This dataset is collected by DataCluster Labs. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai

This dataset is an extremely challenging set of over 5000+ images of people wearing gloves, which are captured and crowdsourced from over 2000+ urban and rural locations, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. It contains a wide variety of gloves in various colors and textures.

Optimized for Generative AI, Visual Question Answering, Image Classification, and LMM development, this dataset provides a strong basis for achieving robust model performance.

Dataset Features

• Dataset size : 5000+ images
• Captured by : Over 2000+ crowdsource contributors
• Resolution : HD and above (1920x1080 and above)
• Location : Captured with 100+ locations across India
• Diversity : Various lighting conditions like day, night, varied distances, view points etc.
• Device used : Captured using mobile phones in 2020-2022
• Usage : Gloves Detection, Body Accessories Detection, PPE Detection, Covid Safety Items, Object Detection, Computer Vision, etc.

Available Annotation formats

COCO, YOLO, PASCAL-VOC, Tf-Record

The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. Contact us at sales@datacluster.ai Visit www.datacluster.ai to know more.

Designed for industrial safety applications, this dataset provides high-quality, well-annotated data focusing on the detection of Personal Protective Equipment (PPE) and is particularly suitable for the training and application of computer vision models. The dataset contains 2,286 images of 640×640 pixels and focuses on the detection of PPE, such as the wearing of helmets and reflective undershirts. The data comes from a variety of sources, including public platforms such as GitHub, Kaggle, and Roboflow, as well as real-life photographs from different scenarios, to ensure that the data is diverse and can be adapted to a variety of scenarios and applications. The dataset is labeled and categorized according to the official YOLO specification, and the data can be directly applied to mainstream object detection frameworks such as YOLOv8 and YOLOv11, making it an important resource for researchers, developers, and practitioners. This dataset can be used to improve industrial safety monitoring systems and enhance construction site safety.