| --- |
| license: apache-2.0 |
| pipeline_tag: image-segmentation |
| tags: |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
| --- |
| |
| # EdgeCrafter: ECSeg-L |
|
|
| EdgeCrafter is a unified compact Vision Transformer (ViT) framework designed for efficient edge dense prediction. This specific model, **ECSeg-L**, is optimized for instance segmentation on resource-constrained devices. It is part of the work presented in [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/2603.18739). |
|
|
| - **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/2603.18739) |
| - **Repository:** [https://github.com/Intellindust-AI-Lab/EdgeCrafter](https://github.com/Intellindust-AI-Lab/EdgeCrafter) |
| - **Project Page:** [https://intellindust-ai-lab.github.io/projects/EdgeCrafter/](https://intellindust-ai-lab.github.io/projects/EdgeCrafter/) |
|
|
| ## Model Description |
| EdgeCrafter addresses the performance gap between compact ViTs and CNN-based architectures like YOLO on edge devices. By using task-specialized distillation and an edge-friendly encoder-decoder design, EdgeCrafter models achieve a strong accuracy-efficiency tradeoff. ECSeg-L provides a high-performance balance for instance segmentation tasks. |
|
|
| ## Usage |
| To use this model, please refer to the [official GitHub repository](https://github.com/Intellindust-AI-Lab/EdgeCrafter) for installation instructions. You can run inference using the following command: |
|
|
| ```bash |
| cd ecdetseg |
| # Run PyTorch inference |
| # Make sure to replace `path/to/your/image.jpg` with an actual image path and provide the path to the weights |
| python tools/inference/torch_inf.py -c configs/ecseg/ecseg_l.yml -r /path/to/ecseg_l.pth -i path/to/your/image.jpg |
| ``` |
|
|
| For loading models directly via the Hugging Face Hub, check the [hf_models.ipynb](https://github.com/Intellindust-AI-Lab/EdgeCrafter/blob/main/hf_models.ipynb) notebook in the repository. |
|
|
| ## 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} |
| } |
| ``` |
