许可证: agpl-3.0 任务标签: 零样本目标检测 标签:

• 模型中心混合
• PyTorch模型中心混合

模型描述

YOLOE: 实时视觉万物

• arXiv: https://arxiv.org/abs/2503.07465
• GitHub: https://github.com/THU-MIG/yoloe

安装

pip install git+https://github.com/THU-MIG/yoloe.git#subdirectory=third_party/CLIP
pip install git+https://github.com/THU-MIG/yoloe.git#subdirectory=third_party/ml-mobileclip
pip install git+https://github.com/THU-MIG/yoloe.git#subdirectory=third_party/lvis-api
pip install git+https://github.com/THU-MIG/yoloe.git

wget https://docs-assets.developer.apple.com/ml-research/datasets/mobileclip/mobileclip_blt.pt

预测

请参考 https://github.com/THU-MIG/yoloe?tab=readme-ov-file#prediction。

迁移学习

请参考 https://github.com/THU-MIG/yoloe?tab=readme-ov-file#transferring。

验证

请参考 https://github.com/THU-MIG/yoloe?tab=readme-ov-file#validation。

训练

请参考 https://github.com/THU-MIG/yoloe?tab=readme-ov-file#training。

BibTeX 条目及引用信息

@misc{wang2025yoloerealtimeseeing,
      title={YOLOE: 实时视觉万物}, 
      author={王傲 and 刘立浩 and 陈辉 and 林子佳 and 韩军功 and 丁贵广},
      year={2025},
      eprint={2503.07465},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.07465}, 
}