metadata
license: apache-2.0
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
EdgeCrafter: ECSeg
EdgeCrafter is a unified compact Vision Transformer (ViT) framework designed for efficient dense prediction tasks on edge devices. This specific checkpoint is part of the ECSeg series, which focuses on high-performance instance segmentation using a distilled compact backbone and an edge-friendly encoder-decoder design.
- Project Page: EdgeCrafter
- Paper: EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation
- Repository: GitHub - Intellindust-AI-Lab/EdgeCrafter
Performance (Instance Segmentation on COCO2017)
| Model | Size | AP50:95 | #Params | GFLOPs | Latency (ms) |
|---|---|---|---|---|---|
| ECSeg-S | 640 | 43.0 | 10M | 33 | 6.96 |
| ECSeg-M | 640 | 45.2 | 20M | 64 | 9.85 |
| ECSeg-L | 640 | 47.1 | 34M | 111 | 12.56 |
| ECSeg-X | 640 | 48.4 | 50M | 168 | 14.96 |
Note: Latency is measured on an NVIDIA T4 GPU with batch size 1 under FP16 precision using TensorRT (v10.6).
Usage
To run inference with this model, follow the instructions in the official repository. You can use the provided inference script:
# 1. Clone the repository and install dependencies
git clone https://github.com/Intellindust-AI-Lab/EdgeCrafter
cd EdgeCrafter/ecdetseg
pip install -r requirements.txt
# 2. Run PyTorch inference
# Replace `path/to/your/image.jpg` with an actual image path
python tools/inference/torch_inf.py -c configs/ecseg/ecseg_s.yml -r ecdet_s.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.