We used EfficientNet [B0-B6], Resnest,Resnext, with Sizes 192x192 256x256 384x384 512x512 768x768 384x512[HxW]
- Heavy TTA (X20)
- Cutmix
- Coarse dropout
- SWA(Stochastic Weight Averaging)
- Loss-Label Smoothing, BCE
- Optimizers - AdamW, Adam
- 2018, 2020 and malignant datasets
- 5 checkpoints' prediction averaging(stabalised our model's predictions)
- some models were trained with different height width ratios
- Loss functions-Focal loss, dice loss
- Optimizer- Ranger
- Hair removal/addition
- Pseudo labelling
- 2019 dataset
- Preprocessing techniques from Aptos Competition
- Progressive learning
- Weighted average
- Power Average
- Minmax ensemble(didn't help)
15+ pytorch model (with context) and 15+ tf models (without context) - 0.9627 (public LB) 0.9470 (private LB) 0.9618 (CV) It could have achieved us 6th rank on the private leader board of the competition.