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.

• 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.

We write a chinese blog about this toolbox in zhihu

See INSTALL.md.

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.

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

Fastreid is released under the Apache 2.0 license.