Transfer Learning in JAX and Flax

In this mini-project, we’ll be working on developing a neural network using JAX and Flax libraries. The primary objective of this project is to provide you with hands-on experience in applying transfer learning techniques to classification problems, even with limited datasets. We will utilize the ResNet-50 model, which has already been trained from over a million images. This prior training gives the model a deep understanding of visual concepts, allowing it to accurately pick out essential features from new images.

Moreover, you’ll have a dataset containing 24 images of cars and bikes, with 12 samples for each category. The number of samples within the dataset may seem insignificant. However, this provides a perfect scenario to take advantage of the training of the ResNet-50 model.

Furthermore, you’ll use TensorBoard to inspect and analyze various behaviors of the neural network. You will be able to monitor scalar values, such as loss and accuracy over time, visualize the computational graph of your network, inspect the distribution of tensor values through histograms, and even explore embeddings for a better grasp of high-dimensional data.