---
license: apache-2.0
pipeline_tag: object-detection
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- vision
- vit
- edge-ai
---

# EdgeCrafter: ECDet-S

EdgeCrafter is a unified compact Vision Transformer (ViT) framework for edge dense prediction, introduced in the paper [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/2603.18739).

**ECDet-S** is an object detection model within this framework, featuring a distilled compact backbone and an edge-friendly encoder-decoder design. On the COCO dataset, it achieves **51.7 AP** with fewer than **10M parameters** using only COCO annotations.

- **Paper:** [arXiv:2603.18739](https://arxiv.org/abs/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
Deploying high-performance dense prediction models on resource-constrained edge devices is challenging. EdgeCrafter addresses this by using task-specialized distillation to enhance task-specific representation learning in small-scale ViTs. This approach allows compact ViTs to achieve accuracy-efficiency trade-offs competitive with traditional CNN-based architectures like YOLO.

## Sample Usage (Inference)

To run inference on a sample image using the provided scripts in the official repository:

```bash
# 1. Clone the repository and install dependencies
git clone https://github.com/Intellindust-AI-Lab/EdgeCrafter
cd EdgeCrafter
pip install -r requirements.txt

# 2. Run PyTorch inference
cd ecdetseg
# 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
```

This model was pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.

## 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}
}
```