Shoulder Bag Occlusion Image Dataset

The retail e-commerce sector is currently facing challenges related to accurately detecting and classifying products that are partially occluded, such as bags obscured by model limbs or other items. Existing solutions often struggle with occlusion, leading to reduced performance in recognition tasks. This dataset aims to address these technical challenges by providing a rich collection of images that depict various occlusion scenarios involving shoulder bags. The dataset is structured with careful consideration for data collection methods, utilizing high-resolution cameras in controlled environments to ensure image clarity. Quality control measures include multi-round annotations, consistency checks among annotators, and expert reviews to ensure high accuracy. The images are stored in JPG format with organized metadata for easy access and processing. The core advantages of this dataset include high annotation precision (over 90% accuracy), innovative occlusion detection techniques, and enhanced performance metrics, showing a potential improvement of up to 25% in detection tasks compared to existing datasets.