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

# EdgeCrafter: ECPose

EdgeCrafter is a unified framework for compact Vision Transformers (ViTs) designed for high-performance dense prediction on resource-constrained edge devices. This specific checkpoint is for **ECPose**, a model specialized for pose estimation (keypoint detection).

- **Project Page:** [EdgeCrafter](https://intellindust-ai-lab.github.io/projects/EdgeCrafter/)
- **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)

## Model Description
The EdgeCrafter framework addresses the challenge of deploying Vision Transformers on edge devices through task-specialized distillation and edge-aware encoder-decoder designs. ECPose models achieve a strong accuracy-efficiency trade-off, often outperforming CNN-based architectures like YOLO while maintaining low latency on edge hardware.

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

## Citation

If you find this work useful, 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}
}
```