metadata
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
- Paper: EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation
- Code: GitHub Repository
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 integration.
Citation
If you find this work useful, please consider citing:
@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}
}