GreenSort – Automated Deep Learning Waste Classifier
Deep convolutional neural network classifying municipal waste into organic and recyclable streams with 92.04% test accuracy.
Problem Statement
Improper municipal waste segregation causes severe landfill overflow and contamination of recyclable materials. Automated, low-latency visual sorting is vital to relieve human sorting burdens and enhance recycling throughput.
Methodology & Implementation
Built and trained a 4-stage convolutional neural network (CNN) in TensorFlow/Keras on a curated dataset of 25,077 real-world images. Replaced heavy dense layers with GlobalAveragePooling2D, reducing model parameter count by 99.3% while maintaining high discriminatory feature representation. Deployed with an asynchronous FastAPI inference service featuring image preprocessing, batch predictions, and automated Swagger OpenAPI documentation.
Technical Architecture Highlights
- 4-stage convolutional feature extraction hierarchy with batch normalization and dropout regularization
- GlobalAveragePooling2D topology slashing total model parameters by 99.3% to enable lightweight edge inference
- Asynchronous FastAPI REST endpoints processing image payloads with sub-50ms inference latency
- Interactive client dashboard supporting real-time single-item uploads and multi-file batch image classification