This repository gives access to the tools to train the models presented at the BCN 20000's dataset scientific publication. The dataset itself is available for download at the ISIC archive.
The code for the cropping technique used on the dermatoscopies can be found at:
The csv's with the image filename must be passed as a --csv_dir argument when executing the code.
In order to train a model, one should set the model's name at utils/settings.yaml for one of the following:
| Settings name | Model |
|---|---|
| res18 | ResNet 18 |
| res34 | ResNet 34 |
| res50 | ResNet 50 |
| effb0 | EfficientNet b0 |
| effb1 | EfficientNet b1 |
| effb2 | EfficientNet b2 |
In the same file you can change the proposed learning_rate and regularization values. The code will save a model everytime it surpasses the highest balanced accuracy of the validation set. The checkpoints are saved at saved_models/.