OpenGVLab/InternImage

Health v2 · maintenance only

Adoption not part of the score

2841 stars · 265 forks observed · 2026-08-28

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

InternImage is a large-scale CNN-based vision foundation model that uses deformable convolutions as its core operator, released with pretrained weights and code. It achieves state-of-the-art results on ImageNet classification, COCO object detection, and ADE20K semantic segmentation.

Use cases

• pretrained backbone for object detection
• semantic segmentation model for ADE20K or Cityscapes
• image classification with a large-scale CNN
• fine-tune a vision foundation model on custom detection data
• deformable convolution implementation in PyTorch
• state-of-the-art COCO detection model

When to choose

• you need a strong pretrained vision backbone for detection or segmentation
• you want CNN-based alternatives to ViT foundation models
• you need top accuracy on COCO or ADE20K benchmarks

When to avoid

• you need lightweight models for edge or mobile deployment
• you lack GPU resources for large-scale training or inference
• you need a general-purpose ML framework rather than a specific model family

Facets

library · maturity stable

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning image-processing python vision-backbone deformable-convolution object-detection semantic-segmentation foundation-model cnn pretrained-models gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="OpenGVLab/InternImage")