Trash Classification Project Overview This project focuses on trash classification using machine learning, leveraging multiple datasets with a total of 8,895 images from diverse sources. Datasets 1. Drinking Waste Classification Images : 4,832 Organization : Directory based sorting Classes : 4 recyclable categories Aluminium Cans Glass Bottles PET (Plastic) Bottles HDPE (Plastic) Milk Bottles 2. TACO (Trash Annotations in Context) Images : 1,530 Environment : Diverse settings (woods, roads, beaches) Format : Raw images with annotation JSON Note : Requires category mapping for proper sorting 3. TrashNet Images : 2,533 Organization : Directory based sorting Classes : 6 categories Cardboard Glass Metal Paper Plastic Trash (miscellaneous) 4. Google Images API Images : 4,500 Organization : Directory based sorting Collection : Scraping images from google images API to expand dataset. Data Processing Pipeline Image Augmentation We apply 14 different manipulations to expand the dataset: Transformation Description Grayscale Convert to grayscale Rotation 90°, 180°, 270° rotations Flipping Horizontal and vertical flips Noise Add random noise Blur Apply Gaussian blur Brightness Brighten and da…
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