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This repository contains the codebase for my approach on recent SIIM-ISIC Melanoma Classification kaggle competition.

A complete summary and walkthrough can be find in my blog post here.

To be able to replicate the experiments and re run the code, you will need to download the data from here.

To create folds, simply run, python folds.py or use the triple stratified training folds file that is part of this repo. This will split the training data into 5 folds.

To preprocess and resize images, simply run: python resize_images.py --input_folder <path_to_input_folder> --output_folder <path_to_output_folder> --mantain_aspect_ratio --sz 224

This will resize all images such that the shorter side of the image is of size 224px while mantaining the aspect ratio of the image. This kind of preprocessing was also used by ISIC 2019 winners as in the research paper here.

To also add color constancy, simply add -- cc flag to the command.

For a complete list of parameters, run python resize_images.py -h in the src directory of this GitHub repo.

To train the model, run the following command:

python train.py --model_name efficient_net \
--arch_name efficientnet-b3 --device cuda --metric 'auc' \
--training_folds_csv <path_to_training_folds_csv> \
--train_data_dir <path_to_preprocessed_data> \
--kfold 0,1,2,3,4 --pretrained imagenet --train_batch_size 64 \
--valid_batch_size 64 --learning_rate  5e-4 \
--epochs 100 --sz 384 --accumulation_steps 8 \
--loss 'weighted_focal_loss'  

To run inference,

python predict.py --model_name efficient_net --arch_name efficientnet-b3 \
--model_path <path_to_trained_model_output_from_training_script> \
--test_data_dir <path_to_preprocessed_test_data> --sz 256 --test_batch_size 64 

To predict using TTA, just add --tta flag to the above command.