This tutorial will show you how to create an image dataset from Google and train an image classifier in FastAI.
By the end of the tutorial, you will have a state-of-the-art deep learning model that can classify dandelions and grass. You can then export this model and deploy it to the web or and edge device for inference!
Click the above badge to access the Kaggle kernel. If you haven't yet, sign up for a Kaggle account, and then create a copy of the kernel to follow along.
This tutorial is hosted on Kaggle as a Kaggle kernel. After trying alternatives like Paperspace Gradient, Google Cloud Platform, and Binder, I chose to use Kaggle kernels to run my notebook because it was completely free (will always be free), provided free GPU use (every other alternative had no to limited GPU support, or wasn't free), accessible (anyone around the world can run it by signing up for Kaggle), and had a community around it (Kaggle's data science community rocks).
Special thanks to FastAI and Jeremy Howard and Rachel Thomas for the amazing library and course!