Runmila-AI-Institute-minoHealth-AI-Labs-Tuberculosis-Classification-via-X-Rays-Challenge

competition website

• Tools: Google Colab, Pytorch, Albumentation, Pytorch pretrained models, Resnet152

• Techniques: Transfer Learning, Data Augmentation

• See TB.ipynb for package installation details, stored in notebook folder

• The data used is the small version: train_small.zip/ test_small.zip stored in the inputs folder

• Training: 5 folds cross-validation , however, fold 2 is the one that achieved the highest submitted accuracy

• TB_stratified_kfold.csv is the 5 folds cross-validation file stored in the inputs folder, you can generate it using the train_fold.py file stored in the folder

• Submission results are stored in nsub.TB.csv in the inputs folder

• Training engine is in utils.py stored in source folder alongside with train_folds.py

• The training models are saved in the models folder

• To run the code please store your data according to the folders as disccused above and don't forget to change paths in the main notebook.

• You can add inputs folder to store your data and another folder called models to save your best trained models

• My Zindi Profile.