github上YOLOv10的地址如下
https://github.com/THU-MIG/yolov10
下载源码，直接用下面的代码就可以导出ONNX格式模型了，发现导出以后还没来得及把格式搞清楚，输出得居然是动态得ONNX格式表示，但是实际上
输入支持格式为:1x3x640x640输出格式为1x300x6
x1 y1 x2 y2 score classid
因为YOLOv10模型不需要NMS了，所以就直接输出最终得预测结果，支持默认最大得boxes数目是300， 这个应该是可以改得，我还没仔细研究，不然显然支持得预测框太少，这点跟YOLOv8预测框相比少了很多。模型转换代码如下：
from ultralytics import RTDETR, YOLO
"""Test exporting the YOLO model to ONNX format."""
f = YOLO("yolov10s.pt").export(format="onnx", dynamic=True)
单纯从推理上看 YOLOv10的确比YOLOv8简单很多，有点SSD模型得既视感。推理代码实现如下：
import cv2 as cv
import numpy as np
from openvino.runtime import Core
# load model
labels = load_classes()
ie = Core()
for device in ie.available_devices:
print(device)
model = ie.read_model(model="yolov10n.onnx")
compiled_model = ie.compile_model(model=model, device_name="CPU")
output_layer = compiled_model.output(0)
frame = cv.imread("D:/images/1234.jpg")
image = format_yolov10(frame)
h, w, c = image.shape
x_factor = w / 640.0
y_factor = h / 640.0
# 检测 2/255.0, NCHW = 1x3x640x640
blob = cv.dnn.blobFromImage(image, 1 / 255.0, (640, 640), swapRB=True, crop=False)
# 设置网络输入
cvOut = compiled_model([blob])[output_layer]
# [left,top, right, bottom, score, classId]
print(cvOut.shape)
for row in cvOut[0,:,:]:
score = float(row[4])
objIndex = int(row[5])
if score > 0.5:
left, top, right, bottom = row[0].item(), row[1].item(), row[2].item(), row[3].item()
left = int(left * x_factor)
top = int(top * y_factor)
right = int(right * x_factor)
bottom = int(bottom * y_factor)
# 绘制
cv.rectangle(frame, (int(left), int(top)), (int(right), int(bottom)), (255, 0, 0), thickness=2)
cv.putText(frame, "score:%.2f, %s"%(score, labels[objIndex]),
(int(left) - 10, int(top) - 5), cv.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2, 8);
cv.imshow('YOLOv10 Object Detection', frame)
cv.imwrite("D:/result.png", frame)
cv.waitKey(0)
cv.destroyAllWindows()
总结与看法
个人认为，这个就是一个YOLOv8的魔改版本，直接叫YOLOv10有点过分，这样魔改下去，马上YOLOv20也不会太远了。然后下载它的源码之后，你会发现里面很多YOLOv8的包跟代码注释连名字都还没有改过来，特别是推理的演示代码里面还是YOLOv8的，我晕倒，能专业点不！
