Improve model card and add metadata

README.md

@@ -1,10 +1,52 @@
 ---
+license: apache-2.0
+pipeline_tag: object-detection
 tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
+- vision
+- vit
+- edge-ai
 ---
 
-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: ECDet-S
+
+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://arxiv.org/abs/2603.18739).
+
+**ECDet-S** is an object detection model within this framework, featuring a distilled compact backbone and an edge-friendly encoder-decoder design. On the COCO dataset, it achieves **51.7 AP** with fewer than **10M parameters** using only COCO annotations.
+
+- **Paper:** [arXiv:2603.18739](https://arxiv.org/abs/2603.18739)
+- **GitHub Repository:** [Intellindust-AI-Lab/EdgeCrafter](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 resource-constrained edge devices is challenging. EdgeCrafter addresses this by using task-specialized distillation to enhance task-specific representation learning in small-scale ViTs. This approach allows compact ViTs to achieve accuracy-efficiency trade-offs competitive with traditional CNN-based architectures like YOLO.
+
+## Sample Usage (Inference)
+
+To run inference on a sample image using the provided scripts in 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
+cd ecdetseg
+# Replace `path/to/your/image.jpg` with an actual image path
+python tools/inference/torch_inf.py -c configs/ecdet/ecdet_s.yml -r ecdet_s.pth -i path/to/your/image.jpg
+```
+
+This model was pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.
+
+## Citation
+
+```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}
+}
+```