YOLOv8.md

YOLOv8 usage

NOTE: The yaml file is not required.

Convert model

1. Install Ultralytics YOLOv8 and dependencies

pip3 install ultralytics[export]
pip3 install onnxsim cmake

NOTE: It is recommended to use Python virtualenv or the Ultralytics container

2. Clone the Lumeo models-conversion repository

git clone https://github.com/lumeohq/models-conversion
cd models-conversion

3. Download the model

Download the pt file from YOLOv8 releases (example for YOLOv8s) to the "models-conversion" folder

wget https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt

NOTE: You can use instead your trained model weights.

4. Convert model

Generate the ONNX model file (example for YOLOv8s)

python3 ./yolo/onnx/export_yoloV8.py -w yolov8s.pt --dynamic

NOTE: To change the inference size (defaut: 640)

-s SIZE
--size SIZE
-s HEIGHT WIDTH
--size HEIGHT WIDTH

Example for 1280

-s 1280

or

-s 1280 1280

NOTE: To simplify the ONNX model

--simplify

NOTE: To use dynamic batch-size

--dynamic

NOTE: To use implicit batch-size (example for batch-size = 4)

--batch 4

NOTE: The default opset is 16.

--opset 12

5. Upload the generated ONNX file

On Lumeo's Console click Design -> AI Models -> Add model and fill the form using those settings:

Types & Weights tab

• Format: ONNX Lumeo YOLO
• Capability: Detection
• Architecture: YOLOv8
• Labels: Type the labels or import the labels file (each line represents a different model's class)
• Weights: Upload the previously generated ONNX file

Parameters tab

• Net scale factor: 0.0039215698 (click in 1/256)
• Color format: RGB
• Network precision: Float16
• Clustering algorithm: NMS