---
license: apache-2.0
pipeline_tag: keypoint-detection
library_name: pytorch
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- vision
- pose-estimation
---

# EdgeCrafter (ECPose)

EdgeCrafter is a unified compact Vision Transformer (ViT) framework designed for edge dense prediction tasks like object detection, instance segmentation, and pose estimation. This specific checkpoint is for **ECPose**, a variant specialized for high-performance human pose estimation on resource-constrained devices.

- **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/2603.18739)
- **Code:** [GitHub Repository](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 edge devices is challenging due to strict limits on computation and memory. EdgeCrafter addresses this gap using task-specific representation learning and a distilled compact backbone. 

For pose estimation, the **ECPose-X** variant reaches **74.8 AP** on the COCO dataset, significantly outperforming YOLO-based architectures (e.g., YOLOv8-Pose-X at 71.6 AP) while maintaining an efficiency profile suitable for edge deployment.

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

For inference examples and detailed setup, please refer to the official [GitHub repository](https://github.com/Intellindust-AI-Lab/EdgeCrafter). A CLI inference example is provided below:

```bash
# Refer to GitHub for installation and config requirements
cd ecpose
python tools/inference/torch_inf.py -c configs/ecpose/ecpose_s_coco.yml -r ecpose_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}
}
```