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Bacteria Classification with fast.ai

Using fast.ai to build a state of the art bacterial classifier with the DIBaS dataset

The Gradient Team


In this tutorial we demonstrate how to to build a state of the art bacterial classification model on Gradient using the fastai machine learning library. The dataset used, DIBaS, contains 660 images of 33 different species of bacteria. Using fastai and ResNet-50, we can achieve state of the art results with 98.5% accuracy.

This entry has a complementary tutorial, Building a State of the Art Bacterial Classifier with Paperspace Gradient and fast.ai.