  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 https://serdaryildiz.github.io/ENTIRe-ID
