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FiftyOne Datasets <> Hugging Face Hub Integration!
FiftyOne Datasets <> Hugging Face Hub Integration!
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As of yesterday's release of FiftyOne 
As of yesterday's release of FiftyOne 
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`0.23.8`
0.23.8
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, the FiftyOne open source library for dataset curation and visualization is now integrated with the Hugging Face Hub!
, the FiftyOne open source library for dataset curation and visualization is now integrated with the Hugging Face Hub!
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You can now load Parquet datasets from the hub and have them converted directly into FiftyOne datasets. To load MNIST, for example:
You can now load Parquet datasets from the hub and have them converted directly into FiftyOne datasets. To load MNIST, for example:
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```bash
pip install -U fiftyone
```
pip install -U fiftyone
bash
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```py
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub(
    "mnist",
    format="ParquetFilesDataset",
    classification_fields="label",
)
session = fo.launch_app(dataset)
```
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub(
    "mnist",
    format="ParquetFilesDataset",
    classification_fields="label",
)
session = fo.launch_app(dataset)
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You can also load FiftyOne datasets directly from the hub. Here's how you load the first 1000 samples from the VisDrone dataset:
You can also load FiftyOne datasets directly from the hub. Here's how you load the first 1000 samples from the VisDrone dataset:
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```py
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub("jamarks/VisDrone2019-DET", max_samples=1000)

# Launch the App
session = fo.launch_app(dataset)
```
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub("jamarks/VisDrone2019-DET", max_samples=1000)

# Launch the App
session = fo.launch_app(dataset)
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And tying it all together, you can push your FiftyOne datasets directly to the hub:
And tying it all together, you can push your FiftyOne datasets directly to the hub:
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```py
import fiftyone.zoo as foz
import fiftyone.utils.huggingface as fouh

dataset = foz.load_zoo_dataset("quickstart")
fouh.push_to_hub(dataset, "my-dataset")
```
import fiftyone.zoo as foz
import fiftyone.utils.huggingface as fouh

dataset = foz.load_zoo_dataset("quickstart")
fouh.push_to_hub(dataset, "my-dataset")
py
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Major thanks to 
Major thanks to 
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@tomaarsen
tomaarsen
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mention
@davanstrien
davanstrien
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@severo
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@osanseviero
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 and 
 and 
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@julien-c
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 for helping to make this happen!!!
 for helping to make this happen!!!
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Full documentation and details here: 
Full documentation and details here: 
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https://docs.voxel51.com/integrations/huggingface.html#huggingface-hub
https://docs.voxel51.com/integrations/huggingface.html#huggingface-hub
FiftyOne Datasets <> Hugging Face Hub Integration!

As of yesterday's release of FiftyOne `0.23.8`, the FiftyOne open source library for dataset curation and visualization is now integrated with the Hugging Face Hub!

You can now load Parquet datasets from the hub and have them converted directly into FiftyOne datasets. To load MNIST, for example:

```bash
pip install -U fiftyone
```

```py
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub(
    "mnist",
    format="ParquetFilesDataset",
    classification_fields="label",
)
session = fo.launch_app(dataset)
```

You can also load FiftyOne datasets directly from the hub. Here's how you load the first 1000 samples from the VisDrone dataset:

```py
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

dataset = fouh.load_from_hub("jamarks/VisDrone2019-DET", max_samples=1000)

# Launch the App
session = fo.launch_app(dataset)
```

And tying it all together, you can push your FiftyOne datasets directly to the hub:

```py
import fiftyone.zoo as foz
import fiftyone.utils.huggingface as fouh

dataset = foz.load_zoo_dataset("quickstart")
fouh.push_to_hub(dataset, "my-dataset")
```

Major thanks to @tomaarsen @davanstrien @severo @osanseviero and @julien-c for helping to make this happen!!!

Full documentation and details here: https://docs.voxel51.com/integrations/huggingface.html#huggingface-hub
🔥
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Tom Aarsen
tomaarsen
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