论文标题
Fastreid：用于一般实例重新识别的Pytorch工具箱
FastReID: A Pytorch Toolbox for General Instance Re-identification
论文作者
论文摘要
一般实例重新识别是计算机视觉中的一项非常重要的任务，可以在许多实际应用中广泛使用，例如人/车辆重新识别，面部识别，野生动植物保护，商品跟踪和快照等。以满足对重新识别的不断增长的应用程序需求，我们在JD AI研究中介绍了Fastreid作为广泛使用的软件系统。在法斯特雷德，高度模块化和可扩展的设计使研究人员可以轻松地实现新的研究思想。友好的可管理系统配置和工程部署功能使从业人员可以将模型快速部署到生产中。我们已经实施了一些最先进的项目，包括人员重新ID，部分重新ID，跨域重新ID和车辆重新ID，并计划在多个基准数据集上发布这些预训练的模型。 Fastreid是迄今为止最一般，最高的绩效工具箱，支持单个和多个GPU服务器，您可以非常轻松地重现我们的项目结果，非常欢迎使用它，可以在https://github.com/jdai-c.com/jdai-cv/fast-fast-fast-red-reid上找到代码和型号。
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.
