An Explorative Analysis of SVM Classifier and ResNet50 Architecture on African Food Classification

TL;DR

This study compares deep learning and traditional machine learning methods, specifically ResNet50 and SVM, for classifying African foods, providing insights into their effectiveness and limitations.

Contribution

It is the first to evaluate and compare ResNet50 and SVM models specifically for African food classification, filling a research gap.

Findings

ResNet50 achieved higher accuracy than SVM.

Both models showed strengths and limitations in different metrics.

The study provides a benchmark for future African food recognition systems.

Abstract

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.