What is transfer learning in image recognition?

What is transfer learning in image recognition?

Transfer learning is a technique where a model developed for a specific task is reused as the starting point for a model on a second task, especially useful in image recognition.

How does it improve the accuracy of image recognition models?

It allows models to leverage learned features from large datasets, reducing the need for extensive training on smaller datasets.

Can you give an example of transfer learning in action?

Sure! For instance, using a pre-trained model like VGG16 on a new dataset for classifying medical images can significantly speed up training and improve accuracy.

What are some popular pre-trained models used for this?

Some popular pre-trained models include VGG16, ResNet, Inception, and MobileNet, each with unique architectures suited for different tasks.