license: agpl-3.0 tags:

• 目标检测
• 计算机视觉
• yolov10
• pytorch_model_hub_mixin datasets:
• detection-datasets/coco library_name: yolov10 inference: false

模型描述

YOLOv10：实时端到端目标检测

• arXiv: https://arxiv.org/abs/2405.14458v1
• github: https://github.com/THU-MIG/yolov10

安装

pip install git+https://github.com/THU-MIG/yolov10.git

训练与验证

from ultralytics import YOLOv10

model = YOLOv10.from_pretrained('jameslahm/yolov10x')
# 训练
model.train(...)
# 训练完成后，可推送至模型中心
model.push_to_hub("your-hf-username/yolov10-finetuned")

# 验证
model.val(...)

推理

以下是一个端到端的示例，展示对猫图片的推理过程：

from ultralytics import YOLOv10

model = YOLOv10.from_pretrained('jameslahm/yolov10x')
source = 'http://images.cocodataset.org/val2017/000000039769.jpg'
model.predict(source=source, save=True)

结果展示：

BibTeX条目及引用信息

@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}
}