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# FastReID
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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](https://github.com/michuanhaohao/reid-strong-baseline).
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## What's New
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- Remove [ignite](https://github.com/pytorch/ignite)(a high-level library) dependency and powered by [PyTorch](https://pytorch.org/).
- Includes more features such as circle loss, visualizing ranklist and label, SoTA results on intra-domain, cross-domain and partial reid, testing on multi-datasets at the same time, etc.
- Can be used as a library to support [different projects](https://github.com/JDAI-CV/fast-reid/tree/master/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
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See [INSTALL.md](https://github.com/JDAI-CV/fast-reid/blob/master/INSTALL.md).
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## Quick Start
The designed architecture follows this guide [PyTorch-Project-Template](https://github.com/L1aoXingyu/PyTorch-Project-Template), you can check each folder's purpose by yourself.
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See [GETTING_STARTED.md](https://github.com/JDAI-CV/fast-reid/blob/master/GETTING_STARTED.md).
Change architecture: 1. delete redundant preprocess 2. add data prefetcher to accelerate data loading 3. fix minor bug of triplet sampler when only one image for one id
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Learn more at out [documentation](). And see [projects/](https://github.com/JDAI-CV/fast-reid/tree/master/projects) for some projects that are build on top of fastreid.
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## Model Zoo and Baselines
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We provide a large set of baseline results and trained models available for download in the [Fastreid Model Zoo](https://github.com/JDAI-CV/fast-reid/blob/master/MODEL_ZOO.md).