Transfer Learning

Training a deep CNN from scratch to recognize complex objects requires Millions of images and Weeks of supercomputer GPU time. Transfer Learning bypasses this by taking a mathematical model that someone else (usually Google, Meta, or Microsoft) has already spent millions of dollars training, and tweaking it for your specific use case.

1. How Transfer Learning Works

When a massive model like ResNet50 or VGG16 is trained on ImageNet (a dataset of 14 million images), its early Convolutional layers learn to detect universal visual features: straight lines, curved edges, gradients, and contrasting colors. Its middle layers learn to detect textures and simple object parts (eyes, wheels). Its final layers learn to detect specific ImageNet classes (e.g., "Golden Retriever", "Boeing 747").

If you want to build an AI to detect defective microchips on a factory line, you do not train a CNN from scratch. 1. You download ResNet50. 2. You geometrically "chop off" its final classification layer (the part that predicts Dogs and Airplanes). 3. You "Freeze" the weights of the early layers (so they retain their ability to detect edges and textures). 4. You attach a new classification layer on top, designed to predict "Defective" or "Normal". 5. You train ONLY that new layer using a small dataset of just a few hundred microchip images.

Because the model already mathematically understands "vision", it can learn a new specific task with less than 1% of the data usually required.

2. Fine-Tuning

A more advanced step. After you have trained your new top classification layer, you "unfreeze" the top few Convolutional layers of the original ResNet50 model. You then train the entire network using a microscopic Learning Rate. This gently fine-tunes the generic texture-detectors to be slightly more attuned to microchip textures, resulting in maximum possible accuracy.

How to execute the examples:

Go to the Examples/ folder and run the script: python TransferLearning_ResNet.py