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Introduction

MMTracking is an open source video perception toolbox by PyTorch. It is a part of OpenMMLab project.

It supports 4 video tasks:

  • Video object detection (VID)

  • Single object tracking (SOT)

  • Multiple object tracking (MOT)

  • Video instance segmentation (VIS)

Major features

  • The First Unified Video Perception Platform

    We are the first open source toolbox that unifies versatile video perception tasks including video object detection, multiple object tracking, single object tracking and video instance segmentation.

  • Modular Design

    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 official implementations.

Getting Started

Please refer to get_started.md for the basic usage of MMTracking.

A Colab tutorial is provided. You may preview the notebook here or directly run it on Colab.

User Guides

There are some basic usage guides, including:

If you want to learn more advanced guides, you can refer to:

Benchmark and model zoo

Results and models are available in the model zoo.

Contributing

We appreciate all contributions to improve MMTracking. Please refer to CONTRIBUTING.md for the contributing guideline and this discussion for development roadmap.

FAQ

If you encounter any problems in the process of using MMTracking, you can firstly refer to FAQ. If not solved, you can post an issue and we will give a response as soon as possible.

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