#
FiftyOne

FiftyOne is the leading open-source toolkit for curating, visualizing, and managing unstructured visual data. The library streamlines data-centric workflows, from finding low-confidence predictions to identifying poor-quality samples and uncovering hidden patterns in your data. The library supports all sorts of visual data, from images and videos to PDFs, point clouds, and meshes.

Whereas tabular data formats like a pandas DataFrame or a Parquet file consist of rows and columns, FiftyOne datasets are considerably more flexible, accomodating object detections, keypoints, polylines, etc. and custom schemas.

FiftyOne is integrated with the Hugging Face Hub, so you can load and share FiftyOne datasets directly from the Hub.

🚀 Try the FiftyOne 🤝 Hugging Face Integration in Colab!

##
Prerequisites

First login with your Hugging Face account:

    huggingface-cli login

Make sure you have `fiftyone>=0.24.0` installed:

    pip install -U fiftyone

##
Loading Visual Datasets from the Hub

With `load_from_hub()` from FiftyOne’s Hugging Face utils, you can load:

- Any FiftyOne dataset uploaded to the hub
- Most image-based datasets stored in Parquet files (which is the standard for datasets uploaded to the hub via the `datasets` library)

###
Loading FiftyOne datasets from the Hub

Any dataset pushed to the hub in one of FiftyOne’s supported common formats should have all of the necessary configuration info in its dataset repo on the hub, so you can load the dataset by specifying its `repo_id`. As an example, to load the VisDrone detection dataset, all you need is:

    import fiftyone as fo
    from fiftyone.utils import load_from_hub

    ## load from the hub
    dataset = load_from_hub("Voxel51/VisDrone2019-DET")

    ## visualize in app
    session = fo.launch_app(dataset)

You can customize the download process, including the number of samples to download, the name of the created dataset object, whether or not it is persisted to disk, and more!

You can list all the available FiftyOne datasets on the Hub using:

    from huggingface_hub import HfApi
    api = HfApi()
    api.list_datasets(tags="fiftyone")

###
Loading Parquet Datasets from the Hub with FiftyOne

You can also use the `load_from_hub()` function to load datasets from Parquet files. Type conversions are handled for you and images are downloaded from URLs if necessary.

With this functionality, you can load any of the following:

- FiftyOne-Compatible Image Classification Datasets, like Food101 and ImageNet-Sketch
- FiftyOne-Compatible Object Detection Datasets like CPPE-5 and WIDER FACE
- FiftyOne-Compatible Segmentation Datasets like SceneParse150 and Sidewalk Semantic
- FiftyOne-Compatible Image Captioning Datasets like COYO-700M and New Yorker Caption Contest
- FiftyOne-Compatible Visual Question-Answering Datasets like TextVQA and ScienceQA

And many more!

As a simple example, we can load the first 1,000 samples from the WikiArt dataset into FiftyOne with:

    import fiftyone as fo
    from fiftyone.utils.huggingface import load_from_hub

    dataset = load_from_hub(
        "huggan/wikiart",  ## repo_id
        format="parquet",  ## for Parquet format
        classification_fields=["artist", "style", "genre"], ## columns to treat as classification labels
        max_samples=1000,  # number of samples to load
        name="wikiart",  # name of the dataset in FiftyOne
    )

##
Pushing FiftyOne Datasets to the Hub

Pushing a dataset to the hub is as simple as:

    import fiftyone as fo
    import fiftyone.zoo as foz
    from fiftyone.utils.huggingface import push_to_hub

    ## load example dataset
    dataset = foz.load_zoo_dataset("quickstart")

    ## push to hub
    push_to_hub(dataset, "my-hf-dataset")

When you call `push_to_hub()`, the dataset will be uploaded to the repo with the specified repo name under your username, and the repo will be created if necessary. A Dataset Card will automatically be generated and populated with instructions for loading the dataset from the hub. You can even upload a thumbnail image/gif to appear on the Dataset Card with the `preview_path` argument.

Here’s an example using many of these arguments, which would upload the first three samples of FiftyOne’s Quickstart Video dataset to the private repo `username/my-quickstart-video-dataset` with tags, an MIT license, a description, and a preview image:

    dataset = foz.load_from_zoo("quickstart-video", max_samples=3)

    push_to_hub(
        dataset,
        "my-quickstart-video-dataset",
        tags=["video", "tracking"],
        license="mit",
        description="A dataset of video samples for tracking tasks",
        private=True,
        preview_path="<path/to/preview.png>"
    )

##
📚 Resources

- 🚀 Code-Along Colab Notebook
- 🗺️ User Guide for FiftyOne Datasets
- 🤗 FiftyOne 🤝 Hub Integration Docs
- 🤗 FiftyOne 🤝 Transformers Integration Docs
- 🧩 FiftyOne Hugging Face Hub Plugin

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