SHI-Labs/OneFormer

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1736 stars · 155 forks observed · 2026-08-28

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

OneFormer is a universal image segmentation framework (CVPR 2023) that unifies semantic, instance, and panoptic segmentation in a single transformer model trained once with task tokens and contrastive learning. It provides PyTorch code, pretrained checkpoints, and HuggingFace/Colab integrations for ADE20K, Cityscapes, and COCO.

Use cases

• run panoptic segmentation on images
• semantic segmentation of street scenes
• instance segmentation on COCO images
• segment images with a single universal model
• fine-tune a segmentation transformer on my dataset
• compare segmentation models on ADE20K benchmarks

When to choose

• you need one model handling semantic, instance, and panoptic segmentation
• you want state-of-the-art transformer segmentation with pretrained checkpoints
• you want HuggingFace transformers integration for easy inference

When to avoid

• you need lightweight real-time segmentation on edge devices
• you only need simple classical segmentation like thresholding
• you cannot use GPU resources for training or inference

Facets

library · maturity stable

machine-learning computer-vision image-processing deep-learning computer-vision image-processing deep-learning artificial-intelligence python cross-platform image-segmentation panoptic-segmentation semantic-segmentation instance-segmentation transformer pytorch cvpr-2023 universal-segmentation huggingface gpu

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