ENTIRe-ID: An Extensive and Diverse Dataset for Person Re-Identification

Serdar Yildiz, Ahmet Nezih Kasim
5/30/2024
cs.CVcs.AIcs.LG

No official code found. Best implementation: serdaryildiz/ENTIRe-ID (25 stars, last commit 2024-07-13).

Abstract

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

View on arXivView PDF

Code Implementations(1)

serdaryildiz/ENTIRe-IDCommunity100%

ENTIRe-ID

250CSS, HTMLApr 18, 20242 years ago

Similar Papers

Person Re-ID in 2025: Supervised, Self-Supervised, and Language-Aligned. What Works?

86% match1 repo22026

When Person Re-Identification Meets Event Camera: A Benchmark Dataset and An Attribute-guided Re-Identification Framework

85% match1 repo182025

CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis

84% match1 repo132025

DynaMix: Generalizable Person Re-identification via Dynamic Relabeling and Mixed Data Sampling

84% match2025

Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-Training

83% match1 repo112024

Exploring the Camera Bias of Person Re-identification

82% match2026

Cite this paper

@article{yildiz2024entireid,
  title  = {ENTIRe-ID: An Extensive and Diverse Dataset for Person Re-Identification},
  author = {Serdar Yildiz and Ahmet Nezih Kasim},
  year   = {2024},
  eprint = {2405.20465},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.20465v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2405.20465.svg)](https://codeofpaper.com/paper/2405.20465)

Discussion