How to use this

Overview

• select some great and some bad photos from your library
• train a classification model using train.py
• classify a bunch of photos from your library using the trained model with classify.py
• improve trained model

Setup

• install required Python 3 packages

  pip install tensorflow scikit-learn Pillow
  

• on macOS you need tensorflow-macos

Selecting photos for training

• copy randomly selected photos from your library to the photos folder

  find /path/to/photo/library -type f -iname "*.jpg" -print0 | shuf -z -n 100 | xargs -0 -I{} cp -v {} ./photos

• manually move great ones to the ./photos/great folder
• manually move bad ones to the ./photos/bad folder

Train model

• make sure you have at least 100 photos in the photos folder (the more, the merrier)
• launch training

  python3 train.py

• model will be saved to model.h5

Classify photos

• make sure you have some photos in the classified_photos folder

  find /path/to/photo/library -type f -iname "*.jpg" -print0 | shuf -z -n 100 | xargs -0 -I{} cp -v {} ./classified_photos

• launch classification

  python3 classify.py

• verify if photos in classified_photos/bad folder are actually bad and classified_photos/great photos are actually great.

• incorrectly classified photos should be added as photos for training to the respective folder in photos

Improving model

• delete sub-folders from data folder
• run train.py again

  python3 train.py

Contributing

• looking forward to your PR!

Troubleshooting

• on macOS, you should use macOS built-in Python 3 and not the brew version for tensorflow to work