GFNet for Alzheimers

[J-Ath1]

GFNet for binary classification of Alzheimers and non-afflicted MRI scans.

Model:
The model uses the Global Filtering variety of a ViT to replace attention mechanisms by filtering global information across all spatial locations, allowing it to capture long-range dependencies more effectively than traditional ViTs. This approach aids in reducing redundant computations and improving focus on relevant spatial features.

[wangzhaomxy]

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

File Organizing: Wrong file path. All student-constructed documents should be packed in the ‘PatternAnalysis-2024/recognition/<student_constructed_folder>/’ folder. Please reorganize your files, otherwise it won't be merged into Shakes' branch.

Problem Solving:

• The algorithm solves the problem appropriately.
• Accuracy in testing dataset: 0.68.

Model and functions:

• It correctly uses PyTorch to construct the GFNet models and functions.
• With data augmentation.

Code design: Good.

Code comment and docstring:

• Good code comments
• Good function docstrings

Difficulty: Hard.

Additional Comments:

• intermediate numbers of commits
• Good ReadMe design

[aniketgupta17]

Observational Feedback

Pull Request:
Correctly created the Pull request from Topic Recognition Branch .
The pull request includes NO description about the file structure .

File Organizing: Wrong file path. All student-constructed documents should be packed in the ‘PatternAnalysis-2024/recognition/<student_constructed_folder>/’ folder. Please reorganize your files, otherwise it won't be merged into Shakes' branch.

Commit Log:
Commit messages could be more progressive for the Recognition Problem solved using 4 files .
Commits show a logical Flow .

Documentation:

Code comments and docstrings are included.
Proper GitHub markdown formatting is used, with organized headings, lists, and code blocks.
Can add about future improvements and works in the ReadMe

[gayanku]

Marking

Good/OK/Fair Practice (Design/Commenting, TF/Torch Usage)
	Good design and implementation.
	Spacing and comments.
	No Header blocks.	-1
Recognition Problem
	Good solution to problem.
	Driver Script present.
	File structure present.
	Good Usage & Demo & Visualisation & Data usage. Usage missing	-1
	Module present.
	Commenting present.
	No Data leakage found.
	Difficulty : Hard. GFNet (Hard Difficulty)
Commit Log
	Good Meaningful commit messages.
	Some/Adequate Progressive commits.	-1
Documentation
	Readme :Good.
	Model/technical explanation :Good.
	Description and Comments :Good.
	Markdown used and PDF submitted. PDF Checked.
Pull Request
	Pull Request has problems. Initial PR used wrong branch. Last submission.	-2
	Feedback action required: Feedback marks possible +2 if the requested changes are made. Remove cache files for merge.	-2
	Request Description is good.
TOTAL		-7

Marked as per the due date and changes after which aren't necessarily allowed to contribute to grade for fairness.
Subject to approval from Shakes

[gayanku]

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

[hanemma7moud]

Jackson Athanasiadis

[shakes76]

Incorrect folder structure, should use own folder -2, feedback applied +2

[shakes76]

Cant merge because of conflicting changes to main repo files (README). Please update for merge, doesn't affect grade.

[shakes76]

Approved extension +2