Urban Solid Waste Classification Dataset for Convolutional Neural Networks. 2
Abstract
Convolutional Neural Network (CNN) models require processed data that learns image patterns to avoid memorization. This research presents the creation of a dataset of 3,208 images for the classification of Urban Solid Waste (MSW) into organic and inorganic forms for CNN models. Processing was carried out using a three-phase methodology: 1. Dataset identification and selection: Kaggle and Github; 2. Dataset creation: image uniformity and color adjustments; and 3. Creation of the organic and inorganic waste dataset. The results obtained were the organic MSW datasets consisting of 1,574 images and 1,634 inorganic images. This will enable the training of Deep Learning models for binary MSW classification.
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