A Deep Learning-Based Waste Classification Using Ensemble and Vision Transformer Models

Abstract

This research presents a deep learning approach for waste categorization using three pre-trained models such as MobileNetV2, DenseNet121, and ResNet50, a transformer model (Vision Transformer or ViT), and an ensemble model. The seven-class waste dataset was used, which is publicly available, with the preprocessing steps including resizing, normalization, augmentation, and class balancing. Hyperparameter tuning was applied to all models using Grid Search, Random Search, and Bayesian Optimization. Among them, the ensemble model had a test accuracy of 97.52%, surpassing single models by synergistically combining their predictions by weighted averaging soft voting. The models were made robust using label smoothing, mix-up augmentation, and class weighting. Evaluation was carried out on accuracy, precision, recall, F1-score, and confusion matrices. Issues such as class imbalance and intra-class visual similarity in visual waste classification are addressed by the study. The future work will use the system in an IoT-capable intelligent dustbin for actual implementation.