DEKR

DEKR estimates multi-person poses from image-wide keypoint heatmaps and offsets.

任务: pose
尺寸: w32 at 640 px
安装: pip install libreyolo
支持层级: 仅推理，自 v1.6.0 起。仅支持预测、验证和导出。训练相关的功能不适用。
上游: DEKR，由 HRNet authors 发布，采用 MIT 许可。论文、源码
许可: 代码采用 MIT，权重采用 No separate artifact grant recorded。商用

Install

bash

pip install "libreyolo"

Predict

Python

from libreyolo import LibreYOLO, SAMPLE_IMAGE # Downloads the W32 checkpoint from the upstream CDN and checks its SHA-256model = LibreYOLO("LibreDEKRw32-pose.pt", device="cpu")result = model(SAMPLE_IMAGE)print(result.keypoints)

The adapter uses the W32 no-deformable-convolution graph and 17 COCO person keypoints. Boxes enclose confident decoded joints. This is an inference-only family; the original deformable graph is a different checkpoint architecture.

Validate

Python

from libreyolo import LibreYOLO # Downloads the W32 checkpoint from the upstream CDN and checks its SHA-256model = LibreYOLO("LibreDEKRw32-pose.pt", device="cpu")# coco8-pose.yaml builds a 4-image COCO keypoint split on first usemetrics = model.val(data="coco8-pose.yaml", allow_download_scripts=True, workers=0)print(metrics)

Use the dataset format for this task. Validation explains the dataset requirements and returned metrics.

Export

任务	ONNX	TorchScript	ExecuTorch	TensorRT	OpenVINO	Paddle	MNN	RKNN	ncnn	TFLite	CoreML	Core AI
Pose	Pose to ONNX：支持	Pose to TorchScript：支持	Pose to ExecuTorch：不支持	Pose to TensorRT：支持	Pose to OpenVINO：支持	Pose to Paddle：不支持	Pose to MNN：不支持	Pose to RKNN：不支持	Pose to ncnn：不支持	Pose to TFLite：不支持	Pose to CoreML：不支持	Pose to Core AI：不支持

Python

from libreyolo import LibreYOLO # Downloads the W32 checkpoint from the upstream CDN and checks its SHA-256model = LibreYOLO("LibreDEKRw32-pose.pt", device="cpu")model.export(format="onnx")

Export setup lists format dependencies and loading exported artifacts.

Licensing

请检查你所下载的具体权重在 Hugging Face 仓库中的许可。LibreYOLO 组织里的每个检查点都附有许可，同一家族内也不一定相同。该仓库是权威来源；以下摘要说明本页上次验证时适用的情况。

这里只说明涉及的许可证，不构成法律意见。如果答案对商用很重要，请自行阅读许可证并咨询法律顾问。

原始工作: DEKR, HRNet authors
上游许可: MIT
上游来源: github.com/HRNet/DEKR
LibreYOLO 代码: MIT
权重: 采用 No separate artifact grant recorded 许可，由作者分发。LibreYOLO 不托管或镜像这些权重。
解读: The upstream implementation is MIT. The adapter retrieves the released checkpoint from its source CDN.

Citation

@inproceedings{GengSXZW21,
  title={Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression},
  author={Zigang Geng, Ke Sun, Bin Xiao, Zhaoxiang Zhang, Jingdong Wang},
  booktitle={CVPR},
  year={2021}
}

复制自作者在 raw.githubusercontent.com/HRNet/DEKR/main/README.md 上提供的引用块。