/kaggle-recursion-cellular-image-classification

Solution of Kaggle Cellular Image Classification Challenge (https://rxrx.ai)

Primary LanguageJupyter Notebook

Kaggle Recursion Cellular Image Classification Challenge

The competition has an objective of image classification in experimantal noise of biological signals. Here the proposed algorithms detects different genetic perturbations.

Hardware/ Software

  • GPU: 1xTesla K80
  • PyTorch, albumentations

Training

As the cellular images have origin from 4 types of experiments (HEPG2, HUVEC, RPE, U2OS) we have trained 4 different models in parallel for each experiment and then concatenated the predictions.

Solution

The solution represents:

  • models:
    • EfficientNet-B0
  • augmentations:
    • Albumentations library
    • Rotate90, HorizontalFlip, Brightness, Contrast, ColorJitter
  • optimizer: Adam
  • loss: CrossEntropyLoss
  • batch size: 16