open-mmlab/mmtracking

Health v2 · maintenance only

Adoption not part of the score

3897 stars · 621 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

MMTracking is OpenMMLab's PyTorch-based toolbox for video perception tasks, unifying video object detection, multiple object tracking, single object tracking, and video instance segmentation in one framework. It offers a modular design, pretrained model zoo, and integration with other OpenMMLab projects like MMDetection.

Use cases

• track multiple objects across video frames
• run single object tracking on videos
• detect objects in video streams
• perform video instance segmentation
• train custom MOT models on my dataset
• benchmark state-of-the-art tracking models
• build a video analytics pipeline with pretrained detectors

When to choose

• you need a unified PyTorch toolbox covering VID, MOT, SOT, and VIS
• you want pretrained state-of-the-art tracking models with a model zoo
• you already use OpenMMLab tools like MMDetection or MMPose
• you need modular components to compose custom tracking methods

When to avoid

• you need real-time tracking on edge devices with minimal dependencies
• you want a actively developed project - it has been superseded by mmdetection 3.x and sees little activity
• you need non-PyTorch frameworks like TensorFlow or JAX
• you only need image-level detection without video/temporal modeling

Facets

framework · maturity maintenance

computer-vision machine-learning video-processing deep-learning computer-vision machine-learning python cross-platform object-tracking multi-object-tracking single-object-tracking video-object-detection video-instance-segmentation pytorch openmmlab model-zoo video gpu linux

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markdown · JSON · MCP: product_card(name="open-mmlab/mmtracking")