| --- |
| license: apache-2.0 |
| pipeline_tag: object-detection |
| tags: |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
| --- |
| |
| # EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation |
|
|
| EdgeCrafter is a unified compact Vision Transformer (ViT) framework designed for high-performance dense prediction (object detection, instance segmentation, and pose estimation) on resource-constrained edge devices. |
|
|
| - **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://huggingface.co/papers/2603.18739) |
| - **Project Page:** [https://intellindust-ai-lab.github.io/projects/EdgeCrafter/](https://intellindust-ai-lab.github.io/projects/EdgeCrafter/) |
| - **Repository:** [https://github.com/Intellindust-AI-Lab/EdgeCrafter](https://github.com/Intellindust-AI-Lab/EdgeCrafter) |
|
|
| ## Introduction |
| Deploying high-performance dense prediction models on edge devices is challenging due to strict computation and memory limits. EdgeCrafter introduces a framework centered on **ECDet**, a detection model built from a distilled compact backbone and an edge-friendly encoder-decoder design. On the COCO dataset, ECDet-S achieves 51.7 AP with fewer than 10M parameters. |
|
|
| ## Quick Start (Inference) |
| The easiest way to test EdgeCrafter is to run inference on a sample image using the provided tools in the official repository. |
|
|
| ### Installation |
| ```bash |
| # Create conda environment |
| conda create -n ec python=3.11 -y |
| conda activate ec |
| |
| # Install dependencies |
| pip install -r requirements.txt |
| ``` |
|
|
| ### Inference |
| ```bash |
| # 1. Download a pre-trained model (e.g., ECDet-L) |
| cd ecdetseg |
| wget https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecdet_l.pth |
| |
| # 2. Run PyTorch inference |
| # Make sure to replace `path/to/your/image.jpg` with an actual image path |
| python tools/inference/torch_inf.py -c configs/ecdet/ecdet_l.yml -r ecdet_l.pth -i path/to/your/image.jpg |
| ``` |
|
|
| ## 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} |
| } |
| ``` |
|
|
| --- |
| This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration. |
