BloodAxe/pytorch-toolbelt

Health v2 · maintenance only

Adoption not part of the score

1574 stars · 126 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python library of PyTorch extensions providing building blocks for fast R&D prototyping, including encoder-decoder architectures, specialized losses, and GPU-friendly test-time augmentation. It is designed to complement high-level frameworks like Catalyst, Ignite, or fast.ai rather than replace them.

Use cases

• build U-Net style encoder-decoder models for image segmentation
• apply focal, Dice, Jaccard, or Lovasz losses to segmentation training
• run test-time augmentation on GPU for classification or segmentation
• run inference on very large images like 5000x5000 tiles
• fix and restore random seeds for reproducible experiments
• prototype Kaggle competition pipelines quickly

When to choose

• you already use PyTorch and want reusable modules, losses, and TTA utilities
• you compete in Kaggle vision competitions and want battle-tested building blocks
• you need GPU-friendly inference on huge images

When to avoid

• you want a full high-level training framework like Catalyst, Ignite, or fast.ai
• your project does not use PyTorch
• you need non-vision deep learning tooling

Facets

library · maturity active

machine-learning deep-learning image-processing computer-vision data-science deep-learning computer-vision machine-learning image-processing python pytorch kaggle segmentation losses test-time-augmentation encoder-decoder unet

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markdown · JSON · MCP: product_card(name="BloodAxe/pytorch-toolbelt")