Project code for Udacity's AI Programming with Python Nanodegree program. In this project, students first develop code for an image classifier built with PyTorch, then convert it into a command line application.
Install requirements into a new conda environment.
conda env create -f enviroment.yml [-n aipnd]
Run setup script to download and unzip flowers datasets into flowers directory.
./misc/setup.sh
Note: setup.sh uses wget to download the compressed archive. If on a mac
install wget first:
brew install wget
train.py
Train a new image classifier or continue training a classifier using a previously saved checkpoint.
predict.py
Load a saved model checkpoint and predict an image's top K most probable classes.
misc/batch_training.sh and misc/batch_training.py
Train each of the model architectures using the hyperparameters loaded from a
yaml file. Training output is written to stdout and saved to a logfile for each
architecture. Example yaml file and logs are in batch_example
Both scripts take the same options. I just wanted to see which I liked writing more.
To save disk space, you can run the cleanup script to remove the three image
datasets from flowers/
./misc/cleanup.sh