| --- |
| license: apache-2.0 |
| pipeline_tag: object-detection |
| tags: |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
| - vision |
| - vit |
| - edge-ai |
| --- |
| |
| # EdgeCrafter: ECDet-S |
|
|
| EdgeCrafter is a unified compact Vision Transformer (ViT) framework for edge dense prediction, introduced in the paper [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/2603.18739). |
|
|
| **ECDet-S** is an object detection model within this framework, featuring a distilled compact backbone and an edge-friendly encoder-decoder design. On the COCO dataset, it achieves **51.7 AP** with fewer than **10M parameters** using only COCO annotations. |
|
|
| - **Paper:** [arXiv:2603.18739](https://arxiv.org/abs/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 |
| Deploying high-performance dense prediction models on resource-constrained edge devices is challenging. EdgeCrafter addresses this by using task-specialized distillation to enhance task-specific representation learning in small-scale ViTs. This approach allows compact ViTs to achieve accuracy-efficiency trade-offs competitive with traditional CNN-based architectures like YOLO. |
|
|
| ## Sample Usage (Inference) |
|
|
| To run inference on a sample image using the provided scripts in the official repository: |
|
|
| ```bash |
| # 1. Clone the repository and install dependencies |
| git clone https://github.com/Intellindust-AI-Lab/EdgeCrafter |
| cd EdgeCrafter |
| pip install -r requirements.txt |
| |
| # 2. Run PyTorch inference |
| cd ecdetseg |
| # Replace `path/to/your/image.jpg` with an actual image path |
| python tools/inference/torch_inf.py -c configs/ecdet/ecdet_s.yml -r ecdet_s.pth -i path/to/your/image.jpg |
| ``` |
|
|
| This model was pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration. |
|
|
| ## 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} |
| } |
| ``` |
