Electronic Accessories Classification Dataset: A Comprehensive Collection for Accessory Recognition and Categorization
Description
Objective: This dataset has been curated to advance research in the recognition and classification of electronic accessories. It aims to support the development of machine learning models by providing diverse, high-quality images suitable for various computer vision tasks, such as object classification, segmentation, and identification. Description: The dataset comprises images of 10 electronic accessories, captured from various angles and perspectives to ensure a wide range of scenarios. It includes both raw images in PNG format and augmented images in PNG format, making it ideal for building robust machine learning models. Classes: Charger, Game Controller, Headphone, Keyboard, Laptop, Monitor, Mouse, Smartphone, Smartwatch, Speaker. Key Features: Total Images: 14027 (4027 raw and 10000 augmented images) Number of Classes: 10 classes. File Formats: Raw Images: PNG Augmented Images: PNG Data Type: Image data Applications: Machine Learning: Training models for image classification, object detection, and segmentation in computer vision. E-Commerce: Assisting in product recognition for online marketplaces. Inventory Management: Automating accessory identification and sorting for warehouses. Smartphone Applications: Enabling mobile apps for real-time accessory recognition. Source: The dataset was compiled from publicly available images sourced from social media platforms and second-hand selling marketplaces, including eBay. Augmentation Details: The raw images were processed to enhance their utility for machine learning applications. The augmented data includes variations in rotation, zoom, brightness, contrast, and other transformations, providing clean and focused images in PNG format. Relevance and Benefits: This dataset bridges a critical gap in the availability of diverse, high-quality data for electronic accessory classification. By providing balanced and augmented images, it enables researchers and developers to build more accurate models for accessory recognition. The inclusion of multiple formats and diverse perspectives ensures its adaptability across a range of use cases, from academic research to real-world applications in commerce and automation.