| --- |
| license: apache-2.0 |
| pipeline_tag: image-segmentation |
| tags: |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
| --- |
| |
| # EdgeCrafter: Compact ViTs for Edge Dense Prediction |
|
|
| EdgeCrafter is a unified framework for compact Vision Transformers (ViTs) designed for high-performance dense prediction (detection, instance segmentation, and pose estimation) on resource-constrained edge devices. This specific model, **ECSeg-S**, is a lightweight instance segmentation model. |
|
|
| - **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://huggingface.co/papers/2603.18739) |
| - **GitHub Repository:** [Intellindust-AI-Lab/EdgeCrafter](https://github.com/Intellindust-AI-Lab/EdgeCrafter) |
| - **Project Page:** [EdgeCrafter Project Page](https://intellindust-ai-lab.github.io/projects/EdgeCrafter/) |
|
|
| ## Model Description |
| ECSeg-S is built using a distilled compact backbone and an edge-friendly encoder-decoder design. It achieves a strong accuracy-efficiency tradeoff, making it suitable for real-time applications on edge hardware. For instance segmentation, it achieves performance comparable to RF-DETR while using significantly fewer parameters. |
|
|
| ## Quick Start (Inference) |
|
|
| To run inference on a sample image, follow the instructions from the official repository: |
|
|
| ### 1. Installation |
| ```bash |
| # Create conda environment |
| conda create -n ec python=3.11 -y |
| conda activate ec |
| |
| # Install dependencies |
| pip install -r requirements.txt |
| ``` |
|
|
| ### 2. Run Inference |
| ```bash |
| # Navigate to the detection/segmentation folder |
| cd ecdetseg |
| |
| # Run PyTorch inference |
| # Replace `path/to/your/image.jpg` with an actual image path |
| python tools/inference/torch_inf.py -c configs/ecseg/ecseg_s.yml -r /path/to/ecseg_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} |
| } |
| ``` |
