  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.
