Ultralytics HUB Integrations
Learn about Ultralytics HUB integrations with various platforms and formats.
Datasets
Seamlessly import your datasets in Ultralytics HUB for model training.
After a dataset is imported in Ultralytics HUB, you can train a model on your dataset just like you would using the Ultralytics HUB datasets.
Roboflow
You can easily filter the 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 you export a Roboflow dataset, select the Ultralytics HUB format. This action will redirect you to Ultralytics HUB and trigger the Dataset Import dialog.
You can import your Roboflow dataset by clicking on the Import button.
Next, train a model on your dataset.
Remove
Navigate to the Dataset page of the Roboflow dataset you want to remove, open the dataset actions dropdown and click on the Remove option.
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 on the Integrations button in the sidebar.
Type your Roboflow Workspace private API key and click on the Add button.
Tip
You can click on the Get my API key button which will redirect you to the settings of your Roboflow Workspace from where you can obtain your private API key.
This will connect your Ultralytics HUB account with your Roboflow Workspace and make your Roboflow datasets available in Ultralytics HUB.
Next, train a model on your dataset.
Remove
Navigate to the Integrations page by clicking on the Integrations button in the sidebar and click on the Unlink button of the Roboflow Workspace you want to remove.
Tip
You can remove a connected Roboflow Workspace directly from the Dataset page of one of the datasets from your Roboflow Workspace.
Models
Exports
After you train a model, you can export it to 13 different formats, including ONNX, OpenVINO, CoreML, TensorFlow, Paddle and many others.
The available export formats are presented in the table below.
| Format | format Argument | Model | Metadata | Arguments |
|---|---|---|---|---|
| PyTorch | - | yolo11n.pt | ✅ | - |
| TorchScript | torchscript | yolo11n.torchscript | ✅ | imgsz, optimize, batch |
| ONNX | onnx | yolo11n.onnx | ✅ | imgsz, half, dynamic, simplify, opset, batch |
| OpenVINO | openvino | yolo11n_openvino_model/ | ✅ | imgsz, half, int8, batch |
| TensorRT | engine | yolo11n.engine | ✅ | imgsz, half, dynamic, simplify, workspace, int8, batch |
| CoreML | coreml | yolo11n.mlpackage | ✅ | imgsz, half, int8, nms, batch |
| TF SavedModel | saved_model | yolo11n_saved_model/ | ✅ | imgsz, keras, int8, batch |
| TF GraphDef | pb | yolo11n.pb | ❌ | imgsz, batch |
| TF Lite | tflite | yolo11n.tflite | ✅ | imgsz, half, int8, batch |
| TF Edge TPU | edgetpu | yolo11n_edgetpu.tflite | ✅ | imgsz |
| TF.js | tfjs | yolo11n_web_model/ | ✅ | imgsz, half, int8, batch |
| PaddlePaddle | paddle | yolo11n_paddle_model/ | ✅ | imgsz, batch |
| MNN | mnn | yolo11n.mnn | ✅ | imgsz, batch, int8, half |
| NCNN | ncnn | yolo11n_ncnn_model/ | ✅ | imgsz, half, batch |
| IMX500 | imx | yolo11n_imx_model/ | ✅ | imgsz, int8 |
Exciting New Features on the Way 🎉
- Additional Dataset Integrations
- Detailed Export Integration Guides
- Step-by-Step Tutorials for Each Integration
Stay Updated 🚧
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