Improve model card

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

The changes include:
- Adding the `pipeline_tag: object-detection` to the YAML metadata for better discoverability.
- Specifying the `license: apache-2.0` based on the project's repository.
- Adding links to the paper, project page, and source code.
- Including a summary and sample usage instructions derived from the official GitHub README.

README.md

@@ -1,10 +1,55 @@
 ---
+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 via Task-Specialized Distillation
+
+EdgeCrafter is a unified compact Vision Transformer (ViT) framework designed for high-performance dense prediction (object detection, instance segmentation, and pose estimation) on resource-constrained edge devices. 
+
+- **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)
+
+## Introduction
+Deploying high-performance dense prediction models on edge devices is challenging due to strict computation and memory limits. EdgeCrafter introduces a framework centered on **ECDet**, a detection model built from a distilled compact backbone and an edge-friendly encoder-decoder design. On the COCO dataset, ECDet-S achieves 51.7 AP with fewer than 10M parameters.
+
+## Quick Start (Inference)
+The easiest way to test EdgeCrafter is to run inference on a sample image using the provided tools in the official repository.
+
+### Installation
+```bash
+# Create conda environment
+conda create -n ec python=3.11 -y
+conda activate ec
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+### Inference
+```bash
+# 1. Download a pre-trained model (e.g., ECDet-L)
+cd ecdetseg
+wget https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecdet_l.pth
+
+# 2. Run PyTorch inference
+# Make sure to replace `path/to/your/image.jpg` with an actual image path
+python tools/inference/torch_inf.py -c configs/ecdet/ecdet_l.yml -r ecdet_l.pth -i path/to/your/image.jpg
+```
+
+## 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}
+}
+```
+
+---
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.