Transfer learning: Pretrained embedding, fine-tuning, differential learning | Computer Vision Series

Miro notes: https://miro.com/app/board/uXjVI9ZVABA=/?share_link_id=268989054435 Code files: 1) Transfer learning without fine-tuning: https://colab.research.google.com/drive/1cn3FaWKGlxvogX-98PTaOodjoDQ6cfCr?usp=sharing 2) Transfer learning with fine-tuning: https://colab.research.google.com/drive/1-IR2YTu2hVACZ-stMgtg6jQmDQFMlHlb?usp=sharing 3) Transfer learning with fine-tuning + learning rate rescheduling: https://colab.research.google.com/drive/1kjzc6kBjOBasJntjUQTJIto0HjPcDME_?usp=sharing 4) Transfer learning with fine-tuning + differential learning rate: https://colab.research.google.com/drive/1QTbyfi9YnV1cjYHcUtbIQt2ymtCBBuya?usp=sharing In this lecture from our "Computer Vision from Scratch" series, we dive deep into the world of transfer learning - a powerful idea that can dramatically improve your model's performance without starting from zero. Over the past few lectures, we experienced firsthand the struggle of building image classifiers from scratch. Despite introducing deeper networks, regularization techniques like batch normalization and dropout, and careful training strategies, we could only push validation accuracy up to 50-60 percent. The reality became clear - training from scratch on a small dataset is like trying to build a skyscraper without enough bricks. This is where transfer learning changes everything. In today's session, we will cover: The intuition behind transfer learning and pre-trained embeddings. The challenges of training deep networks from scratch on small datasets. How to leverage large, extensively trained models like MobileNetV2. Two key methods: Fixed Feature Extraction Fine-Tuning of Pretrained Models How learning rate strategies can further enhance performance: Learning Rate Decay (Exponential) Differential Learning Rates for different parts of the model We start by freezing the base model (MobileNetV2 trained on ImageNet) and only training a new classifier head for our Five Flowers dataset. This alone pushes validation accuracy up to an impressive 82 percent. Then, we move on to fine-tuning, where we unfreeze parts of the pretrained model, retrain them gently, and achieve even better performance - 87 percent validation accuracy and 100 percent training accuracy. Finally, we explore how learning rate decay and differential learning rates can stabilize and improve training further, ensuring that the model learns fast where needed and conservatively where it must. Major Takeaways: Transfer learning is not optional if you are working with small to medium-sized datasets in computer vision. Pre-trained embeddings offer a strong foundation, similar to hiring an employee who already understands the basics of your business. Fine-tuning becomes critical when your new task is moderately different from the original training task. Smart learning rate strategies can help achieve faster convergence without risking overfitting. If you have struggled with training from scratch or wondered why your deep learning models underperform on small datasets, this lecture will be a complete eye-opener. Code and Resources: We use TensorFlow and Keras to implement everything from loading MobileNetV2 to training and evaluating the models. Logging and experiment tracking is demonstrated using Weights and Biases (optional). At the end of this lecture, you will not only understand transfer learning theoretically but will also have practical code to directly apply it to your own datasets. Recommended Flow: Watch the lecture fully. Code along step by step. Experiment with learning rate decay and differential learning yourself. Reflect on when to use feature extraction and when to perform fine-tuning. By the end of this session, you will have a strong practical and conceptual foundation in transfer learning - one of the most important skills in modern deep learning.