Shoe Cabinet Push-to-Open Detection Dataset

The current retail e-commerce industry faces increasingly fierce competition, especially in terms of user experience and product display. Many e-commerce platforms lack effective intelligent recognition technology, unable to respond promptly to user needs and behaviors, leading to a decline in user experience. Existing target detection datasets often lack annotations for specific scenarios, making them difficult to meet practical application needs. This dataset aims to provide high-quality shoe cabinet push-to-open detection data to help researchers and developers improve the performance of target detection models in retail e-commerce scenarios. Data collection is conducted using high-resolution cameras in real retail environments to ensure the authenticity and diversity of images. The annotation process undergoes multiple rounds of review, using consistency checks to ensure annotation quality. Data is stored in JPEG format, organized by image ID for quick retrieval and use.