-
Notifications
You must be signed in to change notification settings - Fork 397
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
Improved UNet segmentaion on ISIC 2017 data set #431
base: topic-recognition
Are you sure you want to change the base?
Improved UNet segmentaion on ISIC 2017 data set #431
Conversation
… next thing to do is develop functions to process the data to feed to the model (should have started this assignment a bit earlier hey
…n for disc parameter and then start training.
…ome bugs in modules.py, still working through errors in modules.py.
…me to train and make some nice plots
…ed train.py functionality. Need to do predict.py and make plots+images
… functions in util.py to plot accuracy and loss. Last thing for funcionality is predict.py
This is an initial inspection, no action is required at this point
|
Good Practice (Design/Commenting, TF/Torch Usage)Adequate use and implementation (no prediction) -2 Recognition ProblemSolves problem (no outputs, can't verify working) -3 Commit LogMeaningful commit messages DocumentationReadMe minimal, broken links, could be more informative -2 Pull RequestSuccessful Pull Request (Working Algorithm Delivered on Time in Correct Branch) |
This is my solution to task 1.
The model takes in rgb images of skin moles and segments out the mole using the Improved UNet. When train.py is provided the data locations in the correct format as specified in train.py. Training metrics will be plotted against the training and validation test sets.
I believe there will be a chance to receive feedback which I will gladly accept and act upon.
Michael :)