zhanghang1989/ResNeSt

Health v2 · maintenance only

Adoption not part of the score

3261 stars · 495 forks observed · 2026-08-28

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

ResNeSt is a PyTorch implementation of the Split-Attention Network, a ResNet variant that applies channel-wise attention across network branches for improved visual recognition. It ships pretrained ImageNet models and serves as a drop-in backbone boosting downstream tasks like Mask R-CNN, Cascade R-CNN, and DeepLabV3.

Use cases

• image classification backbone with pretrained weights
• object detection on COCO with a stronger ResNet backbone
• semantic segmentation on ADE20K or Cityscapes
• instance and panoptic segmentation with Mask R-CNN
• transfer learning for custom vision tasks
• compare ResNet variants like EfficientNet vs ResNeSt

When to choose

• you need a strong CNN backbone for detection or segmentation in PyTorch
• you want pretrained ImageNet weights with proven COCO/LVIS challenge results
• you want a drop-in ResNet replacement with better accuracy-latency tradeoff

When to avoid

• you need a transformer-based vision model like ViT or Swin
• you need a framework-agnostic solution outside PyTorch
• you need actively maintained code with recent updates

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision deep-learning computer-vision image-processing python resnet-variant split-attention pytorch pretrained-models backbone-network image-classification object-detection semantic-segmentation gpu

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