FiftyOne TutorialsΒΆ
Welcome to FiftyOne tutorials!
Each tutorial below is a curated demonstration of how FiftyOne can help refine your datasets and turn your good models into great models.
Evaluating object detections
Aggregate statistics aren't sufficient for object detection. This tutorial shows how to use FiftyOne to perform powerful evaluation workflows on your detector.
Evaluating a classifier
Evaluation made easy. This tutorial walks through an end-to-end example of fine-tuning a classifier and understanding its failure modes using FiftyOne.
Using image embeddings
Visualize your data in new ways. This tutorial shows how to use FiftyOne's powerful embeddings visualization capabilities to improve your image datasets.
Annotating with CVAT
So you've loaded and explored your data in FiftyOne... but now what? See how to send it off to CVAT for annotation in just one line of code.
Annotating with Labelbox
Unlock the power of the Labelbox platform. See how you can get your FiftyOne datasets annotated with just one line of code.
Downloading and evaluating Open Images
Expand your data lake and evaluate your object detection models with Google's Open Images dataset and evaluation protocol, all natively within FiftyOne.
Exploring image uniqueness
Your models need diverse data. This tutorial shows how FiftyOne can remove near-duplicate images and recommend unique samples for model training.
Finding classification mistakes
Better models start with better data. This tutorial shows how FiftyOne can automatically find label mistakes in your classification datasets.
Finding detection mistakes
How good are your ground truth objects? Use the FiftyOne Brain's mistakenness feature to find annotation errors in your object detections.
Note
Check out the fiftyone-examples repository for more examples of using FiftyOne!