plant-pathology-2020-fgvc7-pytorch

This is a solution to kaggle competition on categorizing foliar disease in apple tree leaves. For data and further competition info : https://www.kaggle.com/c/plant-pathology-2020-fgvc7

Results

In private leaderboard, an ensemble solution achieved 0.967 score with top 26%.

About Competition

Develop a model that uses images of plant leaves and categorizes them into different diseased categories. For further info about dataset: https://arxiv.org/abs/2004.11958

The prediction categories as as follows:

• health
• rust
• scab
• multiple : this conists of both rust and scab diseases

The data was highly imbalanced

Solution

In order to train the model, first have a look at config.py and modify variables accordingly.

Key things:

• Weighted sampling of data for data imbalance.
• Variety of data random data augmentation strategies to make robust mdoel.
• Use of Label Smoothing to address better predictions: https://arxiv.org/abs/1512.00567
• Efficientnet as feature extractor : https://arxiv.org/abs/1905.11946
• Pre-trained models using noisy-student method: https://arxiv.org/abs/1911.04252
• Optimization of cost function is based on modified version of Adam : https://arxiv.org/abs/1711.05101

To use or not use can be configured in config.py:

        'criterion':'label_smooth', # other : cross_entropy
        'optimizer':'adamw', # other : adam, radam
        'lr': 3e-4, # learning rate 
        'wd': 1e-5, # weight decay parameter
        'lr_schedule':'reduce_plateau', # cyclic_lr , 
        'arch': 'tf_efficientnet_b5_ns',  # backend architecture

Requirements

• torch : 1.5.0a0+8f84ded
• torchvision : 0.6.0a0

others are in requirements.txt

Improvements

• largest model that I could use was tf_efficientnet_b5_ns due to limited GPU memory, there can be further improvements using efficinet-b6, b7 or b8.
• training is done in straight forward manner, but improvements can be made using noisy student training or utilizing self-supervised.

Acknowledgements

• pytorch pre-trained image models : https://github.com/rwightman/pytorch-image-models
• data augmentations : https://albumentations.readthedocs.io/en/latest/
• logging and utils : https://github.com/catalyst-team/catalyst