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@LargePenguin034
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Improved Unet model that is used to map a mask to skin Lessions in the ISIC dataset

Contains

dataset.py - Function for data loading
modules.py - Improved Unet model
predict.py - Used to use model after training
train.py - Used to train model

Aaron Reynolds
Student ID: 47479049

@wangzhaomxy
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wangzhaomxy commented Nov 6, 2023

This is an initial inspection, no action is required at this point.

  • Files are a bit chaos, but containing all necessary ones. Trained model is included in the repository, which is not allowed as per the spec.

  • Algorithm solves the problem appropriately. Average Dice Coefficient in testing dataset: 0.88, but no evidence.

  • Correctly using PyTorch to construct the Improved U-Net models and functions.

  • Good coding design. Well handwriting code.

  • Sufficient docstring and comment in the code.

  • Easy difficulty problem, no higher difficulty marks.

  • Wrong pull request branch, from local:topic-recognition branch to shakes76:main branch.

@shakes76
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Marking

Good Practice (Design/Commenting, TF/Torch Usage)

Adequate design and implementation
Good spacing and comments
Header blocks missing -1

Recognition Problem

Solves problem
Driver Script present
File structure present
Shows Usage & Demo & Visualisation & Data usage
Module present
Commenting minimal -1
No Data leakage
Difficulty: Easy -10

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe OK, missing usage and other info -2
Model/technical explanation, could be better -1
Good Description and Comments
Markdown used and PDF submitted

Pull Request

Successful Pull Request (Working Algorithm Delivered on Time in InCorrect Branch) -2
Feedback required, remove model files and correct branch and remove gitignore files -2
Request Description OK

@shakes76
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Feedback marks possible +2 if the requested changes are made (see above).

@shakes76 shakes76 added the question Further information is requested label Nov 20, 2023
@wangzhaomxy
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No feedback attempt and no feedback marks granted.

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4 participants