human-protein

Human Protein Atlas Image Classification

Overview

Data

• Channels: RGB
• Oversampling
• External data: http://v18.proteinatlas.org

Augmentation

• Resize, Rotate, RandomRotate90, HorizontalFlip, RandomBrightnessContrast, Normalize

Model design

• Backbone: Resnet50 pretrained on ImageNet
• Head: 2 linear layers with batch normalization and dropout

Loss

• Binary Cross Entropy Loss

Training

• 5-fold CV
• Optimizer: Adam
• Different learning rates for different layers
• Head fine-tuning with frozen backbone (1 epoch)
• Scheduler: Cyclical Learning Rates

Stage 1:

• Image size: 256
• Batch size: 128
• Epochs: 16

Stage 2:

• Image size: 512
• Batch size: 32
• Epochs: 6

Prediciton

• TTA: 8
• TTA augmentation: Resize, Rotate, RandomRotate90, HorizontalFlip, Normalize
• The mean of the predictions
• Threshold: 0.2

Result

• Training takes ~35 hours on Tesla v100
• Public LB: 0.595
• Private LB: 0.523

Observations

• Mixed precision works poorly
• External data helps a lot
• BCE Loss with oversampling is much better than Focal Loss
• Resnet50 outperforms Resnet18 and Resnet34
• 5 folds improve score by 0.024
• TTA helps too

Installation

First, clone the repository

git clone https://github.com/rebryk/kaggle.git
cd kaggle/human-protein

Second, install requirements

pip install -r requirements.txt

Third, install apex

git clone https://github.com/NVIDIA/apex.git
cd apex
python setup.py install --cuda_ext --cpp_ext

And last but not least, update config files in the configs folder to match your preferences!

External data

Use scripts/external_data.py and scripts/convert_data.py to download and convert external data.

Training

# Stage 1
cp configs/train256.py config.py
python train.py

# Stage 2
cp configs/train512.py config.py
python train.py

Prediction

cp configs/test.py config.py
python test.py

Submissions are saved in the submissions folder.