Improve model card: add metadata, links and paper info

README.md

@@ -1,10 +1,39 @@
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+license: apache-2.0
+pipeline_tag: keypoint-detection
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
 ---
 
-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: Compact ViTs for Edge Dense Prediction
+
+EdgeCrafter is a unified compact Vision Transformer (ViT) framework for edge dense prediction, introduced in the paper [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://huggingface.co/papers/2603.18739). 
+
+This repository contains a pose estimation model (**ECPose**) optimized for high performance on resource-constrained edge devices.
+
+- **Project Page:** [EdgeCrafter](https://intellindust-ai-lab.github.io/projects/EdgeCrafter/)
+- **Github Repository:** [Intellindust-AI-Lab/EdgeCrafter](https://github.com/Intellindust-AI-Lab/EdgeCrafter)
+
+## Overview
+
+EdgeCrafter addresses the performance gap between compact ViTs and traditional CNN-based architectures (like YOLO) on edge hardware. It introduces a task-specialized distillation approach and edge-friendly encoder-decoder designs. 
+
+The **ECPose** family focuses on human pose estimation. For example, ECPose-X reaches 74.8 AP on the COCO dataset, significantly outperforming lightweight CNN-based models while maintaining efficiency for real-time 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 and training instructions, please refer to the [official GitHub repository](https://github.com/Intellindust-AI-Lab/EdgeCrafter).
+
+## Citation
+
+If you find this project 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}
+}
+```