YOLOv10 的突破就在于从后处理和模型架构方面进一步提升了 YOLO 的性能 - 效率边界。为此，研究团队首次提出了 YOLO 无 NMS 训练的一致双重分配（consistent dual assignment），这使得 YOLO 在性能和推理延迟方面有所改进。研究团队为 YOLO 提出了整体效率 - 准确率驱动的模型设计策略，从效率和准确率两个角度全面...
fromultralyticsimportYOLOv10model=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/down...
YOLOv10-M:通用中型版本。 YOLOv10-B:平衡型,宽度增加,精度更高。 YOLOv10-L:大型版本,精度更高,但计算资源增加。 YOLOv10-X:超大型版本可实现最高精度和性能。 性能 在准确性和效率方面,YOLOv10 优于YOLO 以前的版本和其他最先进的模型。例如,在 COCO 数据集上,YOLOv10-S 的速度是RT-DETR-R18 的 ...
30 + 详见[yolov10预处理配置文件](./_base_/yolov10_reader.yml)。 31 + 32 + ## 使用教程 33 + 34 + ### 0. **一键运行全流程** 35 + 36 + 将以下命令写在一个脚本文件里如```run.sh```,一键运行命令为:```sh run.sh```,也可命令行一句句去运行。 37 + 38 + ```bas...
fromultralyticsimportYOLOv10model=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/down...
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/yolo...
YOLOv10: Real-Time End-to-End Object Detection. Contribute to LongerVision/yolov10 development by creating an account on GitHub.
wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt python app.py # Please visit http://127.0.0.1:7860 Validation yolov10n.pt yolov10s.pt yolov10m.pt yolov10b.pt yolov10l.pt yolov10x.pt yolo val model=yolov10n/s/m/b/l/x.pt data=coco.yaml batch=256...
YOLOv10问世,登顶GiTHub!性能飞升,【多尺度目标检测】值得大看特看! 【多尺度目标检测】是近年来在深度学习领域中备受关注的一项技术,它通过处理图像中不同尺度的目标,显著提升了模型在复杂场景中的检测精度和鲁棒性。多尺度目标检测技术已经在自动驾驶、安防监控和遥感图像分析等多个领域取得了显著成果,其独特的方法...
fromultralyticsimportYOLOv10model=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/down...