If you are interested in getting updates, please join our mailing list here.
- [2024/02/08] Tech report of EfficientViT-SAM is available: arxiv.
- [2024/02/07] We released EfficientViT-SAM, the first accelerated SAM model that matches/outperforms SAM-ViT-H's zero-shot performance, delivering the SOTA performance-efficiency trade-off.
- [2023/11/20] EfficientViT is available in the NVIDIA Jetson Generative AI Lab.
- [2023/09/12] EfficientViT is highlighted by MIT home page and MIT News.
- [2023/07/18] EfficientViT is accepted by ICCV 2023.
EfficientViT is a new family of ViT models for efficient high-resolution dense prediction vision tasks. The core building block of EfficientViT is a lightweight, multi-scale linear attention module that achieves global receptive field and multi-scale learning with only hardware-efficient operations, making EfficientViT TensorRT-friendly and suitable for GPU deployment.
conda create -n efficientvit python=3.10
conda activate efficientvit
conda install -c conda-forge mpi4py openmpi
pip install -r requirements.txt
pip install -e . # add --config-settings --editable-mode=compat if vscode doesn't resolve the imports
Han Cai: [email protected]
- ImageNet Pretrained models
- Segmentation Pretrained models
- ImageNet training code
- EfficientViT L series, designed for cloud
- EfficientViT for segment anything
- EfficientViT for image generation
- EfficientViT for CLIP
- EfficientViT for super-resolution
- Segmentation training code
If EfficientViT is useful or relevant to your research, please kindly recognize our contributions by citing our paper:
@article{cai2022efficientvit,
title={Efficientvit: Enhanced linear attention for high-resolution low-computation visual recognition},
author={Cai, Han and Gan, Chuang and Han, Song},
journal={arXiv preprint arXiv:2205.14756},
year={2022}
}