README.md

YOLO-World_EfficientViT-SAM

Getting Started

HuggingFace Space

Installation

download the pretrained weights from the following links and save them in the weights directory. https://huggingface.co/han-cai/efficientvit-sam/resolve/main/xl1.pt

Use Anaconda to create a new environment and install the required packages.

# Create and activate a python 3.10 environment.
conda create -n yolo-world-efficientvit-sam python=3.10 -y

conda activate yolo-world-efficientvit-sam

pip install -r requirements.txt

Running the Project

python app.py

Core Models

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.

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.

Citation

@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}
}

@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}
}