| --- |
| 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} |
| } |
| ``` |
