Home Projector Style Recognition Dataset

In the current retail e-commerce sector, distinguishing between various styles of home projectors such as portable, laser, and home theater models poses significant challenges due to the increasing variety and complexity of products. Existing solutions often rely on manual classification methods, which are time-consuming and prone to errors, leading to inconsistencies in product listings. This dataset aims to address these challenges by providing a robust collection of labeled images that can be used to train machine learning models for automatic style recognition. The dataset is constructed through a systematic process where images are collected from reputable online retailers, ensuring a diverse representation of projector styles. Quality control measures include multiple rounds of annotation by trained personnel, consistency checks, and expert review to ensure high accuracy in labeling. The data is stored in JPG format, organized by style categories, ensuring easy accessibility and usability for model training.