Dataset de clasificación de Residuos Sólidos Urbanos para Redes Neuronales Convolucionales.
Resumen
Los modelos de Redes Neuronales Convolucionales (RNC) requieren datos procesados que aprendan patrones de imágenes para evitar la memorización. Esta investigación presenta la conformación de un dataset de 3208 imágenes de clasificación de Residuos Sólidos Urbanos (RSU) en orgánicos e inorgánicos para modelos de RNC. El procesamiento se realizó con una metodología de tres fases: 1. Identificación y selección de dataset: Kaggle y Github; 2. Creación del dataset: uniformidad de imágenes y ajustes de color; y 3. Conformación del dataset de residuos orgánicos e inorgánicos. Los resultados obtenidos fueron los conjuntos de datos de RSU orgánicos conformado por 1574 imágenes y 1634 inorgánicos. Esto permitirá el entrenamiento en modelos de Deep Learning para la clasificación binaria de RSU.
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