YOLOv10 的官方 PyTorch 实现。NeurIPS 2024。
在延迟-精度（左）和大小-精度（右）权衡方面与其他模型的对比。
YOLOv10: Real-Time End-to-End Object Detection。
Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, and Guiguang Ding
v10Detect中不必要的cv2和cv3操作，YOLOv10的速度会存在偏差。COCO
| 模型 | 测试尺寸 | 参数数量 | 计算量 | APval | 延迟 |
|---|---|---|---|---|---|
| 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 |
推荐使用 conda 虚拟环境。
conda create -n yolov10 python=3.9
conda activate yolov10
pip install -r requirements.txt
pip install -e .python app.py
# Please visit http://127.0.0.1:7860yolov10n yolov10s yolov10m yolov10b yolov10l yolov10x
yolo val model=jameslahm/yolov10{n/s/m/b/l/x} data=coco.yaml batch=256或
from ultralytics import YOLOv10
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.val(data='coco.yaml', batch=256)yolo detect train data=coco.yaml model=yolov10n/s/m/b/l/x.yaml epochs=500 batch=256 imgsz=640 device=0,1,2,3,4,5,6,7或者
from ultralytics import YOLOv10
model = 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='coco.yaml', epochs=500, batch=256, imgsz=640)你也可以选择将微调后的模型作为公开或私有模型推送到 Hugging Face hub：
# let's say you have fine-tuned a model for crop detection
model.push_to_hub("<your-hf-username-or-organization/yolov10-finetuned-crop-detection")
# you can also pass `private=True` if you don't want everyone to see your model
model.push_to_hub("<your-hf-username-or-organization/yolov10-finetuned-crop-detection", private=True)请注意，可以设置较小的置信度阈值来检测较小的物体或远处的物体。详细信息请参考此处。
yolo predict model=jameslahm/yolov10{n/s/m/b/l/x}或
from ultralytics import YOLOv10
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.predict()# 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或
from ultralytics import YOLOv10
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.export(...)本代码库基于 ultralytics 和 RT-DETR 构建。
感谢这些出色的实现！
如果我们的代码或模型对您的工作有所帮助，请引用我们的论文：
@article{wang2024yolov10,
title={YOLOv10: Real-Time End-to-End Object Detection},
author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
journal={arXiv preprint arXiv:2405.14458},
year={2024}
}