FastReID is a research platform that implements state-of-the-art re-identification algorithms. It is a groud-up rewrite of the previous verson, reid strong baseline.

What's New

Remove ignite(a high-level library) dependency and powered by PyTorch.
Includes more features such as circle loss, abundant visualization methods and evaluation metrics, SoTA results on conventional, cross-domain, partial and vehicle re-id, testing on multi-datasets simultaneously, etc.
Can be used as a library to support different projects on top of it. We'll open source more research projects in this way.
It trains much faster.

See our zhihu blog to learn more about fastreid.

Installation

See INSTALL.md.

Quick Start

The designed architecture follows this guide PyTorch-Project-Template, you can check each folder's purpose by yourself.

See GETTING_STARTED.md.

Learn more at out documentation. And see projects/ for some projects that are build on top of fastreid.

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the Fastreid Model Zoo.

License

Fastreid is released under the Apache 2.0 license.

Languages

Python 86.7%

C++ 11%

Cython 1.3%

CMake 0.6%

Dockerfile 0.4%