan/.gitignore @@ -8,4 +8,6 @@ wandb .DS_Store .venv -.env \ No newline at end of file +.env + +*.pt \ No newline at end of file diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index c31747604..a38d2ae62 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -74,6 +74,11 @@ dataset/ └── img_n.txt ``` +The prepocessed dataset can be downloaded from this link: +https://drive.google.com/uc?id=1YI3pwanX35i7NCIxKnfXBozXiyQZcGbL + +Which also happens to be the default argument to downloand_and_unzip() from the file dataset_utils.py + ### Usage diff --git a/lesion_detection_larsmoan/check_torch.py b/lesion_detection_larsmoan/check_torch.py index 3925c9e3e..c068631d7 100644 --- a/lesion_detection_larsmoan/check_torch.py +++ b/lesion_detection_larsmoan/check_torch.py @@ -2,4 +2,4 @@ import torch print(torch.__version__) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') -print(device) +print(device) \ No newline at end of file diff --git a/lesion_detection_larsmoan/dataset_utils.py b/lesion_detection_larsmoan/dataset_utils.py index cf54ee70a..f90d633b7 100644 --- a/lesion_detection_larsmoan/dataset_utils.py +++ b/lesion_detection_larsmoan/dataset_utils.py @@ -197,7 +197,7 @@ def copy_txt_files(source_folder, destination_folder): #Used to download and unzip compressed dataset from google drive if the dataset folder doesn't exist -def download_and_unzip(dataset_folder, download_url="https://drive.google.com/uc?id=19HcgRBuXyhzxsukE2jHPnBkC0hEk_aMJ"): +def download_and_unzip(dataset_folder, download_url="https://drive.google.com/uc?id=1YI3pwanX35i7NCIxKnfXBozXiyQZcGbL"): if not os.path.exists(dataset_folder): print(f"Folder {dataset_folder} does not exist. Downloading the zip file...") @@ -215,3 +215,10 @@ def download_and_unzip(dataset_folder, download_url="https://drive.google.com/uc print(f"Files have been extracted to {dataset_folder}") else: print(f"Folder {dataset_folder} already exists.") + +if __name__ == "__main__": + download_and_unzip("/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/data/") + weights_url = "https://drive.google.com/uc?id=1mAu29ZlOTn3csjnZ5fmro10kY3XxAddC" + weights_out = "yolov7_training.pt" + gdown.download(weights_url, weights_out, quiet=False) + \ No newline at end of file diff --git a/lesion_detection_larsmoan/requirements.txt b/lesion_detection_larsmoan/requirements.txt new file mode 100644 index 000000000..1b47cc8f1 --- /dev/null +++ b/lesion_detection_larsmoan/requirements.txt @@ -0,0 +1,96 @@ +absl-py==1.4.0 +aiohttp==3.8.5 +aiosignal==1.2.0 +appdirs==1.4.4 +async-timeout==4.0.2 +attrs==23.1.0 +beautifulsoup4==4.12.2 +blinker==1.6.2 +Bottleneck==1.3.5 +brotlipy==0.7.0 +cachetools==4.2.2 +certifi==2023.7.22 +cffi==1.15.1 +charset-normalizer==2.0.4 +click==8.1.7 +colorama==0.4.6 +contourpy==1.0.5 +cryptography==41.0.3 +cycler==0.11.0 +docker-pycreds==0.4.0 +filelock==3.9.0 +fonttools==4.25.0 +frozenlist==1.3.3 +gdown==4.7.1 +gitdb==4.0.10 +GitPython==3.1.38 +gmpy2==2.1.2 +google-auth==2.22.0 +google-auth-oauthlib==0.5.2 +grpcio==1.48.2 +idna==3.4 +Jinja2==3.1.2 +kiwisolver==1.4.4 +Markdown==3.4.1 +MarkupSafe==2.1.1 +matplotlib==3.7.2 +mkl-fft==1.3.8 +mkl-random==1.2.4 +mkl-service==2.4.0 +mpmath==1.3.0 +multidict==6.0.2 +munkres==1.1.4 +networkx==3.1 +numexpr==2.8.7 +numpy==1.26.0 +oauthlib==3.2.2 +opencv-python==4.8.1.78 +packaging==23.1 +pandas==2.1.1 +pathtools==0.1.2 +Pillow==10.0.1 +pip==23.3 +ply==3.11 +protobuf==3.20.3 +psutil==5.9.0 +pyasn1==0.4.8 +pyasn1-modules==0.2.8 +pycparser==2.21 +PyJWT==2.4.0 +pyOpenSSL==23.2.0 +pyparsing==3.0.9 +PyQt5-sip==12.11.0 +PySocks==1.7.1 +python-dateutil==2.8.2 +pytz==2023.3.post1 +PyYAML==6.0 +requests==2.31.0 +requests-oauthlib==1.3.0 +rsa==4.7.2 +scipy==1.11.3 +seaborn==0.12.2 +sentry-sdk==1.32.0 +setproctitle==1.2.2 +setuptools==68.0.0 +sip==6.6.2 +six==1.16.0 +smmap==3.0.5 +soupsieve==2.5 +sympy==1.11.1 +tensorboard==2.12.1 +tensorboard-data-server==0.7.0 +tensorboard-plugin-wit==1.8.1 +toml==0.10.2 +torch==2.0.1 +torchaudio==2.0.2 +torchvision==0.15.2 +tornado==6.3.3 +tqdm==4.66.1 +triton==2.0.0 +typing_extensions==4.7.1 +tzdata==2023.3 +urllib3==1.26.16 +wandb==0.15.12 +Werkzeug==2.2.3 +wheel==0.41.2 +yarl==1.8.1 \ No newline at end of file diff --git a/lesion_detection_larsmoan/run_custom_train.sh b/lesion_detection_larsmoan/run_custom_train.sh index 94cdc1593..4fd7fa488 100755 --- a/lesion_detection_larsmoan/run_custom_train.sh +++ b/lesion_detection_larsmoan/run_custom_train.sh @@ -11,6 +11,7 @@ #SBATCH --gres=gpu:1 # Activate Python environment (optional) -conda activate gpu_fix +conda activate torch-gpu +module load cuda -python3 yolov7/train.py --device 0 --workers 8 --batch-size 2 --data data/ISIC_2017_downsampled/isic.yaml --img 256 256 --cfg yolov7_isic_cfg.yaml --weights '' --name yolov7 --hyp hyp.scratch.p5.yaml \ No newline at end of file +python3 yolov7/train.py --device 0 --workers 8 --batch-size 2 --data data/ISIC_2017_0.5/isic.yaml --img 1024 1024 --cfg yolov7_isic_cfg.yaml --weights 'yolov7_training.pt' --name yolov7_downsampled_rangpur --hyp hyp.scratch.p5.yaml \ No newline at end of file diff --git a/lesion_detection_larsmoan/slurm.sh b/lesion_detection_larsmoan/slurm.sh index cb3869d59..1a9e20a8f 100644 --- a/lesion_detection_larsmoan/slurm.sh +++ b/lesion_detection_larsmoan/slurm.sh @@ -10,9 +10,11 @@ #SBATCH --partition=test #SBATCH --gres=gpu:1 -conda activate gpu-env +conda activate torch-gpu module load cuda nvidia-smi -python check_torch.py \ No newline at end of file +python dataset_utils.py + + diff --git a/out.txt b/out.txt deleted file mode 100644 index e69de29bb..000000000 From b01e8e32e6d5d727e59eade32becb5bb8187a319 Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Wed, 18 Oct 2023 16:15:55 +1000 Subject: [PATCH 19/42] gitignore change --- lesion_detection_larsmoan/.gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/lesion_detection_larsmoan/.gitignore b/lesion_detection_larsmoan/.gitignore index f08965c56..0e5d4addd 100644 --- a/lesion_detection_larsmoan/.gitignore +++ b/lesion_detection_larsmoan/.gitignore @@ -8,4 +8,5 @@ wandb .DS_Store .venv -.env \ No newline at end of file +.env +*.pt \ No newline at end of file From 0482f05ea1809466cf137de1ea5dfccae414485d Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Wed, 18 Oct 2023 18:25:33 +1000 Subject: [PATCH 20/42] Changed training file to correct batch size, utilizes the GPU better --- lesion_detection_larsmoan/run_custom_train.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lesion_detection_larsmoan/run_custom_train.sh b/lesion_detection_larsmoan/run_custom_train.sh index 4fd7fa488..e1668b5e5 100755 --- a/lesion_detection_larsmoan/run_custom_train.sh +++ b/lesion_detection_larsmoan/run_custom_train.sh @@ -14,4 +14,4 @@ conda activate torch-gpu module load cuda -python3 yolov7/train.py --device 0 --workers 8 --batch-size 2 --data data/ISIC_2017_0.5/isic.yaml --img 1024 1024 --cfg yolov7_isic_cfg.yaml --weights 'yolov7_training.pt' --name yolov7_downsampled_rangpur --hyp hyp.scratch.p5.yaml \ No newline at end of file +python3 yolov7/train.py --device 0 --workers 1 --batch-size 4 --data data/ISIC_2017_0.5/isic.yaml --img 1024 1024 --cfg yolov7_isic_cfg.yaml --weights 'yolov7_training.pt' --name yolov7_downsampled_rangpur --hyp hyp.scratch.p5.yaml \ No newline at end of file From ef84a7818117264ebb77c0e0352f34d7adb25bc3 Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Wed, 18 Oct 2023 18:38:30 +1000 Subject: [PATCH 21/42] Removed some unneccesary stuff in readme --- lesion_detection_larsmoan/README.md | 47 +++++++---------------------- 1 file changed, 11 insertions(+), 36 deletions(-) diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index a38d2ae62..25d33ab5a 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -10,26 +10,21 @@ - [Model Architecture](#model-architecture) - [Training](#training) - [Results](#results) - - [Contributing](#contributing) - - [License](#license) - - [Acknowledgements](#acknowledgements) - -### Todo -- [x] Create my own training script that works on top of yolov7/train.py -- [x] Make a downsampled version of the dataset. -- [x] Manage a way of logging the runs to wandb -- [x] Zip and upload dataset to gdrive, and make functions for fetching the dataset from the cluster. -- [x] Make training scripts for use on the rangpur cluster. -- [ ] Manage to train on the downsampled dataset using the cluster. Need gpu version of CUDA for this to work. ### Installation -- Prerequisites: List any pre-required libraries or software. +- Prerequisites: +python=3.10.12 +torch=2.01 +cuda=11.7 + - Installation steps: - ```bash - pip install -r requirements.txt - ``` +git clone git@github.com:larsmoan/PatternAnalysis-2023.git +git submodule init +git submodule update + +The last two commands are nedded as yolov7 is installed as a git submodule. ### Dataset https://challenge.isic-archive.com/data/#2017 @@ -109,24 +104,4 @@ Training the yolov7 model on the dataset can be done in two different ways: - Summarize the results obtained. - Any metrics used (e.g., accuracy, F1-score). -- Visual results (e.g., plots, graphs) if applicable. - -### Contributing - -- Details on how others can contribute to this project. -- Any specific guidelines for contributing. - -### License - -- Licensing information. For example: - ``` - This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details. - ``` - -### Acknowledgements - -- Any credits or acknowledgments for third-party resources or researchers. - ---- - -Remember, the above is just a basic skeleton. Depending on the project's complexity and breadth, you might want to add more sections or elaborate on the existing ones. \ No newline at end of file +- Visual results (e.g., plots, graphs) if applicable. \ No newline at end of file From 5d757a9d55d3fce6cc361a3e9cdc0b7f1adfe2fc Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Wed, 18 Oct 2023 18:46:02 +1000 Subject: [PATCH 22/42] readme work --- lesion_detection_larsmoan/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index 25d33ab5a..ab6753ff6 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -19,10 +19,13 @@ python=3.10.12 torch=2.01 cuda=11.7 + - Installation steps: +``` git clone git@github.com:larsmoan/PatternAnalysis-2023.git git submodule init git submodule update +``` The last two commands are nedded as yolov7 is installed as a git submodule. From a90f08e3e02a17b2cacec19efb1a63bfcdc6a5c3 Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Wed, 18 Oct 2023 19:08:39 +1000 Subject: [PATCH 23/42] documentation --- lesion_detection_larsmoan/README.md | 48 +++++++++++++++++------------ 1 file changed, 28 insertions(+), 20 deletions(-) diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index ab6753ff6..e977ce21c 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -13,37 +13,45 @@ ### Installation - -- Prerequisites: -python=3.10.12 -torch=2.01 -cuda=11.7 - - -- Installation steps: +- Prerequisites: python=3.10.12 && cuda=11.7 ``` git clone git@github.com:larsmoan/PatternAnalysis-2023.git -git submodule init +git submodule init git submodule update +pip install -r requirements.txt ``` -The last two commands are nedded as yolov7 is installed as a git submodule. +## Dataset + +**Source**: [ISIC 2017 Dataset](https://challenge.isic-archive.com/data/#2017) + +### Overview +Each image comes with corresponding label and segmentation file highlighting the lesion. +- **Training Set**: + - 2000 images. + +- **Validation Set**: + - 600 images. + +- **Test Set**: + - 150 images. -### Dataset -https://challenge.isic-archive.com/data/#2017 -- Description of the dataset. -The original dataset consist of 2000 training images of skin lesions with corresponding labels and segmentation files for the specific part of the picture that is the lesion. -The validation set consist of 600 images with corresponding labels and the test set consists of 150 images with labels. +**Lesion Classes**: +- `Melanoma` +- `Seborrheic Keratosis` +- `Nevi / Uknown`: Technically known as a benign skin lesion. Commonly referred to as a mole. -The different classes that are present in the dataset is: Melanoma, Nevi, and Seborrheic Keratosis. Where Nevi (Unknown) is the technical term for a benign skin lesion, often referred to as a mole. +### Preprocessing +Given that the dataset provides segmentation files, there's a need for preprocessing to convert these labels into YOLO bounding box labels. -- Preprocessing steps (if any). -Since the dataset comes with segmentation files preprocessing is needed to convert these lables into yolo bbox labels with the corresponding class. +Steps include: +1. Identify the maximum and minimum coordinates within the segmentation area. +2. Fit a bounding box around this region. +3. Assign the class based on the label provided in the associated CSV file. -This consist of mainly finding the max and min pixels of the segmentation area and fitting a bounding box to this area. The class is then determined by the label given in the csv file. -The dataset itself also needs to be refactored a bit to work with yolov7, therefore the structure is changed to the following: +The dataset itself also needs to be refactored a bit to work with YOLOV7, therefore the structure is changed to the following: ``` dataset/ │ From 3d29c3de53199918e3309f4e1ae8e71eaba8f421 Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Thu, 19 Oct 2023 11:33:48 +1000 Subject: [PATCH 24/42] Changed some hyperparams for augmenting the data more during training --- lesion_detection_larsmoan/README.md | 27 +-- lesion_detection_larsmoan/dataset_utils.py | 7 +- lesion_detection_larsmoan/err_p6_p100.txt | 159 ++++++++++++++++++ lesion_detection_larsmoan/hyp.scratch.p5.yaml | 2 +- lesion_detection_larsmoan/out_p6_p100.txt | 7 + lesion_detection_larsmoan/run_custom_train.sh | 10 +- lesion_detection_larsmoan/run_inference.sh | 2 + 7 files changed, 194 insertions(+), 20 deletions(-) create mode 100644 lesion_detection_larsmoan/err_p6_p100.txt create mode 100644 lesion_detection_larsmoan/out_p6_p100.txt create mode 100644 lesion_detection_larsmoan/run_inference.sh diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index e977ce21c..7cce3fad4 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -81,23 +81,28 @@ dataset/ ``` The prepocessed dataset can be downloaded from this link: -https://drive.google.com/uc?id=1YI3pwanX35i7NCIxKnfXBozXiyQZcGbL +https://drive.google.com/uc?id=1YI3pwanX35i7NCIxKnfXBozXiyQZcGbL or from [dataset_utils.py](./dataset_utils.py) -Which also happens to be the default argument to downloand_and_unzip() from the file dataset_utils.py ### Usage - -- How to use the code/model. -- Example commands: - ```bash - python your_script_name.py --arg1 value1 --arg2 value2 +- Download the dataset and pretrained yolov7 weights: + ``` + python dataset_utils.py + ``` +- Train the model: + Using rangpur cluster: + ``` + bash run_custom_train.sh + ``` + Using Google Colab GPU: + [isic_train.ipynb](./isic_train.ipynb) +- Run inference on testset + ``` + bash ``` -### Model Architecture -The model and architecture used in this project is the open source yolov7 model: -https://github.com/WongKinYiu/yolov7 - +### Model Architecture: open source [YOLOV7 Model](https://github.com/WongKinYiu/yolov7) ### Training Training the yolov7 model on the dataset can be done in two different ways: diff --git a/lesion_detection_larsmoan/dataset_utils.py b/lesion_detection_larsmoan/dataset_utils.py index f90d633b7..624b9c683 100644 --- a/lesion_detection_larsmoan/dataset_utils.py +++ b/lesion_detection_larsmoan/dataset_utils.py @@ -217,8 +217,9 @@ def download_and_unzip(dataset_folder, download_url="https://drive.google.com/uc print(f"Folder {dataset_folder} already exists.") if __name__ == "__main__": - download_and_unzip("/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/data/") + #download_and_unzip("/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/data/") weights_url = "https://drive.google.com/uc?id=1mAu29ZlOTn3csjnZ5fmro10kY3XxAddC" - weights_out = "yolov7_training.pt" - gdown.download(weights_url, weights_out, quiet=False) + yolov7_weights_out = "yolov7_training.pt" + #gdown.download(weights_url, yolov7_weights_out, quiet=False) + \ No newline at end of file diff --git a/lesion_detection_larsmoan/err_p6_p100.txt b/lesion_detection_larsmoan/err_p6_p100.txt new file mode 100644 index 000000000..422cbad65 --- /dev/null +++ b/lesion_detection_larsmoan/err_p6_p100.txt @@ -0,0 +1,159 @@ +YOLOR 🚀 2023-10-18 torch 2.0.1 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB) + +Namespace(weights='yolov7_training.pt', cfg='yolov7_isic_cfg.yaml', data='data/ISIC_2017_0.5/isic.yaml', hyp='hyp.scratch.p6.yaml', epochs=300, batch_size=32, img_size=[512, 512], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=8, project='runs/train', entity=None, name='yolov7_downsampled_rangpur', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs/train/yolov7_downsampled_rangpur4', total_batch_size=32) +tensorboard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ +hyperparameters: lr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15, loss_ota=1 +wandb: Currently logged in as: lars-ostberg-moan. Use `wandb login --relogin` to force relogin +wandb: - Waiting for wandb.init()... wandb: \ Waiting for wandb.init()... wandb: Tracking run with wandb version 0.15.12 +wandb: Run data is saved locally in /home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/wandb/run-20231019_105156-8n9w9vyq +wandb: Run `wandb offline` to turn off syncing. +wandb: Syncing run yolov7_downsampled_rangpur4 +wandb: ⭐️ View project at https://wandb.ai/lars-ostberg-moan/YOLOR +wandb: 🚀 View run at https://wandb.ai/lars-ostberg-moan/YOLOR/runs/8n9w9vyq + + from n params module arguments + 0 -1 1 928 models.common.Conv [3, 32, 3, 1] + 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] + 2 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] + 4 -1 1 8320 models.common.Conv [128, 64, 1, 1] + 5 -2 1 8320 models.common.Conv [128, 64, 1, 1] + 6 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 7 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 8 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 9 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 10 [-1, -3, -5, -6] 1 0 models.common.Concat [1] + 11 -1 1 66048 models.common.Conv [256, 256, 1, 1] + 12 -1 1 0 models.common.MP [] + 13 -1 1 33024 models.common.Conv [256, 128, 1, 1] + 14 -3 1 33024 models.common.Conv [256, 128, 1, 1] + 15 -1 1 147712 models.common.Conv [128, 128, 3, 2] + 16 [-1, -3] 1 0 models.common.Concat [1] + 17 -1 1 33024 models.common.Conv [256, 128, 1, 1] + 18 -2 1 33024 models.common.Conv [256, 128, 1, 1] + 19 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 20 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 21 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 22 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 23 [-1, -3, -5, -6] 1 0 models.common.Concat [1] + 24 -1 1 263168 models.common.Conv [512, 512, 1, 1] + 25 -1 1 0 models.common.MP [] + 26 -1 1 131584 models.common.Conv [512, 256, 1, 1] + 27 -3 1 131584 models.common.Conv [512, 256, 1, 1] + 28 -1 1 590336 models.common.Conv [256, 256, 3, 2] + 29 [-1, -3] 1 0 models.common.Concat [1] + 30 -1 1 131584 models.common.Conv [512, 256, 1, 1] + 31 -2 1 131584 models.common.Conv [512, 256, 1, 1] + 32 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 33 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 34 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 35 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 36 [-1, -3, -5, -6] 1 0 models.common.Concat [1] + 37 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] + 38 -1 1 0 models.common.MP [] + 39 -1 1 525312 models.common.Conv [1024, 512, 1, 1] + 40 -3 1 525312 models.common.Conv [1024, 512, 1, 1] + 41 -1 1 2360320 models.common.Conv [512, 512, 3, 2] + 42 [-1, -3] 1 0 models.common.Concat [1] + 43 -1 1 262656 models.common.Conv [1024, 256, 1, 1] + 44 -2 1 262656 models.common.Conv [1024, 256, 1, 1] + 45 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 46 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 47 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 48 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 49 [-1, -3, -5, -6] 1 0 models.common.Concat [1] + 50 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] + 51 -1 1 7609344 models.common.SPPCSPC [1024, 512, 1] + 52 -1 1 131584 models.common.Conv [512, 256, 1, 1] + 53 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] + 54 37 1 262656 models.common.Conv [1024, 256, 1, 1] + 55 [-1, -2] 1 0 models.common.Concat [1] + 56 -1 1 131584 models.common.Conv [512, 256, 1, 1] + 57 -2 1 131584 models.common.Conv [512, 256, 1, 1] + 58 -1 1 295168 models.common.Conv [256, 128, 3, 1] + 59 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 60 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 61 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 62[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] + 63 -1 1 262656 models.common.Conv [1024, 256, 1, 1] + 64 -1 1 33024 models.common.Conv [256, 128, 1, 1] + 65 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] + 66 24 1 65792 models.common.Conv [512, 128, 1, 1] + 67 [-1, -2] 1 0 models.common.Concat [1] + 68 -1 1 33024 models.common.Conv [256, 128, 1, 1] + 69 -2 1 33024 models.common.Conv [256, 128, 1, 1] + 70 -1 1 73856 models.common.Conv [128, 64, 3, 1] + 71 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 72 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 73 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 74[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] + 75 -1 1 65792 models.common.Conv [512, 128, 1, 1] + 76 -1 1 0 models.common.MP [] + 77 -1 1 16640 models.common.Conv [128, 128, 1, 1] + 78 -3 1 16640 models.common.Conv [128, 128, 1, 1] + 79 -1 1 147712 models.common.Conv [128, 128, 3, 2] + 80 [-1, -3, 63] 1 0 models.common.Concat [1] + 81 -1 1 131584 models.common.Conv [512, 256, 1, 1] + 82 -2 1 131584 models.common.Conv [512, 256, 1, 1] + 83 -1 1 295168 models.common.Conv [256, 128, 3, 1] + 84 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 85 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 86 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 87[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] + 88 -1 1 262656 models.common.Conv [1024, 256, 1, 1] + 89 -1 1 0 models.common.MP [] + 90 -1 1 66048 models.common.Conv [256, 256, 1, 1] + 91 -3 1 66048 models.common.Conv [256, 256, 1, 1] + 92 -1 1 590336 models.common.Conv [256, 256, 3, 2] + 93 [-1, -3, 51] 1 0 models.common.Concat [1] + 94 -1 1 525312 models.common.Conv [1024, 512, 1, 1] + 95 -2 1 525312 models.common.Conv [1024, 512, 1, 1] + 96 -1 1 1180160 models.common.Conv [512, 256, 3, 1] + 97 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 98 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 99 -1 1 590336 models.common.Conv [256, 256, 3, 1] +100[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] +101 -1 1 1049600 models.common.Conv [2048, 512, 1, 1] +102 75 1 328704 models.common.RepConv [128, 256, 3, 1] +103 88 1 1312768 models.common.RepConv [256, 512, 3, 1] +104 101 1 5246976 models.common.RepConv [512, 1024, 3, 1] +105 [102, 103, 104] 1 44944 models.yolo.IDetect [3, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]] +/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1682343967769/work/aten/src/ATen/native/TensorShape.cpp:3483.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +Model Summary: 415 layers, 37207344 parameters, 37207344 gradients, 105.1 GFLOPS + +Transferred 555/566 items from yolov7_training.pt +Scaled weight_decay = 0.0005 +Optimizer groups: 95 .bias, 95 conv.weight, 98 other + train: Scanning 'data/ISIC_2017_0.5/train_downsampled.cache' images and labels... 2000 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 2000/2000 [00:00 Date: Thu, 19 Oct 2023 11:35:38 +1000 Subject: [PATCH 25/42] Altered gitignore to ignore output train files --- lesion_detection_larsmoan/.gitignore | 3 ++- lesion_detection_larsmoan/err_p6_p100.txt | 6 +++++- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/lesion_detection_larsmoan/.gitignore b/lesion_detection_larsmoan/.gitignore index 0e5d4addd..e651739ba 100644 --- a/lesion_detection_larsmoan/.gitignore +++ b/lesion_detection_larsmoan/.gitignore @@ -2,7 +2,8 @@ data/ __pycache__ out.txt err.txt - +err_p6_p100.txt +out_p6_p100.txt runs/ wandb diff --git a/lesion_detection_larsmoan/err_p6_p100.txt b/lesion_detection_larsmoan/err_p6_p100.txt index 422cbad65..d8fdfa47d 100644 --- a/lesion_detection_larsmoan/err_p6_p100.txt +++ b/lesion_detection_larsmoan/err_p6_p100.txt @@ -156,4 +156,8 @@ Starting training for 300 epochs... Class Images Labels P R mAP@.5 mAP@.5:.95: 0%| | 0/3 [00:00 Date: Fri, 20 Oct 2023 15:43:02 +1000 Subject: [PATCH 26/42] Finished training on the a100 --- lesion_detection_larsmoan/.gitignore | 1 + lesion_detection_larsmoan/README.md | 23 +- lesion_detection_larsmoan/dataset_utils.py | 10 +- lesion_detection_larsmoan/err_p6_p100.txt | 338 +++++++++++++++++- lesion_detection_larsmoan/hyp.scratch.p6.yaml | 2 +- lesion_detection_larsmoan/out_p6_p100.txt | 85 +++++ lesion_detection_larsmoan/run_custom_train.sh | 6 +- lesion_detection_larsmoan/run_test.sh | 16 + lesion_detection_larsmoan/slurm-93861.out | 219 ++++++++++++ lesion_detection_larsmoan/slurm-93881.out | 146 ++++++++ 10 files changed, 826 insertions(+), 20 deletions(-) create mode 100644 lesion_detection_larsmoan/run_test.sh create mode 100644 lesion_detection_larsmoan/slurm-93861.out create mode 100644 lesion_detection_larsmoan/slurm-93881.out diff --git a/lesion_detection_larsmoan/.gitignore b/lesion_detection_larsmoan/.gitignore index e651739ba..fa6f939aa 100644 --- a/lesion_detection_larsmoan/.gitignore +++ b/lesion_detection_larsmoan/.gitignore @@ -4,6 +4,7 @@ out.txt err.txt err_p6_p100.txt out_p6_p100.txt + runs/ wandb diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index 7cce3fad4..fbca32b4a 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -14,6 +14,9 @@ ### Installation - Prerequisites: python=3.10.12 && cuda=11.7 + +**A GPU cluster is used for this project, more specifically rangpur @ UQ. Therefore a lot of the training and inference scripts are based on slurm jobs. If needed this can easily be converted to run locally.** + ``` git clone git@github.com:larsmoan/PatternAnalysis-2023.git git submodule init @@ -22,7 +25,6 @@ pip install -r requirements.txt ``` ## Dataset - **Source**: [ISIC 2017 Dataset](https://challenge.isic-archive.com/data/#2017) ### Overview @@ -50,6 +52,7 @@ Steps include: 2. Fit a bounding box around this region. 3. Assign the class based on the label provided in the associated CSV file. +More information can be found in the file: [dataset_utils.py](./dataset_utils.py) The dataset itself also needs to be refactored a bit to work with YOLOV7, therefore the structure is changed to the following: ``` @@ -93,27 +96,23 @@ https://drive.google.com/uc?id=1YI3pwanX35i7NCIxKnfXBozXiyQZcGbL or from [datase - Train the model: Using rangpur cluster: ``` - bash run_custom_train.sh + sbatch run_custom_train.sh ``` - Using Google Colab GPU: + Or using Google Colab: [isic_train.ipynb](./isic_train.ipynb) -- Run inference on testset +- Run inference on testset: ``` - bash + sbatch run_test.sh ``` ### Model Architecture: open source [YOLOV7 Model](https://github.com/WongKinYiu/yolov7) ### Training -Training the yolov7 model on the dataset can be done in two different ways: -1. Using the isic_train.ipynb script -- This utilizes the free GPU provided by google-colab for training. - -2. Using the rangpur cluster. -- Training can then be done using the run_custom_train.sh script. Here you can specify the number of epochs, batch size etc etc. Statistics from the training will be logged to the runs/ folder and wandb if that is enabled beforehand. +Training was mainly done on the rangpur cluster, - Details about the training process. -- Hyperparameters used. +- Hyperparameters used: + - Both p5 and p6 models were trained. - Training time, hardware details (if relevant). ### Results diff --git a/lesion_detection_larsmoan/dataset_utils.py b/lesion_detection_larsmoan/dataset_utils.py index 624b9c683..f98656555 100644 --- a/lesion_detection_larsmoan/dataset_utils.py +++ b/lesion_detection_larsmoan/dataset_utils.py @@ -197,7 +197,7 @@ def copy_txt_files(source_folder, destination_folder): #Used to download and unzip compressed dataset from google drive if the dataset folder doesn't exist -def download_and_unzip(dataset_folder, download_url="https://drive.google.com/uc?id=1YI3pwanX35i7NCIxKnfXBozXiyQZcGbL"): +def download_dataset(dataset_folder, download_url="https://drive.google.com/uc?id=1YI3pwanX35i7NCIxKnfXBozXiyQZcGbL"): if not os.path.exists(dataset_folder): print(f"Folder {dataset_folder} does not exist. Downloading the zip file...") @@ -217,9 +217,13 @@ def download_and_unzip(dataset_folder, download_url="https://drive.google.com/uc print(f"Folder {dataset_folder} already exists.") if __name__ == "__main__": - #download_and_unzip("/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/data/") + #Download the preprocessed version of ISIC2017 that has YOLO labels and correct structure + path_to_dset = "/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/data/" + download_datset(path_to_dset) + + #Download pretrained YOLOV7 weights (COCO) for transfer learning purposes weights_url = "https://drive.google.com/uc?id=1mAu29ZlOTn3csjnZ5fmro10kY3XxAddC" yolov7_weights_out = "yolov7_training.pt" - #gdown.download(weights_url, yolov7_weights_out, quiet=False) + gdown.download(weights_url, yolov7_weights_out, quiet=False) \ No newline at end of file diff --git a/lesion_detection_larsmoan/err_p6_p100.txt b/lesion_detection_larsmoan/err_p6_p100.txt index d8fdfa47d..9b7007a0b 100644 --- a/lesion_detection_larsmoan/err_p6_p100.txt +++ b/lesion_detection_larsmoan/err_p6_p100.txt @@ -160,4 +160,340 @@ Starting training for 300 epochs... Class Images Labels P R mAP@.5 mAP@.5:.95: 0%| | 0/3 [00:00 + train(hyp, opt, device, tb_writer) + File "/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/train.py", line 255, in train + testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt, # testloader + File "/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/utils/datasets.py", line 85, in create_dataloader + dataloader = loader(dataset, + File "/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/utils/datasets.py", line 103, in __init__ + self.iterator = super().__iter__() + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 441, in __iter__ + return self._get_iterator() + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 388, in _get_iterator + return _MultiProcessingDataLoaderIter(self) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1042, in __init__ + w.start() + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/multiprocessing/process.py", line 121, in start + self._popen = self._Popen(self) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/multiprocessing/context.py", line 224, in _Popen + return _default_context.get_context().Process._Popen(process_obj) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/multiprocessing/context.py", line 281, in _Popen + return Popen(process_obj) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/multiprocessing/popen_fork.py", line 19, in __init__ + self._launch(process_obj) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/multiprocessing/popen_fork.py", line 66, in _launch + self.pid = os.fork() +OSError: [Errno 12] Cannot allocate memory +wandb: Waiting for W&B process to finish... (failed 1). Press Control-C to abort syncing. +wandb: - 0.002 MB of 0.002 MB uploaded (0.000 MB deduped) wandb: \ 0.002 MB of 0.021 MB uploaded (0.000 MB deduped) wandb: | 0.021 MB of 0.021 MB uploaded (0.000 MB deduped) wandb: / 0.021 MB of 0.021 MB uploaded (0.000 MB deduped) wandb: 🚀 View run p_A100_b256 at: https://wandb.ai/lars-ostberg-moan/YOLOR/runs/kztu7cmr +wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s) +wandb: Find logs at: ./wandb/run-20231019_180153-kztu7cmr/logs +Exception in thread NetStatThr: +Traceback (most recent call last): + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/threading.py", line 1016, in _bootstrap_inner +Exception in thread IntMsgThr: +Traceback (most recent call last): + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/threading.py", line 1016, in _bootstrap_inner + self.run() + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/threading.py", line 953, in run + self.run() + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/threading.py", line 953, in run + self._target(*self._args, **self._kwargs) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/wandb_run.py", line 299, in check_internal_messages + self._target(*self._args, **self._kwargs) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/wandb_run.py", line 267, in check_network_status + self._loop_check_status( + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/wandb_run.py", line 223, in _loop_check_status + self._loop_check_status( + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/wandb_run.py", line 223, in _loop_check_status + local_handle = request() + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface.py", line 735, in deliver_network_status + local_handle = request() + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface.py", line 743, in deliver_internal_messages + return self._deliver_network_status(status) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface_shared.py", line 475, in _deliver_network_status + return self._deliver_internal_messages(internal_message) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface_shared.py", line 481, in _deliver_internal_messages + return self._deliver_record(record) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface_shared.py", line 428, in _deliver_record + return self._deliver_record(record) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface_shared.py", line 428, in _deliver_record + handle = mailbox._deliver_record(record, interface=self) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/mailbox.py", line 455, in _deliver_record + handle = mailbox._deliver_record(record, interface=self) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/mailbox.py", line 455, in _deliver_record + interface._publish(record) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface_sock.py", line 51, in _publish + interface._publish(record) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface_sock.py", line 51, in _publish + self._sock_client.send_record_publish(record) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 221, in send_record_publish + self._sock_client.send_record_publish(record) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 221, in send_record_publish + self.send_server_request(server_req) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 155, in send_server_request + self.send_server_request(server_req) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 155, in send_server_request + self._send_message(msg) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 152, in _send_message + self._send_message(msg) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 152, in _send_message + self._sendall_with_error_handle(header + data) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 130, in _sendall_with_error_handle + self._sendall_with_error_handle(header + data) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 130, in _sendall_with_error_handle + sent = self._sock.send(data) +BrokenPipeError: [Errno 32] Broken pipe + sent = self._sock.send(data) +BrokenPipeError: [Errno 32] Broken pipe diff --git a/lesion_detection_larsmoan/slurm-93881.out b/lesion_detection_larsmoan/slurm-93881.out new file mode 100644 index 000000000..7b1043f0d --- /dev/null +++ b/lesion_detection_larsmoan/slurm-93881.out @@ -0,0 +1,146 @@ +YOLOR 🚀 2023-10-18 torch 2.0.1 CUDA:0 (GRID A100-2-10C MIG 2g.10gb, 10235.875MB) + +Namespace(weights='yolov7_training.pt', cfg='yolov7_isic_cfg.yaml', data='data/ISIC_2017_0.5/isic.yaml', hyp='hyp.scratch.p6.yaml', epochs=300, batch_size=-1, img_size=[512, 512], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=8, project='runs/train', entity=None, name='p6_p100_b64', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs/train/p6_p100_b64', total_batch_size=-1) +tensorboard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ +hyperparameters: lr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.5, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15, loss_ota=1 +wandb: Currently logged in as: lars-ostberg-moan. Use `wandb login --relogin` to force relogin +wandb: - Waiting for wandb.init()... wandb: \ Waiting for wandb.init()... wandb: Tracking run with wandb version 0.15.12 +wandb: Run data is saved locally in /home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/wandb/run-20231019_170026-5wwfj06n +wandb: Run `wandb offline` to turn off syncing. +wandb: Syncing run p6_p100_b64 +wandb: ⭐️ View project at https://wandb.ai/lars-ostberg-moan/YOLOR +wandb: 🚀 View run at https://wandb.ai/lars-ostberg-moan/YOLOR/runs/5wwfj06n + + from n params module arguments + 0 -1 1 928 models.common.Conv [3, 32, 3, 1] + 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] + 2 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] + 4 -1 1 8320 models.common.Conv [128, 64, 1, 1] + 5 -2 1 8320 models.common.Conv [128, 64, 1, 1] + 6 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 7 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 8 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 9 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 10 [-1, -3, -5, -6] 1 0 models.common.Concat [1] + 11 -1 1 66048 models.common.Conv [256, 256, 1, 1] + 12 -1 1 0 models.common.MP [] + 13 -1 1 33024 models.common.Conv [256, 128, 1, 1] + 14 -3 1 33024 models.common.Conv [256, 128, 1, 1] + 15 -1 1 147712 models.common.Conv [128, 128, 3, 2] + 16 [-1, -3] 1 0 models.common.Concat [1] + 17 -1 1 33024 models.common.Conv [256, 128, 1, 1] + 18 -2 1 33024 models.common.Conv [256, 128, 1, 1] + 19 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 20 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 21 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 22 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 23 [-1, -3, -5, -6] 1 0 models.common.Concat [1] + 24 -1 1 263168 models.common.Conv [512, 512, 1, 1] + 25 -1 1 0 models.common.MP [] + 26 -1 1 131584 models.common.Conv [512, 256, 1, 1] + 27 -3 1 131584 models.common.Conv [512, 256, 1, 1] + 28 -1 1 590336 models.common.Conv [256, 256, 3, 2] + 29 [-1, -3] 1 0 models.common.Concat [1] + 30 -1 1 131584 models.common.Conv [512, 256, 1, 1] + 31 -2 1 131584 models.common.Conv [512, 256, 1, 1] + 32 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 33 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 34 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 35 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 36 [-1, -3, -5, -6] 1 0 models.common.Concat [1] + 37 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] + 38 -1 1 0 models.common.MP [] + 39 -1 1 525312 models.common.Conv [1024, 512, 1, 1] + 40 -3 1 525312 models.common.Conv [1024, 512, 1, 1] + 41 -1 1 2360320 models.common.Conv [512, 512, 3, 2] + 42 [-1, -3] 1 0 models.common.Concat [1] + 43 -1 1 262656 models.common.Conv [1024, 256, 1, 1] + 44 -2 1 262656 models.common.Conv [1024, 256, 1, 1] + 45 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 46 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 47 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 48 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 49 [-1, -3, -5, -6] 1 0 models.common.Concat [1] + 50 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] + 51 -1 1 7609344 models.common.SPPCSPC [1024, 512, 1] + 52 -1 1 131584 models.common.Conv [512, 256, 1, 1] + 53 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] + 54 37 1 262656 models.common.Conv [1024, 256, 1, 1] + 55 [-1, -2] 1 0 models.common.Concat [1] + 56 -1 1 131584 models.common.Conv [512, 256, 1, 1] + 57 -2 1 131584 models.common.Conv [512, 256, 1, 1] + 58 -1 1 295168 models.common.Conv [256, 128, 3, 1] + 59 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 60 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 61 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 62[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] + 63 -1 1 262656 models.common.Conv [1024, 256, 1, 1] + 64 -1 1 33024 models.common.Conv [256, 128, 1, 1] + 65 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] + 66 24 1 65792 models.common.Conv [512, 128, 1, 1] + 67 [-1, -2] 1 0 models.common.Concat [1] + 68 -1 1 33024 models.common.Conv [256, 128, 1, 1] + 69 -2 1 33024 models.common.Conv [256, 128, 1, 1] + 70 -1 1 73856 models.common.Conv [128, 64, 3, 1] + 71 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 72 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 73 -1 1 36992 models.common.Conv [64, 64, 3, 1] + 74[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] + 75 -1 1 65792 models.common.Conv [512, 128, 1, 1] + 76 -1 1 0 models.common.MP [] + 77 -1 1 16640 models.common.Conv [128, 128, 1, 1] + 78 -3 1 16640 models.common.Conv [128, 128, 1, 1] + 79 -1 1 147712 models.common.Conv [128, 128, 3, 2] + 80 [-1, -3, 63] 1 0 models.common.Concat [1] + 81 -1 1 131584 models.common.Conv [512, 256, 1, 1] + 82 -2 1 131584 models.common.Conv [512, 256, 1, 1] + 83 -1 1 295168 models.common.Conv [256, 128, 3, 1] + 84 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 85 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 86 -1 1 147712 models.common.Conv [128, 128, 3, 1] + 87[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] + 88 -1 1 262656 models.common.Conv [1024, 256, 1, 1] + 89 -1 1 0 models.common.MP [] + 90 -1 1 66048 models.common.Conv [256, 256, 1, 1] + 91 -3 1 66048 models.common.Conv [256, 256, 1, 1] + 92 -1 1 590336 models.common.Conv [256, 256, 3, 2] + 93 [-1, -3, 51] 1 0 models.common.Concat [1] + 94 -1 1 525312 models.common.Conv [1024, 512, 1, 1] + 95 -2 1 525312 models.common.Conv [1024, 512, 1, 1] + 96 -1 1 1180160 models.common.Conv [512, 256, 3, 1] + 97 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 98 -1 1 590336 models.common.Conv [256, 256, 3, 1] + 99 -1 1 590336 models.common.Conv [256, 256, 3, 1] +100[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] +101 -1 1 1049600 models.common.Conv [2048, 512, 1, 1] +102 75 1 328704 models.common.RepConv [128, 256, 3, 1] +103 88 1 1312768 models.common.RepConv [256, 512, 3, 1] +104 101 1 5246976 models.common.RepConv [512, 1024, 3, 1] +105 [102, 103, 104] 1 44944 models.yolo.IDetect [3, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]] +/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1682343967769/work/aten/src/ATen/native/TensorShape.cpp:3483.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] +Model Summary: 415 layers, 37207344 parameters, 37207344 gradients, 105.1 GFLOPS + +Transferred 555/566 items from yolov7_training.pt +Scaled weight_decay = -7.8125e-06 +Optimizer groups: 95 .bias, 95 conv.weight, 98 other + train: Scanning 'data/ISIC_2017_0.5/train_downsampled.cache' images and labels... 2000 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 2000/2000 [00:00 + train(hyp, opt, device, tb_writer) + File "/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/train.py", line 245, in train + dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, + File "/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/utils/datasets.py", line 85, in create_dataloader + dataloader = loader(dataset, + File "/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/utils/datasets.py", line 101, in __init__ + super().__init__(*args, **kwargs) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 357, in __init__ + batch_sampler = BatchSampler(sampler, batch_size, drop_last) + File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/utils/data/sampler.py", line 232, in __init__ + raise ValueError("batch_size should be a positive integer value, " +ValueError: batch_size should be a positive integer value, but got batch_size=-1 +wandb: Waiting for W&B process to finish... (failed 1). Press Control-C to abort syncing. +wandb: - 0.002 MB of 0.002 MB uploaded (0.000 MB deduped) wandb: \ 0.019 MB of 0.019 MB uploaded (0.000 MB deduped) wandb: 🚀 View run p6_p100_b64 at: https://wandb.ai/lars-ostberg-moan/YOLOR/runs/5wwfj06n +wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s) +wandb: Find logs at: ./wandb/run-20231019_170026-5wwfj06n/logs From 9bdc8100c8f445f4749a0c3b6a126eb48a05d528 Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Sat, 21 Oct 2023 13:31:53 +1000 Subject: [PATCH 27/42] Added results from test on p100 and batch 32 --- .gitmodules | 2 + lesion_detection_larsmoan/err_p6_p100.txt | 499 ---- lesion_detection_larsmoan/err_test.txt | 9 + lesion_detection_larsmoan/isic_mrcnn.py | 64 + lesion_detection_larsmoan/out_p6_p100.txt | 92 - lesion_detection_larsmoan/out_test.txt | 16 + .../results/yolov7_b32_p100/F1_curve.png | Bin 0 -> 194944 bytes .../results/yolov7_b32_p100/PR_curve.png | Bin 0 -> 125902 bytes .../results/yolov7_b32_p100/P_curve.png | Bin 0 -> 196251 bytes .../results/yolov7_b32_p100/R_curve.png | Bin 0 -> 158169 bytes .../yolov7_b32_p100/confusion_matrix.png | Bin 0 -> 131683 bytes .../yolov7_b32_p100/test_batch0_labels.jpg | Bin 0 -> 76800 bytes .../yolov7_b32_p100/test_batch0_pred.jpg | Bin 0 -> 76445 bytes .../yolov7_b32_p100/test_batch1_labels.jpg | Bin 0 -> 74711 bytes .../yolov7_b32_p100/test_batch1_pred.jpg | Bin 0 -> 75771 bytes .../yolov7_b32_p100/test_batch2_labels.jpg | Bin 0 -> 75649 bytes .../yolov7_b32_p100/test_batch2_pred.jpg | Bin 0 -> 72968 bytes lesion_detection_larsmoan/run_test.sh | 5 +- lesion_detection_larsmoan/slurm-93861.out | 219 -- lesion_detection_larsmoan/slurm-93881.out | 146 - lesion_detection_larsmoan/utils.py | 2 - poetry.lock | 2615 ----------------- pyproject.toml | 35 - 23 files changed, 94 insertions(+), 3610 deletions(-) delete mode 100644 lesion_detection_larsmoan/err_p6_p100.txt create mode 100644 lesion_detection_larsmoan/err_test.txt create mode 100644 lesion_detection_larsmoan/isic_mrcnn.py delete mode 100644 lesion_detection_larsmoan/out_p6_p100.txt create mode 100644 lesion_detection_larsmoan/out_test.txt create mode 100644 lesion_detection_larsmoan/results/yolov7_b32_p100/F1_curve.png create mode 100644 lesion_detection_larsmoan/results/yolov7_b32_p100/PR_curve.png create mode 100644 lesion_detection_larsmoan/results/yolov7_b32_p100/P_curve.png create mode 100644 lesion_detection_larsmoan/results/yolov7_b32_p100/R_curve.png create mode 100644 lesion_detection_larsmoan/results/yolov7_b32_p100/confusion_matrix.png create mode 100644 lesion_detection_larsmoan/results/yolov7_b32_p100/test_batch0_labels.jpg create mode 100644 lesion_detection_larsmoan/results/yolov7_b32_p100/test_batch0_pred.jpg create mode 100644 lesion_detection_larsmoan/results/yolov7_b32_p100/test_batch1_labels.jpg create mode 100644 lesion_detection_larsmoan/results/yolov7_b32_p100/test_batch1_pred.jpg create mode 100644 lesion_detection_larsmoan/results/yolov7_b32_p100/test_batch2_labels.jpg create mode 100644 lesion_detection_larsmoan/results/yolov7_b32_p100/test_batch2_pred.jpg delete mode 100644 lesion_detection_larsmoan/slurm-93861.out delete mode 100644 lesion_detection_larsmoan/slurm-93881.out delete mode 100644 poetry.lock delete mode 100644 pyproject.toml diff --git a/.gitmodules b/.gitmodules index a574ccd76..03d228cd2 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,5 @@ [submodule "lesion_detection_larsmoan/yolov7"] path = lesion_detection_larsmoan/yolov7 url = https://github.com/WongKinYiu/yolov7 + + diff --git a/lesion_detection_larsmoan/err_p6_p100.txt b/lesion_detection_larsmoan/err_p6_p100.txt deleted file mode 100644 index 9b7007a0b..000000000 --- a/lesion_detection_larsmoan/err_p6_p100.txt +++ /dev/null @@ -1,499 +0,0 @@ -YOLOR 🚀 2023-10-18 torch 2.0.1 CUDA:0 (Tesla P100-PCIE-16GB, 16280.875MB) - -Namespace(weights='yolov7_training.pt', cfg='yolov7_isic_cfg.yaml', data='data/ISIC_2017_0.5/isic.yaml', hyp='hyp.scratch.p6.yaml', epochs=300, batch_size=32, img_size=[512, 512], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=8, project='runs/train', entity=None, name='yolov7_downsampled_rangpur', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs/train/yolov7_downsampled_rangpur4', total_batch_size=32) -tensorboard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ -hyperparameters: lr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15, loss_ota=1 -wandb: Currently logged in as: lars-ostberg-moan. Use `wandb login --relogin` to force relogin -wandb: - Waiting for wandb.init()... wandb: \ Waiting for wandb.init()... wandb: Tracking run with wandb version 0.15.12 -wandb: Run data is saved locally in /home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/wandb/run-20231019_105156-8n9w9vyq -wandb: Run `wandb offline` to turn off syncing. -wandb: Syncing run yolov7_downsampled_rangpur4 -wandb: ⭐️ View project at https://wandb.ai/lars-ostberg-moan/YOLOR -wandb: 🚀 View run at https://wandb.ai/lars-ostberg-moan/YOLOR/runs/8n9w9vyq - - from n params module arguments - 0 -1 1 928 models.common.Conv [3, 32, 3, 1] - 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] - 2 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] - 4 -1 1 8320 models.common.Conv [128, 64, 1, 1] - 5 -2 1 8320 models.common.Conv [128, 64, 1, 1] - 6 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 7 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 8 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 9 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 10 [-1, -3, -5, -6] 1 0 models.common.Concat [1] - 11 -1 1 66048 models.common.Conv [256, 256, 1, 1] - 12 -1 1 0 models.common.MP [] - 13 -1 1 33024 models.common.Conv [256, 128, 1, 1] - 14 -3 1 33024 models.common.Conv [256, 128, 1, 1] - 15 -1 1 147712 models.common.Conv [128, 128, 3, 2] - 16 [-1, -3] 1 0 models.common.Concat [1] - 17 -1 1 33024 models.common.Conv [256, 128, 1, 1] - 18 -2 1 33024 models.common.Conv [256, 128, 1, 1] - 19 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 20 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 21 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 22 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 23 [-1, -3, -5, -6] 1 0 models.common.Concat [1] - 24 -1 1 263168 models.common.Conv [512, 512, 1, 1] - 25 -1 1 0 models.common.MP [] - 26 -1 1 131584 models.common.Conv [512, 256, 1, 1] - 27 -3 1 131584 models.common.Conv [512, 256, 1, 1] - 28 -1 1 590336 models.common.Conv [256, 256, 3, 2] - 29 [-1, -3] 1 0 models.common.Concat [1] - 30 -1 1 131584 models.common.Conv [512, 256, 1, 1] - 31 -2 1 131584 models.common.Conv [512, 256, 1, 1] - 32 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 33 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 34 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 35 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 36 [-1, -3, -5, -6] 1 0 models.common.Concat [1] - 37 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] - 38 -1 1 0 models.common.MP [] - 39 -1 1 525312 models.common.Conv [1024, 512, 1, 1] - 40 -3 1 525312 models.common.Conv [1024, 512, 1, 1] - 41 -1 1 2360320 models.common.Conv [512, 512, 3, 2] - 42 [-1, -3] 1 0 models.common.Concat [1] - 43 -1 1 262656 models.common.Conv [1024, 256, 1, 1] - 44 -2 1 262656 models.common.Conv [1024, 256, 1, 1] - 45 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 46 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 47 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 48 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 49 [-1, -3, -5, -6] 1 0 models.common.Concat [1] - 50 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] - 51 -1 1 7609344 models.common.SPPCSPC [1024, 512, 1] - 52 -1 1 131584 models.common.Conv [512, 256, 1, 1] - 53 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] - 54 37 1 262656 models.common.Conv [1024, 256, 1, 1] - 55 [-1, -2] 1 0 models.common.Concat [1] - 56 -1 1 131584 models.common.Conv [512, 256, 1, 1] - 57 -2 1 131584 models.common.Conv [512, 256, 1, 1] - 58 -1 1 295168 models.common.Conv [256, 128, 3, 1] - 59 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 60 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 61 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 62[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] - 63 -1 1 262656 models.common.Conv [1024, 256, 1, 1] - 64 -1 1 33024 models.common.Conv [256, 128, 1, 1] - 65 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] - 66 24 1 65792 models.common.Conv [512, 128, 1, 1] - 67 [-1, -2] 1 0 models.common.Concat [1] - 68 -1 1 33024 models.common.Conv [256, 128, 1, 1] - 69 -2 1 33024 models.common.Conv [256, 128, 1, 1] - 70 -1 1 73856 models.common.Conv [128, 64, 3, 1] - 71 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 72 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 73 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 74[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] - 75 -1 1 65792 models.common.Conv [512, 128, 1, 1] - 76 -1 1 0 models.common.MP [] - 77 -1 1 16640 models.common.Conv [128, 128, 1, 1] - 78 -3 1 16640 models.common.Conv [128, 128, 1, 1] - 79 -1 1 147712 models.common.Conv [128, 128, 3, 2] - 80 [-1, -3, 63] 1 0 models.common.Concat [1] - 81 -1 1 131584 models.common.Conv [512, 256, 1, 1] - 82 -2 1 131584 models.common.Conv [512, 256, 1, 1] - 83 -1 1 295168 models.common.Conv [256, 128, 3, 1] - 84 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 85 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 86 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 87[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] - 88 -1 1 262656 models.common.Conv [1024, 256, 1, 1] - 89 -1 1 0 models.common.MP [] - 90 -1 1 66048 models.common.Conv [256, 256, 1, 1] - 91 -3 1 66048 models.common.Conv [256, 256, 1, 1] - 92 -1 1 590336 models.common.Conv [256, 256, 3, 2] - 93 [-1, -3, 51] 1 0 models.common.Concat [1] - 94 -1 1 525312 models.common.Conv [1024, 512, 1, 1] - 95 -2 1 525312 models.common.Conv [1024, 512, 1, 1] - 96 -1 1 1180160 models.common.Conv [512, 256, 3, 1] - 97 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 98 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 99 -1 1 590336 models.common.Conv [256, 256, 3, 1] -100[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] -101 -1 1 1049600 models.common.Conv [2048, 512, 1, 1] -102 75 1 328704 models.common.RepConv [128, 256, 3, 1] -103 88 1 1312768 models.common.RepConv [256, 512, 3, 1] -104 101 1 5246976 models.common.RepConv [512, 1024, 3, 1] -105 [102, 103, 104] 1 44944 models.yolo.IDetect [3, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]] -/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. 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line 85, in create_dataloader - dataloader = loader(dataset, - File "/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/utils/datasets.py", line 103, in __init__ - self.iterator = super().__iter__() - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 441, in __iter__ - return self._get_iterator() - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 388, in _get_iterator - return _MultiProcessingDataLoaderIter(self) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1042, in __init__ - w.start() - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/multiprocessing/process.py", line 121, in start - self._popen = self._Popen(self) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/multiprocessing/context.py", line 224, in _Popen - return _default_context.get_context().Process._Popen(process_obj) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/multiprocessing/context.py", line 281, in _Popen - return Popen(process_obj) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/multiprocessing/popen_fork.py", line 19, in __init__ - self._launch(process_obj) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/multiprocessing/popen_fork.py", line 66, in _launch - self.pid = os.fork() -OSError: [Errno 12] Cannot allocate memory -wandb: Waiting for W&B process to finish... (failed 1). Press Control-C to abort syncing. -wandb: - 0.002 MB of 0.002 MB uploaded (0.000 MB deduped) wandb: \ 0.002 MB of 0.021 MB uploaded (0.000 MB deduped) wandb: | 0.021 MB of 0.021 MB uploaded (0.000 MB deduped) wandb: / 0.021 MB of 0.021 MB uploaded (0.000 MB deduped) wandb: 🚀 View run p_A100_b256 at: https://wandb.ai/lars-ostberg-moan/YOLOR/runs/kztu7cmr -wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s) -wandb: Find logs at: ./wandb/run-20231019_180153-kztu7cmr/logs -Exception in thread NetStatThr: -Traceback (most recent call last): - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/threading.py", line 1016, in _bootstrap_inner -Exception in thread IntMsgThr: -Traceback (most recent call last): - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/threading.py", line 1016, in _bootstrap_inner - self.run() - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/threading.py", line 953, in run - self.run() - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/threading.py", line 953, in run - self._target(*self._args, **self._kwargs) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/wandb_run.py", line 299, in check_internal_messages - self._target(*self._args, **self._kwargs) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/wandb_run.py", line 267, in check_network_status - self._loop_check_status( - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/wandb_run.py", line 223, in _loop_check_status - self._loop_check_status( - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/wandb_run.py", line 223, in _loop_check_status - local_handle = request() - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface.py", line 735, in deliver_network_status - local_handle = request() - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface.py", line 743, in deliver_internal_messages - return self._deliver_network_status(status) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface_shared.py", line 475, in _deliver_network_status - return self._deliver_internal_messages(internal_message) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface_shared.py", line 481, in _deliver_internal_messages - return self._deliver_record(record) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface_shared.py", line 428, in _deliver_record - return self._deliver_record(record) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface_shared.py", line 428, in _deliver_record - handle = mailbox._deliver_record(record, interface=self) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/mailbox.py", line 455, in _deliver_record - handle = mailbox._deliver_record(record, interface=self) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/mailbox.py", line 455, in _deliver_record - interface._publish(record) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface_sock.py", line 51, in _publish - interface._publish(record) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/interface/interface_sock.py", line 51, in _publish - self._sock_client.send_record_publish(record) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 221, in send_record_publish - self._sock_client.send_record_publish(record) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 221, in send_record_publish - self.send_server_request(server_req) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 155, in send_server_request - self.send_server_request(server_req) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 155, in send_server_request - self._send_message(msg) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 152, in _send_message - self._send_message(msg) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 152, in _send_message - self._sendall_with_error_handle(header + data) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 130, in _sendall_with_error_handle - self._sendall_with_error_handle(header + data) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/wandb/sdk/lib/sock_client.py", line 130, in _sendall_with_error_handle - sent = self._sock.send(data) -BrokenPipeError: [Errno 32] Broken pipe - sent = self._sock.send(data) -BrokenPipeError: [Errno 32] Broken pipe diff --git a/lesion_detection_larsmoan/slurm-93881.out b/lesion_detection_larsmoan/slurm-93881.out deleted file mode 100644 index 7b1043f0d..000000000 --- a/lesion_detection_larsmoan/slurm-93881.out +++ /dev/null @@ -1,146 +0,0 @@ -YOLOR 🚀 2023-10-18 torch 2.0.1 CUDA:0 (GRID A100-2-10C MIG 2g.10gb, 10235.875MB) - -Namespace(weights='yolov7_training.pt', cfg='yolov7_isic_cfg.yaml', data='data/ISIC_2017_0.5/isic.yaml', hyp='hyp.scratch.p6.yaml', epochs=300, batch_size=-1, img_size=[512, 512], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=8, project='runs/train', entity=None, name='p6_p100_b64', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs/train/p6_p100_b64', total_batch_size=-1) -tensorboard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ -hyperparameters: lr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.5, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15, loss_ota=1 -wandb: Currently logged in as: lars-ostberg-moan. Use `wandb login --relogin` to force relogin -wandb: - Waiting for wandb.init()... wandb: \ Waiting for wandb.init()... wandb: Tracking run with wandb version 0.15.12 -wandb: Run data is saved locally in /home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/wandb/run-20231019_170026-5wwfj06n -wandb: Run `wandb offline` to turn off syncing. -wandb: Syncing run p6_p100_b64 -wandb: ⭐️ View project at https://wandb.ai/lars-ostberg-moan/YOLOR -wandb: 🚀 View run at https://wandb.ai/lars-ostberg-moan/YOLOR/runs/5wwfj06n - - from n params module arguments - 0 -1 1 928 models.common.Conv [3, 32, 3, 1] - 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] - 2 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] - 4 -1 1 8320 models.common.Conv [128, 64, 1, 1] - 5 -2 1 8320 models.common.Conv [128, 64, 1, 1] - 6 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 7 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 8 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 9 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 10 [-1, -3, -5, -6] 1 0 models.common.Concat [1] - 11 -1 1 66048 models.common.Conv [256, 256, 1, 1] - 12 -1 1 0 models.common.MP [] - 13 -1 1 33024 models.common.Conv [256, 128, 1, 1] - 14 -3 1 33024 models.common.Conv [256, 128, 1, 1] - 15 -1 1 147712 models.common.Conv [128, 128, 3, 2] - 16 [-1, -3] 1 0 models.common.Concat [1] - 17 -1 1 33024 models.common.Conv [256, 128, 1, 1] - 18 -2 1 33024 models.common.Conv [256, 128, 1, 1] - 19 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 20 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 21 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 22 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 23 [-1, -3, -5, -6] 1 0 models.common.Concat [1] - 24 -1 1 263168 models.common.Conv [512, 512, 1, 1] - 25 -1 1 0 models.common.MP [] - 26 -1 1 131584 models.common.Conv [512, 256, 1, 1] - 27 -3 1 131584 models.common.Conv [512, 256, 1, 1] - 28 -1 1 590336 models.common.Conv [256, 256, 3, 2] - 29 [-1, -3] 1 0 models.common.Concat [1] - 30 -1 1 131584 models.common.Conv [512, 256, 1, 1] - 31 -2 1 131584 models.common.Conv [512, 256, 1, 1] - 32 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 33 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 34 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 35 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 36 [-1, -3, -5, -6] 1 0 models.common.Concat [1] - 37 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] - 38 -1 1 0 models.common.MP [] - 39 -1 1 525312 models.common.Conv [1024, 512, 1, 1] - 40 -3 1 525312 models.common.Conv [1024, 512, 1, 1] - 41 -1 1 2360320 models.common.Conv [512, 512, 3, 2] - 42 [-1, -3] 1 0 models.common.Concat [1] - 43 -1 1 262656 models.common.Conv [1024, 256, 1, 1] - 44 -2 1 262656 models.common.Conv [1024, 256, 1, 1] - 45 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 46 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 47 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 48 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 49 [-1, -3, -5, -6] 1 0 models.common.Concat [1] - 50 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] - 51 -1 1 7609344 models.common.SPPCSPC [1024, 512, 1] - 52 -1 1 131584 models.common.Conv [512, 256, 1, 1] - 53 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] - 54 37 1 262656 models.common.Conv [1024, 256, 1, 1] - 55 [-1, -2] 1 0 models.common.Concat [1] - 56 -1 1 131584 models.common.Conv [512, 256, 1, 1] - 57 -2 1 131584 models.common.Conv [512, 256, 1, 1] - 58 -1 1 295168 models.common.Conv [256, 128, 3, 1] - 59 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 60 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 61 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 62[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] - 63 -1 1 262656 models.common.Conv [1024, 256, 1, 1] - 64 -1 1 33024 models.common.Conv [256, 128, 1, 1] - 65 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] - 66 24 1 65792 models.common.Conv [512, 128, 1, 1] - 67 [-1, -2] 1 0 models.common.Concat [1] - 68 -1 1 33024 models.common.Conv [256, 128, 1, 1] - 69 -2 1 33024 models.common.Conv [256, 128, 1, 1] - 70 -1 1 73856 models.common.Conv [128, 64, 3, 1] - 71 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 72 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 73 -1 1 36992 models.common.Conv [64, 64, 3, 1] - 74[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] - 75 -1 1 65792 models.common.Conv [512, 128, 1, 1] - 76 -1 1 0 models.common.MP [] - 77 -1 1 16640 models.common.Conv [128, 128, 1, 1] - 78 -3 1 16640 models.common.Conv [128, 128, 1, 1] - 79 -1 1 147712 models.common.Conv [128, 128, 3, 2] - 80 [-1, -3, 63] 1 0 models.common.Concat [1] - 81 -1 1 131584 models.common.Conv [512, 256, 1, 1] - 82 -2 1 131584 models.common.Conv [512, 256, 1, 1] - 83 -1 1 295168 models.common.Conv [256, 128, 3, 1] - 84 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 85 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 86 -1 1 147712 models.common.Conv [128, 128, 3, 1] - 87[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] - 88 -1 1 262656 models.common.Conv [1024, 256, 1, 1] - 89 -1 1 0 models.common.MP [] - 90 -1 1 66048 models.common.Conv [256, 256, 1, 1] - 91 -3 1 66048 models.common.Conv [256, 256, 1, 1] - 92 -1 1 590336 models.common.Conv [256, 256, 3, 2] - 93 [-1, -3, 51] 1 0 models.common.Concat [1] - 94 -1 1 525312 models.common.Conv [1024, 512, 1, 1] - 95 -2 1 525312 models.common.Conv [1024, 512, 1, 1] - 96 -1 1 1180160 models.common.Conv [512, 256, 3, 1] - 97 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 98 -1 1 590336 models.common.Conv [256, 256, 3, 1] - 99 -1 1 590336 models.common.Conv [256, 256, 3, 1] -100[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] -101 -1 1 1049600 models.common.Conv [2048, 512, 1, 1] -102 75 1 328704 models.common.RepConv [128, 256, 3, 1] -103 88 1 1312768 models.common.RepConv [256, 512, 3, 1] -104 101 1 5246976 models.common.RepConv [512, 1024, 3, 1] -105 [102, 103, 104] 1 44944 models.yolo.IDetect [3, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]] -/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1682343967769/work/aten/src/ATen/native/TensorShape.cpp:3483.) - return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] -Model Summary: 415 layers, 37207344 parameters, 37207344 gradients, 105.1 GFLOPS - -Transferred 555/566 items from yolov7_training.pt -Scaled weight_decay = -7.8125e-06 -Optimizer groups: 95 .bias, 95 conv.weight, 98 other - train: Scanning 'data/ISIC_2017_0.5/train_downsampled.cache' images and labels... 2000 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 2000/2000 [00:00 - train(hyp, opt, device, tb_writer) - File "/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/train.py", line 245, in train - dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, - File "/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/utils/datasets.py", line 85, in create_dataloader - dataloader = loader(dataset, - File "/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/utils/datasets.py", line 101, in __init__ - super().__init__(*args, **kwargs) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 357, in __init__ - batch_sampler = BatchSampler(sampler, batch_size, drop_last) - File "/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/utils/data/sampler.py", line 232, in __init__ - raise ValueError("batch_size should be a positive integer value, " -ValueError: batch_size should be a positive integer value, but got batch_size=-1 -wandb: Waiting for W&B process to finish... 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-[tool.poetry.dependencies] -python = ">=3.11,<3.13" -numpy = ">=1.18.5,<1.24.0" -opencv-python = ">=4.1.1" -torch = ">=1.7.0,<1.12.0 || >1.12.0" -pyyaml = ">=5.3.1" -tensorboard = ">=2.4.1" -matplotlib = ">=3.2.2" -requests = ">=2.23.0" -scipy = ">=1.4.1" -torchvision = ">=0.8.1,<0.13.0 || >0.13.0" -tqdm = ">=4.41.0" -protobuf = "<4.21.3" -pandas = ">=1.1.4" -seaborn = ">=0.11.0" -wandb = "^0.15.12" -pillow = "^10.0.1" -gdown = "^4.7.1" -python-dotenv = "^1.0.0" - - -[tool.poetry.group.dev.dependencies] -ipykernel = "^6.25.2" - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" From 933f9dd9fcb40412e86885861941f1b81dcd1386 Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Sat, 21 Oct 2023 16:11:50 +1000 Subject: [PATCH 28/42] Cleanup of old and unnecessary files + test script for model --- .gitignore | 1 + lesion_detection_larsmoan/check_torch.py | 5 ----- lesion_detection_larsmoan/dataset_utils.py | 2 +- lesion_detection_larsmoan/err_test.txt | 9 --------- lesion_detection_larsmoan/install_dep.sh | 18 ----------------- lesion_detection_larsmoan/out_test.txt | 16 --------------- lesion_detection_larsmoan/run_custom_train.sh | 2 -- lesion_detection_larsmoan/run_inference.sh | 2 -- lesion_detection_larsmoan/run_test.sh | 16 +++++++++++++++ lesion_detection_larsmoan/slurm.sh | 20 ------------------- 10 files changed, 18 insertions(+), 73 deletions(-) create mode 100644 .gitignore delete mode 100644 lesion_detection_larsmoan/check_torch.py delete mode 100644 lesion_detection_larsmoan/err_test.txt delete mode 100755 lesion_detection_larsmoan/install_dep.sh delete mode 100644 lesion_detection_larsmoan/out_test.txt delete mode 100644 lesion_detection_larsmoan/run_inference.sh delete mode 100644 lesion_detection_larsmoan/slurm.sh diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..1d17dae13 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +.venv diff --git a/lesion_detection_larsmoan/check_torch.py b/lesion_detection_larsmoan/check_torch.py deleted file mode 100644 index c068631d7..000000000 --- a/lesion_detection_larsmoan/check_torch.py +++ /dev/null @@ -1,5 +0,0 @@ -print("Checking if torch is installed correctly with GPU compatitbility: ") -import torch -print(torch.__version__) -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') -print(device) \ No newline at end of file diff --git a/lesion_detection_larsmoan/dataset_utils.py b/lesion_detection_larsmoan/dataset_utils.py index f98656555..ae4aed485 100644 --- a/lesion_detection_larsmoan/dataset_utils.py +++ b/lesion_detection_larsmoan/dataset_utils.py @@ -219,7 +219,7 @@ def download_dataset(dataset_folder, download_url="https://drive.google.com/uc?i if __name__ == "__main__": #Download the preprocessed version of ISIC2017 that has YOLO labels and correct structure path_to_dset = "/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/data/" - download_datset(path_to_dset) + download_dataset(path_to_dset) #Download pretrained YOLOV7 weights (COCO) for transfer learning purposes weights_url = "https://drive.google.com/uc?id=1mAu29ZlOTn3csjnZ5fmro10kY3XxAddC" diff --git a/lesion_detection_larsmoan/err_test.txt b/lesion_detection_larsmoan/err_test.txt deleted file mode 100644 index 616f7185e..000000000 --- a/lesion_detection_larsmoan/err_test.txt +++ /dev/null @@ -1,9 +0,0 @@ -YOLOR 🚀 2023-10-18 torch 2.0.1 CUDA:0 (GRID A100-2-10C MIG 2g.10gb, 10235.875MB) - -/home/Student/s4827064/miniconda3/envs/torch-gpu/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1682343967769/work/aten/src/ATen/native/TensorShape.cpp:3483.) - return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] -Model Summary: 314 layers, 36492560 parameters, 6194944 gradients, 103.2 GFLOPS - val: Scanning 'data/ISIC_2017_0.5/val_downsampled.cache' images and labels... 150 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 150/150 [00:00 /dev/null; then - echo "Conda is not installed. Please install Conda first." - exit 1 -fi - -conda activate torch-gpu - -# Read the requirements.txt file and try to install packages using conda -while read package; do - if [[ ! -z "$package" && ! "$package" =~ ^\# ]]; then - conda install --yes "$package" || echo "Failed to install $package with conda. Consider installing manually." - fi -done < yolov7/requirements.txt - -echo "Installation finished." diff --git a/lesion_detection_larsmoan/out_test.txt b/lesion_detection_larsmoan/out_test.txt deleted file mode 100644 index a2bcb32a9..000000000 --- a/lesion_detection_larsmoan/out_test.txt +++ /dev/null @@ -1,16 +0,0 @@ -Namespace(weights=['best.pt'], data='data/ISIC_2017_0.5/isic.yaml', batch_size=2, img_size=512, conf_thres=0.001, iou_thres=0.65, task='val', device='0', single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project='runs/test', name='yolov7_b32_testing', exist_ok=False, no_trace=False, v5_metric=False) -Fusing layers... -RepConv.fuse_repvgg_block -RepConv.fuse_repvgg_block -RepConv.fuse_repvgg_block -IDetect.fuse - Convert model to Traced-model... - traced_script_module saved! - model is traced! - - all 150 150 0.7 0.682 0.718 0.438 - melanoma 150 30 0.762 0.533 0.624 0.4 -seborrheic_keratosis 150 42 0.606 0.667 0.7 0.329 - unknown 150 78 0.731 0.846 0.83 0.586 -Speed: 10.7/2.4/13.1 ms inference/NMS/total per 512x512 image at batch-size 2 -Results saved to runs/test/yolov7_b32_testing3 diff --git a/lesion_detection_larsmoan/run_custom_train.sh b/lesion_detection_larsmoan/run_custom_train.sh index 841a11909..f6869919e 100755 --- a/lesion_detection_larsmoan/run_custom_train.sh +++ b/lesion_detection_larsmoan/run_custom_train.sh @@ -5,8 +5,6 @@ #SBATCH --cpus-per-task 1 #SBATCH --mail-type=All #SBATCH --mail-user=l.moan@uqconnect.edu.au - - #SBATCH --partition=test #SBATCH --gres=gpu:1 diff --git a/lesion_detection_larsmoan/run_inference.sh b/lesion_detection_larsmoan/run_inference.sh deleted file mode 100644 index 139597f9c..000000000 --- a/lesion_detection_larsmoan/run_inference.sh +++ /dev/null @@ -1,2 +0,0 @@ - - diff --git a/lesion_detection_larsmoan/run_test.sh b/lesion_detection_larsmoan/run_test.sh index e17ecd654..c03519fde 100644 --- a/lesion_detection_larsmoan/run_test.sh +++ b/lesion_detection_larsmoan/run_test.sh @@ -14,4 +14,20 @@ conda activate torch-gpu module load cuda + +# Define the filename and file URL +filename="best.pt" +file_url="https://drive.google.com/uc?id=1NiVwOQVGDGuy39O3KtKdGOj4JEyHlbV5" + +# Check if the file exists in the current directory +if [ -e "$PWD/$filename" ]; then + echo "File '$filename' already exists in the current directory." +else + # If file doesn't exist, download it + echo "Downloading '$filename'..." + wget "$file_url" + echo "Download complete." +fi + + python yolov7/test.py --data data/ISIC_2017_0.5/isic.yaml --img 512 --batch 2 --conf 0.001 --iou 0.65 --device 0 --weights 'best.pt' --name yolov7_b32_testing diff --git a/lesion_detection_larsmoan/slurm.sh b/lesion_detection_larsmoan/slurm.sh deleted file mode 100644 index 1a9e20a8f..000000000 --- a/lesion_detection_larsmoan/slurm.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/bin/bash -#SBATCH -N 1 -#SBATCH -n 1 -#SBATCH --job-name=torch-gpu -#SBATCH --cpus-per-task 1 -#SBATCH --mail-type=All -#SBATCH --mail-user=l.moan@uqconnect.edu.au -#SBATCH -o out.txt -#SBATCH -e err.txt -#SBATCH --partition=test -#SBATCH --gres=gpu:1 - -conda activate torch-gpu - -module load cuda -nvidia-smi - -python dataset_utils.py - - From bbd7b0db60382a9b5bb0df988387dcd8c91bf216 Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Sat, 21 Oct 2023 17:12:56 +1000 Subject: [PATCH 29/42] Finished working test script, now it automatically downloads the weights if not present in repo --- lesion_detection_larsmoan/.gitignore | 5 ++++- lesion_detection_larsmoan/README.md | 11 ++++++++--- lesion_detection_larsmoan/run_test.sh | 22 +++++++++++----------- 3 files changed, 23 insertions(+), 15 deletions(-) diff --git a/lesion_detection_larsmoan/.gitignore b/lesion_detection_larsmoan/.gitignore index fa6f939aa..323c29de3 100644 --- a/lesion_detection_larsmoan/.gitignore +++ b/lesion_detection_larsmoan/.gitignore @@ -11,4 +11,7 @@ wandb .DS_Store .venv .env -*.pt \ No newline at end of file +*.pt + +err_test.txt +out_test.txt \ No newline at end of file diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index fbca32b4a..a40801e05 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -116,7 +116,12 @@ Training was mainly done on the rangpur cluster, - Training time, hardware details (if relevant). ### Results +#### Confusion Matrix +Description -- Summarize the results obtained. -- Any metrics used (e.g., accuracy, F1-score). -- Visual results (e.g., plots, graphs) if applicable. \ No newline at end of file + +### F1 - curve +Description + +### Precision - Recall curve +Description \ No newline at end of file diff --git a/lesion_detection_larsmoan/run_test.sh b/lesion_detection_larsmoan/run_test.sh index c03519fde..b477f649b 100644 --- a/lesion_detection_larsmoan/run_test.sh +++ b/lesion_detection_larsmoan/run_test.sh @@ -14,20 +14,20 @@ conda activate torch-gpu module load cuda +#Checks if the weights are present in the directory +if [ ! -f "best.pt" ]; then + echo "File does not exist, downloading..." + python3 - < Date: Sat, 21 Oct 2023 17:15:50 +1000 Subject: [PATCH 30/42] commented on the training hyp params --- lesion_detection_larsmoan/README.md | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index a40801e05..f0a147221 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -108,12 +108,9 @@ https://drive.google.com/uc?id=1YI3pwanX35i7NCIxKnfXBozXiyQZcGbL or from [datase ### Model Architecture: open source [YOLOV7 Model](https://github.com/WongKinYiu/yolov7) ### Training -Training was mainly done on the rangpur cluster, +Training was mainly done on the rangpur cluster, using the P100 gpu and a batch size of 32 -- Details about the training process. -- Hyperparameters used: - - Both p5 and p6 models were trained. -- Training time, hardware details (if relevant). +- Hyperparameters that was used is described in: [hyp.scratch.p6.yaml](./hyp.scratch.p6.yaml) ### Results #### Confusion Matrix From 96c4008f37a55d2dc4a2bb77c517e1f9202d7b88 Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Sat, 21 Oct 2023 17:32:00 +1000 Subject: [PATCH 31/42] Added functionality for downloading transfer learning weights if not present --- lesion_detection_larsmoan/dataset_utils.py | 5 ----- lesion_detection_larsmoan/run_custom_train.sh | 18 +++++++++++++++++- 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/lesion_detection_larsmoan/dataset_utils.py b/lesion_detection_larsmoan/dataset_utils.py index ae4aed485..98c58564e 100644 --- a/lesion_detection_larsmoan/dataset_utils.py +++ b/lesion_detection_larsmoan/dataset_utils.py @@ -220,10 +220,5 @@ def download_dataset(dataset_folder, download_url="https://drive.google.com/uc?i #Download the preprocessed version of ISIC2017 that has YOLO labels and correct structure path_to_dset = "/home/Student/s4827064/PatternAnalysis-2023/lesion_detection_larsmoan/data/" download_dataset(path_to_dset) - - #Download pretrained YOLOV7 weights (COCO) for transfer learning purposes - weights_url = "https://drive.google.com/uc?id=1mAu29ZlOTn3csjnZ5fmro10kY3XxAddC" - yolov7_weights_out = "yolov7_training.pt" - gdown.download(weights_url, yolov7_weights_out, quiet=False) \ No newline at end of file diff --git a/lesion_detection_larsmoan/run_custom_train.sh b/lesion_detection_larsmoan/run_custom_train.sh index f6869919e..bb333b8f4 100755 --- a/lesion_detection_larsmoan/run_custom_train.sh +++ b/lesion_detection_larsmoan/run_custom_train.sh @@ -7,9 +7,25 @@ #SBATCH --mail-user=l.moan@uqconnect.edu.au #SBATCH --partition=test #SBATCH --gres=gpu:1 +#SBATCH -o out.txt +#SBATCH -e err.txt # Activate Python environment (optional) conda activate torch-gpu module load cuda -python3 yolov7/train.py --device 0 --workers 8 --batch-size -1 --data data/ISIC_2017_0.5/isic.yaml --img 512 512 --cfg yolov7_isic_cfg.yaml --weights 'yolov7_training.pt' --name p6_p100_b64 --hyp hyp.scratch.p6.yaml \ No newline at end of file +#Checks if the transfer learning weights (YOLOV7 weights (COCO)) are present in the dir, if not downloads them +if [ ! -f "yolov7_training.pt" ]; then + echo "File does not exist, downloading..." + python3 - < Date: Thu, 9 Nov 2023 15:20:09 +1000 Subject: [PATCH 32/42] Removed unnecessary notebook file for training on google colab + documentation for the original YOLOV1 concepts utilized in the YOLOV7 model --- lesion_detection_larsmoan/README.md | 35 +- .../figures/grid_cell_yolo.png | Bin 0 -> 246911 bytes lesion_detection_larsmoan/figures/yolov1.png | Bin 0 -> 369642 bytes lesion_detection_larsmoan/isic_train.ipynb | 1681 ----------------- lesion_detection_larsmoan/run_test.sh | 0 5 files changed, 31 insertions(+), 1685 deletions(-) create mode 100644 lesion_detection_larsmoan/figures/grid_cell_yolo.png create mode 100644 lesion_detection_larsmoan/figures/yolov1.png delete mode 100644 lesion_detection_larsmoan/isic_train.ipynb mode change 100644 => 100755 lesion_detection_larsmoan/run_test.sh diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index fbca32b4a..ea7a49da1 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -1,13 +1,15 @@ ## Lesion detection and classification using YOLOV7 on the ISIC2017 dataset ### Table of Contents -- [Project Title](#project-title) +- [Lesion detection and classification using YOLOV7 on the ISIC2017 dataset](#lesion-detection-and-classification-using-yolov7-on-the-isic2017-dataset) - [Table of Contents](#table-of-contents) - - [Todo](#todo) - [Installation](#installation) - - [Dataset](#dataset) +- [Dataset](#dataset) + - [Overview](#overview) + - [Preprocessing](#preprocessing) - [Usage](#usage) - - [Model Architecture](#model-architecture) + - [Model Architecture: open source YOLOV7 Model](#model-architecture-open-source-yolov7-model) + - [Core ideas used in the YOLOV1 paper:](#core-ideas-used-in-the-yolov1-paper) - [Training](#training) - [Results](#results) @@ -107,6 +109,31 @@ https://drive.google.com/uc?id=1YI3pwanX35i7NCIxKnfXBozXiyQZcGbL or from [datase ### Model Architecture: open source [YOLOV7 Model](https://github.com/WongKinYiu/yolov7) +YOLOV7 is based on the original YOLO paper: [YOLOV1](https://arxiv.org/abs/1506.02640) which was published in 2015 and presented a leap in inference speed for object detection models. The main reason for this was that it was one of the first models that did object detection in a single stage, hence the name YOLO ( you only look once ) in contrast to the two stage models that were popular at the time. Note that some single stage models were present, such as SSD, but they had relatively accuracy performance. + +#### Core ideas used in the YOLOV1 paper: + +The original paper was trained on input images of size 448X448 and these images where parsed into a grid of 7x7 grid cells. +
    + Example Image +
    Original grid cells on 448x448 image.
    +
    + +The idea was that each grid cell was responsible for predicting a object if the center of that object was within the given grid cell. +**In this example the red grid cell would be responsible to detecting the car present in the top right corner** + + In the paper it was also proposed that each grid cell's output was two bounding boxes each with their own confidence / objectness score + a class probability vector. I.e was each grid cell only capable of predicting one detection, altough it could predict two bounding boxes. The bounding box with the highest objectness score was chosen in addition to the class with the highest probability. + +
    + Example Image +
    YOLOV1 object detection [Joseph Redmon et al]
    +
    + +In the figure above the core ideas is presented, each grid cell proposing two bounding boxes (in black) but only one class. The "best" box and class over a certain treshold is then used as the final prediction. + + + + ### Training Training was mainly done on the rangpur cluster, diff --git a/lesion_detection_larsmoan/figures/grid_cell_yolo.png b/lesion_detection_larsmoan/figures/grid_cell_yolo.png new file mode 100644 index 0000000000000000000000000000000000000000..318049b29479a24f6f8acb17ac87ac93f4eca635 GIT binary patch literal 246911 zcmV*UKwH0wP)|% zfKSBXf`S!S5C{MPrhEF_GBYCF-L71ynMa=L#wkfTS$QHO+|5i?fBp4Wb@{*k&;O%+ z`SWM?`%2E4RvQ2!P-~^N1~giO+$sfVt zLLUDnf{5tLf!02}$A>@CU#HioZ%=4H1rzVadC3|tv?%k0`kMnKGs1H#+$T{@AAXt9d~?uZhL&_V?RV_t@e8z`^Iqw zq_4-1mpj%>j&*Al8giTsXwV9c`nad#>$ex%{yX1~m&#xMtl!h)cmB%n{pQm?-v4oP zA3o#9cW5B}K7V+Z$3Dpq+w)<6j`#Yo<{}7koRg0iFziHawcpimZfD%9?)`86t^IHc zK79Ll?c*L=zy8LDOJkh5k01Qo^=h;qZR0oBa`=f4Yg=o@Ij29ajXKU6qBe;1v-vn{ zzg^GAY3z40j@Mtc@V{J&->y|_4I}2qL*D;q*z>Tn;RlD!eOSfEb^hk{dU0bvfhHe( z?f5MDu>QmE{I~_jat{CUTmSP{_N!5cvpTk4#%F(g?GN9y$Lh(k630Ia51XzZ)`ITK 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z_2Va^$xpwVD+~kOCSwI@JrN2=fOlYhj8z)Do8)rm;9)8Yh!0cRb;+^^)>eg07=t}0T_S+*Q z%1lP{d)k<~_})SQ>x6nU2lkq7f@wgM_o(GseRpeH&Rl9+ly?CM=pGg}hlL_4CBAQr z_GLi+oh+CDP68%1w|!%9eS*>s>9_DIU2e5B#t60MM6;l-H5grRCi4nctQa~uiBqR} zaa*k%3T*sYrdVhvF(<V5MJ0Y=6_l4KMebY zYXyY=*Jb#>ul#2-{rd#U1JkYlNSlA3klHk-+{O2m)IsPlT|GC7= laYp>#(A9tb%x%o`t286qb2R2}S_m&M6$K6X8d\"Open" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JLki5YXMdF6Y", - "outputId": "c1250389-170e-4762-ce31-d4edb4c6f0eb" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cloning into 'PatternAnalysis-2023'...\n", - "remote: Enumerating objects: 65, done.\u001b[K\n", - "remote: Counting objects: 100% (65/65), done.\u001b[K\n", - "remote: Compressing objects: 100% (38/38), done.\u001b[K\n", - "remote: Total 65 (delta 24), reused 63 (delta 24), pack-reused 0\u001b[K\n", - "Receiving objects: 100% (65/65), 83.76 KiB | 3.81 MiB/s, done.\n", - "Resolving deltas: 100% (24/24), done.\n" - ] - } - ], - "source": [ - "!git clone \"https://github.com/larsmoan/PatternAnalysis-2023.git\"\n", - "!cd PatternAnalysis-2023" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "wgWX0QHsfnV6", - "outputId": "e550fdf9-6178-445b-90ba-a4c03ed6cfb8" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Submodule 'lesion_detection_larsmoan/yolov7' (https://github.com/WongKinYiu/yolov7) registered for path 'lesion_detection_larsmoan/yolov7'\n", - "Cloning into '/content/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7'...\n", - "Submodule path 'lesion_detection_larsmoan/yolov7': checked out '84932d70fb9e2932d0a70e4a1f02a1d6dd1dd6ca'\n" - ] - } - ], - "source": [ - "!cd PatternAnalysis-2023/ && git submodule update --init" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ss3upKMvk_9Z", - "outputId": "64a05888-36b3-4e76-d177-7f1764ef8142" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dataset.py\t hyp.scratch.p5.yaml predict.py\t slurm.sh yolov7_isic_cfg.yaml\n", - "dataset_utils.ipynb hyp.scratch.p6.yaml README.md\t utils.py\n", - "downsample.py\t modules.py\t\t run_custom_train.sh yolov7\n" - ] - } - ], - "source": [ - "import os\n", - "os.chdir('/content/PatternAnalysis-2023/lesion_detection_larsmoan/')\n", - "!ls" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "mErp_yNneMej" - }, - "outputs": [], - "source": [ - "import gdown\n", - "import os\n", - "import zipfile\n", - "def download_and_unzip(folder_path, download_url):\n", - " \"\"\"\n", - " Checks if a folder exists at the specified path.\n", - " If not, downloads a zip file from the provided URL using gdown and unzips it.\n", - "\n", - " Parameters:\n", - " - folder_path: Path to the folder you want to check.\n", - " - download_url: The gdown URL of the zip file to download if the folder doesn't exist.\n", - " \"\"\"\n", - "\n", - " # Check if folder exists\n", - " if not os.path.exists(folder_path):\n", - " print(f\"Folder {folder_path} does not exist. Downloading the zip file...\")\n", - "\n", - " # Download zip file using gdown\n", - " output = \"temp_download.zip\"\n", - " gdown.download(download_url, output, quiet=False)\n", - "\n", - "\n", - " # Unzip the downloaded file\n", - " print(\"Unzipping the downloaded file...\")\n", - " with zipfile.ZipFile(output, 'r') as zip_ref:\n", - " zip_ref.extractall(folder_path)\n", - "\n", - " # Delete the downloaded zip file\n", - " os.remove(output)\n", - " print(f\"Files have been extracted to {folder_path}\")\n", - " else:\n", - " print(f\"Folder {folder_path} already exists.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "GAROAZdheOBE", - "outputId": "c94c1a0e-1de9-4bd3-c51f-b8a9f851fa93" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Folder data does not exist. Downloading the zip file...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Downloading...\n", - "From: https://drive.google.com/uc?id=19HcgRBuXyhzxsukE2jHPnBkC0hEk_aMJ\n", - "To: /content/PatternAnalysis-2023/lesion_detection_larsmoan/temp_download.zip\n", - "100%|██████████| 71.8M/71.8M [00:00<00:00, 76.7MB/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unzipping the downloaded file...\n", - "Files have been extracted to data\n" - ] - } - ], - "source": [ - "dataset_folder = \"data\"\n", - "url = \"https://drive.google.com/uc?id=19HcgRBuXyhzxsukE2jHPnBkC0hEk_aMJ\"\n", - "url_05= \"https://drive.google.com/uc?id=1YI3pwanX35i7NCIxKnfXBozXiyQZcGbL\"\n", - "download_and_unzip(dataset_folder, url)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "K71MTSz2eykw", - "outputId": "c7846616-6844-49e3-c9f3-85815367f286" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-10-16 04:52:10.996744: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-10-16 04:52:11.936076: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "YOLOR 🚀 v0.1-126-g84932d7 torch 2.0.1+cu118 CUDA:0 (Tesla T4, 15101.8125MB)\n", - "\n", - "Namespace(weights='', cfg='yolov7_isic_cfg.yaml', data='data/ISIC_2017_downsampled/isic.yaml', hyp='hyp.scratch.p5.yaml', epochs=100, batch_size=16, img_size=[256, 256], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=32, project='runs/train', entity=None, name='yolov7_isic', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs/train/yolov7_isic9', total_batch_size=16)\n", - "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n", - "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15, loss_ota=1\n", - "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)\n", - "\n", - " from n params module arguments \n", - " 0 -1 1 928 models.common.Conv [3, 32, 3, 1] \n", - " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", - " 2 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", - " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", - " 4 -1 1 8320 models.common.Conv [128, 64, 1, 1] \n", - " 5 -2 1 8320 models.common.Conv [128, 64, 1, 1] \n", - " 6 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", - " 7 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", - " 8 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", - " 9 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", - " 10 [-1, -3, -5, -6] 1 0 models.common.Concat [1] \n", - " 11 -1 1 66048 models.common.Conv [256, 256, 1, 1] \n", - " 12 -1 1 0 models.common.MP [] \n", - " 13 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", - " 14 -3 1 33024 models.common.Conv [256, 128, 1, 1] \n", - " 15 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", - " 16 [-1, -3] 1 0 models.common.Concat [1] \n", - " 17 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", - " 18 -2 1 33024 models.common.Conv [256, 128, 1, 1] \n", - " 19 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", - " 20 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", - " 21 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", - " 22 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", - " 23 [-1, -3, -5, -6] 1 0 models.common.Concat [1] \n", - " 24 -1 1 263168 models.common.Conv [512, 512, 1, 1] \n", - " 25 -1 1 0 models.common.MP [] \n", - " 26 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", - " 27 -3 1 131584 models.common.Conv [512, 256, 1, 1] \n", - " 28 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", - " 29 [-1, -3] 1 0 models.common.Concat [1] \n", - " 30 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", - " 31 -2 1 131584 models.common.Conv [512, 256, 1, 1] \n", - " 32 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", - " 33 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", - " 34 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", - " 35 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", - " 36 [-1, -3, -5, -6] 1 0 models.common.Concat [1] \n", - " 37 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] \n", - " 38 -1 1 0 models.common.MP [] \n", - " 39 -1 1 525312 models.common.Conv [1024, 512, 1, 1] \n", - " 40 -3 1 525312 models.common.Conv [1024, 512, 1, 1] \n", - " 41 -1 1 2360320 models.common.Conv [512, 512, 3, 2] \n", - " 42 [-1, -3] 1 0 models.common.Concat [1] \n", - " 43 -1 1 262656 models.common.Conv [1024, 256, 1, 1] \n", - " 44 -2 1 262656 models.common.Conv [1024, 256, 1, 1] \n", - " 45 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", - " 46 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", - " 47 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", - " 48 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", - " 49 [-1, -3, -5, -6] 1 0 models.common.Concat [1] \n", - " 50 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] \n", - " 51 -1 1 7609344 models.common.SPPCSPC [1024, 512, 1] \n", - " 52 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", - " 53 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", - " 54 37 1 262656 models.common.Conv [1024, 256, 1, 1] \n", - " 55 [-1, -2] 1 0 models.common.Concat [1] \n", - " 56 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", - " 57 -2 1 131584 models.common.Conv [512, 256, 1, 1] \n", - " 58 -1 1 295168 models.common.Conv [256, 128, 3, 1] \n", - " 59 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", - " 60 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", - " 61 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", - " 62[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] \n", - " 63 -1 1 262656 models.common.Conv [1024, 256, 1, 1] \n", - " 64 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", - " 65 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", - " 66 24 1 65792 models.common.Conv [512, 128, 1, 1] \n", - " 67 [-1, -2] 1 0 models.common.Concat [1] \n", - " 68 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", - " 69 -2 1 33024 models.common.Conv [256, 128, 1, 1] \n", - " 70 -1 1 73856 models.common.Conv [128, 64, 3, 1] \n", - " 71 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", - " 72 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", - " 73 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", - " 74[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] \n", - " 75 -1 1 65792 models.common.Conv [512, 128, 1, 1] \n", - " 76 -1 1 0 models.common.MP [] \n", - " 77 -1 1 16640 models.common.Conv [128, 128, 1, 1] \n", - " 78 -3 1 16640 models.common.Conv [128, 128, 1, 1] \n", - " 79 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", - " 80 [-1, -3, 63] 1 0 models.common.Concat [1] \n", - " 81 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", - " 82 -2 1 131584 models.common.Conv [512, 256, 1, 1] \n", - " 83 -1 1 295168 models.common.Conv [256, 128, 3, 1] \n", - " 84 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", - " 85 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", - " 86 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", - " 87[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] \n", - " 88 -1 1 262656 models.common.Conv [1024, 256, 1, 1] \n", - " 89 -1 1 0 models.common.MP [] \n", - " 90 -1 1 66048 models.common.Conv [256, 256, 1, 1] \n", - " 91 -3 1 66048 models.common.Conv [256, 256, 1, 1] \n", - " 92 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", - " 93 [-1, -3, 51] 1 0 models.common.Concat [1] \n", - " 94 -1 1 525312 models.common.Conv [1024, 512, 1, 1] \n", - " 95 -2 1 525312 models.common.Conv [1024, 512, 1, 1] \n", - " 96 -1 1 1180160 models.common.Conv [512, 256, 3, 1] \n", - " 97 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", - " 98 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", - " 99 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", - "100[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] \n", - "101 -1 1 1049600 models.common.Conv [2048, 512, 1, 1] \n", - "102 75 1 328704 models.common.RepConv [128, 256, 3, 1] \n", - "103 88 1 1312768 models.common.RepConv [256, 512, 3, 1] \n", - "104 101 1 5246976 models.common.RepConv [512, 1024, 3, 1] \n", - "105 [102, 103, 104] 1 44944 models.yolo.IDetect [3, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]]\n", - "Model Summary: 415 layers, 37207344 parameters, 37207344 gradients\n", - "\n", - "Scaled weight_decay = 0.0005\n", - "Optimizer groups: 95 .bias, 95 conv.weight, 98 other\n", - "\u001b[34m\u001b[1mtrain: \u001b[0mScanning 'data/ISIC_2017_downsampled/train_downsampled.cache' images and labels... 2000 found, 0 missing, 0 empty, 0 corrupted: 100% 2000/2000 [00:00\n", - " train(hyp, opt, device, tb_writer)\n", - " File \"/content/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/train.py\", line 363, in train\n", - " loss, loss_items = compute_loss_ota(pred, targets.to(device), imgs) # loss scaled by batch_size\n", - " File \"/content/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/utils/loss.py\", line 585, in __call__\n", - " bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs)\n", - " File \"/content/PatternAnalysis-2023/lesion_detection_larsmoan/yolov7/utils/loss.py\", line 695, in build_targets\n", - " pxywh = torch.cat([pxy, pwh], dim=-1)\n", - "KeyboardInterrupt\n", - "^C\n" - ] - } - ], - "source": [ - "!python3 yolov7/train.py --epochs 100 --workers 32 --device 0 --batch-size 16 --data data/ISIC_2017_downsampled/isic.yaml --img 256 256 --cfg yolov7_isic_cfg.yaml --weights '' --name yolov7_isic --hyp hyp.scratch.p5.yaml" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OOFoLaI9uCof", - "outputId": "8151d7a6-ddaa-4c70-e0f1-dac59f440d8c" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Namespace(weights=['runs/train/yolov7_isic9/weights/best.pt'], source='data/ISIC_2017_downsampled/test_downsampled', img_size=640, conf_thres=0.2, iou_thres=0.45, device='', view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', exist_ok=False, no_trace=False)\n", - "YOLOR 🚀 v0.1-126-g84932d7 torch 2.0.1+cu118 CUDA:0 (Tesla T4, 15101.8125MB)\n", - "\n", - "Fusing layers... \n", - "RepConv.fuse_repvgg_block\n", - "RepConv.fuse_repvgg_block\n", - "RepConv.fuse_repvgg_block\n", - "IDetect.fuse\n", - "Model Summary: 314 layers, 36492560 parameters, 6194944 gradients\n", - " Convert model to Traced-model... \n", - " traced_script_module saved! \n", - " model is traced! \n", - "\n", - "/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n", - " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n", - "Done. (16.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012086.jpg\n", - "5 unknowns, Done. (16.4ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012092.jpg\n", - "1 unknown, Done. (16.4ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012095.jpg\n", - "1 unknown, Done. (16.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012134.jpg\n", - "2 unknowns, Done. (16.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012136.jpg\n", - "Done. (16.4ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012147.jpg\n", - "Done. (16.4ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012149.jpg\n", - "1 unknown, Done. (16.4ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012152.jpg\n", - "1 unknown, Done. (16.4ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012178.jpg\n", - "1 unknown, Done. (11.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012199.jpg\n", - "Done. (11.4ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012207.jpg\n", - "3 unknowns, Done. (11.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012215.jpg\n", - "6 unknowns, Done. (11.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012216.jpg\n", - "Done. (11.4ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012223.jpg\n", - "Done. (11.3ms) Inference, (0.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012240.jpg\n", - "Done. (11.2ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012248.jpg\n", - "8 unknowns, Done. (11.3ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012258.jpg\n", - "2 unknowns, Done. (11.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012265.jpg\n", - "1 unknown, Done. (9.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012266.jpg\n", - "1 unknown, Done. (9.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012272.jpg\n", - "1 unknown, Done. (9.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012273.jpg\n", - "Done. (9.5ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012314.jpg\n", - "2 unknowns, Done. (9.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012323.jpg\n", - "1 unknown, Done. (9.4ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012330.jpg\n", - "Done. (9.3ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012356.jpg\n", - "2 unknowns, Done. (9.3ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012357.jpg\n", - "11 unknowns, Done. (9.3ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012358.jpg\n", - "Done. (9.4ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012364.jpg\n", - "4 unknowns, Done. (8.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012369.jpg\n", - "3 unknowns, Done. (8.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012372.jpg\n", - "2 unknowns, Done. (8.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012375.jpg\n", - "1 unknown, Done. (8.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012387.jpg\n", - "1 unknown, Done. (8.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012388.jpg\n", - "1 unknown, Done. (8.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012395.jpg\n", - "Done. (8.8ms) Inference, (0.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012414.jpg\n", - "2 unknowns, Done. (8.8ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012425.jpg\n", - "2 unknowns, Done. (8.7ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012428.jpg\n", - "Done. (8.6ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012432.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012447.jpg\n", - "2 unknowns, Done. (8.8ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012448.jpg\n", - "Done. (8.8ms) Inference, (0.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012484.jpg\n", - "2 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012493.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012510.jpg\n", - "1 unknown, Done. (8.3ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012522.jpg\n", - "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012537.jpg\n", - "1 unknown, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012548.jpg\n", - "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012551.jpg\n", - "3 unknowns, Done. (8.2ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012654.jpg\n", - "6 unknowns, Done. (8.4ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012656.jpg\n", - "1 unknown, Done. (8.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012705.jpg\n", - "6 unknowns, Done. (8.8ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012708.jpg\n", - "1 unknown, Done. (8.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012722.jpg\n", - "1 unknown, Done. (8.9ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012757.jpg\n", - "1 unknown, Done. (11.3ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012758.jpg\n", - "4 unknowns, Done. (10.0ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012786.jpg\n", - "5 unknowns, Done. (9.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012803.jpg\n", - "1 unknown, Done. (8.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012836.jpg\n", - "2 unknowns, Done. (8.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012837.jpg\n", - "3 unknowns, Done. (8.3ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012848.jpg\n", - "1 unknown, Done. (8.2ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012852.jpg\n", - "2 unknowns, Done. (8.1ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012903.jpg\n", - "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012904.jpg\n", - "1 unknown, Done. (8.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012928.jpg\n", - "Done. (8.2ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012941.jpg\n", - "1 unknown, Done. (8.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012955.jpg\n", - "Done. (8.1ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012967.jpg\n", - "1 unknown, Done. (8.8ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012974.jpg\n", - "1 unknown, Done. (8.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0012989.jpg\n", - "Done. (8.1ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013030.jpg\n", - "Done. (8.1ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013035.jpg\n", - "5 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013045.jpg\n", - "4 unknowns, Done. (8.6ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013070.jpg\n", - "4 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013072.jpg\n", - "6 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013073.jpg\n", - "2 unknowns, Done. (8.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013085.jpg\n", - "5 unknowns, Done. (8.8ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013109.jpg\n", - "4 unknowns, Done. (8.6ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013159.jpg\n", - "5 unknowns, Done. (8.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013164.jpg\n", - "2 unknowns, Done. (8.3ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013169.jpg\n", - "Done. (8.2ms) Inference, (0.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013170.jpg\n", - "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013176.jpg\n", - "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013191.jpg\n", - "5 unknowns, Done. (8.6ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013203.jpg\n", - "3 unknowns, Done. (8.9ms) Inference, (1.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013216.jpg\n", - "1 unknown, Done. (8.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013226.jpg\n", - "2 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013230.jpg\n", - "3 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013242.jpg\n", - "7 unknowns, Done. (8.6ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013269.jpg\n", - "10 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013270.jpg\n", - "1 unknown, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013271.jpg\n", - "5 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013277.jpg\n", - "Done. (8.3ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013281.jpg\n", - "5 unknowns, Done. (8.2ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013291.jpg\n", - "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013319.jpg\n", - "1 unknown, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013321.jpg\n", - "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013325.jpg\n", - "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013374.jpg\n", - "4 unknowns, Done. (8.2ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013393.jpg\n", - "1 unknown, Done. (8.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013399.jpg\n", - "Done. (8.5ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013411.jpg\n", - "5 unknowns, Done. (8.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013414.jpg\n", - "2 unknowns, Done. (8.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013416.jpg\n", - "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013455.jpg\n", - "1 unknown, Done. (8.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013457.jpg\n", - "15 unknowns, Done. (8.2ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013459.jpg\n", - "6 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013465.jpg\n", - "1 unknown, Done. (8.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013472.jpg\n", - "1 unknown, Done. (8.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013473.jpg\n", - "2 unknowns, Done. (8.7ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013511.jpg\n", - "5 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013512.jpg\n", - "1 unknown, Done. (8.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013529.jpg\n", - "Done. (8.9ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013565.jpg\n", - "2 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013577.jpg\n", - "4 unknowns, Done. (8.3ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013588.jpg\n", - "3 unknowns, Done. (8.2ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013600.jpg\n", - "5 unknowns, Done. (8.2ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013602.jpg\n", - "2 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013615.jpg\n", - "2 unknowns, Done. (8.3ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013617.jpg\n", - "1 unknown, Done. (8.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013636.jpg\n", - "1 unknown, Done. (8.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013673.jpg\n", - "5 unknowns, Done. (8.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013678.jpg\n", - "2 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013696.jpg\n", - "1 unknown, Done. (8.3ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013708.jpg\n", - "3 unknowns, Done. (8.3ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013733.jpg\n", - "2 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013738.jpg\n", - "4 unknowns, Done. (8.4ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013739.jpg\n", - "3 unknowns, Done. (8.3ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013764.jpg\n", - "5 unknowns, Done. (8.3ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013766.jpg\n", - "Done. (8.5ms) Inference, (0.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013767.jpg\n", - "1 unknown, Done. (8.7ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013794.jpg\n", - "7 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013809.jpg\n", - "Done. (8.5ms) Inference, (0.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013813.jpg\n", - "Done. (8.6ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013814.jpg\n", - "Done. (8.7ms) Inference, (0.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013833.jpg\n", - "2 unknowns, Done. (8.7ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013842.jpg\n", - "3 unknowns, Done. (8.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013867.jpg\n", - "2 unknowns, Done. (8.8ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013891.jpg\n", - "5 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013897.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013908.jpg\n", - "2 unknowns, Done. (8.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013911.jpg\n", - "1 unknown, Done. (8.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013917.jpg\n", - "6 unknowns, Done. (8.8ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013925.jpg\n", - "1 unknown, Done. (8.8ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013948.jpg\n", - "Done. (8.7ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013953.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013966.jpg\n", - "2 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013977.jpg\n", - "3 unknowns, Done. (8.3ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013987.jpg\n", - "2 unknowns, Done. (8.2ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013988.jpg\n", - "4 unknowns, Done. (8.3ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0013998.jpg\n", - "Done. (8.5ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014006.jpg\n", - "Done. (8.3ms) Inference, (0.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014027.jpg\n", - "1 unknown, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014059.jpg\n", - "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014077.jpg\n", - "1 unknown, Done. (8.1ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014090.jpg\n", - "4 unknowns, Done. (8.1ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014103.jpg\n", - "3 unknowns, Done. (8.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014110.jpg\n", - "3 unknowns, Done. (8.1ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014117.jpg\n", - "Done. (8.1ms) Inference, (0.8ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014129.jpg\n", - "1 unknown, Done. (8.2ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014148.jpg\n", - "3 unknowns, Done. (8.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014160.jpg\n", - "Done. (8.6ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014177.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014181.jpg\n", - "6 unknowns, Done. (8.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014186.jpg\n", - "3 unknowns, Done. (8.3ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014219.jpg\n", - "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014221.jpg\n", - "3 unknowns, Done. (9.8ms) Inference, (1.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014233.jpg\n", - "Done. (8.2ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014251.jpg\n", - "2 unknowns, Done. (8.2ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014255.jpg\n", - "5 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014270.jpg\n", - "3 unknowns, Done. (8.6ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014278.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014284.jpg\n", - "2 unknowns, Done. (8.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014288.jpg\n", - "5 unknowns, Done. (8.3ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014319.jpg\n", - "Done. (8.3ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014336.jpg\n", - "1 unknown, Done. (8.2ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014349.jpg\n", - "1 unknown, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014369.jpg\n", - "Done. (8.1ms) Inference, (0.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014386.jpg\n", - "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014392.jpg\n", - "3 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014409.jpg\n", - "Done. (8.1ms) Inference, (0.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014419.jpg\n", - "3 unknowns, Done. (8.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014423.jpg\n", - "Done. (8.1ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014434.jpg\n", - "2 unknowns, Done. (8.3ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014454.jpg\n", - "6 unknowns, Done. (8.7ms) Inference, (2.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014457.jpg\n", - "3 unknowns, Done. (10.4ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014470.jpg\n", - "2 unknowns, Done. (10.8ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014474.jpg\n", - "3 unknowns, Done. (8.3ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014478.jpg\n", - "1 unknown, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014489.jpg\n", - "2 unknowns, Done. (8.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014500.jpg\n", - "Done. (8.2ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014503.jpg\n", - "Done. (8.3ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014506.jpg\n", - "2 unknowns, Done. (8.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014513.jpg\n", - "5 unknowns, Done. (8.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014541.jpg\n", - "Done. (8.7ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014542.jpg\n", - "Done. (8.6ms) Inference, (0.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014546.jpg\n", - "2 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014548.jpg\n", - "Done. (8.5ms) Inference, (0.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014559.jpg\n", - "5 unknowns, Done. (8.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014567.jpg\n", - "2 unknowns, Done. (9.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014574.jpg\n", - "2 unknowns, Done. (9.8ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014575.jpg\n", - "2 unknowns, Done. (9.6ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014586.jpg\n", - "5 unknowns, Done. (9.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014587.jpg\n", - "4 unknowns, Done. (9.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014588.jpg\n", - "2 unknowns, Done. (9.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014590.jpg\n", - "3 unknowns, Done. (9.3ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014600.jpg\n", - "2 unknowns, Done. (9.3ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014619.jpg\n", - "1 unknown, Done. (9.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014626.jpg\n", - "1 unknown, Done. (9.3ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014627.jpg\n", - "3 unknowns, Done. (9.1ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014629.jpg\n", - "1 unknown, Done. (9.0ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014631.jpg\n", - "2 unknowns, Done. (8.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014634.jpg\n", - "1 unknown, Done. (9.0ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014643.jpg\n", - "6 unknowns, Done. (9.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014644.jpg\n", - "1 unknown, Done. (9.0ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014645.jpg\n", - "1 unknown, Done. (9.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014647.jpg\n", - "3 unknowns, Done. (10.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014648.jpg\n", - "4 unknowns, Done. (10.3ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014649.jpg\n", - "2 unknowns, Done. (9.6ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014652.jpg\n", - "Done. (9.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014653.jpg\n", - "Done. (8.7ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014663.jpg\n", - "1 unknown, Done. (8.9ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014666.jpg\n", - "5 unknowns, Done. (8.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014675.jpg\n", - "4 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014677.jpg\n", - "4 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014687.jpg\n", - "2 unknowns, Done. (8.6ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014693.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014695.jpg\n", - "2 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014697.jpg\n", - "3 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014698.jpg\n", - "3 unknowns, Done. (8.7ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014703.jpg\n", - "4 unknowns, Done. (8.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014720.jpg\n", - "4 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014725.jpg\n", - "2 unknowns, Done. (9.2ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014727.jpg\n", - "1 unknown, Done. (10.8ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014728.jpg\n", - "1 unknown, Done. (8.7ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014729.jpg\n", - "1 unknown, Done. (11.0ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014740.jpg\n", - "6 unknowns, Done. (10.9ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014743.jpg\n", - "3 unknowns, Done. (8.7ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014746.jpg\n", - "6 unknowns, Done. (8.7ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014749.jpg\n", - "3 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014753.jpg\n", - "6 unknowns, Done. (8.7ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014755.jpg\n", - "4 unknowns, Done. (9.4ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014765.jpg\n", - "4 unknowns, Done. (9.3ms) Inference, (1.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014766.jpg\n", - "5 unknowns, Done. (8.8ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014768.jpg\n", - "5 unknowns, Done. (8.7ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014772.jpg\n", - "3 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014773.jpg\n", - "8 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014780.jpg\n", - "5 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014784.jpg\n", - "2 unknowns, Done. (8.8ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014786.jpg\n", - "6 unknowns, Done. (8.8ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014787.jpg\n", - "Done. (8.7ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014790.jpg\n", - "1 unknown, Done. (8.7ms) Inference, (2.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014792.jpg\n", - "4 unknowns, Done. (9.4ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014796.jpg\n", - "Done. (9.3ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014798.jpg\n", - "4 unknowns, Done. (8.8ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014800.jpg\n", - "2 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014807.jpg\n", - "5 unknowns, Done. (8.7ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014814.jpg\n", - "5 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014815.jpg\n", - "6 unknowns, Done. (13.1ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014820.jpg\n", - "1 unknown, Done. (13.4ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014822.jpg\n", - "Done. (13.3ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014826.jpg\n", - "Done. (13.3ms) Inference, (0.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014833.jpg\n", - "5 unknowns, Done. (13.6ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014835.jpg\n", - "4 unknowns, Done. (13.5ms) Inference, (1.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014844.jpg\n", - "5 unknowns, Done. (13.6ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014853.jpg\n", - "6 unknowns, Done. (13.9ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014854.jpg\n", - "1 unknown, Done. (13.9ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014862.jpg\n", - "5 unknowns, Done. (14.0ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014863.jpg\n", - "4 unknowns, Done. (14.3ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014867.jpg\n", - "1 unknown, Done. (14.4ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014868.jpg\n", - "Done. (14.3ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014872.jpg\n", - "3 unknowns, Done. (14.4ms) Inference, (1.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014876.jpg\n", - "2 unknowns, Done. (14.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014879.jpg\n", - "Done. (14.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014883.jpg\n", - "4 unknowns, Done. (14.6ms) Inference, (1.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014901.jpg\n", - "3 unknowns, Done. (15.1ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014907.jpg\n", - "3 unknowns, Done. (15.0ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014910.jpg\n", - "3 unknowns, Done. (15.0ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014912.jpg\n", - "4 unknowns, Done. (15.0ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014921.jpg\n", - "2 unknowns, Done. (15.0ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014927.jpg\n", - "3 unknowns, Done. (15.0ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014928.jpg\n", - "4 unknowns, Done. (15.0ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014932.jpg\n", - "11 unknowns, Done. (15.0ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014936.jpg\n", - "10 unknowns, Done. (14.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014938.jpg\n", - "5 unknowns, Done. (15.3ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014940.jpg\n", - "1 unknown, Done. (15.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014941.jpg\n", - "5 unknowns, Done. (15.3ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014942.jpg\n", - "6 unknowns, Done. (15.3ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014943.jpg\n", - "5 unknowns, Done. (15.3ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014944.jpg\n", - "9 unknowns, Done. (15.3ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014947.jpg\n", - "1 unknown, Done. (15.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014948.jpg\n", - "4 unknowns, Done. (15.3ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014949.jpg\n", - "3 unknowns, Done. (15.3ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014952.jpg\n", - "16 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014955.jpg\n", - "4 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014956.jpg\n", - "1 unknown, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014957.jpg\n", - "10 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014958.jpg\n", - "6 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014959.jpg\n", - "2 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014961.jpg\n", - "3 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014962.jpg\n", - "4 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014963.jpg\n", - "1 unknown, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014964.jpg\n", - "5 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014966.jpg\n", - "3 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014968.jpg\n", - "5 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014969.jpg\n", - "5 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014973.jpg\n", - "7 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014974.jpg\n", - "5 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014977.jpg\n", - "4 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014982.jpg\n", - "4 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014992.jpg\n", - "3 unknowns, Done. (15.9ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014994.jpg\n", - "5 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0014998.jpg\n", - "Done. (15.8ms) Inference, (0.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015002.jpg\n", - "4 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015003.jpg\n", - "Done. (15.9ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015004.jpg\n", - "3 unknowns, Done. (15.8ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015007.jpg\n", - "Done. (15.8ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015008.jpg\n", - "1 unknown, Done. (15.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015009.jpg\n", - "Done. (15.8ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015011.jpg\n", - "Done. (15.8ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015013.jpg\n", - "5 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015015.jpg\n", - "5 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015016.jpg\n", - "4 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015018.jpg\n", - "3 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015019.jpg\n", - "2 unknowns, Done. (15.8ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015020.jpg\n", - "3 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015021.jpg\n", - "6 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015023.jpg\n", - "1 unknown, Done. (15.8ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015026.jpg\n", - "6 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015030.jpg\n", - "Done. (16.1ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015031.jpg\n", - "4 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015034.jpg\n", - "2 unknowns, Done. (16.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015035.jpg\n", - "2 unknowns, Done. (16.1ms) Inference, (1.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015037.jpg\n", - "5 unknowns, Done. (16.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015040.jpg\n", - "5 unknowns, Done. (16.2ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015041.jpg\n", - "6 unknowns, Done. (16.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015046.jpg\n", - "Done. (16.1ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015050.jpg\n", - "4 unknowns, Done. (16.2ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015051.jpg\n", - "6 unknowns, Done. (16.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015056.jpg\n", - "3 unknowns, Done. (16.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015057.jpg\n", - "2 unknowns, Done. (16.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015060.jpg\n", - "3 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015064.jpg\n", - "7 unknowns, Done. (16.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015071.jpg\n", - "Done. (16.1ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015078.jpg\n", - "Done. (16.1ms) Inference, (0.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015089.jpg\n", - "1 unknown, Done. (16.1ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015102.jpg\n", - "2 unknowns, Done. (16.3ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015115.jpg\n", - "Done. (16.5ms) Inference, (0.8ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015118.jpg\n", - "3 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015119.jpg\n", - "6 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015125.jpg\n", - "2 unknowns, Done. (16.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015127.jpg\n", - "4 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015129.jpg\n", - "Done. (16.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015130.jpg\n", - "2 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015132.jpg\n", - "6 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015133.jpg\n", - "3 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015136.jpg\n", - "14 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015139.jpg\n", - "1 unknown, Done. (16.4ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015140.jpg\n", - "2 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015142.jpg\n", - "11 unknowns, Done. (16.6ms) Inference, (1.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015146.jpg\n", - "5 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015149.jpg\n", - "4 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015150.jpg\n", - "Done. (16.6ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015152.jpg\n", - "Done. (16.5ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015155.jpg\n", - "3 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015156.jpg\n", - "5 unknowns, Done. (16.5ms) Inference, (1.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015157.jpg\n", - "Done. (16.5ms) Inference, (0.8ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015160.jpg\n", - "2 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015161.jpg\n", - "2 unknowns, Done. (16.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015163.jpg\n", - "4 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015167.jpg\n", - "Done. (16.5ms) Inference, (0.8ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015171.jpg\n", - "Done. (16.5ms) Inference, (0.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015173.jpg\n", - "5 unknowns, Done. (16.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015174.jpg\n", - "1 unknown, Done. (15.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015175.jpg\n", - "Done. (15.8ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015176.jpg\n", - "3 unknowns, Done. (15.8ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015179.jpg\n", - "4 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015180.jpg\n", - "4 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015184.jpg\n", - "Done. (15.8ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015185.jpg\n", - "2 unknowns, Done. (15.8ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015193.jpg\n", - "4 unknowns, Done. (15.8ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015201.jpg\n", - "2 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015202.jpg\n", - "Done. (15.5ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015203.jpg\n", - "11 unknowns, Done. (15.6ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015206.jpg\n", - "6 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015207.jpg\n", - "3 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015208.jpg\n", - "4 unknowns, Done. (15.7ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015212.jpg\n", - "1 unknown, Done. (15.5ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015215.jpg\n", - "Done. (15.5ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015216.jpg\n", - "5 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015217.jpg\n", - "5 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015218.jpg\n", - "6 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015223.jpg\n", - "1 unknown, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015224.jpg\n", - "1 unknown, Done. (15.6ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015226.jpg\n", - "1 unknown, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015229.jpg\n", - "2 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015232.jpg\n", - "1 unknown, Done. (15.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015237.jpg\n", - "4 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015241.jpg\n", - "2 unknowns, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015244.jpg\n", - "4 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015245.jpg\n", - "4 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015250.jpg\n", - "4 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015251.jpg\n", - "4 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015254.jpg\n", - "Done. (15.6ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015255.jpg\n", - "6 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015258.jpg\n", - "5 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015264.jpg\n", - "7 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015270.jpg\n", - "5 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015273.jpg\n", - "4 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015274.jpg\n", - "6 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015276.jpg\n", - "4 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015279.jpg\n", - "1 unknown, Done. (15.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015283.jpg\n", - "5 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015291.jpg\n", - "2 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015293.jpg\n", - "5 unknowns, Done. (15.6ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015298.jpg\n", - "6 unknowns, Done. (15.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015309.jpg\n", - "1 unknown, Done. (15.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015310.jpg\n", - "Done. (15.6ms) Inference, (0.8ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015311.jpg\n", - "4 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015312.jpg\n", - "4 unknowns, Done. (15.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015330.jpg\n", - "6 unknowns, Done. (15.6ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015331.jpg\n", - "3 unknowns, Done. (15.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015347.jpg\n", - "1 unknown, Done. (15.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015353.jpg\n", - "Done. (15.5ms) Inference, (0.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015355.jpg\n", - "4 unknowns, Done. (15.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015357.jpg\n", - "3 unknowns, Done. (15.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015360.jpg\n", - "14 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015363.jpg\n", - "3 unknowns, Done. (15.8ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015364.jpg\n", - "Done. (15.8ms) Inference, (0.8ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015368.jpg\n", - "4 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015369.jpg\n", - "3 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015383.jpg\n", - "2 unknowns, Done. (15.8ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015386.jpg\n", - "2 unknowns, Done. (15.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015390.jpg\n", - "Done. (15.8ms) Inference, (0.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015395.jpg\n", - "6 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015403.jpg\n", - "Done. (15.8ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015404.jpg\n", - "5 unknowns, Done. (15.9ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015411.jpg\n", - "2 unknowns, Done. (15.9ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015412.jpg\n", - "Done. (15.8ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015416.jpg\n", - "1 unknown, Done. (15.9ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015417.jpg\n", - "5 unknowns, Done. (16.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015418.jpg\n", - "6 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015419.jpg\n", - "5 unknowns, Done. (16.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015436.jpg\n", - "4 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015440.jpg\n", - "Done. (16.1ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015447.jpg\n", - "1 unknown, Done. (16.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015455.jpg\n", - "3 unknowns, Done. (16.1ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015464.jpg\n", - "Done. (16.1ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015466.jpg\n", - "5 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015468.jpg\n", - "3 unknowns, Done. (16.1ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015476.jpg\n", - "5 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015481.jpg\n", - "Done. (16.1ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015482.jpg\n", - "11 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015485.jpg\n", - "1 unknown, Done. (16.1ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015510.jpg\n", - "5 unknowns, Done. (16.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015526.jpg\n", - "4 unknowns, Done. (16.2ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015537.jpg\n", - "2 unknowns, Done. (16.2ms) Inference, (1.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015544.jpg\n", - "5 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015559.jpg\n", - "4 unknowns, Done. (16.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015563.jpg\n", - "2 unknowns, Done. (16.4ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015566.jpg\n", - "2 unknowns, Done. (16.5ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015568.jpg\n", - "5 unknowns, Done. (16.5ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015582.jpg\n", - "Done. (16.5ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015593.jpg\n", - "7 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015603.jpg\n", - "1 unknown, Done. (16.4ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015607.jpg\n", - "4 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015614.jpg\n", - "2 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015617.jpg\n", - "6 unknowns, Done. (16.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015625.jpg\n", - "Done. (16.5ms) Inference, (0.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015631.jpg\n", - "Done. (16.4ms) Inference, (0.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015636.jpg\n", - "4 unknowns, Done. (16.5ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015638.jpg\n", - "5 unknowns, Done. (16.6ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015641.jpg\n", - "2 unknowns, Done. (16.6ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015645.jpg\n", - "2 unknowns, Done. (16.5ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015936.jpg\n", - "1 unknown, Done. (16.4ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015937.jpg\n", - "3 unknowns, Done. (16.4ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015938.jpg\n", - "5 unknowns, Done. (14.3ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015939.jpg\n", - "1 unknown, Done. (14.3ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015940.jpg\n", - "4 unknowns, Done. (14.3ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015941.jpg\n", - "1 unknown, Done. (14.3ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015942.jpg\n", - "1 unknown, Done. (14.3ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015943.jpg\n", - "2 unknowns, Done. (14.3ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015944.jpg\n", - "2 unknowns, Done. (14.3ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015945.jpg\n", - "2 unknowns, Done. (14.3ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015946.jpg\n", - "3 unknowns, Done. (10.8ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015947.jpg\n", - "2 unknowns, Done. (10.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015948.jpg\n", - "3 unknowns, Done. (10.8ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015949.jpg\n", - "1 unknown, Done. (10.8ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015950.jpg\n", - "2 unknowns, Done. (10.8ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015951.jpg\n", - "1 unknown, Done. (10.7ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015952.jpg\n", - "1 unknown, Done. (10.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015953.jpg\n", - "1 unknown, Done. (10.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015954.jpg\n", - "4 unknowns, Done. (10.6ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015955.jpg\n", - "3 unknowns, Done. (10.7ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015956.jpg\n", - "3 unknowns, Done. (9.2ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015957.jpg\n", - "1 unknown, Done. (9.0ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015958.jpg\n", - "10 unknowns, Done. (8.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015959.jpg\n", - "16 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015960.jpg\n", - "27 unknowns, Done. (8.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015961.jpg\n", - "3 unknowns, Done. (8.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015962.jpg\n", - "1 unknown, Done. (8.7ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015963.jpg\n", - "5 unknowns, Done. (8.8ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015964.jpg\n", - "2 unknowns, Done. (8.9ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015965.jpg\n", - "4 unknowns, Done. (8.8ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015966.jpg\n", - "2 unknowns, Done. (8.8ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015967.jpg\n", - "Done. (8.6ms) Inference, (0.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015968.jpg\n", - "5 unknowns, Done. (8.4ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015969.jpg\n", - "3 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015971.jpg\n", - "1 unknown, Done. (8.3ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015972.jpg\n", - "1 unknown, Done. (10.0ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015973.jpg\n", - "1 unknown, Done. (10.2ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015974.jpg\n", - "2 unknowns, Done. (10.0ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015975.jpg\n", - "3 unknowns, Done. (9.6ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015976.jpg\n", - "1 unknown, Done. (8.9ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015978.jpg\n", - "1 unknown, Done. (8.5ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015979.jpg\n", - "4 unknowns, Done. (8.4ms) Inference, (0.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015980.jpg\n", - "10 unknowns, Done. (8.4ms) Inference, (1.1ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015981.jpg\n", - "3 unknowns, Done. (8.7ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015982.jpg\n", - "5 unknowns, Done. (9.3ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015983.jpg\n", - "10 unknowns, Done. (8.5ms) Inference, (1.0ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015984.jpg\n", - "4 unknowns, Done. (9.8ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015985.jpg\n", - "2 unknowns, Done. (9.4ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015986.jpg\n", - "2 unknowns, Done. (9.4ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015987.jpg\n", - "6 unknowns, Done. (9.0ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015988.jpg\n", - "4 unknowns, Done. (8.8ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015989.jpg\n", - "5 unknowns, Done. (9.0ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015990.jpg\n", - "3 unknowns, Done. (10.6ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015991.jpg\n", - "2 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015992.jpg\n", - "5 unknowns, Done. (8.5ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015993.jpg\n", - "1 unknown, Done. (9.5ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015994.jpg\n", - "3 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015995.jpg\n", - "2 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015996.jpg\n", - "2 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015997.jpg\n", - "2 unknowns, Done. (10.2ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015998.jpg\n", - "1 unknown, Done. (10.6ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0015999.jpg\n", - "3 unknowns, Done. (8.6ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016000.jpg\n", - "6 unknowns, Done. (8.6ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016001.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016002.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016003.jpg\n", - "4 unknowns, Done. (8.5ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016004.jpg\n", - "3 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016005.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016006.jpg\n", - "2 unknowns, Done. (8.5ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016007.jpg\n", - "3 unknowns, Done. (8.5ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016008.jpg\n", - "1 unknown, Done. (9.8ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016009.jpg\n", - "3 unknowns, Done. (8.5ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016011.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016012.jpg\n", - "2 unknowns, Done. (8.6ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016013.jpg\n", - "1 unknown, Done. (8.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016014.jpg\n", - "1 unknown, Done. (8.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016015.jpg\n", - "1 unknown, Done. (8.5ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016016.jpg\n", - "1 unknown, Done. (8.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016017.jpg\n", - "4 unknowns, Done. (8.5ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016018.jpg\n", - "Done. (8.5ms) Inference, (0.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016019.jpg\n", - "6 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016022.jpg\n", - "4 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016023.jpg\n", - "2 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016024.jpg\n", - "7 unknowns, Done. (8.6ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016025.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016026.jpg\n", - "1 unknown, Done. (8.5ms) Inference, (2.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016027.jpg\n", - "2 unknowns, Done. (8.6ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016028.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016029.jpg\n", - "2 unknowns, Done. (8.8ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016030.jpg\n", - "3 unknowns, Done. (8.5ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016031.jpg\n", - "5 unknowns, Done. (8.5ms) Inference, (1.2ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016033.jpg\n", - "3 unknowns, Done. (10.8ms) Inference, (1.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016034.jpg\n", - "3 unknowns, Done. (11.3ms) Inference, (1.7ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016035.jpg\n", - "4 unknowns, Done. (9.6ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016036.jpg\n", - "3 unknowns, Done. (8.6ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016037.jpg\n", - "6 unknowns, Done. (8.6ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016038.jpg\n", - "2 unknowns, Done. (8.6ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016040.jpg\n", - "5 unknowns, Done. (10.9ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016041.jpg\n", - "2 unknowns, Done. (10.9ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016042.jpg\n", - "4 unknowns, Done. (8.6ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016043.jpg\n", - "5 unknowns, Done. (8.6ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016044.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016045.jpg\n", - "1 unknown, Done. (8.9ms) Inference, (1.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016046.jpg\n", - "1 unknown, Done. (8.6ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016048.jpg\n", - "4 unknowns, Done. (9.4ms) Inference, (1.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016049.jpg\n", - "5 unknowns, Done. (9.3ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016050.jpg\n", - "2 unknowns, Done. (9.3ms) Inference, (1.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016051.jpg\n", - "7 unknowns, Done. (9.1ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016052.jpg\n", - "4 unknowns, Done. (12.3ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016053.jpg\n", - "5 unknowns, Done. (8.6ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016054.jpg\n", - "2 unknowns, Done. (11.3ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016055.jpg\n", - "3 unknowns, Done. (10.0ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016056.jpg\n", - "5 unknowns, Done. (9.9ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016057.jpg\n", - "Done. (9.9ms) Inference, (0.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016058.jpg\n", - "1 unknown, Done. (9.9ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016059.jpg\n", - "4 unknowns, Done. (9.9ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016060.jpg\n", - "5 unknowns, Done. (9.9ms) Inference, (2.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016061.jpg\n", - "10 unknowns, Done. (10.2ms) Inference, (1.9ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016062.jpg\n", - "4 unknowns, Done. (10.1ms) Inference, (1.5ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016063.jpg\n", - "2 unknowns, Done. (10.1ms) Inference, (1.6ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016064.jpg\n", - "3 unknowns, Done. (10.0ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016065.jpg\n", - "1 unknown, Done. (10.2ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016066.jpg\n", - "3 unknowns, Done. (10.0ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016068.jpg\n", - "4 unknowns, Done. (10.1ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016069.jpg\n", - "2 unknowns, Done. (10.4ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016070.jpg\n", - "7 unknowns, Done. (10.2ms) Inference, (1.3ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016071.jpg\n", - "2 unknowns, Done. (11.2ms) Inference, (1.4ms) NMS\n", - " The image with the result is saved in: runs/detect/exp5/ISIC_0016072.jpg\n", - "Done. (20.704s)\n" - ] - } - ], - "source": [ - "!python yolov7/detect.py --weights runs/train/yolov7_isic9/weights/best.pt --conf 0.2 --img-size 640 --source data/ISIC_2017_downsampled/test_downsampled" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "authorship_tag": "ABX9TyPeFJdJGSone4v6j83wW1tV", - "gpuType": "T4", - "include_colab_link": true, - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/lesion_detection_larsmoan/run_test.sh b/lesion_detection_larsmoan/run_test.sh old mode 100644 new mode 100755 From 134fdf79023007c5b0eabb8f3dede0fbe80fed60 Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Thu, 9 Nov 2023 15:40:36 +1000 Subject: [PATCH 33/42] Added more explenation about the YOLO architecture --- .gitignore | 1 + lesion_detection_larsmoan/README.md | 19 +++++++++++++++--- .../figures/output-tensor-yolov1.png.webp | Bin 0 -> 50968 bytes 3 files changed, 17 insertions(+), 3 deletions(-) create mode 100644 lesion_detection_larsmoan/figures/output-tensor-yolov1.png.webp diff --git a/.gitignore b/.gitignore index 1d17dae13..4083cc55b 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,2 @@ .venv +.DS_Store \ No newline at end of file diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index 28d9c96f2..a1d42ba2f 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -10,8 +10,12 @@ - [Usage](#usage) - [Model Architecture: open source YOLOV7 Model](#model-architecture-open-source-yolov7-model) - [Core ideas used in the YOLOV1 paper:](#core-ideas-used-in-the-yolov1-paper) + - [Output](#output) - [Training](#training) - [Results](#results) + - [Confusion Matrix](#confusion-matrix) + - [F1 - curve](#f1---curve) + - [Precision - Recall curve](#precision---recall-curve) ### Installation @@ -116,7 +120,7 @@ YOLOV7 is based on the original YOLO paper: [YOLOV1](https://arxiv.org/abs/1506. The original paper was trained on input images of size 448X448 and these images where parsed into a grid of 7x7 grid cells.

    Example Image -
    Original grid cells on 448x448 image.
    +
    Original grid cells on 448x448 image. Source: "You Only Look Once by Joseph Redmon et al., CVPR 2016.
    The idea was that each grid cell was responsible for predicting a object if the center of that object was within the given grid cell. @@ -125,14 +129,23 @@ The idea was that each grid cell was responsible for predicting a object if the In the paper it was also proposed that each grid cell's output was two bounding boxes each with their own confidence / objectness score + a class probability vector. I.e was each grid cell only capable of predicting one detection, altough it could predict two bounding boxes. The bounding box with the highest objectness score was chosen in addition to the class with the highest probability.
    - Example Image -
    YOLOV1 object detection [Joseph Redmon et al]
    + Example Image +
    YOLOv1 output tensor. Source: "You Only Look Once by Joseph Redmon et al., CVPR 2016.
    In the figure above the core ideas is presented, each grid cell proposing two bounding boxes (in black) but only one class. The "best" box and class over a certain treshold is then used as the final prediction. +### Output +Following from the description above about what the model outputs. Because the model uses a 7x7 grid, the output is a tensor of shape (batch_size, 7, 7, 30). Where 30 is the number of parameters that is predicted for each grid cell. The 30 parameters per grid cell are: +[objectness_score, box_x, box_y, width, height] + [objectness_score, box_x, box_y, width, height] + [class_probabilities]. +Two bounding boxes + a vector of class probabilities. In the case of the original YOLO paper, the model was trained on the [PASCAL dataset](http://host.robots.ox.ac.uk/pascal/VOC/) where there are 20 classes present. Hence the 30 parameters per grid cell. +
    + YOLOv1 Output Tensor Visualization +
    YOLOv1 output tensor. Source: "You Only Look Once by Joseph Redmon et al., CVPR 2016.
    +
    + ### Training Training was mainly done on the rangpur cluster, using the P100 gpu and a batch size of 32 diff --git a/lesion_detection_larsmoan/figures/output-tensor-yolov1.png.webp b/lesion_detection_larsmoan/figures/output-tensor-yolov1.png.webp new file mode 100644 index 0000000000000000000000000000000000000000..5fdd581e19477f45950c6da0ba205e8cb74e31e3 GIT binary patch literal 50968 zcmV(>K-j-hNk&Ex#{d9VMM6+kP&iBk#{d8?wt{m2|Kh5)ZB=>hF6bWts6u%HxVv4y zch_JW*|w68JEWv^L;nT1MK160?R{6ZZL7+2*MBmQfQqysjsLU@8F^bZIa|2LrAlRO#NLB&9s57KBuyK%c`mOYY~u}r}t;j)~xaxO)djo_4QOA(yeR)>i%hA421A7m&hLKx(;2>uY% z2v`n#_@mS4=<^saw*@+g~h=>W0ZL0?ms->zj+m>~19=_5iJem(c2dT`$wv$9jP`6A4h~6A0b$ae$+P3FS zvu%r*u_5&83iE($EDM|vO5#RLHGUh&kwDG!4FCUc9LKir$9~w7(A)PC(SKQxZG~-f zU-DW}J2_9090kD>BK^{~ZIhfhwsor9uAav;&q7d0LSi25)dgN&cJFyZ84w7;8UFv@ z$ao`_s$Sc@K4xS@^xqb3yJ6c(mwA{_W2aZC|M za(%R4-nDIy+s3xGP!a)j)V)9^m?wsI-hrV;?4a~5ydu7~ag;)yvn=Lx5H|AA!L9jPe z1qRqI32cE6+8A#0LSzP(xl56ipf5!#Mv_Y~aR^IG!9J#p@C{f@9z7E42 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for loss and performance from Wandb --- lesion_detection_larsmoan/README.md | 20 +++++++++++++++--- .../figures/metrics-mAP0.5_0.95.png | Bin 0 -> 668262 bytes .../figures/metrics-mAP_0.5.png | Bin 0 -> 635272 bytes .../figures/metrics-precision.png | Bin 0 -> 843601 bytes .../figures/metrics-recall.png | Bin 0 -> 768844 bytes .../figures/train-boxloss.png | Bin 0 -> 505453 bytes .../figures/train-clsloss.png | Bin 0 -> 530596 bytes .../figures/train-objloss.png | Bin 0 -> 519336 bytes 8 files changed, 17 insertions(+), 3 deletions(-) create mode 100644 lesion_detection_larsmoan/figures/metrics-mAP0.5_0.95.png create mode 100644 lesion_detection_larsmoan/figures/metrics-mAP_0.5.png create mode 100644 lesion_detection_larsmoan/figures/metrics-precision.png create mode 100644 lesion_detection_larsmoan/figures/metrics-recall.png create mode 100644 lesion_detection_larsmoan/figures/train-boxloss.png create mode 100644 lesion_detection_larsmoan/figures/train-clsloss.png create mode 100644 lesion_detection_larsmoan/figures/train-objloss.png diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index a1d42ba2f..43130472f 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -133,10 +133,10 @@ The idea was that each grid cell was responsible for predicting a object if the
    YOLOv1 output tensor. Source: "You Only Look Once by Joseph Redmon et al., CVPR 2016.
    -In the figure above the core ideas is presented, each grid cell proposing two bounding boxes (in black) but only one class. The "best" box and class over a certain treshold is then used as the final prediction. +In the figure above the core ideas is presented, each grid cell proposing two bounding boxes (in black) but only one class. The "best" box and class over a certain treshold is then used as the final prediction. -> Single stage object detection and classification! ### Output -Following from the description above about what the model outputs. Because the model uses a 7x7 grid, the output is a tensor of shape (batch_size, 7, 7, 30). Where 30 is the number of parameters that is predicted for each grid cell. The 30 parameters per grid cell are: +Because the model uses a 7x7 grid, the output is a tensor of shape (batch_size, 7, 7, 30). Where 30 is the number of parameters that is predicted for each grid cell. The 30 parameters per grid cell are: [objectness_score, box_x, box_y, width, height] + [objectness_score, box_x, box_y, width, height] + [class_probabilities]. Two bounding boxes + a vector of class probabilities. In the case of the original YOLO paper, the model was trained on the [PASCAL dataset](http://host.robots.ox.ac.uk/pascal/VOC/) where there are 20 classes present. Hence the 30 parameters per grid cell. @@ -148,10 +148,24 @@ Two bounding boxes + a vector of class probabilities. In the case of the origina ### Training -Training was mainly done on the rangpur cluster, using the P100 gpu and a batch size of 32 +Training was done on the rangpur cluster using a variety of different GPU's, the two main training runs shown in the plots below was done using the A100 and the P100. (A100 for the batch size of 8 and P100 for the batch size of 32). - Hyperparameters that was used is described in: [hyp.scratch.p6.yaml](./hyp.scratch.p6.yaml) +
    + Example Image +
    Box loss.
    +
    +
    + Example Image +
    Class loss.
    +
    +
    + Example Image +
    Object loss.
    +
    + + ### Results #### Confusion Matrix Description diff --git a/lesion_detection_larsmoan/figures/metrics-mAP0.5_0.95.png b/lesion_detection_larsmoan/figures/metrics-mAP0.5_0.95.png new file mode 100644 index 0000000000000000000000000000000000000000..57ca42d382a1c2f3cbd734215ae725e13216ce7f GIT binary patch literal 668262 zcmeFZXIPWbwk-?@77zgyP!LcNQHm(NgQ%eN-it_+F1-g(5$U}Pq4(ZO zX~e?9|9Jf>@XSQ2hdJ;A+eThY1S`Mq!7A{Nw+5;o45g&77=Xvuv9P_3v2f4c0(?;b zUszaI60xza06($Mo=e2}=c{)b6R-U9G5*K1H>xJ%3S(gjV|{r0TG0`E1L_hSvF~(k z`av#m53h5#K}9`gHqw`V$Of^n1LPqKlyOsL@{CPgxCM`R}CzK z=Ur?($LBpF|1TCr7`W>DmskC-NV{C?|CDu?5%O1lTt>)cg#7p1bQvL+5po$Je}U$| z`1RjDxMZ2X7VHv3E-~a1L;gyMe+wP|Y7{Rq3hFoIEC5Bv9s+X1Or8Dkt59TE|UUK6lH(qk%B{yDj<0Us77sZ~3O=?5VKa}D}UtSOJ=;#YM=Q5bs$%WYnh^PGzZdf3ERoPH)p zPKyQvO-e~g$%c7ct&`2D6L+kvtkt~2y-a4jthkEjbWsOWC$r4!Bdmi<#{8lL%kl?K zZT%sy_kYUde}6%;roa*UGO}`^3;g~*ym~97z~Icx;BwB%rW;ZW!B}u`H<3DRl{kM;I+xMM$2i|^y zi%M!Q9dYl@5+)!a@fS!_``eC-5h@POc`PjKTq;K3dVM~l?dwe1#i_Y*@q%vZYU@mo 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zYYC7I<{u>RYjKey%s+^W9AU@?^J@u^4dx#t@N03ABg{XDiyUFd2J>qPkPYS^BtQ^X z%j&!GEn7}T$e;OLTTL=X`afyh z{hJqgaeK=}3KYpq%76S;|5kQ570i|i`x(;C_P=?VfBQ({!pY{n*^9~Mz1e=q=Do?~ z$kBVV>yV@Ori4q5-kUQa8Juj6uw-zu*>%X+dlTFxgOkm!Lk1^gaI(o!$lzpCJSA3r z#1CX}LIx+BD~QAuZqe+ RFzvKO{;cwul+%~~_ Date: Thu, 9 Nov 2023 16:30:53 +1000 Subject: [PATCH 35/42] Added metric plots for mAP, precision and recall --- lesion_detection_larsmoan/README.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index 43130472f..4ae4e1ae8 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -166,6 +166,23 @@ Training was done on the rangpur cluster using a variety of different GPU's, the +
    + Example Image +
    mAP 0.5:0.95.
    +
    +
    + Example Image +
    mAP@0.5.
    +
    +
    + Example Image +
    Precision
    +
    +
    + Example Image +
    Recall
    +
    + ### Results #### Confusion Matrix Description From 46a67cf59b81564f2b2ebc1cad13b7af48b115ba Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Thu, 9 Nov 2023 16:52:09 +1000 Subject: [PATCH 36/42] Added commenting on the training plots and the result plots --- lesion_detection_larsmoan/README.md | 27 +++++++++++++++++++++++++-- 1 file changed, 25 insertions(+), 2 deletions(-) diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index 4ae4e1ae8..724cb2b59 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -12,10 +12,12 @@ - [Core ideas used in the YOLOV1 paper:](#core-ideas-used-in-the-yolov1-paper) - [Output](#output) - [Training](#training) + - [Training discussion](#training-discussion) - [Results](#results) - [Confusion Matrix](#confusion-matrix) - [F1 - curve](#f1---curve) - [Precision - Recall curve](#precision---recall-curve) + - [Results discussion](#results-discussion) ### Installation @@ -152,6 +154,8 @@ Training was done on the rangpur cluster using a variety of different GPU's, the - Hyperparameters that was used is described in: [hyp.scratch.p6.yaml](./hyp.scratch.p6.yaml) +***In the plots below, the term step refers to epoch number*** +
    Example Image
    Box loss.
    @@ -166,6 +170,13 @@ Training was done on the rangpur cluster using a variety of different GPU's, the
    +### Training discussion +From the plots showing the box, class and object loss it is clear that they indeed was decreasing when the training was halted. With that said they decreased by small amounts, almost negligible. In the case of the A100 training run, that took about 48 hours. + +An interesting observation is the fact that during both runs the model's loss reduced drastically for the first 10 or so epochs, after this the reduction was almost 0. This can be an indication that given the data the models were presented they actually fitted really quickly, but that the data itself might have very complex structures. Hence the model was not able to learn more from the data. + + +### Results
    Example Image
    mAP 0.5:0.95.
    @@ -183,7 +194,7 @@ Training was done on the rangpur cluster using a variety of different GPU's, the
    Recall
    -### Results + #### Confusion Matrix Description @@ -192,4 +203,16 @@ Training was done on the rangpur cluster using a variety of different GPU's, the Description ### Precision - Recall curve -Description \ No newline at end of file +Description + +### Results discussion +The results are not very good, and far off what I anticipated when first embarking on this project. The best result was an mAP@0.5 of about 0.718 which is quite poor. + +An important observation is the instability in training related to precision and recall, we can see that for thease metrics the models doesnt improve much past the 10 first epochs, but it has huge variety from epoch to epoch. + +Initially I thought these issues were related to the small batch size, that was the main driver for increasing it to 32 during the second training run. +However this did not improve the results drastically, although it had a significant effect on mAP@0.5. + +I think the reason for the results being as poor as they are is the inherent complexity of the dataset, when I have went through the data personally I often find it really challenging to understand the labels in the ISIC2017 dataset. + +The label unknown / benign is especially tricky to understand as a human because there are a lot of artifacts present in the dataset that is not labeled as benign skin lesions. \ No newline at end of file From 13c9e2cca95760f4c51beeb4471ca49368602828 Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Thu, 9 Nov 2023 17:12:00 +1000 Subject: [PATCH 37/42] Did a new test run on batch size 9 for readme --- .../metrics-mAP0.5_0.95.png | Bin .../{figures => results}/metrics-mAP_0.5.png | Bin .../{figures => results}/metrics-precision.png | Bin .../{figures => results}/metrics-recall.png | Bin .../{figures => results}/train-boxloss.png | Bin .../{figures => results}/train-clsloss.png | Bin .../{figures => results}/train-objloss.png | Bin .../results/yolov7_b32_testing7/F1_curve.png | Bin 0 -> 197529 bytes .../results/yolov7_b32_testing7/PR_curve.png | Bin 0 -> 128087 bytes .../results/yolov7_b32_testing7/P_curve.png | Bin 0 -> 197271 bytes .../results/yolov7_b32_testing7/R_curve.png | Bin 0 -> 157406 bytes .../yolov7_b32_testing7/confusion_matrix.png | Bin 0 -> 130262 bytes .../yolov7_b32_testing7/test_batch0_labels.jpg | Bin 0 -> 186618 bytes 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--gres=gpu:1 # Activate Python environment (optional) @@ -30,4 +30,4 @@ else fi -python yolov7/test.py --data data/ISIC_2017_0.5/isic.yaml --img 512 --batch 2 --conf 0.001 --iou 0.8 --device 0 --weights 'best.pt' --name yolov7_b32_testing \ No newline at end of file +python yolov7/test.py --data data/ISIC_2017_0.5/isic.yaml --img 512 --batch 9 --conf 0.001 --iou 0.8 --device 0 --weights 'best.pt' --name yolov7_b32_testing \ No newline at end of file From c1793f466c39fe2b88f0566b108d00f500727e02 Mon Sep 17 00:00:00 2001 From: Lars Moan Rangpur HPC Date: Thu, 9 Nov 2023 17:19:48 +1000 Subject: [PATCH 38/42] Results from new test run --- .../results/yolov7_b32_testing7/F1_curve.png | Bin 197529 -> 0 bytes .../results/yolov7_b32_testing7/PR_curve.png | Bin 128087 -> 0 bytes .../results/yolov7_b32_testing7/P_curve.png | Bin 197271 -> 0 bytes .../results/yolov7_b32_testing7/R_curve.png | Bin 157406 -> 0 bytes .../yolov7_b32_testing7/confusion_matrix.png | Bin 130262 -> 0 bytes .../yolov7_b32_testing7/test_batch2_pred.jpg | Bin 153567 -> 0 bytes .../results/yolov7_b32_testing8/F1_curve.png | Bin 0 -> 144712 bytes .../results/yolov7_b32_testing8/PR_curve.png | Bin 0 -> 118940 bytes .../results/yolov7_b32_testing8/P_curve.png | Bin 0 -> 142231 bytes .../results/yolov7_b32_testing8/R_curve.png | Bin 0 -> 137132 bytes .../yolov7_b32_testing8/confusion_matrix.png | Bin 0 -> 135315 bytes .../test_batch0_labels.jpg | Bin .../test_batch0_pred.jpg | Bin 187391 -> 187042 bytes .../test_batch1_labels.jpg | Bin .../test_batch1_pred.jpg | Bin 163214 -> 157816 bytes .../test_batch2_labels.jpg | Bin .../yolov7_b32_testing8/test_batch2_pred.jpg | Bin 0 -> 149497 bytes lesion_detection_larsmoan/run_test.sh | 2 +- 18 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 lesion_detection_larsmoan/results/yolov7_b32_testing7/F1_curve.png delete mode 100644 lesion_detection_larsmoan/results/yolov7_b32_testing7/PR_curve.png delete mode 100644 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yolov7_b32_testing \ No newline at end of file +python yolov7/test.py --data data/ISIC_2017_0.5/isic.yaml --img 512 --batch 9 --conf 0.35 --iou 0.2 --device 0 --weights 'best.pt' --name yolov7_b32_testing \ No newline at end of file From 0dc546337552bbc513f3517b8060903cebc45c4d Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Thu, 9 Nov 2023 17:43:08 +1000 Subject: [PATCH 39/42] Added plot for showing the misalignement with the anchor boxes --- lesion_detection_larsmoan/README.md | 38 +++++++++++++++++- .../figures/anchor_comparison.png | Bin 0 -> 188778 bytes 2 files changed, 37 insertions(+), 1 deletion(-) create mode 100644 lesion_detection_larsmoan/figures/anchor_comparison.png diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index 724cb2b59..3c892e227 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -18,6 +18,8 @@ - [F1 - curve](#f1---curve) - [Precision - Recall curve](#precision---recall-curve) - [Results discussion](#results-discussion) + - [Anchor boxes misaligned](#anchor-boxes-misaligned) + - [Example outputs](#example-outputs) ### Installation @@ -215,4 +217,38 @@ However this did not improve the results drastically, although it had a signific I think the reason for the results being as poor as they are is the inherent complexity of the dataset, when I have went through the data personally I often find it really challenging to understand the labels in the ISIC2017 dataset. -The label unknown / benign is especially tricky to understand as a human because there are a lot of artifacts present in the dataset that is not labeled as benign skin lesions. \ No newline at end of file +The label unknown / benign is especially tricky to understand as a human because there are a lot of artifacts present in the dataset that is not labeled as benign skin lesions. + +### Anchor boxes misaligned +After diving deeper into the intrinsics of the YOLO architecture I have found one major factor which I suspect could enhance the performance by a ton. Using another set of anchor boxes! +The standard anchor boxes utilized by YOLOV7 does not fit that well with the labels present in the ISIC dataset, where we can see that often a bounding box takes up almost the complete image. + +To mitigate this issue I should have performed a custom annchor box analyzis, by doing a clustering on the boxes present in the ISIC dataset and therefore "allow" the model to do bigger predictions. This is something I will try to implement in the future. + +### Example outputs +

    + Example Image +
    Batch 0 - Labels
    +
    +
    + Example Image +
    Batch 0 - Predictions
    +
    + +
    + Example Image +
    Batch 1 - Labels
    +
    +
    + Example Image +
    Batch 1 - Predictions
    +
    + + +In these outputs what I discussed related to anchor boxes above is clear, a lot of the labels take up almost all of the image. This is quite far off from the YOLO standard anchor boxes. + +Another way of visualizing this is by the plot below, here we can clearly see an example of how the anchor boxes are misaligned with the labels present in the ISIC dataset. +
    + Example Image +
    Anchor comparison
    +
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40/42] Minor readme changes --- lesion_detection_larsmoan/README.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index 3c892e227..8f0c12eaf 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -117,11 +117,11 @@ https://drive.google.com/uc?id=1YI3pwanX35i7NCIxKnfXBozXiyQZcGbL or from [datase ### Model Architecture: open source [YOLOV7 Model](https://github.com/WongKinYiu/yolov7) -YOLOV7 is based on the original YOLO paper: [YOLOV1](https://arxiv.org/abs/1506.02640) which was published in 2015 and presented a leap in inference speed for object detection models. The main reason for this was that it was one of the first models that did object detection in a single stage, hence the name YOLO ( you only look once ) in contrast to the two stage models that were popular at the time. Note that some single stage models were present, such as SSD, but they had relatively accuracy performance. +YOLOV7 is based on the original YOLO paper: [YOLOV1](https://arxiv.org/abs/1506.02640) which was published in 2015 and presented a leap in inference speed for object detection models. The main reason for this was that it was one of the first models that did object detection in a single stage, hence the name YOLO ( you only look once ) in contrast to the two stage models that were popular at the time. Note that some single stage models were present, such as SSD, but they had relatively poor accuracy performance. #### Core ideas used in the YOLOV1 paper: -The original paper was trained on input images of size 448X448 and these images where parsed into a grid of 7x7 grid cells. +The original paper was trained on input images of size 448x448 and these images where parsed into a grid of 7x7 grid cells.

    -### Training discussion +#### Training discussion From the plots showing the box, class and object loss it is clear that they indeed was decreasing when the training was halted. With that said they decreased by small amounts, almost negligible. In the case of the A100 training run, that took about 48 hours. An interesting observation is the fact that during both runs the model's loss reduced drastically for the first 10 or so epochs, after this the reduction was almost 0. This can be an indication that given the data the models were presented they actually fitted really quickly, but that the data itself might have very complex structures. Hence the model was not able to learn more from the data. @@ -202,10 +202,10 @@ An interesting observation is the fact that during both runs the model's loss re Description -### F1 - curve +#### F1 - curve Description -### Precision - Recall curve +#### Precision - Recall curve Description ### Results discussion @@ -224,7 +224,7 @@ The label unknown / benign is especially tricky to understand as a human because The original dataset too large to fit on a student node at rangpur, since the storage limit there is 16gb. Therefore I dowsampled the dataset by a factor of 2 and used this during both training and testing. Originally I thought this wouldn't pose any problem as the original dataset has images of really high resolution, often in the range og 5MB per image. That being said, the downsamplign is in itself a loss of information as the dimensionality is reduced, so it could be a factor that has contributed to the poor results. -### Anchor boxes misaligned +#### Anchor boxes misaligned After diving deeper into the intrinsics of the YOLO architecture I have found one major factor which I suspect could enhance the performance by a ton. Using another set of anchor boxes! The standard anchor boxes utilized by YOLOV7 does not fit that well with the labels present in the ISIC dataset, where we can see that often a bounding box takes up almost the complete image.
    Example Image
    Original grid cells on 448x448 image. Source: "You Only Look Once by Joseph Redmon et al., CVPR 2016.
    @@ -159,15 +159,15 @@ Training was done on the rangpur cluster using a variety of different GPU's, the ***In the plots below, the term step refers to epoch number***
    - Example Image + Example Image
    Box loss.
    - Example Image + Example Image
    Class loss.
    - Example Image + Example Image
    Object loss.
    @@ -180,19 +180,19 @@ An interesting observation is the fact that during both runs the model's loss re ### Results
    - Example Image + Example Image
    mAP 0.5:0.95.
    - Example Image + Example Image
    mAP@0.5.
    - Example Image + Example Image
    Precision
    - Example Image + Example Image
    Recall
    From 1e7628033760a10da1571aa7211c19ef584cc375 Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Fri, 10 Nov 2023 11:43:52 +1000 Subject: [PATCH 41/42] Added reflection on downsampling the dataset# --- lesion_detection_larsmoan/README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index 8f0c12eaf..394a4eb31 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -18,6 +18,7 @@ - [F1 - curve](#f1---curve) - [Precision - Recall curve](#precision---recall-curve) - [Results discussion](#results-discussion) + - [Downsampling](#downsampling) - [Anchor boxes misaligned](#anchor-boxes-misaligned) - [Example outputs](#example-outputs) @@ -219,6 +220,10 @@ I think the reason for the results being as poor as they are is the inherent com The label unknown / benign is especially tricky to understand as a human because there are a lot of artifacts present in the dataset that is not labeled as benign skin lesions. +#### Downsampling +The original dataset too large to fit on a student node at rangpur, since the storage limit there is 16gb. Therefore I dowsampled the dataset by a factor of 2 and used this during both training and testing. Originally I thought this wouldn't pose any problem as the original dataset has images of really high resolution, often in the range og 5MB per image. +That being said, the downsamplign is in itself a loss of information as the dimensionality is reduced, so it could be a factor that has contributed to the poor results. + ### Anchor boxes misaligned After diving deeper into the intrinsics of the YOLO architecture I have found one major factor which I suspect could enhance the performance by a ton. Using another set of anchor boxes! The standard anchor boxes utilized by YOLOV7 does not fit that well with the labels present in the ISIC dataset, where we can see that often a bounding box takes up almost the complete image. From 89caaf8a9597e0d389cf06a6ae56740d531a0552 Mon Sep 17 00:00:00 2001 From: Lars Moan Date: Fri, 10 Nov 2023 11:45:07 +1000 Subject: [PATCH 42/42] Minor --- lesion_detection_larsmoan/README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/lesion_detection_larsmoan/README.md b/lesion_detection_larsmoan/README.md index 394a4eb31..139e17d9f 100644 --- a/lesion_detection_larsmoan/README.md +++ b/lesion_detection_larsmoan/README.md @@ -12,14 +12,14 @@ - [Core ideas used in the YOLOV1 paper:](#core-ideas-used-in-the-yolov1-paper) - [Output](#output) - [Training](#training) - - [Training discussion](#training-discussion) + - [Training discussion](#training-discussion) - [Results](#results) - [Confusion Matrix](#confusion-matrix) - - [F1 - curve](#f1---curve) - - [Precision - Recall curve](#precision---recall-curve) + - [F1 - curve](#f1---curve) + - [Precision - Recall curve](#precision---recall-curve) - [Results discussion](#results-discussion) - [Downsampling](#downsampling) - - [Anchor boxes misaligned](#anchor-boxes-misaligned) + - [Anchor boxes misaligned](#anchor-boxes-misaligned) - [Example outputs](#example-outputs) @@ -173,7 +173,7 @@ Training was done on the rangpur cluster using a variety of different GPU's, the

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