Improve model card: add pipeline tag, license, and project links

README.md

@@ -1,10 +1,33 @@
 ---
+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: ECPose
+
+EdgeCrafter is a unified framework for compact Vision Transformers (ViTs) designed for high-performance dense prediction on resource-constrained edge devices. This specific checkpoint is for **ECPose**, a model specialized for pose estimation (keypoint detection).
+
+- **Project Page:** [EdgeCrafter](https://intellindust-ai-lab.github.io/projects/EdgeCrafter/)
+- **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)
+
+## Model Description
+The EdgeCrafter framework addresses the challenge of deploying Vision Transformers on edge devices through task-specialized distillation and edge-aware encoder-decoder designs. ECPose models achieve a strong accuracy-efficiency trade-off, often outperforming CNN-based architectures like YOLO while maintaining low latency on edge hardware.
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.
+
+## Citation
+
+If you find this work useful, 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}
+}
+```