The Human Wrist Images dataset is designed for detection and augmented reality use cases. It includes images captured by mobile phones in real-world scenarios, featuring diverse lighting conditions, weather, indoor, and outdoor environments. The dataset supports applications such as Augmented Reality, Mixed Reality, Rakhi Detection, Wrist-watch Detection, and Hand-band Detection.
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Dataset Features
Captured by 3000+ unique users: Ensures a wide variety of perspectives and diversity.
Rich in diversity: Features male and female wrists with various items like watches, bands, and bracelets.
Mobile phone viewpoint: Images captured from the typical angles and viewpoints of mobile phone users.
HD Resolution: High-definition images provide clarity and detail.
Various lighting conditions: Images taken in different lighting scenarios, enhancing robustness for model training.
Indoor and Outdoor scenes: Captures wrists in multiple environments for versatile applications.
Annotations and Formats
Classification and detection annotations: Available to support various machine learning tasks.
Multiple category annotations: Allows for detailed and comprehensive labeling.
Supported Formats: COCO, PASCAL VOC, and YOLO formats to ensure compatibility with different model training pipelines.
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