Add metadata and improve model card

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}
+}
+```