chisarie/efficientvit

EfficientViT is a new family of vision models for efficient high-resolution vision.

EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction (paper, poster)

News

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• [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.

About EfficientViT Models

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.

Third-Party Implementation/Integration

• NVIDIA Jetson Generative AI Lab
• timm: link
• X-AnyLabeling: link

Getting Started

conda create -n efficientvit python=3.10
conda activate efficientvit
conda install -c conda-forge mpi4py openmpi
pip install -r requirements.txt

EfficientViT Applications

Segment Anything

Image Classification

Semantic Segmentation

Contact

Han Cai: [email protected]

TODO

• 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

Citation

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}
}