zhanghang1989/PyTorch-Encoding

Health v2 · maintenance only

Adoption not part of the score

2044 stars · 448 forks observed · 2026-08-28

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

A PyTorch computer vision toolkit with optimized CUDA backend, providing encoding layers, synchronized multi-GPU batch normalization, and pretrained models for semantic segmentation and image classification. It accompanies research papers such as ResNeSt, Context Encoding for Semantic Segmentation, and Deep TEN.

Use cases

• run semantic segmentation on ADE20K or PASCAL Context
• use ResNeSt split-attention networks in PyTorch
• synchronized batch normalization across multiple GPUs
• pretrained image classification models for PyTorch
• texture encoding networks for deep texture recognition
• style transfer with MSG-Net

When to choose

• you need the official implementations of ResNeSt or EncNet models
• you want synchronized multi-GPU batch normalization in PyTorch
• you need pretrained segmentation and classification model zoos

When to avoid

• you need a general-purpose actively developed CV framework rather than paper-specific code
• you don't use PyTorch or CUDA GPUs
• you need Windows support or frequent updates

Facets

library · maturity maintenance

deep-learning machine-learning image-processing gpu-computing deep-learning computer-vision image-processing machine-learning python pytorch semantic-segmentation synchronized-batchnorm encoding-layer resnest computer-vision-toolkit cuda linux macos gpu docker

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