Train a Custom Object Detector with Detectron2 and FiftyOne

In recent years, every aspect of the Machine Learning (ML) lifecycle has had tooling developed to make it easier to bring a custom model from an idea to a reality. The most exciting part is that the community has a propensity for open-source tools, like Pytorch and Tensorflow, allowing the model development process to be more transparent and replicable. In this post, we take a look at how to integrate two open-source tools tackling different parts of an ML project: FiftyOne and Detectron2. Detectron2 is a library developed by Facebook AI Research designed to allow you to easily train state-of-the-art detection and segmentation algorithms on your own data. FiftyOne is a toolkit designed to let you easily visualize your data, curate high-quality datasets, and analyze your model results.