Hi everyone. I recently trained an image classifier on a Japanese character dataset called KMNIST and achieved 97% validation accuracy within a few minutes using a suite of modern deep learning tools and techniques.

I explain all the techniques my blog post, published on the weights and biases site.

I kept this article relatively short and straightforward, so it should be quite accessible to beginners and is likely to improve the performance of your deep learning models.

Primarily, I used the learning rate finder and 1cycle learning rate policy taught by Jeremy Howard in the fast.ai practical deep learning for coders course along with visualization and monitoring tools from a library called Weights & Biases.

Hope you enjoy (and your poor GPU that’s been computing for days) it!