Awesome

Open-vocabulary-Segment-Anything

An interesting demo by combining OWL-ViT of Google and Segment Anything of Meta!

Detect Result Segment Anything Result Inpainting Result

prompt: a bird with a yellow wing

Highlight

Detect and Segment everything with Language!
Detect objects in more details (small objects)
Image-conditioned detection and Text-condition detection
Use Stable Diffusion to do inpainting

Catelog

OWL-ViT + Segment-Anything Demo
Image-conditioned detection
Inpainting using StableDiffusion
Huggingface Demo

Installation

The code requires python>=3.8, as well as pytorch>=1.7 and torchvision>=0.8. Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.

Install Segment Anything:

python -m pip install -e segment_anything

Install OWL-ViT (the OWL-ViT is included in transformer library):

pip install transformers

More details can be found in installation segment anything

Run Demo

download segment-anything checkpoint

wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

Run demo

bash run_demo.sh

Detect Result Segment Anything Result

Reference

Please give applause for IDEA-Research and OWL-ViT on HuggingFace

Citation

If you find this project helpful for your research, please consider citing the following BibTeX entry.

@article{kirillov2023segany,
  title={Segment Anything}, 
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
  journal={arXiv:2304.02643},
  year={2023}
}

@misc{minderer2022simple,
      title={Simple Open-Vocabulary Object Detection with Vision Transformers}, 
      author={Matthias Minderer and Alexey Gritsenko and Austin Stone and Maxim Neumann and Dirk Weissenborn and Alexey Dosovitskiy and Aravindh Mahendran and Anurag Arnab and Mostafa Dehghani and Zhuoran Shen and Xiao Wang and Xiaohua Zhai and Thomas Kipf and Neil Houlsby},
      year={2022},
      eprint={2205.06230},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}