---
license: apache-2.0
pipeline_tag: object-detection
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---

# EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation

EdgeCrafter is a unified compact Vision Transformer (ViT) framework designed for high-performance dense prediction (object detection, instance segmentation, and pose estimation) on resource-constrained edge devices. 

- **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://huggingface.co/papers/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)

## Introduction
Deploying high-performance dense prediction models on edge devices is challenging due to strict computation and memory limits. EdgeCrafter introduces a framework centered on **ECDet**, a detection model built from a distilled compact backbone and an edge-friendly encoder-decoder design. On the COCO dataset, ECDet-S achieves 51.7 AP with fewer than 10M parameters.

## Quick Start (Inference)
The easiest way to test EdgeCrafter is to run inference on a sample image using the provided tools in the official repository.

### Installation
```bash
# Create conda environment
conda create -n ec python=3.11 -y
conda activate ec

# Install dependencies
pip install -r requirements.txt
```

### Inference
```bash
# 1. Download a pre-trained model (e.g., ECDet-L)
cd ecdetseg
wget https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecdet_l.pth

# 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_l.yml -r ecdet_l.pth -i path/to/your/image.jpg
```

## 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}
}
```

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