An evaluation of pre-trained models for feature extraction in image classification

• URL: http://arxiv.org/abs/2310.02037v1
• Date: Tue, 3 Oct 2023 13:28:14 GMT
• Title: An evaluation of pre-trained models for feature extraction in image classification
• Authors: Erick da Silva Puls, Matheus V. Todescato, Joel L. Carbonera
• Abstract summary: This work aims to compare the performance of different pre-trained neural networks for feature extraction in image classification tasks. Our results demonstrate that the best general performance along the datasets was achieved by CLIP-ViT-B and ViT-H-14, where the CLIP-ResNet50 model had similar performance but with less variability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, we have witnessed a considerable increase in performance in image classification tasks. This performance improvement is mainly due to the adoption of deep learning techniques. Generally, deep learning techniques demand a large set of annotated data, making it a challenge when applying it to small datasets. In this scenario, transfer learning strategies have become a promising alternative to overcome these issues. This work aims to compare the performance of different pre-trained neural networks for feature extraction in image classification tasks. We evaluated 16 different pre-trained models in four image datasets. Our results demonstrate that the best general performance along the datasets was achieved by CLIP-ViT-B and ViT-H-14, where the CLIP-ResNet50 model had similar performance but with less variability. Therefore, our study provides evidence supporting the choice of models for feature extraction in image classification tasks.

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