There are deep learning models that are pre-trained on millions of image data. These models reduce the effort to train the custom deep learning model from scratch, you need to fine-tune them and they are ready to be trained on your dataset.
Keras provides a high-level API for using pre-trained models. You can easily load these models with their pre-trained weights and adapt them to your specific tasks by adding custom classification layers on top of the pre-trained layers. This allows you to perform transfer learning efficiently.
In this article, you’ll see which of the four commonly used pre-trained models (VGG, Inception, Xception, and ResNet) is more accurate with their default settings. You’ll train these models on the image dataset and at the end you will able to conclude which model performed the best.