Transfer Learning Concepts and Strategies
Feature extraction vs fine-tuning, frozen layers, when transfer learning helps.
What Is Transfer Learning?
Transfer learning reuses a model trained on a large dataset as the starting point for a new, related task. Instead of learning visual features from scratch, you borrow features a network already learned.
This dramatically cuts the data and compute you need to reach high accuracy.
The ImageNet Foundation
Most pretrained vision models are trained on ImageNet: roughly 1.4 million images across 1000 classes.
During this training the network learns a hierarchy of features: edges and colors in early layers, textures in the middle, and object parts near the top. These low-level features transfer well to almost any image task.
All lessons in this course
- Transfer Learning Concepts and Strategies
- Using VGG16 and ResNet50 as Base Models
- Fine-tuning: Unfreezing and Retraining
- MobileNet and EfficientNet for Edge Deployment