Flower image classifier with Pytorch

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.

Jupyter Notebook Files

Command line application

Train classifier:

python train.py <data_dir> --save_dir <checkpoint folder> -g

Example:
python train.py flowers --save_dir checkpoints -g

Argument	Short	Default	Description
data_dir			Folder path for the flower images
--save_dir		checkpoints	Folder path to save the checkpoints
--arch		vgg16	CNN Model Architecture (vgg16 or densenet121)
--learning_rate	-l	0.001	Learning rate
--epochs	-e	1	Epochs to train the model
--hidden_units_01	-h1	4096	Hidden units of the first layer
--hidden_units_02	-h2	1024	Hidden units of the second layer
--checkpoint_path	-cp	None	Path of a checkpoint you want to reuse
--gpu	-g	False	Use gpu if available

Predict image:

python predict.py <image_path> <checkpoint_path> -g

Example:
python predict.py flowers/test/1/image_06764.jpg checkpoints/checkpoint_best_accuracy.pth -g

Argument	Short	Default	Description
image_path			Image path for the prediction
checkpoint_path		checkpoints/checkpoint_best_accuracy.pth	Checkpoint path
--top_k	-k	1	Number of the top k most likely classes
--json_path	-json	cat_to_name.json	JSON file path to map categories to real names
--gpu	-g	False	Use gpu if available

License