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Image classification using tensorflow.

Project description

Image classification (not object detection) using tensorflow.

Based on example code located here:


  • install virtual environment:

    virtualenv -p /usr/bin/python3.7 venv
  • install tensorflow (1.x or 2.x works)

    • with GPU (1.x):

      ./venv/bin/pip install "tensorflow-gpu<2.0.0"
    • with GPU (2.x):

      ./venv/bin/pip install "tensorflow-gpu>=2.0.0"
    • CPU only (1.x):

      ./venv/bin/pip install "tensorflow<2.0.0"
    • CPU only (2.x):

      ./venv/bin/pip install "tensorflow>=2.0.0"
  • install library

    • via pip:

      ./venv/bin/pip install wai.tfimageclass
    • from source (from within the directory containing the script):

      ./venv/bin/pip install .


All scripts support –help option to list all available options.


  • For training, use module wai.tfimageclass.train.retrain or console script tfic-retrain
  • For evaluating a built model, use module wai.tfimageclass.train.stats or console script tfic-stats

Training data

All the data for building the model must be located in a single directory, with each sub-directory representing a label. For instance for building a model for distinguishing flowers (daisy, dandelion, roses, sunflowers, tulip), the data directory looks like this:

+- flowers
   +- daisy
   +- dandelion
   +- roses
   +- sunflowers
   +- tulip


Once you have built a model, you can use it as follows:

  • For making predictions for a single image, use module wai.tfimageclass.predict.label_image or console script tfic-labelimage
  • For polling images in a directory and making continous predictions with CSV companion files, use module wai.tfimageclass.predict.poll or console script tfic-poll


0.0.2 (2019-11-14)

  • added missing

0.0.1 (2019-11-01)

  • initial release

Project details

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