WasteClassify is an edge AI mobile application for real-time waste classification, detection, and segmentation. Trained on an augmented TACO dataset via Roboflow using YOLOv5 and ResNet-50, models were converted from PyTorch to TensorFlow Lite (now LiteRT) via ONNX and deployed on Android for fully on-device inference without any cloud dependency.
- Real-time solid waste classification, detection, and segmentation
- YOLOv5 for detection and instance segmentation
- Roboflow-based data augmentation (flip, rotate, brightness, etc.)
- Edge AI deployment pipeline: PyTorch → ONNX → TensorFlow Lite
- Android app using TensorFlow Lite runtime (LiteRT) for local inference
| Task | Tools & Libraries |
|---|---|
| Training | PyTorch, Roboflow, TACO |
| Conversion | ONNX, TensorFlow Lite |
| App Development | Android Studio, Java |
| Models | ResNet-50 (classification), YOLOv5 (detection + segmentation) |
App1/— Android app codeImage_Classification_ResNet50_x_Waste_Cl...— Classification notebookObject_Detection_YOLOv5_x_TACO.ipynb— Detection and segmentation notebook
- YOLOv5 segmentation mAP@0.5: 0.4744
- Input image size: 736px, batch size: 7
- ResNet-50 classification accuracy (3-class): 84.36%
Developed through the Computer Science and Informatics Summer Research Experience (CSIRE) Program at Stony Brook University
Mentored by Dr. Ruwen Qin and Dr. Muhammad Monjurul Karim