This project is my attempt at the Human Protein Atlas Classification competition on Kaggle. To make the model work better, I've used multiple approaches and optimizations such as:

1. Multilabel stratification
2. 4 channel Resnets
3. Making transfer learning work for 4 channels
4. Data Augmentation
5. One Cycle Learning rate scheduling (Cosine Annealing)
6. Progressive resizing
7. Discriminative layer training

At the time of taking part in this competition, I had to suddenly stop working on this project due to some unforeseen circumstances. As a result I couldn't train the model for a significant amount of time. I managed to get an accuracy of about 88% on the multilabel classification task invloving 29 classes. The best fbeta score was 0.412.

This is an incomplete / dropped project