Create Your Own Image Classifier

Submission Files

The submission includes all required files. (Model checkpoints not required.)

Package Imports

All the necessary packages and modules are imported in the first cell of the notebook

Training data augmentation

torchvision transforms are used to augment the training data with random scaling, rotations, mirroring, and/or cropping

Data normalization

The training, validation, and testing data is appropriately cropped and normalized

Data batching

The data for each set is loaded with torchvision's DataLoader

Data loading

The data for each set (train, validation, test) is loaded with torchvision's ImageFolder

Pre-trained Network

A pretrained network such as VGG16 is loaded from torchvision.models and the parameters are frozen

Feedforward Classifier

A new feedforward network is defined for use as a classifier using the features as input

Training the network

The parameters of the feedforward classifier are appropriately trained, while the parameters of the feature network are left static

Testing Accuracy

The network's accuracy is measured on the test data

Validation Loss and Accuracy

During training, the validation loss and accuracy are displayed

Saving the model

The trained model is saved as a checkpoint along with associated hyperparameters and the class_to_idx dictionary

Loading checkpoints

There is a function that successfully loads a checkpoint and rebuilds the model

Image Processing

The process_image function successfully converts a PIL image into an object that can be used as input to a trained model

Class Prediction

The predict function successfully takes the path to an image and a checkpoint, then returns the top K most probably classes for that image

Sanity Checking with matplotlib

A matplotlib figure is created displaying an image and its associated top 5 most probable classes with actual flower names

Training a network

train.py successfully trains a new network on a dataset of images and saves the model to a checkpoint

Training validation log

The training loss, validation loss, and validation accuracy are printed out as a network trains

Model architecture

The training script allows users to choose from at least two different architectures available from torchvision.models

Model hyperparameters

The training script allows users to set hyperparameters for learning rate, number of hidden units, and training epochs

Training with GPU

The training script allows users to choose training the model on a GPU

Predicting classes

The predict.py script successfully reads in an image and a checkpoint then prints the most likely image class and it's associated probability

Top K classes

The predict.py script allows users to print out the top K classes along with associated probabilities

Displaying class names

The predict.py script allows users to load a JSON file that maps the class values to other category names

Predicting with GPU

The predict.py script allows users to use the GPU to calculate the predictions