| --- |
| license: apache-2.0 |
| pipeline_tag: object-detection |
| tags: |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
| --- |
| |
| # EdgeCrafter: Compact ViTs for Edge Dense Prediction |
|
|
| EdgeCrafter is a unified compact ViT framework for edge dense prediction tasks. This repository specifically contains the **ECDet-S** model, an object detection architecture built from a distilled compact backbone and an edge-friendly encoder-decoder design. |
|
|
| - **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/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) |
|
|
| ## Model Description |
|
|
| EdgeCrafter bridges the accuracy-efficiency gap between compact Vision Transformers (ViTs) and CNN-based architectures (like YOLO) on resource-constrained devices. By employing task-specialized distillation and edge-aware architectural designs, ECDet achieves high performance with minimal parameters. ECDet-S, for instance, reaches 51.7 AP on the COCO dataset with fewer than 10M parameters. |
|
|
| ### COCO2017 Validation Results (Object Detection) |
|
|
| | Model | Size | AP<sub>50:95</sub> | #Params | GFLOPs | Latency (ms) | |
| |:-----:|:----:|:--:|:-------:|:------:|:------------:| |
| | **ECDet-S** | 640 | 51.7 | 10 | 26 | 5.41 | |
| | **ECDet-M** | 640 | 54.3 | 18 | 53 | 7.98 | |
| | **ECDet-L** | 640 | 57.0 | 31 | 101 | 10.49 | |
| | **ECDet-X** | 640 | 57.9 | 49 | 151 | 12.70 | |
|
|
| *Note: Latency is measured on an NVIDIA T4 GPU with batch size 1 under FP16 precision using TensorRT (v10.6).* |
|
|
| ## Installation |
|
|
| ```bash |
| # Create conda environment |
| conda create -n ec python=3.11 -y |
| conda activate ec |
| |
| # Install dependencies |
| pip install -r requirements.txt |
| ``` |
|
|
| ## Quick Start (Inference) |
|
|
| You can run inference on a sample image using the provided scripts: |
|
|
| ```bash |
| # 1. Download the pre-trained model (if not already present) |
| # 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_s.yml -r ecdet_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} |
| } |
| ``` |
|
|
| This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration. |
