Improve model card and add metadata (#1)

- Improve model card and add metadata (8bf24ed9ff15ac5af8b21bdb93e4ef8e160d7ee1)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

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 ---
+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
+
+EdgeCrafter is a unified compact ViT framework for edge dense prediction tasks. This repository specifically contains the **ECDet-S** model, an object detection architecture built from a distilled compact backbone and an edge-friendly encoder-decoder design.
+
+- **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/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)
+
+## Model Description
+
+EdgeCrafter bridges the accuracy-efficiency gap between compact Vision Transformers (ViTs) and CNN-based architectures (like YOLO) on resource-constrained devices. By employing task-specialized distillation and edge-aware architectural designs, ECDet achieves high performance with minimal parameters. ECDet-S, for instance, reaches 51.7 AP on the COCO dataset with fewer than 10M parameters.
+
+### COCO2017 Validation Results (Object Detection)
+
+| Model | Size | AP<sub>50:95</sub> | #Params | GFLOPs | Latency (ms) |
+|:-----:|:----:|:--:|:-------:|:------:|:------------:|
+| **ECDet-S** | 640 | 51.7 | 10 | 26 | 5.41 |
+| **ECDet-M** | 640 | 54.3 | 18 | 53 | 7.98 |
+| **ECDet-L** | 640 | 57.0 | 31 | 101 | 10.49 |
+| **ECDet-X** | 640 | 57.9 | 49 | 151 | 12.70 |
+
+*Note: Latency is measured on an NVIDIA T4 GPU with batch size 1 under FP16 precision using TensorRT (v10.6).*
+
+## Installation
+
+```bash
+# Create conda environment
+conda create -n ec python=3.11 -y
+conda activate ec
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+## Quick Start (Inference)
+
+You can run inference on a sample image using the provided scripts:
+
+```bash
+# 1. Download the pre-trained model (if not already present)
+# 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_s.yml -r ecdet_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}
+}
+```
+
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.