Extracting Features

As a quick recap from the previous module, feature extraction is a fundamental transfer learning technique in which a pre-trained model extracts meaningful representations from input data without any additional model training. Instead of training a model from scratch, we leverage the learned representations from a deep network trained on a large dataset (such as ImageNet) and apply them to a new task. This method is particularly beneficial when working with smaller datasets, as it allows us to use well-established feature hierarchies without requiring extensive computational resources.

Feature extraction is widely used in computer vision because early layers in deep neural networks learn generic image features like edges, textures, and shapes, which remain useful across different tasks. By freezing these layers and only training a new classifier on top, we can efficiently adapt pre-trained models to new domains with minimal computational overhead.

Feature Extraction Workflow

Here is a rundown of the general steps for performing feature extraction.

1. Select a Pre-Trained Model: Choose a model trained on a large dataset, such as ResNet, EfficientNet, or BLIP, depending on the target task. The closer the Source model is to your Target task and domain, the less work you’ll need to do!
2. Load the Pre-Trained Model: Use frameworks like PyTorch or TensorFlow to load the model with pre-trained weights.
3. Freeze the Early Layers: Lock (“freeze”) the weights of the initial pre-trained model’s layers to retain their learned features.
4. Modify the Output Layer(s): Replace the top layers of the network to adapt the network for the specific task. For example, if re-using a trained image classification model (such as ResNet) for a new classification task, replace the original fully connected layer with a new one containing the classes you’re looking for.
5. Preprocess the Input Data: Ensure the dataset being used is suitable for your Source model. This may mean preprocessing data, ensuring it’s normalized and formatted to match the input specifications of the model.
6. Extract Features: Pass input data through the frozen layers to obtain feature representations.
7. Train the New Output Layers: Use the extracted features as input to train the model’s new output layers on the target dataset.
8. Evaluate and Optimize: Assess model performance on validation data, and fine-tune hyperparameters as necessary.

Return to Module 2 or Continue to Time for a tune-up