Topic recognition

[ryanjohnson08]

Improved UNET Model to segment the 2017 ISIC dataset.
Achieved a dice score of 0.8 on the test dataset.
Predictions were able to be made using the model, segmenting the testing dataset.
By running predictions.py the model is trained and predictions are made, outputting dice coefficient and loss graphs as well as the testing dice coefficient score.

[SiyuLiu0329]

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

• Code: OK
• Results: OK
• Readme: OK
• Commit Messages: some could use more details
• Other comments: N/A

[shakes76]

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

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

Recognition Problem

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

Commit Log

Meaningful commit messages, could be more descriptive -1
Progressive commits used

Documentation

ReadMe OK, could have more info/background -1
Good Description and Comments
Markdown used PDF submitted

Pull Request

Successful Pull Request (Working Algorithm Delivered on Time in Correct Branch)
Feedback required, revert changes to other student files. -2
Request Description OK, could be more informative -1