Awesome
FiftyOne Examples
FiftyOne is an open source ML tool created by Voxel51 that helps you build high-quality datasets and computer vision models. You can check out the main github repository for the project here.
This repository contains examples of using FiftyOne to accomplish various common tasks.
Usage
Each example in this repository is provided as a Jupyter Notebook. The table of contents below provides handy links for each example:
<img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> Click this link to run the notebook in Google Colab (no setup required!)
<img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> Click this link to view the notebook in Jupyter nbviewer
<img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> Click this link to download the notebook
Running examples locally
You can always clone this repository:
git clone https://github.com/voxel51/fiftyone-examples
and run any example locally. Make sure you have Jupyter installed and then run:
jupyter notebook examples/an_awesome_example.ipynb
Table of contents
<!-- Autogenerated by `scripts/make_examples.py` --> <table> <tr> <th align="center">Shortcuts</th> <th align="center">Examples</th> <th align="center">Description</th> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/quickstart.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/quickstart.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/quickstart.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/quickstart.ipynb">quickstart</a></td> <td>A quickstart example for getting your feet wet with FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/walkthrough.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/walkthrough.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/walkthrough.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/walkthrough.ipynb">walkthrough</a></td> <td>A more in-depth alternative to the quickstart that covers the basics of FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/zilliz_advent_of_code.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/zilliz_advent_of_code.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/zilliz_advent_of_code.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/zilliz_advent_of_code.ipynb">zilliz_advent_of_code</a></td> <td>Welcome to FiftyOne: Zilliz Advent of Open Source Code 2023</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/ai_telephone.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/ai_telephone.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/ai_telephone.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/ai_telephone.ipynb">ai_telephone</a></td> <td>Play multimodal AI telephone with text-to-image models, image-to-text models, and Fiftyone</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/clean_conceptual_captions.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/clean_conceptual_captions.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/clean_conceptual_captions.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/clean_conceptual_captions.ipynb">clean_conceptual_captions</a></td> <td>Clean Google's Conceptual Captions Dataset with Fiftyone to train your own ControlNet</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/segment_anything_openvino.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/segment_anything_openvino.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/segment_anything_openvino.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/segment_anything_openvino.ipynb">segment_anything_openvino</a></td> <td>Add object masks to a FiftyOne dataset with OpenVINO-optimized Segment Anything Model</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/comparing_YOLO_and_EfficientDet.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/comparing_YOLO_and_EfficientDet.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/comparing_YOLO_and_EfficientDet.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/comparing_YOLO_and_EfficientDet.ipynb">comparing_YOLO_and_EfficientDet</a></td> <td>Compares the YOLOv4 and EfficientDet object detection models on the COCO dataset</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/digging_into_coco.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/digging_into_coco.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/digging_into_coco.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/digging_into_coco.ipynb">digging_into_coco</a></td> <td>A simple example of how to find mistakes in your detection datasets</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/deepfakes_in_politics.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/deepfakes_in_politics.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/deepfakes_in_politics.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/deepfakes_in_politics.ipynb">deepfakes_in_politics</a></td> <td>Evaluating deepfakes using a deepfake detection algorithm and visualizing the results in FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/emotion_recognition_presidential_debate.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/emotion_recognition_presidential_debate.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/emotion_recognition_presidential_debate.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/emotion_recognition_presidential_debate.ipynb">emotion_recognition_presidential_debate</a></td> <td>Analyzing the 2020 US Presidential Debates using an emotion recognition model</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/image_uniqueness.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/image_uniqueness.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/image_uniqueness.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/image_uniqueness.ipynb">image_uniqueness</a></td> <td>Using FiftyOne's image uniqueness method to analyze and extract insights from unlabeled datasets</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/structured_noise_injection.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/structured_noise_injection.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/structured_noise_injection.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/structured_noise_injection.ipynb">structured_noise_injection</a></td> <td>Visually exploring a method for structured noise injection in GANs from CVPR 2020</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/visym_pip_175k.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/visym_pip_175k.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/visym_pip_175k.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/visym_pip_175k.ipynb">visym_pip_175k</a></td> <td>Exploring the People in Public 175K Dataset from Visym Labs with FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/wrangling_datasets.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/wrangling_datasets.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/wrangling_datasets.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/wrangling_datasets.ipynb">wrangling_datasets</a></td> <td>Using FiftyOne to load, manipulate, and export datasets in common formats</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/open_images_evaluation/open_images_evaluation.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/open_images_evaluation/open_images_evaluation.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/open_images_evaluation/open_images_evaluation.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/open_images_evaluation/open_images_evaluation.ipynb">open_images_evaluation</a></td> <td>Evaluating the quality of the ground truth annotations of the Open Images Dataset with FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/working_with_feature_points.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/working_with_feature_points.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/working_with_feature_points.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/working_with_feature_points.ipynb">working_with_feature_points</a></td> <td>A simple example of computing feature points for images and visualizing them in FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/image_deduplication.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/image_deduplication.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/image_deduplication.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/image_deduplication.ipynb">image_deduplication</a></td> <td>Find and remove duplicate images in your image datasets with FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/exploring_classification_hardness.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/exploring_classification_hardness.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/exploring_classification_hardness.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/exploring_classification_hardness.ipynb">hardness_for_image_classification</a></td> <td>Use the FiftyOne Brain to mine the hardest images in your classification dataset</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/pytorch_detection_training.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/pytorch_detection_training.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/pytorch_detection_training.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/pytorch_detection_training.ipynb">pytorch_detection_training</a></td> <td>Using FiftyOne datasets to train a PyTorch object detection model</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/pytorchvideo_tutorial.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/pytorchvideo_tutorial.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/pytorchvideo_tutorial.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/pytorchvideo_tutorial.ipynb">pytorchvideo_model_evaluation</a></td> <td>Evaluate and visualize PyTorchVideo models with FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/training_clearml_detector.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/training_clearml_detector.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/training_clearml_detector.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/training_clearml_detector.ipynb">training_clearml_detector</a></td> <td>Train a model with ClearML and FiftyOne to detect DRAGONS!</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/convert_tags_to_classifications.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/convert_tags_to_classifications.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/convert_tags_to_classifications.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/convert_tags_to_classifications.ipynb">converting_tags_to_classifications</a></td> <td>Convert classifications to tags and back to annotate them right in the FiftyOne App</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/Qdrant_FiftyOne_Recipe.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/Qdrant_FiftyOne_Recipe.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/Qdrant_FiftyOne_Recipe.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/Qdrant_FiftyOne_Recipe.ipynb">Qdrant_FiftyOne_Recipe</a></td> <td>Nearest neighbor classification of embeddings with Qdrant</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/armbench_defect_detection.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/armbench_defect_detection.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/armbench_defect_detection.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/armbench_defect_detection.ipynb">armbench_defect_detection</a></td> <td>Visualizing Defects in Amazon’s ARMBench Dataset Using Embeddings and OpenAI’s CLIP Model</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/openvino_detection_with_fiftyone.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/openvino_detection_with_fiftyone.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/openvino_detection_with_fiftyone.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/openvino_detection_with_fiftyone.ipynb">openvino_model_horizontal_text_detection</a></td> <td>Horizontal text detection on Total-Text Dataset using OpenVino Model</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/chest_xray14.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/chest_xray14.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/chest_xray14.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/chest_xray14.ipynb">chest_xray14</a></td> <td>Load and explore the NIH's ChestX-ray14 dataset in FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/football_player_segmentation.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/football_player_segmentation.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/football_player_segmentation.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/football_player_segmentation.ipynb">football_player_segmentation</a></td> <td>Detection and Segmentation on Football Player Segmentation Dataset using SAM</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/wildme_conservation_datasets.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/wildme_conservation_datasets.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/wildme_conservation_datasets.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/wildme_conservation_datasets.ipynb">wildme_conservation_datasets</a></td> <td>Create a 'meta' dataset out of three WildMe conservation datasets in FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/Tips_and_Tricks_CLI.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/Tips_and_Tricks_CLI.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/Tips_and_Tricks_CLI.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/Tips_and_Tricks_CLI.ipynb">CLI Tips & Tricks</a></td> <td>Use FiftyOne's Command Line Interface to expedite your workflows</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/Grouped Datasets.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/Grouped Datasets.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/Grouped Datasets.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/Grouped Datasets.ipynb">Grouped Dataset Tips & Tricks</a></td> <td>Learn how to work with grouped datasets in FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/Keypoints.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/Keypoints.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/Keypoints.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/Keypoints.ipynb">Keypoint Tips & Tricks</a></td> <td>Learn how to work with keypoint skeletons in FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/3D Detections.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/3D Detections.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/3D Detections.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/3D Detections.ipynb">3D Detections Tips & Tricks</a></td> <td>Make your first 3D detection in point clouds using FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/heatmaps.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/heatmaps.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/heatmaps.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/heatmaps.ipynb">Heatmaps Tips & Tricks</a></td> <td>Learn how to use heatmaps with a body pose estimation example</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/Video Labels.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/examples/Video Labels.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/examples/Video Labels.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="examples/Video Labels.ipynb">Video Labels Tips & Tricks</a></td> <td>Learn different label types in video datasets with ASL videos</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/Tracking_Datasets.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/Tracking_Datasets.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/Tracking_Datasets.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="Tracking_Datasets.ipynb">Tracking Datasets with FiftyOne</a></td> <td>Learn how to load and work with tracking datasets with the help of FiftyOne</td> </tr> <tr> <td> <a target="_blank" href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/GradCam + More Tutorial.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791629-6e618700-5769-11eb-857f-d176b37d2496.png" height="22" width="22"> </a> <a target="_blank" href="https://nbviewer.jupyter.org/github/voxel51/fiftyone-examples/blob/master/GradCam + More Tutorial.ipynb"> <img src="https://user-images.githubusercontent.com/25985824/104791634-6efa1d80-5769-11eb-8a4c-71d6cb53ccf0.png" height="22" width="22"> </a> <a id="raw-url" href="https://raw.githubusercontent.com/voxel51/fiftyone-examples/master/GradCam + More Tutorial.ipynb" download> <img src="https://user-images.githubusercontent.com/25985824/104792428-60f9cc00-576c-11eb-95a4-5709d803023a.png" height="22" width="22"> </a> </td> <td><a href="GradCam + More Tutorial.ipynb">GradCam and More with FiftyOne</a></td> <td>Apply Model Explainability techniques to your workflows with FiftyOne and GradCam!</td> </tr> </table>Contributing
This repository is open source and community contributions are welcome!
Check out the contribution guide to learn how to get involved.
Citation
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).