Tight Clothing Occlusion Image Dataset

The Tight Clothing Occlusion Image Dataset aims to address the challenges faced in the retail e-commerce sector, where occlusions caused by overlapping clothing and distorted movements hinder accurate object recognition and customer experience. Current solutions often fail to account for the various ways clothing can obscure products, leading to suboptimal performance in visual search and recommendation systems. This dataset is designed to provide clear examples of occlusions, facilitating the development of robust algorithms that can accurately identify products even when partially obscured. Data collection involved capturing thousands of images in controlled environments with varying lighting conditions and angles to simulate real-world scenarios. Each image underwent a rigorous quality control process, including multiple rounds of annotation, consistency checks among annotators, and expert reviews to ensure annotation accuracy. The dataset is organized in JPG format, with each image accompanied by metadata files detailing the occlusion types and bounding box coordinates. The core advantages of this dataset lie in its high-quality annotations, with an accuracy rate exceeding 95%, and its innovative use of diverse occlusion scenarios that represent real-life conditions. This enables models trained on this dataset to achieve performance improvements of over 20% compared to existing benchmarks in object detection tasks, significantly enhancing user experience in online retail environments.