Improve model card and add metadata

Hi! I'm Niels from the Hugging Face community science team.

This PR improves the model card for this EdgeCrafter model by adding:
- Metadata for the `pipeline_tag` (`image-segmentation`) and `license` (Apache 2.0).
- Links to the paper, project page, and GitHub repository.
- A brief introduction to the EdgeCrafter framework.
- The BibTeX citation from the paper.

This ensures the model is correctly categorized on the Hub and provides essential documentation for researchers and developers.

README.md

@@ -1,10 +1,38 @@
 ---
+license: apache-2.0
+pipeline_tag: image-segmentation
 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 via Task-Specialized Distillation
+
+EdgeCrafter is a unified framework for high-performance dense prediction on resource-constrained edge devices. It introduces compact Vision Transformers (ViTs) that compete with CNN-based architectures like YOLO by using task-specialized distillation and edge-aware encoder-decoder designs.
+
+This repository contains a checkpoint for **ECSeg**, the instance segmentation variant of the framework, which achieves a strong accuracy-efficiency tradeoff.
+
+- **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/2603.18739)
+- **Project Page:** [EdgeCrafter Project Page](https://intellindust-ai-lab.github.io/projects/EdgeCrafter/)
+- **Code:** [GitHub Repository](https://github.com/Intellindust-AI-Lab/EdgeCrafter)
+
+## Model Description
+
+Deploying high-performance dense prediction models on resource-constrained edge devices remains challenging due to strict limits on computation and memory. EdgeCrafter addresses this by introducing a framework centered on distilled compact backbones and edge-friendly encoder-decoder designs. For instance segmentation, ECSeg achieves performance comparable to RF-DETR while using substantially fewer parameters, proving that compact ViTs can be a practical and competitive option for edge deployment.
+
+## Usage
+
+This model is compatible with the `PytorchModelHubMixin`. For detailed instructions on installation, training, and running inference, 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}
+}
+```