Transfer Learning in Computer Vision

Transfer learning enables us to reuse models trained on large datasets for new tasks with limited data. Instead of building a neural network from scratch, we leverage pre-trained models to improve efficiency and performance. Throughout this course, you have already encountered similar approaches in previous sections, which laid the foundation for applying transfer learning effectively.

What is Transfer Learning?

Transfer learning is a technique where a model trained on one task is adapted to another related task. In computer vision, models pre-trained on large datasets like ImageNet can be fine-tuned for specific applications such as medical imaging or autonomous driving.

Why is Transfer Learning Important?

• Reduces Training Time: since the model has already learned general features, only slight adjustments are needed;
• Requires Less Data: useful for cases where obtaining labeled data is expensive;
• Boosts Performance: pre-trained models offer robust feature extraction, improving accuracy.

Workflow of Transfer Learning

The typical workflow of transfer learning involves several key steps:

1. Selecting a Pre-trained Model

   • Choose a model trained on a large dataset (e.g., ResNet, VGG, YOLO);
   • These models have learned useful representations that can be adapted for new tasks.

2. Modifying the Pre-trained Model

   • Feature Extraction: freeze early layers and only retrain later layers for the new task;
   • Fine-Tuning: unfreeze some or all layers and retrain them on the new dataset.

3. Training on the New Dataset

   • Train the modified model using a smaller dataset specific to the target task;
   • Optimize using techniques like backpropagation and loss functions.

4. Evaluation and Iteration

   • Assess performance using metrics like accuracy, precision, recall, and mAP;
   • Fine-tune further if needed to improve results.

Popular Pre-Trained Models

Some of the most widely used pre-trained models for computer vision include:

• ResNet: deep residual networks that enable training very deep architectures;
• VGG: a simple architecture with uniform convolution layers;
• EfficientNet: optimized for high accuracy with fewer parameters;
• YOLO: State-of-the-art real-time object detection.

Fine-Tuning vs. Feature Extraction

Feature Extraction:

• Uses pre-trained model layers as fixed feature extractors;
• Typically removes the final classification layer and replaces it with a task-specific one.

Fine-Tuning:

• Unfreezes some layers and retrains them on the new dataset;
• Allows the model to adapt learned features to the new task.

Applications of Transfer Learning

1. Image Classification

Image classification involves assigning labels to images based on their visual content. Pre-trained models like ResNet and EfficientNet can be adapted for specific tasks such as medical imaging or wildlife classification.

Example:

• Select a pre-trained model (e.g., ResNet);
• Modify the classification layer to match the target classes;
• Fine-tune with a lower learning rate.

2. Object Detection

Object detection involves both identifying objects and localizing them within an image. Transfer learning enables models like Faster R-CNN, SSD, and YOLO to detect specific objects in new datasets efficiently.

Example:

• Use a pre-trained object detection model (e.g., YOLOv8);
• Fine-tune on a custom dataset with new object classes;
• Evaluate performance and optimize accordingly.

3. Semantic Segmentation

Semantic segmentation classifies each pixel in an image into predefined categories. Models like U-Net and DeepLab are widely used in applications like autonomous driving and medical imaging.

Example:

• Use a pre-trained segmentation model (e.g., U-Net);
• Train on a domain-specific dataset;
• Adjust hyperparameters for better accuracy.

4. Style Transfer

Style transfer applies the visual style of one image to another while preserving its original content. This technique is commonly used in digital art and image enhancement, leveraging pre-trained models like VGG.

Example:

• Select a style transfer model (e.g., VGG);
• Input content and style images;
• Optimize for visually appealing results.