计算机科学
鉴定(生物学)
模块化设计
工具箱
软件部署
水准点(测量)
领域(数学分析)
软件工程
软件
任务(项目管理)
追踪
服务器
编码(集合论)
人工智能
程序设计语言
操作系统
系统工程
地理
工程类
集合(抽象数据类型)
数学分析
大地测量学
生物
植物
数学
作者
Lingxiao He,Xingyu Liao,Wu Liu,Xinchen Liu,Cheng Peng,Tao Mei
出处
期刊:Cornell University - arXiv
日期:2020-01-01
被引量:99
标识
DOI:10.48550/arxiv.2006.02631
摘要
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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