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November 15, 2025
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March 30, 2026
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Abstract

The problem of waste management continues to increase along with population growth and lifestyle changes, highlighting the need for a fast and accurate waste classification system to support recycling processes. This study implements a transfer learning approach using seven Convolutional Neural Network (CNN) architectures: MobileNet, MobileNetV2, Xception, EfficientNetB0, VGG16, VGG19, and ResNet50 to classify waste into two categories: organic and recyclable. Each model is modified by adding a Global Average Pooling layer followed by a fully connected layer with 256 neurons before the output layer. The models are trained twice using 30 epochs, a batch size of 2, the Adam optimizer, and a learning rate of 0.0001. Experimental results show that ResNet50 achieves the best performance, with an accuracy of 89.84%, precision of 96.34%, recall of 82.82%, and an F1-score of 89.07%, followed by MobileNet with an accuracy of 89.25%. In contrast, Xception demonstrates the lowest performance, with an accuracy of 83.81%. Analysis of training and validation curves indicates that ResNet50 and MobileNet exhibit better stability and lower overfitting tendencies compared to other models.

Keywords

CNN global average pooling organic recyclable transfer learning

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