（一）模型性能

YOLOv10模型库-COCO

Model	Test Size	Params	FLOPs	APval	Latency
YOLOv10-N	640	2.3M	6.7G	38.5%	1.84ms
YOLOv10-S	640	7.2M	21.6G	46.3%	2.49ms
YOLOv10-M	640	15.4M	59.1G	51.1%	4.74ms
YOLOv10-B	640	19.1M	92.0G	52.5%	5.74ms
YOLOv10-L	640	24.4M	120.3G	53.2%	7.28ms
YOLOv10-X	640	29.5M	160.4G	54.4%	10.70ms

（二）安装与使用

（1）环境安装

git clone https://github.com/THU-MIG/yolov10.git

conda virtual environment is recommended.

conda create -n yolov10 python=3.9
conda activate yolov10
pip install -r requirements.txt

（2）快速使用

python app.py
# Please visit http://127.0.0.1:7860

（3）模型评估Validation

yolov10n yolov10s yolov10m yolov10b yolov10l yolov10x

yolo val model=jameslahm/yolov10{n/s/m/b/l/x} data=coco128.yaml batch=16

Or

from ultralytics import YOLOv10model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
# or
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')model.val(data='coco128.yaml', batch=16)

（4）模型训练Training

yolo detect train data=coco128.yaml model=yolov10n/s/m/b/l/x.yaml epochs=500 batch=16 imgsz=640 device=0,1,2,3,4,5,6,7

Or

from ultralytics import YOLOv10model = YOLOv10()
# If you want to finetune the model with pretrained weights, you could load the 
# pretrained weights like below
# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
# or
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
# model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')model.train(data='coco128.yaml', epochs=500, batch=16, imgsz=640)

（5）模型预测Prediction

Note that a smaller confidence threshold can be set to detect smaller objects or objects in the distance. Please refer to here for details.

yolo predict model=jameslahm/yolov10{n/s/m/b/l/x}

Or

from ultralytics import YOLOv10model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
# or
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')model.predict()

（6）模型转换Export

# End-to-End ONNX
yolo export model=jameslahm/yolov10{n/s/m/b/l/x} format=onnx opset=13 simplify
# Predict with ONNX
yolo predict model=yolov10n/s/m/b/l/x.onnx# End-to-End TensorRT
yolo export model=jameslahm/yolov10{n/s/m/b/l/x} format=engine half=True simplify opset=13 workspace=16
# or
trtexec --onnx=yolov10n/s/m/b/l/x.onnx --saveEngine=yolov10n/s/m/b/l/x.engine --fp16
# Predict with TensorRT
yolo predict model=yolov10n/s/m/b/l/x.engine

Or

from ultralytics import YOLOv10model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
# or
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')model.export(...)