OpenMMLab Video Perception Toolbox. It supports Single Object Tracking (SOT), Multiple Object Tracking (MOT), Video Object Detection (VID) with a unified framework.
|size (curr.)||930 kB|
|license||Apache License 2.0|
MMTracking is an open source video perception toolbox based on PyTorch. It is a part of the OpenMMLab project.
The master branch works with PyTorch 1.3 to 1.7.
The First Unified Video Perception Platform
We are the first open source toolbox that unifies versatile video perception tasks include video object detection, single object tracking, and multiple object tracking.
We decompose the video perception framework into different components and one can easily construct a customized method by combining different modules.
Simple, Fast and Strong
Simple: MMTracking interacts with other OpenMMLab projects. It is built upon MMDetection that we can capitalize any detector only through modifying the configs.
Fast: All operations run on GPUs. The training and inference speeds are faster than or comparable to other implementations.
Strong: We reproduce state-of-the-art models and some of them even outperform the offical implementations.
This project is released under the Apache 2.0 license.
v0.5.0 was released in 04/01/2021. Please refer to changelog.md for details and release history.
Benchmark and model zoo
Results and models are available in the model zoo.
Supported methods of video object detection:
Supported methods of multi object tracking:
Supported methods of single object tracking:
Please refer to install.md for install instructions.
We appreciate all contributions to improve MMTracking. Please refer to CONTRIBUTING.md for the contributing guideline.
MMTracking is an open source project that welcome any contribution and feedback. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible as well as standardized toolkit to reimplement existing methods and develop their own new video perception methods.