| --- |
| license: apache-2.0 |
| pipeline_tag: image-segmentation |
| tags: |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
| --- |
| |
| # EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation |
|
|
| EdgeCrafter is a unified framework for high-performance dense prediction on resource-constrained edge devices. It introduces compact Vision Transformers (ViTs) that compete with CNN-based architectures like YOLO by using task-specialized distillation and edge-aware encoder-decoder designs. |
|
|
| This repository contains a checkpoint for **ECSeg**, the instance segmentation variant of the framework, which achieves a strong accuracy-efficiency tradeoff. |
|
|
| - **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/2603.18739) |
| - **Project Page:** [EdgeCrafter Project Page](https://intellindust-ai-lab.github.io/projects/EdgeCrafter/) |
| - **Code:** [GitHub Repository](https://github.com/Intellindust-AI-Lab/EdgeCrafter) |
|
|
| ## Model Description |
|
|
| Deploying high-performance dense prediction models on resource-constrained edge devices remains challenging due to strict limits on computation and memory. EdgeCrafter addresses this by introducing a framework centered on distilled compact backbones and edge-friendly encoder-decoder designs. For instance segmentation, ECSeg achieves performance comparable to RF-DETR while using substantially fewer parameters, proving that compact ViTs can be a practical and competitive option for edge deployment. |
|
|
| ## Usage |
|
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| This model is compatible with the `PytorchModelHubMixin`. For detailed instructions on installation, training, and running inference, please refer to the [official GitHub repository](https://github.com/Intellindust-AI-Lab/EdgeCrafter). |
|
|
| ## Citation |
|
|
| If you find this project 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} |
| } |
| ``` |
