EWWW - Eco Way to a Waste Free World Smart Waste Management using Deep Learning Models

The urgency for efficient waste segregation has increased as a result of accelerated urbanisation and consumption levels. Manual sorting is still the norm in many urban areas, but it is often slow and erratic. Deep learning algorithms have shown great promise in the automated categorisation of waste. Yet, most prior work has been hampered by issues like limited dataset size, uneven class distribution, computationally intensive architectures, and a lack of generalisation to uncontrolled, real-world settings. In this research, a comparative analysis is presented, covering classical machine learning and custom deep learning algorithms, transfer learning, and transformer-based models, evaluated on the widely used TrashNet benchmark dataset. The methods covered include feature-based neural networks and custom convolutional models such as ResNet-style variants , as well as traditional classifiers including SVM, KNN, Random Forest, Logistic Regression, and Naïve Bayes. In addition, numerous pretrained ImageNet models are included such as ResNet50, DenseNet121, MobileNetV2, InceptionV3, Xception, and EfficientNet-B0. Hyperparameter tuning is applied to EfficientNet-B0 using Optuna. An ensemble performance study is conducted on soft-voting networks combining EfficientNet-B0, Xception, and ResNet50, along with transformer models such as ViT, ConvNeXt, and Swin Transformers. Model performance is evaluated based on classification accuracy, robustness, and feasibility for deployment. This study contributes as a benchmarked comparative work in the field of intelligent, data-driven waste segregation research.