工程基于Ultralytics仓库用于做yolo系列的QAT训练；

model	map@50-95	map@50
yolov11s.pt	0.466	0.635
yolov11s_8w8f_qdq.onnx	0.456	0.628

环境安装

基于官方工程，安装ultralytics库

pip install -r requirements.txt

安装额外库

pip install ultralytics

我们发现 onnxruntime 和 onnxscript 的其他版本可能引起精度误差和导出错误，因此pytorch==2.6; onnxruntime==1.21.0 onnxscript==0.4.0 是必须的。

数据集路径修改

修改 ./ultralytics/cfg/datasets/coco.yaml 中的数据集路径;

QAT训练

python train.py

onnx eval

python eval.py

eval精度如下：

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.456
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.628
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.495
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.286
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.498
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.633
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.354
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.591
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.645
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.463
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.698
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.810

onnx test

python test.py

test会加载根目录下的bus.jpg文件进行推理，然后输出推理结果