My solution for the following Kaggle Competition:
Cassava Leaf Disease Classification https://www.kaggle.com/c/cassava-leaf-disease-classification
kaggle competitions download -c cassava-leaf-disease-classification
I also used the images from the 2019 competition as additional training data:
kaggle competitions download -c cassava-disease
After extensive cross-validation settled on:
- Efficientnet B4
- Larger image size (446x446)
- Standard augmentations + cutmix (cutmix made a big difference)
- Self-distillation on out-of-fold predicted soft targets
- Cosine annealing learning rate schedule, model converges after 10 epochs
Two-step training gcp_446.py
and gcp_distillation_final10.py
for the final distillated model.
In notebook leaf_inference.ipynb
, usign light test-time augmentation.