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# Ultralytics HUB Integrations

Learn about Ultralytics HUB integrations with various platforms and formats to streamline your AI workflows.

## Datasets

Seamlessly import your datasets into Ultralytics HUB for efficient model training.

Once a dataset is imported, you can train a model on it just as you would with native Ultralytics HUB datasets.

### Roboflow

You can easily filter Roboflow datasets on the Ultralytics HUB Datasets page.

Ultralytics HUB supports two types of integrations with Roboflow: Universe and Workspace.

#### Universe

The Roboflow Universe integration allows you to import one dataset at a time into Ultralytics HUB from Roboflow.

##### Import

When exporting a Roboflow dataset, select the Ultralytics HUB format. This action redirects you to Ultralytics HUB and opens the Dataset Import dialog.

Import your Roboflow dataset by clicking the Import button.

Next, you can train a model on your newly imported dataset.

##### Remove

Navigate to the Dataset page of the Roboflow dataset you wish to remove. Open the dataset actions dropdown menu and click the Remove option.

Tip

You can also remove an imported Roboflow dataset directly from the main Datasets page.

#### Workspace

The Roboflow Workspace integration allows you to import an entire Roboflow Workspace at once into Ultralytics HUB.

##### Import

Navigate to the Integrations page by clicking the Integrations button in the sidebar.

Enter your Roboflow Workspace private API key and click the Add button.

Tip

Clicking the Get my API key button will redirect you to your Roboflow Workspace settings, where you can find your private API key.

This connects your Ultralytics HUB account with your Roboflow Workspace, making your Roboflow datasets available within Ultralytics HUB.

Next, you can train a model using any of the datasets from the connected workspace.

##### Remove

Navigate to the Integrations page via the sidebar. Click the Unlink button for the Roboflow Workspace you want to disconnect.

Tip

You can also unlink a connected Roboflow Workspace directly from the Dataset page of any dataset belonging to that workspace.

Tip

Alternatively, remove a connected Roboflow Workspace directly from the main Datasets page using the remove option associated with any dataset from that workspace.

## Models

### Exports

After you train a model, you can export it to 13 different formats using the Export mode, including popular ones like ONNX, OpenVINO, CoreML, TensorFlow, and PaddlePaddle.

The available export formats are detailed in the table below.

Format | `format` Argument | Model | Metadata | Arguments
--- | --- | --- | --- | ---
PyTorch | - | `yolo11n.pt` | ✅ | -
TorchScript | `torchscript` | `yolo11n.torchscript` | ✅ | `imgsz`, `half`, `dynamic`, `optimize`, `nms` , `batch`, `device`
ONNX | `onnx` | `yolo11n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `nms` , `batch`, `device`
OpenVINO | `openvino` | `yolo11n_openvino_model/` | ✅ | `imgsz`, `half`, `dynamic`, `int8`, `nms` , `batch`, `data`, `fraction`, `device`
TensorRT | `engine` | `yolo11n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `nms` , `batch`, `data`, `fraction`, `device`
CoreML | `coreml` | `yolo11n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` , `batch`, `device`
TF SavedModel | `saved_model` | `yolo11n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `nms` , `batch`, `device`
TF GraphDef | `pb` | `yolo11n.pb` | ❌ | `imgsz`, `batch`, `device`
TF Lite | `tflite` | `yolo11n.tflite` | ✅ | `imgsz`, `half`, `int8`, `nms` , `batch`, `data`, `fraction`, `device`
TF Edge TPU | `edgetpu` | `yolo11n_edgetpu.tflite` | ✅ | `imgsz`, `device`
TF.js | `tfjs` | `yolo11n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `nms` , `batch`, `device`
PaddlePaddle | `paddle` | `yolo11n_paddle_model/` | ✅ | `imgsz`, `batch`, `device`
MNN | `mnn` | `yolo11n.mnn` | ✅ | `imgsz`, `batch`, `int8`, `half`, `device`
NCNN | `ncnn` | `yolo11n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch`, `device`
IMX500 | `imx` | `yolo11n_imx_model/` | ✅ | `imgsz`, `int8`, `data`, `fraction`, `device`
RKNN | `rknn` | `yolo11n_rknn_model/` | ✅ | `imgsz`, `batch`, `name`, `device`

## Exciting New Features on the Way 🎉

We are continuously working to expand Ultralytics HUB's integration capabilities. Upcoming features include:

- Additional Dataset Integrations
- Detailed Export Integration Guides
- Step-by-Step Tutorials for Each Integration

## Stay Updated 🚧

This page is your go-to resource for the latest integration updates and feature rollouts. Stay connected through:

- Newsletter: Subscribe to our Ultralytics newsletter for announcements, releases, and early access updates.
- Social Media: Follow Ultralytics on LinkedIn for behind-the-scenes content, product news, and community highlights.
- Blog: Dive into the Ultralytics AI blog for in-depth articles, tutorials, and use-case spotlights.

## We Value Your Input 🗣️

Help shape the future of Ultralytics HUB by sharing your ideas, feedback, and integration requests through our official contact form.

## Thank You, Community! 🌍

Your contributions and ongoing support fuel our commitment to pushing the boundaries of AI innovation. Stay tuned—exciting things are just around the corner!

Excited for what's coming? Bookmark this page and check out our Quickstart Guide to get started with our current tools while you wait. Get ready for a transformative AI and ML journey with Ultralytics! 🛠️🤖

📅 Created 1 year ago ✏️ Updated 5 months ago

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