对比SVM与ResNet50在6类非洲食物识别中的表现
An Explorative Analysis of SVM Classifier and ResNet50 Architecture on African Food Classification
- 用ResNet50和SVM分别分类6类非洲食物图像
- ResNet50准确率高于SVM，F1-score达0.92
- 为非洲饮食识别提供可复现的模型比较基准
食物识别系统在西式菜肴上已取得显著进展，但对非洲食物的应用仍研究不足。本研究填补该空白，通过对比深度学习与传统机器学习方法对非洲食物进行分类。采用包含6类非洲食物共1,658张图像的数据集，评估了微调后的ResNet50模型与支持向量机（SVM）分类器的表现。使用混淆矩阵、F1分数、准确率、召回率和精确率五项关键指标进行性能分析。结果揭示了两类方法在非洲食物识别中的优劣，为非洲菜系识别技术的发展提供了重要参考。
原文摘要 · Abstract (English)
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.
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