root@c6f94564c001:/home/xxx/demo_3.0/yolov8#pip install ultralytics
root@c6f94564c001:/home/xxx/demo_3.0/yolov8# python generat_onnx.py
Ultralytics YOLOv8.0.163 🚀 Python-3.8.10 torch-2.0.1+cu117 CPU (Intel Core(TM) i7-7800X 3.50GHz)
YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients

PyTorch: starting from 'yolov8n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)

ONNX: starting export with onnx 1.14.0 opset 17...
============= Diagnostic Run torch.onnx.export version 2.0.1+cu117 =============
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================

ONNX: export success ✅ 0.5s, saved as 'yolov8n.onnx' (12.2 MB)

Export complete (3.2s)
Results saved to /home/xxx/demo_3.0/yolov8
Predict:         yolo predict task=detect model=yolov8n.onnx imgsz=640
Validate:        yolo val task=detect model=yolov8n.onnx imgsz=640 data=coco.yaml
Visualize:       https://netron.app
root@c6f94564c001:/home/xxx/demo_3.0/yolov8# ls
generat_onnx.py  yolov8n.onnx  yolov8n.pt
root@c6f94564c001:/home/xxx/demo_3.0/yolov8# cat generat_onnx.py
from ultralytics import YOLO
model = YOLO("./yolov8n.pt")
success = model.export(format="onnx")

* batch = 1 is important here,
    even export support dynamic axis using dynamic=True,
    sometimes it just fail to export
model.export(format='onnx', simplify=True, imgsz=[640,640], batch=1)

import os
from ultralytics import YOLO
model = YOLO("yolov8s.yaml")
model=YOLO("../pretrained_model/yolov8s.pt")
#success=model.export(format="onnx")
success=model.export(format="onnx", half=False, dynamic=True, opset=17)
#success=model.export(format="onnx", dynamic=True)

print("demo")