Evaluation framework for testing segmentation networks in PyTorch

Related tags

Deep Learning benchmark keras segmentation unet
Overview

Segmentation networks benchmark

Evaluation framework for testing segmentation networks in PyTorch. What segmentation network to choose for next Kaggle competition? This benchmark knows the answer!

Deprecation notice

This repository is not maintained. Please refer to https://github.com/BloodAxe/pytorch-toolbelt instead.

What all this code is about?

It tries to show pros & cons of many existing segmentation networks implemented in Keras and PyTorch for different applications (biomed, sattelite, autonomous driving, etc). Briefly, it does the following:

for model in [Unet, Tiramisu, DenseNet, ...]:
    for dataset in [COCO, LUNA, STARE, ...]:
        for optimizer in [SGD, Adam]:
            history = train(model, dataset, optimizer)
            results.append(history)

summarize(results)

Roadmap

  • Write Keras train pipeline
  • Write Pytorch train pipeline

Models

Datasets

  • Add DSB2018 (stage1) dataset
  • Add COCO dataset
  • Add STARE dataset
  • Add LUNA16 dataset
  • Add Inria dataset
  • Add Cityscapes dataset
  • Add PASCAL VOC2012 dataset

Reporting

  • Add fancy plots

Credits

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Comments
  • About Segcaps

    About Segcaps

    Hi, I'm wondering if your segcaps implementation in pytorch for segmentation task can work? I also do some research about segmentation and want to test SegCaps performance in our dataset

    opened by ierosodin 1
Owner
Eugene Khvedchenya
AI/ML Advisor, Entrepreneur, Kaggle Master. Author of pytorch-toolbelt. Core maintainer of albumentations. Catalyst contributor.
Eugene Khvedchenya
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