An Explorative Analysis of SVM Classifier and ResNet50 Architecture on African Food Classification
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915293646815232 |
|---|---|
| author | Mbonu, Chinedu Emmanuel Anigbogu, Kenechukwu Asogwa, Doris Belonwu, Tochukwu |
| author_facet | Mbonu, Chinedu Emmanuel Anigbogu, Kenechukwu Asogwa, Doris Belonwu, Tochukwu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13923 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Explorative Analysis of SVM Classifier and ResNet50 Architecture on African Food Classification Mbonu, Chinedu Emmanuel Anigbogu, Kenechukwu Asogwa, Doris Belonwu, Tochukwu Computer Vision and Pattern Recognition 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. |
| title | An Explorative Analysis of SVM Classifier and ResNet50 Architecture on African Food Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.13923 |