Improve model card and add metadata

Hi, I'm Niels from the Hugging Face community team. I've updated the model card for EdgeCrafter (ECSeg-S) to include:
- Relevant metadata such as `pipeline_tag: image-segmentation` and `license: apache-2.0`.
- Links to the original paper, GitHub repository, and project page.
- Inference instructions and the BibTeX citation for research use.

This will ensure the model is correctly categorized on the Hub and easy for users to find and use.

README.md

@@ -1,10 +1,55 @@
 ---
+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
+
+EdgeCrafter is a unified framework for compact Vision Transformers (ViTs) designed for high-performance dense prediction (detection, instance segmentation, and pose estimation) on resource-constrained edge devices. This specific model, **ECSeg-S**, is a lightweight instance segmentation model.
+
+- **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://huggingface.co/papers/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
+ECSeg-S is built using a distilled compact backbone and an edge-friendly encoder-decoder design. It achieves a strong accuracy-efficiency tradeoff, making it suitable for real-time applications on edge hardware. For instance segmentation, it achieves performance comparable to RF-DETR while using significantly fewer parameters.
+
+## Quick Start (Inference)
+
+To run inference on a sample image, follow the instructions from the official repository:
+
+### 1. Installation
+```bash
+# Create conda environment
+conda create -n ec python=3.11 -y
+conda activate ec
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+### 2. Run Inference
+```bash
+# Navigate to the detection/segmentation folder
+cd ecdetseg
+
+# Run PyTorch inference
+# Replace `path/to/your/image.jpg` with an actual image path
+python tools/inference/torch_inf.py -c configs/ecseg/ecseg_s.yml -r /path/to/ecseg_s.pth -i path/to/your/image.jpg
+```
+
+## Citation
+
+If you find EdgeCrafter 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}
+}
+```