---
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.