Add paper info, license, and pipeline tag

README.md

@@ -1,10 +1,46 @@
 ---
+license: apache-2.0
+pipeline_tag: keypoint-detection
+library_name: pytorch
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
+- vision
+- pose-estimation
 ---
 
-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: https://github.com/Intellindust-AI-Lab/EdgeCrafter
-- Paper: https://arxiv.org/abs/2603.18739
-- Docs: [More Information Needed]
+# EdgeCrafter (ECPose)
+
+EdgeCrafter is a unified compact Vision Transformer (ViT) framework designed for edge dense prediction tasks like object detection, instance segmentation, and pose estimation. This specific checkpoint is for **ECPose**, a variant specialized for high-performance human pose estimation on resource-constrained devices.
+
+- **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/2603.18739)
+- **Code:** [GitHub Repository](https://github.com/Intellindust-AI-Lab/EdgeCrafter)
+- **Project Page:** [EdgeCrafter Project Page](https://intellindust-ai-lab.github.io/projects/EdgeCrafter/)
+
+## Model Description
+Deploying high-performance dense prediction models on edge devices is challenging due to strict limits on computation and memory. EdgeCrafter addresses this gap using task-specific representation learning and a distilled compact backbone. 
+
+For pose estimation, the **ECPose-X** variant reaches **74.8 AP** on the COCO dataset, significantly outperforming YOLO-based architectures (e.g., YOLOv8-Pose-X at 71.6 AP) while maintaining an efficiency profile suitable for edge deployment.
+
+## Usage
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration. 
+
+For inference examples and detailed setup, please refer to the official [GitHub repository](https://github.com/Intellindust-AI-Lab/EdgeCrafter). A CLI inference example is provided below:
+
+```bash
+# Refer to GitHub for installation and config requirements
+cd ecpose
+python tools/inference/torch_inf.py -c configs/ecpose/ecpose_s_coco.yml -r ecpose_s.pth -i path/to/your/image.jpg
+```
+
+## Citation
+If you find EdgeCrafter useful in your research, please consider citing:
+
+```bibtex
+@article{liu2026edgecrafter,
+  title={EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation},
+  author={Liu, Longfei and Hou, Yongjie and Li, Yang and Wang, Qirui and Sha, Youyang and Yu, Yongjun and Wang, Yinzhi and Ru, Peizhe and Yu, Xuanlong and Shen, Xi},
+  journal={arXiv},
+  year={2026}
+}
+```