60-second card
Transfer learning
Transfer learning starts from a model trained on millions of images and adapts it to your task.
- Early layers already know edges and textures.
- With little data, freeze most layers and train a new final layer.
- With more data, fine-tune more layers with a small learning rate.
Example: 500 crop-disease photos: take a ResNet trained on ImageNet, replace its last layer with 4 disease classes, train that layer first.
Remember: It's the default for small image datasets.
Common mistake: Fine-tuning with a big learning rate, which wipes out the pre-trained knowledge.