> For the complete documentation index, see [llms.txt](https://nx.docs.scailable.net/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nx.docs.scailable.net/ai-manager-v6.1.3/ai-models-support/importing-models/from-ultralytics.md).

# From Ultralytics

<figure><img src="/files/fny7adCeTkmXg2LfNMGn" alt="Ultralytics logo" width="500"><figcaption></figcaption></figure>

Ultralytics develops and maintains popular open-source computer vision libraries including YOLOv8 and YOLO11. These are state-of-the-art object detection models well suited for edge deployment with the AI Manager.

For a list of pre-built YOLO models that are already available in the AI Cloud catalogue, see [Common Models](/ai-manager-v6.1.3/ai-models-support/importing-models/common-models.md).

## Exporting a YOLO model for the AI Manager

To use a custom or fine-tuned YOLO model with the AI Manager, export it to ONNX format and then upload it to the AI Cloud.

### Step 1: Install Ultralytics

```bash
pip install ultralytics
```

### Step 2: Export the model to ONNX

Replace `yolov8n.pt` with your own model checkpoint if you have trained a custom model:

```bash
yolo export model=yolov8n.pt format=onnx
```

This produces a `yolov8n.onnx` file in the current directory. The ONNX export is compatible with the AI Manager's CPU, Intel OpenVino, and NVIDIA CUDA runtimes.

{% hint style="info" %}
If your model uses a non-standard input size, pass `imgsz=<size>` to the export command, for example `yolo export model=yolov8n.pt format=onnx imgsz=640`.
{% endhint %}

### Step 3: Upload to AI Cloud

Upload the `.onnx` file to the AI Cloud. The platform will automatically convert it for all supported AI accelerators.

{% content-ref url="/pages/Z7ndCO8n1txSBW7CSIq7" %}
[Upload your model](/ai-manager-v6.1.3/ai-cloud-ui/upload-your-model.md)
{% endcontent-ref %}