EDICA: A Hybrid Ensemble Architecture Using Deep Learning Models for Fine-Grained Image Classification

Juan Paulo Sánchez Hernández, Alan J. González Hernández, Juan Frausto Solis (), Deny Lizbeth Hernández Rabadán, Javier González-Barbosa and Guadalupe Castilla Valdez
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Juan Paulo Sánchez Hernández: Informatic, Electronic and Telecommunications Division, Universidad Politécnica del Estado de Morelos, Jiutepec 62574, Mexico
Alan J. González Hernández: Graduate Program Division, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero 89440, Mexico
Juan Frausto Solis: Graduate Program Division, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero 89440, Mexico
Deny Lizbeth Hernández Rabadán: Informatic, Electronic and Telecommunications Division, Universidad Politécnica del Estado de Morelos, Jiutepec 62574, Mexico
Javier González-Barbosa: Graduate Program Division, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero 89440, Mexico
Guadalupe Castilla Valdez: Graduate Program Division, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero 89440, Mexico

Mathematics, 2025, vol. 13, issue 22, 1-20

Abstract: This work presents EDICA, a two-stage architecture for fine-grained image classification, which is a hybrid model for the detection and classification task. The model employs YOLOv8 for the detection stage and an ensemble deep learning model that utilizes a majority voting strategy for fine-grained image classification. The proposed model aims to enhance the precision of classification by integrating classification models that have been trained with the same classes. This approach enables the utilization of the strengths of these classification models for a range of test instances. The experiment involved a diverse set of classes, encompassing a variety of types, including dogs, cats, birds, fruits, frogs, and foliage; each class is divided into subclasses for finer-grained classification, such as specific dogs, cat breeds, bird species, and fruit types. The experimental results show that the hybrid model outperforms classification approaches that use only one model, thereby demonstrating greater robustness relating to ambiguous complex images and uncontrolled environments.

Keywords: deep learning; YOLO; hybrid ensemble model; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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