wai.tfimageclass 0.0.1

Image classification using tensorflow.

Image classification (not object detection) using tensorflow.

Based on example code located here:

https://www.tensorflow.org/hub/tutorials/image_retraining

Installation

• install virtual environment:

  virtualenv -p /usr/bin/python3.7 venv

• install dependencies:

  ./venv/bin/pip install -r requirements.txt

• 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 setup.py script):

    ./venv/bin/pip install .

Usage

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

Train

• 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

Predict

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

Changelog

0.0.1 (2019-11-01)

• initial release