Improve model card

Hi! I'm Niels from the community science team at Hugging Face. I'm opening this PR to improve the model card for EdgeCrafter.

Specifically, I have:
- Added the `object-detection` pipeline tag.
- Added the `apache-2.0` license.
- Provided links to the research paper, the official project page, and the GitHub repository.
- Added a brief description and inference instructions based on the official documentation.

This will help users better understand and use your model!

README.md

@@ -1,10 +1,48 @@
 ---
+license: apache-2.0
+pipeline_tag: object-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 framework for compact Vision Transformers (ViTs) optimized for edge dense prediction tasks like object detection, instance segmentation, and pose estimation. It bridges the performance gap between compact ViTs and CNN-based architectures (like YOLO) through task-specialized distillation and edge-friendly architectural designs.
+
+- **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://huggingface.co/papers/2603.18739)
+- **Project Page:** [https://intellindust-ai-lab.github.io/projects/EdgeCrafter/](https://intellindust-ai-lab.github.io/projects/EdgeCrafter/)
+- **Repository:** [https://github.com/Intellindust-AI-Lab/EdgeCrafter](https://github.com/Intellindust-AI-Lab/EdgeCrafter)
+
+## Model Description
+
+Compact ViTs often struggle with task-specific representation learning on edge devices. EdgeCrafter addresses this by introducing **ECDet**, a detection model built from a distilled compact backbone. On the COCO dataset, ECDet-S achieves 51.7 AP with fewer than 10M parameters. This framework demonstrates that when paired with task-specialized distillation, compact ViTs are highly competitive for real-time edge applications.
+
+## Quick Start (Inference)
+
+To run inference on a sample image using the pre-trained model, follow these steps from the official repository:
+
+```bash
+# 1. Clone the repository and install dependencies
+git clone https://github.com/Intellindust-AI-Lab/EdgeCrafter
+cd EdgeCrafter
+pip install -r requirements.txt
+
+# 2. Run PyTorch inference
+# Make sure to replace `path/to/your/image.jpg` with an actual image path
+cd ecdetseg
+python tools/inference/torch_inf.py -c configs/ecdet/ecdet_l.yml -r ecdet_l.pth -i path/to/your/image.jpg
+```
+
+## 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}
+}
+```