CLIP-SAM-Streamlit

CLIP + SAM for open vocabulary image segmentation

Installation:

• Simply run the command pip install -r requirements.txt to install the dependencies.

Usage:

1. Clone this repository and install the dependencies as mentioned above.
2. Create a model directory and save the model checkpoints which can be downloaded from here.
3. Simply run the command:

streamlit run app.py

4. Navigate to http://localhost:8501 in your web-browser.
5. By default, streamlit allows us to upload files of max. 200MB. If you want to have more size for uploading images, execute the command :

streamlit run app.py --server.maxUploadSize=1028

Running the Dockerized App

1. Ensure you have Docker Installed and Setup in your OS (Windows/Mac/Linux). For detailed Instructions, please refer this.
2. Navigate to the folder where you have cloned this repository ( where the Dockerfile is present ).
3. Build the Docker Image (don't forget the dot!! 😄 ):

docker build -f Dockerfile -t app:latest .

4. Run the docker:

docker run -p 8501:8501 app:latest

This will launch the dockerized app. Navigate to http://localhost:8501/ in your browser to have a look at your application. You can check the status of your all available running dockers by:

docker 

Citation

@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{radford2021learning,
      title={Learning Transferable Visual Models From Natural Language Supervision}, 
      author={Alec Radford and Jong Wook Kim and Chris Hallacy and Aditya Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
      year={2021},
      eprint={2103.00020},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}