Deep Learning based Waste Segregation Project to classify waste images into different classes.
Attentional Learning of Trash Classification
Classification of trash images using Convolutional Neural nets
WasteAI: Snap, Sort, Succeed || Waste Segregation
This project utilizes MobileNet and the TrashNet dataset to classify waste into different categories and provide recycling suggestions. Built with TensorFlow and Streamlit, it allows users to upload an image of trash and get instant classification results along with eco-friendly disposal recommendations.
Waste image classification using CNN (MobileNetV2 & DenseNet121) on the TrashNet dataset with augmentation and class weighting.
Waste AI: Snap, Sort, Succeed || Waste Segregation
The cleaned Trashnet dataset has been annotated using Pascal VOC standard for object detection
YOLO11 waste detection -- six-class detector (plastic, paper, cardboard, metal, glass, organic) fine-tuned on public datasets only, with open-set 'rest' rejection and ONNX/TensorRT export
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