-
Notifications
You must be signed in to change notification settings - Fork 391
Image Segmentation ISIC Melanoma Dataset #296
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Image Segmentation ISIC Melanoma Dataset #296
Conversation
|
README
General Comments
|
TF/Torch UsageAdequate use and implementation Good Practice (Design/Commenting)Good spacing and comments AlgorithmDriver Script present Commit LogMeaningful commit messages DocumentationGood Description and Comments Pull RequestSuccessful Pull Request (Working Algorithm Delivered on Time in Correct Branch) |
|
Hi Shakes, I think I ran into a slight error, I posted it on Edstem but I will copy it here as well. I was attempting to fix my project as per the required feedback, but I believed I accidentally pressed on "Fetch Upstream", and my repository on Github was updated but my local repository is not. I only noticed this afterwards and I am now unable to perform any git operations since the 2 repositories are different. Is there any advice on how to fix this? Or if possible can I make an entirely new submission because I am unsure on what I should do at this point. As you can refer above, the 3 "Merge branch __ into __" were not supposed to happen. Thanks! |
|
Model files are still present though for some reason. Not sure what that error is I'm afraid. Feedback attempted so will give you the marks, but can't merge until solved. |
|
Hi Shakes, I managed to fix the problem. As per requested by Boyeong and you, I have added the Dice Score Coefficient in the README file as well as removed the model file that would clog up the repository. Please have a look when you are available. Thank you! |
Hi Shakes,
Please review this pull request. My chosen task was Image Segmentation using ISIC Melanoma Dataset using an Improved UNET Model. The number of epochs used was 30, and an average dice coefficient score obtained is 0.94. A description for the attached files are included.
Thank you!
Joshua Yu Xuan Soo
S4571796