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FastReID: A Pytorch Toolbox for General Instance Re-identification

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arxiv 2006.02631 v4 pith:BBU2G6OJ submitted 2020-06-04 cs.CV

Lingxiao He , Xingyu Liao , Wu Liu , Xinchen Liu , Peng Cheng , Tao Mei This is my paper · ORCID
classification cs.CV
keywords fastreidgeneralre-idre-identificationinstancemodelsverymultiple
verification ladder T0 review T1 audit T2 compute T3 formal

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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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Forward citations

Cited by 9 Pith papers

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    A single model learns per-part person representations guided by semantic ID prototypes, improving attribute-based re-identification and also performing person attribute recognition and partial attribute-based search.

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    A lightweight YOLO11-based tracker learns appearance embeddings without identity labels and reports competitive MOT17/MOT20 tracking accuracy at high speed.

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