FiftyOne Tutorials¶

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.

pandas-style queries in FiftyOne

Translate your pandas knowledge to FiftyOne. This tutorial gives a side-by-side comparison of performing common opertaions in pandas and FiftyOne.

Filtering,Dataset-Evaluation

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.

Model-Evaluation

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.

Model-Evaluation

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.

Visualization,Brain,Embeddings

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.

Annotation,Dataset-Evaluation

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.

Annotation,Dataset-Evaluation

Training with Detectron2

Put your FiftyOne datasets to work and learn how to train and evaluate Detectron2 models directly on your data.

Model-Training,Model-Evaluation

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.

Dataset-Evaluation,Model-Evaluation,Dataset-Zoo

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.

Dataset-Evaluation,Brain

Finding classification mistakes

Better models start with better data. This tutorial shows how FiftyOne can automatically find label mistakes in your classification datasets.

Dataset-Evaluation,Brain

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.

Dataset-Evaluation,Brain

Nearest Neighbor Embeddings Classification with Qdrant

Easily pre-annotate your FiftyOne datasets using approximate nearest neighbors search on embeddings with Qdrant.

Dataset-Evaluation,Model-Evaluation,Embeddings

Note

Check out the fiftyone-examples repository for more examples of using FiftyOne!