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Detectron2 for Active Learning in Object Detection

Usage

  1. Clone the repository with all the submodules:
    git clone --recurse-submodules git@github.com:lolipopshock/Detectron2_AL.git
  2. Install dependencies:
    1. Installing object detection environment with according to your environment
      • The tested version of pytorch is 1.4.0 with CUDA 10
      • And you must install Detectron2 with version 0.1.1. Newer versions has different APIs.
        pip install detectron2==0.1.1 \
            -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu100/torch1.4/index.html
    2. Installing other necessary dependencies:
          pip install -r requirements.txt
    3. Installing UI components
      cd src/label-studio
      pip install -e .
  3. Setting up the label-studio server and modeling backend
    1. Initialize the labeling server (If your image folder is ./data)
      label-studio init labeling/tk-labeling \
              --input-path=./data \
              --input-format=image-dir \
              --allow-serving-local-files --force \
              --label-config=extra/config.xml \
              --ml-backends http://localhost:9090
      And you can start the server via
      label-studio start labeling/tk-labeling
      
    2. Initialize the model backend server
      label-studio-ml init labeling/backend_model --script extra/backend_model.py
      And similarly, you can start the backend server by
      label-studio-ml start labeling/backend_model 
      # There's a relative import of the libraries
      # So you have to run this command in the project project
      # root path to avoid import errors
  4. Start using active learning for annotation

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Active Learning for Object Detection using Detectron2

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