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A minimalistic webapp to perform zero shot object detection based on textual prompts using GroundingDINO

Python 79.90% C++ 2.02% Cuda 17.91% Dockerfile 0.10% Procfile 0.01% Shell 0.06%
grounding-dino opensourceforgood python3 streamlit-webapp idea-research

zero-shot-object-detection-streamlit's Introduction

Zero Shot Object Detection (GroundingDINO) Streamlit Project Status: Active

A minimalistic webapp to perform zero shot object detection based on textual prompts using GroundingDINO

demo

Installation:

  • Simply run the command pip install -r requirements.txt to install the dependencies.
  • Make sure you have CUDA installed and setup. For more details in setting GroundingDINO, please refer this.

Usage:

  1. Clone this repository and install the dependencies as mentioned above.
  2. Create a weights directory and save the model checkpoints which can be downloaded from here.
  3. Simply run the command:
streamlit run app.py
  1. Navigate to http://localhost:8501 in your web-browser.
  2. 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

Results:

Text Prompt: orange, pear, bowl, plum, pomegranate

187e188849730cff557f867c955d82445045c2dd42e4ecf46fc2727c

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!! ๐Ÿ˜„ ):
docker build -f Dockerfile -t app:latest .
  1. 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{liu2023grounding,
  title={Grounding dino: Marrying dino with grounded pre-training for open-set object detection},
  author={Liu, Shilong and Zeng, Zhaoyang and Ren, Tianhe and Li, Feng and Zhang, Hao and Yang, Jie and Li, Chunyuan and Yang, Jianwei and Su, Hang and Zhu, Jun and others},
  journal={arXiv preprint arXiv:2303.05499},
  year={2023}
}

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