Topic recognition

[larsmoan]

Detection and classification of skin lesions on the ISIC2017 dataset using YOLOV7 architecture

[nathasha-naranpanawa]

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

Difficulty: Normal

Readme: Poor

• no insights/discussion/interpretation of results
• no description about how the YOLOv7 algorithm works
• no training/testing loss or accuracy(mAP) plots shown
• no detection results shown in readme (some images are included in the reults folder)

Commit messages: OK

Code:

• Only has Jupyter notebooks, does not have the file structure required
• Uses an open source YOLOv7 implementation by someone else, with command line arguments
• No hyperparameter tuning or attempts to improve performance

Functionality/Performance:

• Classification accuracy is below 0.8 for all classes
• No detection results shown/No IoU metrics shown

[larsmoan]

Thanks for the feedback, obviously a lot of stuff I didn't deliver on during the initial push. I have now done a series of changes, most related to the readme and documentation of the project. I hope this is sufficient.
When it comes to the structure of the project with filenames as model.py, train.pyy etc I find this difficult to implement given the fact that I use the yolov7 library. If I were to follow this rigid structure I would just add more unnecessary code, hope you understand my point of view on this.
Again thanks for the feedback.

[shakes76]

Marking

Some parts marked as per content for due date

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

Adequate design and implementation, YOLOv7 external implementation -3
Good spacing and comments
Header blocks missing -1

Recognition Problem

Solves problem
Driver Script present
File structure not present, needs to be in own folder -2
Shows Usage & Demo & Visualisation & Data usage, no detection results initially shown -2
Module present
Commenting
No Data leakage
Difficulty: Normal -5

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe good but missing refs -1
Model/technical explanation
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]

Cannot be merged as external implementation of YOLOv7, otherwise grade normally.

[larsmoan]

Detection and classification of skin lesions on the ISIC2017 dataset using YOLOV7 architecture

[nathasha-naranpanawa]

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

Difficulty: Normal

Readme: Poor

• no insights/discussion/interpretation of results
• no description about how the YOLOv7 algorithm works
• no training/testing loss or accuracy(mAP) plots shown
• no detection results shown in readme (some images are included in the reults folder)

Commit messages: OK

Code:

• Only has Jupyter notebooks, does not have the file structure required
• Uses an open source YOLOv7 implementation by someone else, with command line arguments
• No hyperparameter tuning or attempts to improve performance

Functionality/Performance:

• Classification accuracy is below 0.8 for all classes
• No detection results shown/No IoU metrics shown

[larsmoan]

Thanks for the feedback, obviously a lot of stuff I didn't deliver on during the initial push. I have now done a series of changes, most related to the readme and documentation of the project. I hope this is sufficient.
When it comes to the structure of the project with filenames as model.py, train.pyy etc I find this difficult to implement given the fact that I use the yolov7 library. If I were to follow this rigid structure I would just add more unnecessary code, hope you understand my point of view on this.
Again thanks for the feedback.

[shakes76]

Marking

Some parts marked as per content for due date

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

Adequate design and implementation, YOLOv7 external implementation -3
Good spacing and comments
Header blocks missing -1

Recognition Problem

Solves problem
Driver Script present
File structure not present, needs to be in own folder -2
Shows Usage & Demo & Visualisation & Data usage, no detection results initially shown -2
Module present
Commenting
No Data leakage
Difficulty: Normal -5

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe good but missing refs -1
Model/technical explanation
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]

Cannot be merged as external implementation of YOLOv7, otherwise grade normally.

[larsmoan]

Detection and classification of skin lesions on the ISIC2017 dataset using YOLOV7 architecture

[nathasha-naranpanawa]

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

Difficulty: Normal

Readme: Poor

• no insights/discussion/interpretation of results
• no description about how the YOLOv7 algorithm works
• no training/testing loss or accuracy(mAP) plots shown
• no detection results shown in readme (some images are included in the reults folder)

Commit messages: OK

Code:

• Only has Jupyter notebooks, does not have the file structure required
• Uses an open source YOLOv7 implementation by someone else, with command line arguments
• No hyperparameter tuning or attempts to improve performance

Functionality/Performance:

• Classification accuracy is below 0.8 for all classes
• No detection results shown/No IoU metrics shown

[larsmoan]

Thanks for the feedback, obviously a lot of stuff I didn't deliver on during the initial push. I have now done a series of changes, most related to the readme and documentation of the project. I hope this is sufficient.
When it comes to the structure of the project with filenames as model.py, train.pyy etc I find this difficult to implement given the fact that I use the yolov7 library. If I were to follow this rigid structure I would just add more unnecessary code, hope you understand my point of view on this.
Again thanks for the feedback.

[shakes76]

Marking

Some parts marked as per content for due date

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

Adequate design and implementation, YOLOv7 external implementation -3
Good spacing and comments
Header blocks missing -1

Recognition Problem

Solves problem
Driver Script present
File structure not present, needs to be in own folder -2
Shows Usage & Demo & Visualisation & Data usage, no detection results initially shown -2
Module present
Commenting
No Data leakage
Difficulty: Normal -5

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe good but missing refs -1
Model/technical explanation
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]

Cannot be merged as external implementation of YOLOv7, otherwise grade normally.

[larsmoan]

Detection and classification of skin lesions on the ISIC2017 dataset using YOLOV7 architecture

[nathasha-naranpanawa]

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

Difficulty: Normal

Readme: Poor

• no insights/discussion/interpretation of results
• no description about how the YOLOv7 algorithm works
• no training/testing loss or accuracy(mAP) plots shown
• no detection results shown in readme (some images are included in the reults folder)

Commit messages: OK

Code:

• Only has Jupyter notebooks, does not have the file structure required
• Uses an open source YOLOv7 implementation by someone else, with command line arguments
• No hyperparameter tuning or attempts to improve performance

Functionality/Performance:

• Classification accuracy is below 0.8 for all classes
• No detection results shown/No IoU metrics shown

[larsmoan]

Thanks for the feedback, obviously a lot of stuff I didn't deliver on during the initial push. I have now done a series of changes, most related to the readme and documentation of the project. I hope this is sufficient.
When it comes to the structure of the project with filenames as model.py, train.pyy etc I find this difficult to implement given the fact that I use the yolov7 library. If I were to follow this rigid structure I would just add more unnecessary code, hope you understand my point of view on this.
Again thanks for the feedback.

[shakes76]

Marking

Some parts marked as per content for due date

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

Adequate design and implementation, YOLOv7 external implementation -3
Good spacing and comments
Header blocks missing -1

Recognition Problem

Solves problem
Driver Script present
File structure not present, needs to be in own folder -2
Shows Usage & Demo & Visualisation & Data usage, no detection results initially shown -2
Module present
Commenting
No Data leakage
Difficulty: Normal -5

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe good but missing refs -1
Model/technical explanation
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]

Cannot be merged as external implementation of YOLOv7, otherwise grade normally.

[larsmoan]

Detection and classification of skin lesions on the ISIC2017 dataset using YOLOV7 architecture

[nathasha-naranpanawa]

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

Difficulty: Normal

Readme: Poor

• no insights/discussion/interpretation of results
• no description about how the YOLOv7 algorithm works
• no training/testing loss or accuracy(mAP) plots shown
• no detection results shown in readme (some images are included in the reults folder)

Commit messages: OK

Code:

• Only has Jupyter notebooks, does not have the file structure required
• Uses an open source YOLOv7 implementation by someone else, with command line arguments
• No hyperparameter tuning or attempts to improve performance

Functionality/Performance:

• Classification accuracy is below 0.8 for all classes
• No detection results shown/No IoU metrics shown

[larsmoan]

Thanks for the feedback, obviously a lot of stuff I didn't deliver on during the initial push. I have now done a series of changes, most related to the readme and documentation of the project. I hope this is sufficient.
When it comes to the structure of the project with filenames as model.py, train.pyy etc I find this difficult to implement given the fact that I use the yolov7 library. If I were to follow this rigid structure I would just add more unnecessary code, hope you understand my point of view on this.
Again thanks for the feedback.

[shakes76]

Marking

Some parts marked as per content for due date

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

Adequate design and implementation, YOLOv7 external implementation -3
Good spacing and comments
Header blocks missing -1

Recognition Problem

Solves problem
Driver Script present
File structure not present, needs to be in own folder -2
Shows Usage & Demo & Visualisation & Data usage, no detection results initially shown -2
Module present
Commenting
No Data leakage
Difficulty: Normal -5

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe good but missing refs -1
Model/technical explanation
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]

Cannot be merged as external implementation of YOLOv7, otherwise grade normally.

[larsmoan]

Detection and classification of skin lesions on the ISIC2017 dataset using YOLOV7 architecture

[nathasha-naranpanawa]

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

Difficulty: Normal

Readme: Poor

• no insights/discussion/interpretation of results
• no description about how the YOLOv7 algorithm works
• no training/testing loss or accuracy(mAP) plots shown
• no detection results shown in readme (some images are included in the reults folder)

Commit messages: OK

Code:

• Only has Jupyter notebooks, does not have the file structure required
• Uses an open source YOLOv7 implementation by someone else, with command line arguments
• No hyperparameter tuning or attempts to improve performance

Functionality/Performance:

• Classification accuracy is below 0.8 for all classes
• No detection results shown/No IoU metrics shown

[larsmoan]

Thanks for the feedback, obviously a lot of stuff I didn't deliver on during the initial push. I have now done a series of changes, most related to the readme and documentation of the project. I hope this is sufficient.
When it comes to the structure of the project with filenames as model.py, train.pyy etc I find this difficult to implement given the fact that I use the yolov7 library. If I were to follow this rigid structure I would just add more unnecessary code, hope you understand my point of view on this.
Again thanks for the feedback.

[shakes76]

Marking

Some parts marked as per content for due date

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

Adequate design and implementation, YOLOv7 external implementation -3
Good spacing and comments
Header blocks missing -1

Recognition Problem

Solves problem
Driver Script present
File structure not present, needs to be in own folder -2
Shows Usage & Demo & Visualisation & Data usage, no detection results initially shown -2
Module present
Commenting
No Data leakage
Difficulty: Normal -5

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe good but missing refs -1
Model/technical explanation
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]

Cannot be merged as external implementation of YOLOv7, otherwise grade normally.

[larsmoan]

Detection and classification of skin lesions on the ISIC2017 dataset using YOLOV7 architecture

[nathasha-naranpanawa]

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

Difficulty: Normal

Readme: Poor

• no insights/discussion/interpretation of results
• no description about how the YOLOv7 algorithm works
• no training/testing loss or accuracy(mAP) plots shown
• no detection results shown in readme (some images are included in the reults folder)

Commit messages: OK

Code:

• Only has Jupyter notebooks, does not have the file structure required
• Uses an open source YOLOv7 implementation by someone else, with command line arguments
• No hyperparameter tuning or attempts to improve performance

Functionality/Performance:

• Classification accuracy is below 0.8 for all classes
• No detection results shown/No IoU metrics shown

[larsmoan]

Thanks for the feedback, obviously a lot of stuff I didn't deliver on during the initial push. I have now done a series of changes, most related to the readme and documentation of the project. I hope this is sufficient.
When it comes to the structure of the project with filenames as model.py, train.pyy etc I find this difficult to implement given the fact that I use the yolov7 library. If I were to follow this rigid structure I would just add more unnecessary code, hope you understand my point of view on this.
Again thanks for the feedback.

[shakes76]

Marking

Some parts marked as per content for due date

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

Adequate design and implementation, YOLOv7 external implementation -3
Good spacing and comments
Header blocks missing -1

Recognition Problem

Solves problem
Driver Script present
File structure not present, needs to be in own folder -2
Shows Usage & Demo & Visualisation & Data usage, no detection results initially shown -2
Module present
Commenting
No Data leakage
Difficulty: Normal -5

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe good but missing refs -1
Model/technical explanation
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]

Cannot be merged as external implementation of YOLOv7, otherwise grade normally.

[larsmoan]

Detection and classification of skin lesions on the ISIC2017 dataset using YOLOV7 architecture

[nathasha-naranpanawa]

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

Difficulty: Normal

Readme: Poor

• no insights/discussion/interpretation of results
• no description about how the YOLOv7 algorithm works
• no training/testing loss or accuracy(mAP) plots shown
• no detection results shown in readme (some images are included in the reults folder)

Commit messages: OK

Code:

• Only has Jupyter notebooks, does not have the file structure required
• Uses an open source YOLOv7 implementation by someone else, with command line arguments
• No hyperparameter tuning or attempts to improve performance

Functionality/Performance:

• Classification accuracy is below 0.8 for all classes
• No detection results shown/No IoU metrics shown

[larsmoan]

Thanks for the feedback, obviously a lot of stuff I didn't deliver on during the initial push. I have now done a series of changes, most related to the readme and documentation of the project. I hope this is sufficient.
When it comes to the structure of the project with filenames as model.py, train.pyy etc I find this difficult to implement given the fact that I use the yolov7 library. If I were to follow this rigid structure I would just add more unnecessary code, hope you understand my point of view on this.
Again thanks for the feedback.

[shakes76]

Marking

Some parts marked as per content for due date

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

Adequate design and implementation, YOLOv7 external implementation -3
Good spacing and comments
Header blocks missing -1

Recognition Problem

Solves problem
Driver Script present
File structure not present, needs to be in own folder -2
Shows Usage & Demo & Visualisation & Data usage, no detection results initially shown -2
Module present
Commenting
No Data leakage
Difficulty: Normal -5

Commit Log

Meaningful commit messages
Progressive commits used

Documentation

ReadMe good but missing refs -1
Model/technical explanation
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]

Cannot be merged as external implementation of YOLOv7, otherwise grade normally.