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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 continuous predictions with CSV companion files, use module wai.tfimageclass.predict.poll or console script tfic-poll

  • For making predictions via Redis (, i.e., broadcasting the image bytes on one channel and receiving the JSON predictions on another channel, use wai.tfimageclass.predict.label_redis or console script tfic-label-redis


0.0.15 (2022-05-10)

  • added wai/tfimageclass/predict/ module (console script: tfic-label-redis) that allows making predictions via Redis backend (image bytes in, JSON predictions out).

0.0.14 (2021-04-20)

  • added –replace_chars option to to better manage what characters get replaced with blanks in labels

0.0.13 (2021-04-16)

  • added ability to to output confusion matrix as well (–output_conf_matrix and –conf_matrix_type)

0.0.12 (2021-04-14)

  • now handles keyboard interrupts properly

  • can use tflite model now as well, using –graph_type tflite

0.0.11 (2021-01-26)

  • can output predictions now in: csv, xml, json

  • can output predictions to stdout or a file and in: plaintext (current), csv, xml, json

0.0.10 (2021-01-25)

  • removed ability to split images into grid from

  • added tfic-export tool to export saved model folder to Tensorflow lite model

  • added support for using tflite models to tfic-poll and tfic-labelimage

0.0.9 (2020-10-21)

  • accidentally redefined variable for resetting the session.

0.0.8 (2020-10-21)

  • now re-initializes the Tensorflow session every X processed images to avoid out of memory problems (–reset_session option).

0.0.7 (2020-09-22)

  • now outputs the top-X predictions with the correct labels/probability

0.0.6 (2020-09-02)

  • in non-continuous mode now works as expected (didn’t scan input directory previously)

0.0.5 (2020-08-06)

  •, and can now re-use the info JSON file generated by to simplify command-line parameters (input_height, input_width, input_layer, output_layer, labels)

  • improved help output of argument parsers: outputting description, command-line and default values now

0.0.4 (2020-08-04)

  • now has new –continuous flag to allow for continuous or single batch predictions

0.0.3 (2020-07-28)

  • added ability to split images into grid of equal sized images, obtaining a classification for each sub-image.

  • fixed license: now uses Apache 2.0 instead of MIT

0.0.2 (2019-11-14)

  • added missing

0.0.1 (2019-11-01)

  • initial release

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