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
- Project Page: https://intellindust-ai-lab.github.io/projects/EdgeCrafter/
- Repository: 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
# Create conda environment
conda create -n ec python=3.11 -y
conda activate ec
# Install dependencies
pip install -r requirements.txt
Inference
# 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
@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 integration.