a1557811266/ITCPR

ITCPR Dataset

Overview

The ITCPR dataset is a comprehensive collection specifically designed for the Composed Person Retrieval (CPR) task. It consists of a total of 2,225 annotated triplets, derived from three distinct datasets: Celeb-reID, PRCC, and LAST.

For more details on how to use this dataset, please refer to: https://github.com/Delong-liu-bupt/Composed_Person_Retrieval

Dataset Scale

• Total Annotated Triplets: 2,225
• Unique Query Combinations: 2,202
• Total Images: 1,151 from Celeb-reID, 146 from PRCC, 905 from LAST
• Total Identities: 512 from Celeb-reID, 146 from PRCC, 541 from LAST
• Target Gallery: 20,510 images with 2,225 corresponding ground truths

Image Sources

The images in the ITCPR dataset are sourced from the following datasets:

• Celeb-reID
• PRCC
• LAST

These are utilized solely for testing purposes in the ZS-CPR task.

Annotation Files

The dataset includes two annotation files: query.json and gallery.json.

query.json Format

Each entry in the query.json file follows this structure:

{
    "file_path": "Celeb-reID/001/1_1_0.jpg",
    "datasets": "Celeb-reID",
    "person_id": 1,
    "instance_id": 1,
    "caption": "Wearing a brown plaid shirt, black leather shoes, another dark gray T-shirt, another blue jeans"
}

• file_path: Reference image path relative to the data root directory.
• datasets: Source dataset of the image.
• person_id: Person ID in the original dataset.
• instance_id: Unique identifier for gallery ground truth matching.
• caption: Relative caption of the reference image.

gallery.json Format

Each entry in the gallery.json file follows this structure:

{
    "file_path": "Celeb-reID/001/1_2_0.jpg",
    "datasets": "Celeb-reID",
    "person_id": 1,
    "instance_id": 1
}

• instance_id: Matches with query.json for target images; -1 for non-matching query instances.
• Others: Correspond to target image path, original dataset, and person ID.

Data Directory Structure

data
|-- Celeb-reID
|   |-- 001
|   |-- 002
|   |-- 003
|   ...
|-- PRCC
|   |-- train
|   |-- val
|   |-- test
|-- LAST
|   |-- 000000
|   |-- 000001
|   |-- 000002
|   ...
|-- query.json
|-- gallery.json

Dataset Download and Preparation

Download and prepare the datasets as follows:

1. Celeb-reID: GitHub Repository
2. PRCC: Google Drive Link
3. LAST: GitHub Repository

After downloading, use the img_process.py script to process Celeb-reID and LAST datasets into the standard format. The PRCC (subfolder PRCC/rgb) dataset can be directly placed in the corresponding directory upon extraction.

Acknowledgments

We are deeply thankful to the creators of the Celebrities-ReID, PRCC, and LAST datasets for their significant contributions to the field of person re-identification. Their commitment to open-sourcing these valuable resources has greatly facilitated advancements in academic and practical research.

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• Celebrities-ReID: "Celebrities-ReID: A Benchmark for Clothes Variation in Long-Term Person Re-Identification" - View Paper
• PRCC: "Person Re-identification by Contour Sketch under Moderate Clothing Change" - View Paper
• LAST: "Large-Scale Spatio-Temporal Person Re-identification: Algorithms and Benchmark" - View Paper

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Citation

If you use our code or dataset, please cite:

@misc{liu2025automaticsyntheticdatafinegrained,
      title={Automatic Synthetic Data and Fine-grained Adaptive Feature Alignment for Composed Person Retrieval}, 
      author={Delong Liu and Haiwen Li and Zhaohui Hou and Zhicheng Zhao and Fei Su and Yuan Dong},
      year={2025},
      eprint={2311.16515},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2311.16515}, 
}