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YOLO Detection of Lesions in ISIC Dataset #188
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Clarified and improved commenting for YOLO training
Adding new output images
This is an initial inspection Difficulty : Normal
Feedback:
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Observational Feedback Pull Request: File Organizing: Incorrect way , See the proper organisation files in the task sheet . Commit Log: Documentation: |
Hi all,I received the first feedback on Saturday evening and I have been in the process of implementing it all day yesterday and today. Please let me know when the final decision for marking will be made, as a comment on Ed by tutors mentioned it would be after a week since the first feedback.Thank you so much for the advice and support. I have had a number of challenges this year in terms of health reasons and I appreciate the opportunity to implement the feedback.
Kind regards,
Mariam
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Hi team, I have attached the link above. Please consider this pull request for my project. I will continue to work on it to implement your feedback, as per Ed's mention of the final date being the end of exam block. If requested I can remove the old files which were incorrectly included in the main branch but for now I will keep them there in case they are required as proof of timely submission dates. Thank you for the opportunity to implement your feedback. Kind regards, |
Hi @mermalade0325 that sounds fine |
Marking
Marked as per the due date and changes after which aren't necessarily allowed to contribute to grade for fairness. |
Feedback marks possible +2 if the requested changes are made (see above). |
Approved extension and fixed PR in #193 +2 |
#This pull request aims to address the problem of detecting skin lesions within the ISIC 2017/2018 dataset. The primary goal is to develop a robust detection and classification network that can accurately identify lesions in medical images, ensuring each detection has a minimum Intersection Over Union (IoU) of 0.8 on the test set, thus achieving a suitable level of accuracy for classification. The project utilizes a YOLO-based detection network (YOLOv7).
Problem Statement
Skin cancer detection relies on identifying lesions accurately in medical images. This project contributes by building a deep learning model focused on detecting lesion locations without classifying lesion types. The main goals are:
Files and Structure
The project is organized into the following files:
Dataset
Regarding the data downloaded from ISIC, it should be organised as follows, with labels generated from dataset.py:
An appendix in the README file links to the ISIC dataset for reference.
Correctly merged pull request: #193 (comment)