FastReID: A Pytorch Toolbox for General Instance Re-identification

Lingxiao He, Xingyu Liao, Wu Liu, Xinchen Liu, Peng Cheng +1 more
6/4/2020
cs.CV

Official code: JDAI-CV/fast-reid (3,997 stars, Apache-2.0, PyTorch, last commit 2024-07-30).

Abstract

General Instance Re-identification is a very important task in the computer vision, which can be widely used in many practical applications, such as person/vehicle re-identification, face recognition, wildlife protection, commodity tracing, and snapshop, etc.. To meet the increasing application demand for general instance re-identification, we present FastReID as a widely used software system in JD AI Research. In FastReID, highly modular and extensible design makes it easy for the researcher to achieve new research ideas. Friendly manageable system configuration and engineering deployment functions allow practitioners to quickly deploy models into productions. We have implemented some state-of-the-art projects, including person re-id, partial re-id, cross-domain re-id and vehicle re-id, and plan to release these pre-trained models on multiple benchmark datasets. FastReID is by far the most general and high-performance toolbox that supports single and multiple GPU servers, you can reproduce our project results very easily and are very welcome to use it, the code and models are available at https://github.com/JDAI-CV/fast-reid.

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Code Implementations(1)

JDAI-CV/fast-reidOfficial100%
PyTorchApache-2.0

SOTA Re-identification Methods and Toolbox

3,997882C++, CMakeJun 6, 20182 years agoApache-2.0
apexbaselinecomputer-visionimage-retrievalimage-search+10 more

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Cite this paper

@article{he2020fastreid,
  title  = {FastReID: A Pytorch Toolbox for General Instance Re-identification},
  author = {Lingxiao He and Xingyu Liao and Wu Liu and Xinchen Liu and Peng Cheng and Tao Mei},
  year   = {2020},
  eprint = {2006.02631},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2006.02631}
}

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