This is an image classifier for this dataset: http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html. It runs from the command line using argparse and pytorch.
The first file, train.py, trains a classifier (densenet or vgg) and saves the trained model as a checkpoint. The second file, predict.py, uses the saved model to predict the class for an input image.
Help:
python train.py -h
Basic usage:
python train.py ./flowers/
Prints out training loss, validation loss, and validation accuracy as the network trains.
Checkpoint saved to model1 directory :
python train.py data_dir --save_dir model1
Choose architecture:
python train.py data_dir --arch vgg11
Set parameters:
python train.py data_dir --learning_rate 0.01 --hidden_units 512 --epochs 10
Enable CUDA:
python train.py data_dir --gpu
Help:
python predict.py -h
Prediction:
python predict.py flowers/test/1/image_06743.jpg checkpoint.pth
Outputs top 7 probabilities:
python predict.py input checkpoint --top_k 7
Uses CUDA for the forward step:
python predict.py input checkpoint --gpu
