An Explorative Analysis of SVM Classifier and ResNet50 Architecture on African Food Classification
This work addresses the gap in food recognition for African cuisines, which is incremental as it applies existing methods to a new dataset.
The study tackled the underexplored problem of African food classification by evaluating a fine-tuned ResNet50 model and an SVM classifier on a dataset of 1,658 images across six categories, providing insights into their strengths and limitations.
Food recognition systems has advanced significantly for Western cuisines, yet its application to African foods remains underexplored. This study addresses this gap by evaluating both deep learning and traditional machine learning methods for African food classification. We compared the performance of a fine-tuned ResNet50 model with a Support Vector Machine (SVM) classifier. The dataset comprises 1,658 images across six selected food categories that are known in Africa. To assess model effectiveness, we utilize five key evaluation metrics: Confusion matrix, F1-score, accuracy, recall and precision. Our findings offer valuable insights into the strengths and limitations of both approaches, contributing to the advancement of food recognition for African cuisines.