Topic recognition

[KnowerSmyf]

Implemented a 2D variant of the improved UNet architecture and used it to perform lesion skin boundary segmentation. I had an extension (hence the late submission), also I feel like I went pretty crazy with the commits... Sorry about that (:

[KnowerSmyf]

I'm new to github, I've tried like 3 pull requests... hopefully its all good

[wangzhaomxy]

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

• Well organized files, but wrong readme.md place.
• Algorithm solves the problem appropriately. Average Dice Coefficient in testing dataset: 0.7364, but no evidence was shown. Predicted Mask output is not good enough, just showing 25 epochs training result in readme, which seems not the best result.
• Correctly using PyTorch to construct the Improved U-Net models and functions.
• Good coding design.
• Sufficient docstring and comment in the code, but need some references.
• Easy difficulty problem, no higher difficulty marks.

[shakes76]

Marking

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

Adequate design and implementation
Good spacing and comments
Header blocks

Recognition Problem

Solves problem but poor performance from predicted mask shown -1
Driver Script present
File structure present
Shows Usage & Demo & Visualisation & Data usage, no training plots etc. -2
Module present
Commenting
No Data leakage
Difficulty: Easy -10

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe acceptable, no refs -1
Model/technical explanation minimal -1
Good Description and Comments
Markdown used and PDF submitted

Pull Request

Successful Pull Request (Working Algorithm Delivered on Time in Correct Branch)
No Feedback required
Request Description minimal -1

[shakes76]

Please revert changes to repo README files for merging, does not affect grade only merge.

[wangzhaomxy]

No feedback attempt and no feedback marks lost.