The growing importance of person reidentification in computer vision has
highlighted the need for more extensive and diverse datasets. In response, we
introduce the ENTIRe-ID dataset, an extensive collection comprising over 4.45
million images from 37 different cameras in varied environments. This dataset
is uniquely designed to tackle the challenges of domain variability and model
generalization, areas where existing datasets for person re-identification have
fallen short. The ENTIRe-ID dataset stands out for its coverage of a wide array
of real-world scenarios, encompassing various lighting conditions, angles of
view, and diverse human activities. This design ensures a realistic and robust
training platform for ReID models. The ENTIRe-ID dataset is publicly available
at this https URL

这篇论文介绍了 ENTIRe-ID 数据集，该数据集包含了超过 4.45 百万张图像，来自 37 个不同的相机，涵盖了各种环境。该数据集独特地设计用于解决领域变异和模型泛化的挑战，是人像重新识别现有数据集的不足之处。ENTIRe-ID 数据集具有广泛的真实世界场景，包括各种光照条件、视角和不同的人类活动，确保了逼真且稳健的 ReID 模型训练平台。该数据集公开可用于此 https URL。