This image classifier project is a requirement to complete the Udacity & AWS AI Programming with Python Nanodegree scholarship. In this project, students first develop code for an image classifier built with PyTorch, then convert it into a command line application. The project utilizes the dataset of about 8200 images of flowers divided into 102 classes.
Image Classifier Project.ipynb
: a Jupyter notebook, contains the whole project code to build and train an image classifier.train.py
: trains a pre-trained network on the dataset and saves the model as a checkpoint.predict.py
: uses a saved model to predict the class for an input image.cat_to_name.json
: a JSON object with dictionary mapping of the flower names.
-
Train a new network on a data set with
train.py
- Basic usage:
python train.py data_directory
- Prints out training loss, validation loss, and validation accuracy as the network trains
- Options:
- Set directory to save checkpoints:
python train.py data_dir --save_dir save_directory
- Choose architecture:
python train.py data_dir --arch "vgg13"
- Set hyperparameters:
python train.py data_dir --learning_rate 0.01 --hidden_units 512 --epochs 20
- Use GPU for training:
python train.py data_dir --gpu
- Set directory to save checkpoints:
- Basic usage:
-
Predict flower name from an image with
predict.py
along with the probability of that name. That is, you'll pass in a single image/path/to/image
and return the flower name and class probability.- Basic usage:
python predict.py /path/to/image checkpoint
- Options:
- Return top K most likely classes:
python predict.py input checkpoint --top_k 3
- Use a mapping of categories to real names:
python predict.py input checkpoint --category_names cat_to_name.json
- Use GPU for inference: :
Use GPU for inference: python predict.py input checkpoint --gpu
- Return top K most likely classes:
- Basic usage: