Awesome

YOLO-World + EfficientViT SAM

🤗 HuggingFace Space

Prerequisites

This project is developed and tested on Python3.10.

# Create and activate a python 3.10 environment.
conda create -n yolo-world-with-efficientvit-sam python=3.10 -y
conda activate yolo-world-with-efficientvit-sam
# Setup packages.
make setup

How to Run

python app.py

Open http://127.0.0.1:7860/ on your web browser.

Core Components

YOLO-World

YOLO-World is an open-vocabulary object detection model with high efficiency. On the challenging LVIS dataset, YOLO-World achieves 35.4 AP with 52.0 FPS on V100, which outperforms many state-of-the-art methods in terms of both accuracy and speed. <img width="1024" src="https://github.com/Curt-Park/yolo-world-with-efficientvit-sam/assets/14961526/fce57405-e18d-45f3-bea8-fc3971faf975">

EfficientViT SAM

EfficientViT SAM is a new family of accelerated segment anything models. Thanks to the lightweight and hardware-efficient core building block, it delivers 48.9× measured TensorRT speedup on A100 GPU over SAM-ViT-H without sacrificing performance.

Powered By

@misc{zhang2024efficientvitsam,
  title={EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss},
  author={Zhuoyang Zhang and Han Cai and Song Han},
  year={2024},
  eprint={2402.05008},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@article{cheng2024yolow,
  title={YOLO-World: Real-Time Open-Vocabulary Object Detection},
  author={Cheng, Tianheng and Song, Lin and Ge, Yixiao and Liu, Wenyu and Wang, Xinggang and Shan, Ying},
  journal={arXiv preprint arXiv:2401.17270},
  year={2024}
}

@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}
}