Transfer learning is a popular technique in deep learning that allows you to leverage pre-trained models to improve the performance of your own models. This tutorial will guide you through the basics of transfer learning and how to implement it using popular deep learning frameworks.

What is Transfer Learning?

Transfer learning is the process of taking a pre-trained model and fine-tuning it on a new dataset. The idea is to use the knowledge gained from the pre-trained model to improve the performance of the model on the new task.

Why Use Transfer Learning?

  • Reduced Training Time: Pre-trained models have already been trained on large datasets, so you don't need to start from scratch.
  • Improved Performance: Pre-trained models often have better performance on new tasks than models trained from scratch.
  • Reduced Data Requirements: You can use transfer learning even if you have a small dataset.

How to Implement Transfer Learning

Step 1: Choose a Pre-trained Model

There are many pre-trained models available, such as VGG, ResNet, and Inception. For this tutorial, we will use the ResNet-50 model.

Step 2: Load the Pre-trained Model

from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions

model = ResNet50(weights='imagenet')

Step 3: Preprocess the Input

img = image.load_img('path/to/your/image.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

Step 4: Predict the Class

predictions = model.predict(x)
print('Predicted:', decode_predictions(predictions, top=3)[0])

Further Reading

For more information on transfer learning, you can read the following tutorials:

Example Image

Here is an example of a pre-trained ResNet-50 model in action:

ResNet50