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detectron2-real-time-instance-segmentation's Introduction

Detectron2 Instance Segmentation

Streamlit App that performs object detection from a camera powered by Detectron2.

Dependencies

Running the application can be done following the instructions above:

  1. To create a Python Virtual Environment (virtualenv) to run the code, type:

    python3 -m venv my-env

  2. Activate the new environment:

    • Windows: my-env\Scripts\activate.bat
    • macOS and Linux: source my-env/bin/activate
  3. Install all the dependencies from requirements.txt:

    pip install -r requirements.txt

Note: First time running the application can take a while because of model download.

Use

Within the virtual environment:

streamlit run app.py

A web application will open in the prompted URL. The user should upload an image file (jpg, jpeg, png) with the button available. Then, the image will be fed to a model which will output tehe original image with the detections drawn on it.

There's another app:

streamlit run app_discriminative.py

It behaves as the other one, but includes the following options:

  • Select which classes to detect: Multiselect to choose which of the classes that the model's been trained on are going to be used in the inference.

Acknowledgments

License

This project is licensed under the MIT License - see the LICENSE.md file for details

detectron2-real-time-instance-segmentation's People

Contributors

xavialex avatar

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