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

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Serdar Yildiz, Ahmet Nezih Kasim

Computer Vision and Pattern Recognition · Submitted 30 May 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim This design ensures a realistic and robust training platform for ReID models.

how it is stated · asserted 0.99 read the record →

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arXiv ID2405.20465 (version 1)
Pith IDERTPCCVJ, Pith's permanent record of this paper and its review
CitationAhmet Nezih Kasim, Serdar Yildiz. "ENTIRe-ID: An Extensive and Diverse Dataset for Person Re-Identification." arXiv:2405.20465 (2024). https://pith.science/paper/ERTPCCVJ
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,881 input tokens.

What the abstract itself states, as probabilities: claims first 0.09 · states limitation 0.05 · human participants 0.35 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.94 · compares with prior results 0.06 · mentions code or data availability 0.95.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The growing importance of person reidentification in computer vision has highlighted the need for more extensive and diverse datasets. background 0.98 0.04 0.05 asserted 0.99
s1 In response, we introduce the ENTIRe-ID dataset, an extensive collection comprising over 4.45 million images from 37 different cameras in varied environments. contribution 1.00 0.95 0.18 asserted 1.00
s2 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. gap 0.53 0.36 0.21 asserted 1.00
s3 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. contribution 0.73 0.69 0.09 asserted 1.00
s4 This design ensures a realistic and robust training platform for ReID models. interpretation 0.91 0.37 0.14 asserted 0.99
s5 The ENTIRe-ID dataset is publicly available at https://serdaryildiz.github.io/ENTIRe-ID contribution 0.91 0.54 0.05 asserted 1.00

What kind of paper the classifier reports this is: dataset or benchmark 1.00 · evidence from observation or experiment 0.91.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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

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