2019/10/22 by Kaiyang Zhou, Tao Xiang, Zhou, Kaiyang +1 · 4 citations
Computer Science · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1910.10093
Tech report
arxiv created 2019/10/22 · openalex publication_date 2019/10/22 · arxiv updated 2019/10/23 · openalex created_date 2019/11/01 · openalex updated_date 2026/07/28
Person re-identification (re-ID), which aims to re-identify people across different camera views, has been significantly advanced by deep learning in recent years, particularly with convolutional neural networks (CNNs). In this paper, we present Torchreid, a software library built on PyTorch that allows fast development and end-to-end training and evaluation of deep re-ID models. As a general-purpose framework for person re-ID research, Torchreid provides (1) unified data loaders that support 15 commonly used re-ID benchmark datasets covering both image and video domains, (2) streamlined pipelines for quick development and benchmarking of deep re-ID models, and (3) implementations of the latest re-ID CNN architectures along with their pre-trained models to facilitate reproducibility as well as future research. With a high-level modularity in its design, Torchreid offers a great flexibility to allow easy extension to new datasets, CNN models and loss functions.