Bath Shower Gel Occlusion Image Dataset

The retail e-commerce industry is increasingly relying on visual recognition technologies to enhance customer experiences and optimize inventory management. However, many existing datasets do not adequately represent occluded product images, which poses a significant challenge for machine learning algorithms in accurately recognizing and classifying products. This dataset aims to address the specific technical issue of training models to recognize products even when partially obscured, meeting the growing business demand for robust visual recognition systems. The data collection involved capturing images of various shower gel bottles from multiple angles and under diverse lighting conditions using high-resolution cameras. Quality control measures included multiple rounds of annotation, consistency checks across annotators, and expert reviews to ensure high accuracy. The data is stored in JPG format, organized with clear naming conventions and metadata files to facilitate easy access and utilization.