Select a pre-trained model, choose a strategy, and selectively unfreeze layers to see how fine-tuning impacts the learning curve and final accuracy.
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Training a Deep Convolutional Neural Network from scratch requires millions of images (like the ImageNet dataset), massive amounts of computing power (GPUs), and weeks of training time. Furthermore, early layers in a CNN generally learn the exact same things regardless of the dataset: generic edges, colors, and basic textures.
Transfer Learning is the practice of taking a pre-trained model (whose weights have already been optimized on a massive dataset) and repurposing it for a new, related task. You are transferring the "knowledge" of visual features to a new domain.
A standard pre-trained model (like ResNet-50 or VGG-16) is split into two conceptual parts:
After removing the original head, we attach a new, randomly initialized Dense layer matching our target class count. Now, we have to decide how to train this new hybrid model.
We ignore pre-trained weights and initialize the entire network randomly. The network has no prior knowledge. The accuracy climbs very slowly, and on a small dataset, heavy models like VGG-16 will likely overfit and fail to reach high performance.
We load the pre-trained weights into the Conv Base and freeze them (set them to untrainable). During training, only the weights of our newly added Dense Head are updated. The frozen base acts as a powerful feature extractor. Training is extremely fast, and accuracy jumps up quickly because the network already knows how to "see".
First, we perform Feature Extraction. Then, we unfreeze specific layers of the Convolutional Base (usually the top layers) and train them alongside the Dense Head using a very small learning rate. (In the app, select Fine-Tune and click layers to unlock them). We keep early layers frozen (generic edges), but let later layers adapt their specific shape-detectors to our custom images. This pushes the accuracy to its absolute maximum limit.
In Fine-Tuning mode, try unfreezing all the Convolutional layers. You will notice the accuracy curve might drop or cap lower than expected. If you unfreeze the entire network and train it on a small dataset, the massive gradients from the randomly initialized Dense Head propagate backward and wreck the delicate, pre-trained weights of the entire base. This is called Catastrophic Forgetting.
Training a Deep Convolutional Neural Network from scratch requires millions of images (like the ImageNet dataset), massive amounts of computing power (GPUs), and weeks of training time. Furthermore, early layers in a CNN generally learn the exact same things regardless of the dataset: generic edges, colors, and basic textures.