What should the Specialist do to initialize the model to re-train it with the custom data?

A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.

What should the Specialist do to initialize the model to re-train it with the custom data?
A . Initialize the model with random weights in all layers including the last fully connected layer
B . Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.
C . Initialize the model with random weights in all layers and replace the last fully connected layer
D . Initialize the model with pre-trained weights in all layers including the last fully connected layer

View Answer

Answer: B

Explanation:

Transfer learning is a technique that allows us to use a model trained for a certain task as a starting point for a machine learning model for a different task. For image classification, a common practice is to use a pre-trained model that was trained on a large and general dataset, such as ImageNet, and then customize it for the specific task. One way to customize the model is to replace the last fully connected layer, which is responsible for the final classification, with a new layer that has the same number of units as the number of classes in the new task. This way, the model can leverage the features learned by the previous layers, which are generic and useful for many image recognition tasks, and learn to map them to the new classes. The new layer can be initialized with random weights, and the rest of the model can be initialized with the pre-trained weights. This method is also known as feature extraction, as it extracts meaningful features from the pre-trained model and uses them for the new task.

Reference: Transfer learning and fine-tuning

Deep transfer learning for image classification: a survey