FiftyOne is an open source ML tool created by Voxel51 that helps you build high-quality datasets and computer vision models. Check out the main github repository for the project at https://github.com/voxel51/fiftyone.
This repository contains various examples of using FiftyOne to accomplish common tasks.
Each example in this repository is provided as a Jupyter Notebook. The table of contents below provides handy links for each example:
Click this link to run the notebook in
Google Colab (no setup required!)
Click this link to view the notebook in
Jupyter nbviewer
Click this link to download the notebook
You can always clone this repository:
git clone https://github.com/voxel51/fiftyone-examplesand run any example locally. Make sure you have Jupyter installed and then run:
jupyter notebook examples/an_awesome_example.ipynb| Shortcuts | Examples | Description |
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quickstart | A quickstart example for getting your feet wet with FiftyOne |
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walkthrough | A more in-depth alternative to the quickstart that covers the basics of FiftyOne |
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comparing_YOLO_and_EfficientDet | Compares the YOLOv4 and EfficientDet object detection models on the COCO dataset |
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digging_into_coco | A simple example of how to find mistakes in your detection datasets |
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deepfakes_in_politics | Evaluating deepfakes using a deepfake detection algorithm and visualizing the results in FiftyOne |
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emotion_recognition_presidential_debate | Analyzing the 2020 US Presidential Debates using an emotion recognition model |
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image_uniqueness | Using FiftyOne's image uniqueness method to analyze and extract insights from unlabeled datasets |
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structured_noise_injection | Visually exploring a method for structured noise injection in GANs from CVPR 2020 |
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visym_pip_175k | Exploring the People in Public 175K Dataset from Visym Labs with FiftyOne |
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wrangling_datasets | Using FiftyOne to load, manipulate, and export datasets in common formats |
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open_images_evaluation | Evaluating the quality of the ground truth annotations of the Open Images Dataset with FiftyOne |
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working_with_feature_points | A simple example of computing feature points for images and visualizing them in FiftyOne |
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image_deduplication | Find and remove duplicate images in your image datasets with FiftyOne |
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hardness_for_image_classification | Use the FiftyOne Brain to mine the hardest images in your classification dataset |
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pytorch_detection_training | Using FiftyOne datasets to train a PyTorch object detection model |
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pytorchvideo_model_evaluation | Evaluate and visualize PyTorchVideo models with FiftyOne |
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training_clearml_detector | Train a model with ClearML and FiftyOne to detect DRAGONS! |
This repository is open source and community contributions are welcome!
Check out the contribution guide to learn how to get involved.
If you use FiftyOne in your research, feel free to cite the project (but only if you love it 😊):
@article{moore2020fiftyone,
title={FiftyOne},
author={Moore, B. E. and Corso, J. J.},
journal={GitHub. Note: https://github.com/voxel51/fiftyone},
year={2020}
}If you use a specific contributed example in this repository, please also cite the author directly (if one is specified).