DETRPose-M-CROWDPOSE
DETRPose-M-CROWDPOSE is a real-time object detection model introduced in the paper DETRPose: Real-Time End-to-End Multi-Person Pose Estimation via Modified Transformer Decoder and Novel Denoising Keypoints.
- Repository: https://github.com/SebastianJanampa/DETRPose
- Paper: https://huggingface.co/papers/2506.13027
📝 Model Description
DETRPose introduces the first real-time end-to-end framework for multi-person pose estimation. By leveraging the hybrid encoder from RT-DETR and the lightweight decoder architecture of D-FINE, DETRPose achieves low-latency inference without sacrificing accuracy. The model introduces two primary methodological advancements:
- Pose-LQE Layer: A specialized head designed to improve confidence scores.
- Advanced Training Paradigm: Incorporates Denoising Keypoints and a custom Keypoint Similarity Varifocal loss function, ensuring robust learning and superior localization performance.
| Model | Dataset | AP | #Params | Latency | GFLOPs |
|---|---|---|---|---|---|
| DETRPose-M | CROWDPOSE | 72.0 | 20.7 M | 6.86 ms | 64.9 |
🚀 Installation
To use this model, you need to install the inference-ready branch of the DETRPose repository.
You can directly install the inference-ready branch using pip:
pip install git+https://github.com/SebastianJanampa/DETRPose.git@inference_only
💻 Usage
This branch is designed to be easy to use for inference. Here is a quick example of how to load a model and run it on a live webcam feed.
from detrpose import DETR
# Initialization
model = DETR(model='detrpose_hgnetv2_m_crowdpose')
# Inference
model(source=0) # inference on a webcam
📜 Citation
If you use DETRPose or its methods in your work, please cite the following BibTeX entries:
@misc{janampa2025detrpose,
title={DETRPose: Real-time end-to-end transformer model for multi-person pose estimation},
author={Sebastian Janampa and Marios Pattichis},
year={2025},
eprint={2506.13027},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.13027},
}
🙏 Acknowledgement
This work was supported in part by Lambda.ai.
Our work is built upon DEIM, D-FINE, Detectron2, and GroupPose.
✨ Feel free to reach out if you have any questions! ✨