Update README.md

README.md

@@ -3,9 +3,77 @@ license: apache-2.0
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
+language:
+- en
+pipeline_tag: keypoint-detection
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+# DETRPose-M-COCO
+
+DETRPose-M-COCO 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](https://huggingface.co/papers/2506.13027). 
+
+- **Repository:** [https://github.com/SebastianJanampa/DETRPose](https://github.com/SebastianJanampa/DETRPose)
+- **Paper:** [https://huggingface.co/papers/2506.13027](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** | COCO | 69.4 | 20.8 M | 7.01 ms | 67.3 |
+
+## 🚀 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`:
+```shell
+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.
+```python
+from detrpose import DETR
+
+# Initialization
+model = DETR(model='detrpose_hgnetv2_m')
+
+# 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:
+
+```latex
+@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](https://lambda.ai).
+
+Our work is built upon [DEIM](https://github.com/Intellindust-AI-Lab/DEIM/tree/main), [D-FINE](https://github.com/Peterande/D-FINE), [Detectron2](https://github.com/facebookresearch/detectron2/tree/main), and [GroupPose](https://github.com/Michel-liu/GroupPose/tree/main).
+
+✨ Feel free to reach out if you have any questions! ✨
+
+<div align="left">
+  <a href="https://lambda.ai" target="_blank">
+  	<img src="./assets/lambda_logo2.png" width=500 >
+  </a>
+</div>