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.
Image Classifier Trainer
positional arguments:
data_dir directory containing testing, validation, and training
images
optional arguments:
-h, --help show this help message and exit
--checkpoint CHECKPOINT
load pre-existing checkpoint for further training
--arch NETWORK pre-trained feature set model name (default: resnet50)
--save_dir SAVE_DIR directory in which to save checkpoint
--learning_rate LEARNING_RATE
optimizer learning rate (default: .001)
--hidden_units HIDDEN_UNITS
number of hidden units (default: 2)
--epochs EPOCHS number of training passes. 0 for no training
--gpu use GPU for computation, if available
--test run tests against testing images
--no-save do not save a checkpoint
Example Usage:
python train.py flowers --epochs 15 --gpu
Will train a model on the ./flowers directory for 15 epochs using the GPU and save a checkpoint to ./checkpoint.pth.
Image Classifier Predictor
positional arguments:
image_path path to test image
optional arguments:
-h, --help show this help message and exit
--checkpoint CHECKPOINT_PATH
path to checkpoint
--category_names JSON_PATH
path to json containing category names
--top_k TOP_K number of predictions to display
--gpu use GPU for computation, if available
Example Usage:
python predict.py flowers/test/1/image_06743.jpg --category_names cat_to_name.json --gpu
Will load the classifier stored in ./checkpoint.pth and provide 5 class predictions for the supplied image and categories using the GPU
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