Vesuvius Kaggle Competition
This repository contains code for Vesuvius Kaggle Competition.
Kaggle Competition - https://www.kaggle.com/competitions/vesuvius-challenge-ink-detection/overview
My results
- Public
- Private
Architectures
Triple MIT_l1 Unet
-> 3 middle layers -> UNet(backbone="mit_b1") ->
9 middle layers -> 3 middle layers -> UNet(backbone="mit_b2") -> Conv2d -> BCELoss -> Predict
-> 3 middle layers -> UNet(backbone="mit_b1") ->
UNet Meanpooled
| mit_b2 | -> MeanPooling(Max, Min) -> | UNet | -> BCE(Dice loss)
| UNet | -> MeanPooling(Max, Min) -> | decoder |
32 middle channels -> | encoder | -> MeanPooling(Max, Min) -> | scse |
| | -> MeanPooling(Max, Min) -> | |
|| -> MeanPooling(Max, Min) -> ||
MaNet Meanpooled
| mit_b2 | -> MeanPooling(Max, Min) -> | MaNet | -> BCE(Dice loss)
| UNet | -> MeanPooling(Max, Min) -> | decoder |
32 middle channels -> | encoder | -> MeanPooling(Max, Min) -> | |
| | -> MeanPooling(Max, Min) -> | |
|| -> MeanPooling(Max, Min) -> ||
UnetPlusPlus Meanpooled
|resnet101d| -> MeanPooling(Max, Min) -> | UNet++ | -> BCE(Dice loss)
| UNet | -> MeanPooling(Max, Min) -> | decoder |
32 middle channels -> | encoder | -> MeanPooling(Max, Min) -> | scse |
| | -> MeanPooling(Max, Min) -> | |
|| -> MeanPooling(Max, Min) -> ||
Scheduler
- GradualWarmupSchedulerV2 (https://www.kaggle.com/code/underwearfitting/single-fold-training-of-resnet200d-lb0-965)
TTA
Rotations :
- 90
- 180
- 270
Add mixup
https://arxiv.org/abs/1710.09412
Add 4 folds
2-nd scroll has been divided into two folds
Final Solution
- UnetMeanpooled 4folds
- 32 channel from 16 to 48 with stride 2
- TTA rotations
- GradualWarmupSchedulerV2
- 8 models in total (4 with best CV F_0.5 and 4 with best BCE)