Dataset Card for "Market1501-Background-Modified"

  Dataset Summary

The Market1501-Background-Modified dataset is a variation of the original Market1501 dataset. It focuses on reducing the influence of background information by replacing the backgrounds in the images with solid colors, noise patterns, or other simplified alternatives. This dataset is designed for person re-identification (ReID) tasks, ensuring models learn person-specific features while ignoring background… See the full description on the dataset page: https://huggingface.co/datasets/ideepankarsharma2003/Market1501-Background-Modified.

Dataset

This dataset was created by 27Wilson

Contents

The Market-1501 dataset is collected in front of a supermarket in Tsinghua University. A total of six cameras were used, including 5 high-resolution cameras, and one low-resolution camera. Field-of-view overlap exists among different cameras. Overall, this dataset contains 32,668 annotated bounding boxes of 1,501 identities. In this open system, images of each identity are captured by at most six cameras. We make sure that each annotated identity is present in at least two cameras, so that cross-camera search can be performed. The Market-1501 dataset has three featured properties:

First, our dataset uses the Deformable Part Model (DPM) as pedestrian detector. Second, in addition to the true positive bounding boxes, we also provde false alarm detection results. Third, each identify may have multiple images under each camera. During cross-camera search, there are multiple queries and multiple ground truths for each identity.

The Market-1501 dataset is a large benchmark for person re-identification. It contains 12,000 images with 1,500 images per person, annotated with bounding boxes and masks for objects.

Dataset

This dataset was created by Sachin Sarkar

Contents

Performance comparison of our method with baselines on the Market1501, DukeMTMC-reID and MSMT17 dataset.

Dataset

This dataset was created by Ali Akay

Contents

Dataset

This dataset was created by Zechuan Lan

Contents

Source

This dataset is collected and annotated in Tsinghua. It has 6 cameras, 1,501 IDs and a distractor set of 500k images. Only one train/test split is used. We also annotate the ID-level attributes for Market-1501

Content

I'm not 100% sure how to interpret filenames in this dataset. If you know it, you can write to me, so I can update the description.

Acknowledgements

Liang Zheng www.liangzheng.org/Project/project_reid

Dataset

This dataset was created by Achraf Abid

Contents

Ablation experiments of our method on the Market1501, DukeMTMC-reID and MSMT17 datasets.

Dataset

This dataset was created by foryolotrain1

Contents

Detailed statistics of Market-1501 and DukeMTMC-reID.

Dataset

This dataset was created by Igor Krashenyi

Released under CC0: Public Domain

Contents

The VIMER-UFO benchmark dataset consists of 8 computer vision tasks: CPLFW, Market1501, DukeMTMC, MSMT-17, Veri-776, VehicleId, VeriWild, and SOP.

Dataset

This dataset was created by Leonardo Naldi

Contents

MARS (Motion Analysis and Re-identification Set) is a large scale video based person reidentification dataset, an extension of the Market-1501 dataset. It has been collected from six near-synchronized cameras. It consists of 1,261 different pedestrians, who are captured by at least 2 cameras. The variations in poses, colors and illuminations of pedestrians, as well as the poor image quality, make it very difficult to yield high matching accuracy. Moreover, the dataset contains 3,248 distractors in order to make it more realistic. Deformable Part Model and GMMCP tracker were used to automatically generate the tracklets (mostly 25-50 frames long).

Dataset

This dataset was created by arunkumar chandran

Contents

El tamaño del mercado global de servicios de centros de datos valía más de USD 83,1 mil millones en 2025 y se prevé que crezca a una CAGR de alrededor del 13,5%, alcanzando ingresos de USD 294,82 mil millones para 2035, impulsado por la demanda de aplicaciones intensivas en datos.

A Culturally-Aware Benchmark for Person Re-Identification in Modest Attire 🧕🏻👨🏻‍💼

Paper Link = https://doi.org/10.1016/j.engappai.2025.111494

The IUST_PersonReId dataset was developed to address limitations in existing person re-identification datasets by including cultural and environmental contexts unique to Islamic countries, especially Iran and Iraq. Unlike common datasets, which don’t reflect the clothing styles common in these regions, such as hijabs and other coverings, the IUST_PersonReID dataset represents this diversity, helping to reduce demographic bias and improve model accuracy. Collected from a variety of real-world settings under different lighting, camera angles, indoor & outdoor, and weather conditions, this dataset provides extensive, overlapping views across multiple cameras. By capturing these unique conditions, IUST_PersonReID offers a valuable resource for developing re-ID models that perform more reliably across diverse environments and populations. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F9623774%2F198a5c58b077a50b5fafa94db20f5356%2FScreenshot%20from%202025-09-18%2014-48-30.png?generation=1758195559915073&alt=media" alt="">

Annotation Statistics

The dataset contains 117,455 images featuring 1,847 unique identities across about 20 different scenes. Many identities are captured from multiple camera views, making this dataset valuable for testing cross-camera re-identification. You can see the distribution of identities across cameras and genders in the images below.

Bounding Box Naming Convention

The IUST_PersonReID dataset adopts a naming convention identical to that of the Market-1501 dataset. The image naming format is structured as follows: