From Ultralytics

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.

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

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:

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.

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.

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.

Upload your model