HIP MRI VQVAE and PIXELCNN implementation into recognition folder

[BHUP-UQ]

Student 48036177

Created Hip_MRI_VQVAE_PixelCNN folder in recognition as assignment submission. Apologies for scuffed git history, I originally made all changes in local repo and had to do a bunch of commits (including renaming) to get the files to the right branch while bringing the commit history.

[wangzhaomxy]

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

File Organizing:

• Please organize all the custom files, including README, images, code scripts and other files, in your project folder at "/PatternAnalysis-2025/recognition/custom_folder_with_project_name_and_SNum". All other files should remain unchanged in their original locations.
• Merge problem from wrong pull request at last year's repo.

Problem Solving:

• For some reason, the main() function is commented out in the script, which prevents the model from being trained. In addition, the testing code loads the same pre-trained weights (trainer.weights.h5) as the initial weights used before training, indicating that the model was not trained properly.
• The reported SSIM is 0.78039; however, no supporting evidence is provided. In addition, the visualized outputs appear unreasonable, suggesting that the SSIM value should not be this high.

Model and functions:

• It correctly uses TensorFlow to construct the VQVAE models and functions. However, failed to train it.
• Good data augmentation.
• Properly use the train/validation/test datasets.

Code design:The source scripts were transformed with limited success.

Code comment and docstring:

• Good code comments
• Good function docstrings
• NO header block

Difficulty: Hard.

Additional Comments:

• Good commits
• The README design can be more structured and provide more comprehensive content. Since this is a report, a proper Discussion and Conclusion section is also expected to clearly summarize your project.

[gayanku]

Marking

Good/OK/Fair Practice (Design/Commenting, TF/Torch Usage)
	Good design and implementation. Unreasonable results.	-1.5
	Spacing and comments.
	No Header blocks.	-1
Recognition Problem
	OK solution to problem. Lacks evidance for train.	-4
	Driver Script NOT present.	-0.5
	File structure NOT present.	-1
	Good Usage & Demo & Visualisation & Data usage.	-0.5
	Module present.
	Commenting present.
	No Data leakage found.
	Difficulty : Hard. Hard. VQVAE
Commit Log
	Some/Adequate Meaningful commit messages. Some	-1
	Good Progressive commits. Most in 1 day.	-2
Documentation
	Readme :Acceptable. Outputs unusual. Lacks install, running steps, references etc.	-4
	Model/technical explanation :Acceptable. Minimal	-2.5
	Description and Comments :Acceptable.	-2.5
	Markdown used and PDF submitted.
Pull Request
	Pull Request has problems. Wrong branch.	-2
	Feedback action require: Feedback marks possible +2 if the requested changes are made. Submit to correct branch.	-2
	Request Description is adequate. Minimal	-1
TOTAL		-25.5

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]

s4803617

[BHUP-UQ]

Student 48036177

Created Hip_MRI_VQVAE_PixelCNN folder in recognition as assignment submission. Apologies for scuffed git history, I originally made all changes in local repo and had to do a bunch of commits (including renaming) to get the files to the right branch while bringing the commit history.

[wangzhaomxy]

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

File Organizing:

• Please organize all the custom files, including README, images, code scripts and other files, in your project folder at "/PatternAnalysis-2025/recognition/custom_folder_with_project_name_and_SNum". All other files should remain unchanged in their original locations.
• Merge problem from wrong pull request at last year's repo.

Problem Solving:

• For some reason, the main() function is commented out in the script, which prevents the model from being trained. In addition, the testing code loads the same pre-trained weights (trainer.weights.h5) as the initial weights used before training, indicating that the model was not trained properly.
• The reported SSIM is 0.78039; however, no supporting evidence is provided. In addition, the visualized outputs appear unreasonable, suggesting that the SSIM value should not be this high.

Model and functions:

• It correctly uses TensorFlow to construct the VQVAE models and functions. However, failed to train it.
• Good data augmentation.
• Properly use the train/validation/test datasets.

Code design:The source scripts were transformed with limited success.

Code comment and docstring:

• Good code comments
• Good function docstrings
• NO header block

Difficulty: Hard.

Additional Comments:

• Good commits
• The README design can be more structured and provide more comprehensive content. Since this is a report, a proper Discussion and Conclusion section is also expected to clearly summarize your project.

[gayanku]

Marking

Good/OK/Fair Practice (Design/Commenting, TF/Torch Usage)
	Good design and implementation. Unreasonable results.	-1.5
	Spacing and comments.
	No Header blocks.	-1
Recognition Problem
	OK solution to problem. Lacks evidance for train.	-4
	Driver Script NOT present.	-0.5
	File structure NOT present.	-1
	Good Usage & Demo & Visualisation & Data usage.	-0.5
	Module present.
	Commenting present.
	No Data leakage found.
	Difficulty : Hard. Hard. VQVAE
Commit Log
	Some/Adequate Meaningful commit messages. Some	-1
	Good Progressive commits. Most in 1 day.	-2
Documentation
	Readme :Acceptable. Outputs unusual. Lacks install, running steps, references etc.	-4
	Model/technical explanation :Acceptable. Minimal	-2.5
	Description and Comments :Acceptable.	-2.5
	Markdown used and PDF submitted.
Pull Request
	Pull Request has problems. Wrong branch.	-2
	Feedback action require: Feedback marks possible +2 if the requested changes are made. Submit to correct branch.	-2
	Request Description is adequate. Minimal	-1
TOTAL		-25.5

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]

s4803617

[BHUP-UQ]

Student 48036177

Created Hip_MRI_VQVAE_PixelCNN folder in recognition as assignment submission. Apologies for scuffed git history, I originally made all changes in local repo and had to do a bunch of commits (including renaming) to get the files to the right branch while bringing the commit history.

[wangzhaomxy]

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

File Organizing:

• Please organize all the custom files, including README, images, code scripts and other files, in your project folder at "/PatternAnalysis-2025/recognition/custom_folder_with_project_name_and_SNum". All other files should remain unchanged in their original locations.
• Merge problem from wrong pull request at last year's repo.

Problem Solving:

• For some reason, the main() function is commented out in the script, which prevents the model from being trained. In addition, the testing code loads the same pre-trained weights (trainer.weights.h5) as the initial weights used before training, indicating that the model was not trained properly.
• The reported SSIM is 0.78039; however, no supporting evidence is provided. In addition, the visualized outputs appear unreasonable, suggesting that the SSIM value should not be this high.

Model and functions:

• It correctly uses TensorFlow to construct the VQVAE models and functions. However, failed to train it.
• Good data augmentation.
• Properly use the train/validation/test datasets.

Code design:The source scripts were transformed with limited success.

Code comment and docstring:

• Good code comments
• Good function docstrings
• NO header block

Difficulty: Hard.

Additional Comments:

• Good commits
• The README design can be more structured and provide more comprehensive content. Since this is a report, a proper Discussion and Conclusion section is also expected to clearly summarize your project.

[gayanku]

Marking

Good/OK/Fair Practice (Design/Commenting, TF/Torch Usage)
	Good design and implementation. Unreasonable results.	-1.5
	Spacing and comments.
	No Header blocks.	-1
Recognition Problem
	OK solution to problem. Lacks evidance for train.	-4
	Driver Script NOT present.	-0.5
	File structure NOT present.	-1
	Good Usage & Demo & Visualisation & Data usage.	-0.5
	Module present.
	Commenting present.
	No Data leakage found.
	Difficulty : Hard. Hard. VQVAE
Commit Log
	Some/Adequate Meaningful commit messages. Some	-1
	Good Progressive commits. Most in 1 day.	-2
Documentation
	Readme :Acceptable. Outputs unusual. Lacks install, running steps, references etc.	-4
	Model/technical explanation :Acceptable. Minimal	-2.5
	Description and Comments :Acceptable.	-2.5
	Markdown used and PDF submitted.
Pull Request
	Pull Request has problems. Wrong branch.	-2
	Feedback action require: Feedback marks possible +2 if the requested changes are made. Submit to correct branch.	-2
	Request Description is adequate. Minimal	-1
TOTAL		-25.5

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]

s4803617

[BHUP-UQ]

Student 48036177

Created Hip_MRI_VQVAE_PixelCNN folder in recognition as assignment submission. Apologies for scuffed git history, I originally made all changes in local repo and had to do a bunch of commits (including renaming) to get the files to the right branch while bringing the commit history.

[wangzhaomxy]

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

File Organizing:

• Please organize all the custom files, including README, images, code scripts and other files, in your project folder at "/PatternAnalysis-2025/recognition/custom_folder_with_project_name_and_SNum". All other files should remain unchanged in their original locations.
• Merge problem from wrong pull request at last year's repo.

Problem Solving:

• For some reason, the main() function is commented out in the script, which prevents the model from being trained. In addition, the testing code loads the same pre-trained weights (trainer.weights.h5) as the initial weights used before training, indicating that the model was not trained properly.
• The reported SSIM is 0.78039; however, no supporting evidence is provided. In addition, the visualized outputs appear unreasonable, suggesting that the SSIM value should not be this high.

Model and functions:

• It correctly uses TensorFlow to construct the VQVAE models and functions. However, failed to train it.
• Good data augmentation.
• Properly use the train/validation/test datasets.

Code design:The source scripts were transformed with limited success.

Code comment and docstring:

• Good code comments
• Good function docstrings
• NO header block

Difficulty: Hard.

Additional Comments:

• Good commits
• The README design can be more structured and provide more comprehensive content. Since this is a report, a proper Discussion and Conclusion section is also expected to clearly summarize your project.

[gayanku]

Marking

Good/OK/Fair Practice (Design/Commenting, TF/Torch Usage)
	Good design and implementation. Unreasonable results.	-1.5
	Spacing and comments.
	No Header blocks.	-1
Recognition Problem
	OK solution to problem. Lacks evidance for train.	-4
	Driver Script NOT present.	-0.5
	File structure NOT present.	-1
	Good Usage & Demo & Visualisation & Data usage.	-0.5
	Module present.
	Commenting present.
	No Data leakage found.
	Difficulty : Hard. Hard. VQVAE
Commit Log
	Some/Adequate Meaningful commit messages. Some	-1
	Good Progressive commits. Most in 1 day.	-2
Documentation
	Readme :Acceptable. Outputs unusual. Lacks install, running steps, references etc.	-4
	Model/technical explanation :Acceptable. Minimal	-2.5
	Description and Comments :Acceptable.	-2.5
	Markdown used and PDF submitted.
Pull Request
	Pull Request has problems. Wrong branch.	-2
	Feedback action require: Feedback marks possible +2 if the requested changes are made. Submit to correct branch.	-2
	Request Description is adequate. Minimal	-1
TOTAL		-25.5

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]

s4803617

[BHUP-UQ]

Student 48036177

Created Hip_MRI_VQVAE_PixelCNN folder in recognition as assignment submission. Apologies for scuffed git history, I originally made all changes in local repo and had to do a bunch of commits (including renaming) to get the files to the right branch while bringing the commit history.

[wangzhaomxy]

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

File Organizing:

• Please organize all the custom files, including README, images, code scripts and other files, in your project folder at "/PatternAnalysis-2025/recognition/custom_folder_with_project_name_and_SNum". All other files should remain unchanged in their original locations.
• Merge problem from wrong pull request at last year's repo.

Problem Solving:

• For some reason, the main() function is commented out in the script, which prevents the model from being trained. In addition, the testing code loads the same pre-trained weights (trainer.weights.h5) as the initial weights used before training, indicating that the model was not trained properly.
• The reported SSIM is 0.78039; however, no supporting evidence is provided. In addition, the visualized outputs appear unreasonable, suggesting that the SSIM value should not be this high.

Model and functions:

• It correctly uses TensorFlow to construct the VQVAE models and functions. However, failed to train it.
• Good data augmentation.
• Properly use the train/validation/test datasets.

Code design:The source scripts were transformed with limited success.

Code comment and docstring:

• Good code comments
• Good function docstrings
• NO header block

Difficulty: Hard.

Additional Comments:

• Good commits
• The README design can be more structured and provide more comprehensive content. Since this is a report, a proper Discussion and Conclusion section is also expected to clearly summarize your project.

[gayanku]

Marking

Good/OK/Fair Practice (Design/Commenting, TF/Torch Usage)
	Good design and implementation. Unreasonable results.	-1.5
	Spacing and comments.
	No Header blocks.	-1
Recognition Problem
	OK solution to problem. Lacks evidance for train.	-4
	Driver Script NOT present.	-0.5
	File structure NOT present.	-1
	Good Usage & Demo & Visualisation & Data usage.	-0.5
	Module present.
	Commenting present.
	No Data leakage found.
	Difficulty : Hard. Hard. VQVAE
Commit Log
	Some/Adequate Meaningful commit messages. Some	-1
	Good Progressive commits. Most in 1 day.	-2
Documentation
	Readme :Acceptable. Outputs unusual. Lacks install, running steps, references etc.	-4
	Model/technical explanation :Acceptable. Minimal	-2.5
	Description and Comments :Acceptable.	-2.5
	Markdown used and PDF submitted.
Pull Request
	Pull Request has problems. Wrong branch.	-2
	Feedback action require: Feedback marks possible +2 if the requested changes are made. Submit to correct branch.	-2
	Request Description is adequate. Minimal	-1
TOTAL		-25.5

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]

s4803617

[BHUP-UQ]

Student 48036177

Created Hip_MRI_VQVAE_PixelCNN folder in recognition as assignment submission. Apologies for scuffed git history, I originally made all changes in local repo and had to do a bunch of commits (including renaming) to get the files to the right branch while bringing the commit history.

[wangzhaomxy]

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

File Organizing:

• Please organize all the custom files, including README, images, code scripts and other files, in your project folder at "/PatternAnalysis-2025/recognition/custom_folder_with_project_name_and_SNum". All other files should remain unchanged in their original locations.
• Merge problem from wrong pull request at last year's repo.

Problem Solving:

• For some reason, the main() function is commented out in the script, which prevents the model from being trained. In addition, the testing code loads the same pre-trained weights (trainer.weights.h5) as the initial weights used before training, indicating that the model was not trained properly.
• The reported SSIM is 0.78039; however, no supporting evidence is provided. In addition, the visualized outputs appear unreasonable, suggesting that the SSIM value should not be this high.

Model and functions:

• It correctly uses TensorFlow to construct the VQVAE models and functions. However, failed to train it.
• Good data augmentation.
• Properly use the train/validation/test datasets.

Code design:The source scripts were transformed with limited success.

Code comment and docstring:

• Good code comments
• Good function docstrings
• NO header block

Difficulty: Hard.

Additional Comments:

• Good commits
• The README design can be more structured and provide more comprehensive content. Since this is a report, a proper Discussion and Conclusion section is also expected to clearly summarize your project.

[gayanku]

Marking

Good/OK/Fair Practice (Design/Commenting, TF/Torch Usage)
	Good design and implementation. Unreasonable results.	-1.5
	Spacing and comments.
	No Header blocks.	-1
Recognition Problem
	OK solution to problem. Lacks evidance for train.	-4
	Driver Script NOT present.	-0.5
	File structure NOT present.	-1
	Good Usage & Demo & Visualisation & Data usage.	-0.5
	Module present.
	Commenting present.
	No Data leakage found.
	Difficulty : Hard. Hard. VQVAE
Commit Log
	Some/Adequate Meaningful commit messages. Some	-1
	Good Progressive commits. Most in 1 day.	-2
Documentation
	Readme :Acceptable. Outputs unusual. Lacks install, running steps, references etc.	-4
	Model/technical explanation :Acceptable. Minimal	-2.5
	Description and Comments :Acceptable.	-2.5
	Markdown used and PDF submitted.
Pull Request
	Pull Request has problems. Wrong branch.	-2
	Feedback action require: Feedback marks possible +2 if the requested changes are made. Submit to correct branch.	-2
	Request Description is adequate. Minimal	-1
TOTAL		-25.5

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]

s4803617

[BHUP-UQ]

Student 48036177

Created Hip_MRI_VQVAE_PixelCNN folder in recognition as assignment submission. Apologies for scuffed git history, I originally made all changes in local repo and had to do a bunch of commits (including renaming) to get the files to the right branch while bringing the commit history.

[wangzhaomxy]

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

File Organizing:

• Please organize all the custom files, including README, images, code scripts and other files, in your project folder at "/PatternAnalysis-2025/recognition/custom_folder_with_project_name_and_SNum". All other files should remain unchanged in their original locations.
• Merge problem from wrong pull request at last year's repo.

Problem Solving:

• For some reason, the main() function is commented out in the script, which prevents the model from being trained. In addition, the testing code loads the same pre-trained weights (trainer.weights.h5) as the initial weights used before training, indicating that the model was not trained properly.
• The reported SSIM is 0.78039; however, no supporting evidence is provided. In addition, the visualized outputs appear unreasonable, suggesting that the SSIM value should not be this high.

Model and functions:

• It correctly uses TensorFlow to construct the VQVAE models and functions. However, failed to train it.
• Good data augmentation.
• Properly use the train/validation/test datasets.

Code design:The source scripts were transformed with limited success.

Code comment and docstring:

• Good code comments
• Good function docstrings
• NO header block

Difficulty: Hard.

Additional Comments:

• Good commits
• The README design can be more structured and provide more comprehensive content. Since this is a report, a proper Discussion and Conclusion section is also expected to clearly summarize your project.

[gayanku]

Marking

Good/OK/Fair Practice (Design/Commenting, TF/Torch Usage)
	Good design and implementation. Unreasonable results.	-1.5
	Spacing and comments.
	No Header blocks.	-1
Recognition Problem
	OK solution to problem. Lacks evidance for train.	-4
	Driver Script NOT present.	-0.5
	File structure NOT present.	-1
	Good Usage & Demo & Visualisation & Data usage.	-0.5
	Module present.
	Commenting present.
	No Data leakage found.
	Difficulty : Hard. Hard. VQVAE
Commit Log
	Some/Adequate Meaningful commit messages. Some	-1
	Good Progressive commits. Most in 1 day.	-2
Documentation
	Readme :Acceptable. Outputs unusual. Lacks install, running steps, references etc.	-4
	Model/technical explanation :Acceptable. Minimal	-2.5
	Description and Comments :Acceptable.	-2.5
	Markdown used and PDF submitted.
Pull Request
	Pull Request has problems. Wrong branch.	-2
	Feedback action require: Feedback marks possible +2 if the requested changes are made. Submit to correct branch.	-2
	Request Description is adequate. Minimal	-1
TOTAL		-25.5

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]

s4803617

[BHUP-UQ]

Student 48036177

Created Hip_MRI_VQVAE_PixelCNN folder in recognition as assignment submission. Apologies for scuffed git history, I originally made all changes in local repo and had to do a bunch of commits (including renaming) to get the files to the right branch while bringing the commit history.

[wangzhaomxy]

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

File Organizing:

• Please organize all the custom files, including README, images, code scripts and other files, in your project folder at "/PatternAnalysis-2025/recognition/custom_folder_with_project_name_and_SNum". All other files should remain unchanged in their original locations.
• Merge problem from wrong pull request at last year's repo.

Problem Solving:

• For some reason, the main() function is commented out in the script, which prevents the model from being trained. In addition, the testing code loads the same pre-trained weights (trainer.weights.h5) as the initial weights used before training, indicating that the model was not trained properly.
• The reported SSIM is 0.78039; however, no supporting evidence is provided. In addition, the visualized outputs appear unreasonable, suggesting that the SSIM value should not be this high.

Model and functions:

• It correctly uses TensorFlow to construct the VQVAE models and functions. However, failed to train it.
• Good data augmentation.
• Properly use the train/validation/test datasets.

Code design:The source scripts were transformed with limited success.

Code comment and docstring:

• Good code comments
• Good function docstrings
• NO header block

Difficulty: Hard.

Additional Comments:

• Good commits
• The README design can be more structured and provide more comprehensive content. Since this is a report, a proper Discussion and Conclusion section is also expected to clearly summarize your project.

[gayanku]

Marking

Good/OK/Fair Practice (Design/Commenting, TF/Torch Usage)
	Good design and implementation. Unreasonable results.	-1.5
	Spacing and comments.
	No Header blocks.	-1
Recognition Problem
	OK solution to problem. Lacks evidance for train.	-4
	Driver Script NOT present.	-0.5
	File structure NOT present.	-1
	Good Usage & Demo & Visualisation & Data usage.	-0.5
	Module present.
	Commenting present.
	No Data leakage found.
	Difficulty : Hard. Hard. VQVAE
Commit Log
	Some/Adequate Meaningful commit messages. Some	-1
	Good Progressive commits. Most in 1 day.	-2
Documentation
	Readme :Acceptable. Outputs unusual. Lacks install, running steps, references etc.	-4
	Model/technical explanation :Acceptable. Minimal	-2.5
	Description and Comments :Acceptable.	-2.5
	Markdown used and PDF submitted.
Pull Request
	Pull Request has problems. Wrong branch.	-2
	Feedback action require: Feedback marks possible +2 if the requested changes are made. Submit to correct branch.	-2
	Request Description is adequate. Minimal	-1
TOTAL		-25.5

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]

s4803617