Add metadata and improve model card

Hi! I'm Niels from the community science team at Hugging Face. I noticed this model card was missing some key information and metadata. This PR adds the relevant pipeline tag and license to the metadata, along with links to the research paper, project page, and GitHub repository. I've also included a sample usage section based on the official documentation to help users get started.

README.md

@@ -1,10 +1,40 @@
 ---
+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: ECSeg-L
+
+EdgeCrafter is a unified compact Vision Transformer (ViT) framework designed for efficient edge dense prediction. This specific model, **ECSeg-L**, is optimized for instance segmentation on resource-constrained devices. It is part of the work presented in [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/2603.18739).
+
+- **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/2603.18739)
+- **Repository:** [https://github.com/Intellindust-AI-Lab/EdgeCrafter](https://github.com/Intellindust-AI-Lab/EdgeCrafter)
+- **Project Page:** [https://intellindust-ai-lab.github.io/projects/EdgeCrafter/](https://intellindust-ai-lab.github.io/projects/EdgeCrafter/)
+
+## Model Description
+EdgeCrafter addresses the performance gap between compact ViTs and CNN-based architectures like YOLO on edge devices. By using task-specialized distillation and an edge-friendly encoder-decoder design, EdgeCrafter models achieve a strong accuracy-efficiency tradeoff. ECSeg-L provides a high-performance balance for instance segmentation tasks.
+
+## Usage
+To use this model, please refer to the [official GitHub repository](https://github.com/Intellindust-AI-Lab/EdgeCrafter) for installation instructions. You can run inference using the following command:
+
+```bash
+cd ecdetseg
+# Run PyTorch inference
+# Make sure to replace `path/to/your/image.jpg` with an actual image path and provide the path to the weights
+python tools/inference/torch_inf.py -c configs/ecseg/ecseg_l.yml -r /path/to/ecseg_l.pth -i path/to/your/image.jpg
+```
+
+For loading models directly via the Hugging Face Hub, check the [hf_models.ipynb](https://github.com/Intellindust-AI-Lab/EdgeCrafter/blob/main/hf_models.ipynb) notebook in the repository.
+
+## 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}
+}
+```