IDEA-Research/MaskDINO

Health v2 · maintenance only

Adoption not part of the score

1557 stars · 162 forks observed · 2026-08-28

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

Official PyTorch implementation of Mask DINO, a unified transformer-based framework for object detection and segmentation, built on detectron2. It provides models and training code achieving state-of-the-art results on COCO instance/panoptic segmentation and ADE20K semantic segmentation.

Use cases

• train a state-of-the-art instance segmentation model on COCO
• run panoptic segmentation on images with a transformer detector
• perform semantic segmentation on ADE20K
• reproduce CVPR 2023 Mask DINO paper results
• fine-tune a unified detection and segmentation model
• use pretrained checkpoints for object detection and segmentation inference

When to choose

• you need top-tier transformer-based detection and segmentation accuracy
• you want a unified model handling instance, panoptic, and semantic segmentation
• your stack already uses detectron2 or detrex
• you need official paper checkpoints for research baselines

When to avoid

• you need a lightweight production detector with fast inference on CPU
• you want actively maintained code with frequent updates
• you need open-vocabulary or text-prompted segmentation (consider OpenSeeD or SAM instead)
• you lack GPU resources for training large vision transformers

Facets

library · maturity maintenance

computer-vision image-processing machine-learning deep-learning computer-vision image-processing deep-learning machine-learning python object-detection instance-segmentation panoptic-segmentation semantic-segmentation transformer detectron2 cvpr-2023 research-code linux gpu

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