In this project I use deep learning with PyTorch to make an image classifier that predicts the top K flower classes and their associated probabilities from a picture.
This project uses the 102 Category Flower Dataset from the University of Oxford. It consist of 102 categories of flowers, each containing 40 to 258 images.
The image_classifier.ipynb file contains the Jupyter Notebook for the design, training, testing and evaluation of the deep learning model.
The files model_ic.py, utils_ic.py, train.py, predict.py convert the model into a command line application. train.py trains a new network on a dataset and saves the model as a checkpoint. predict.py uses a trained network to predict the class for an input image.
I completed this project as a part of the Udacity Data Scientist Nanodegree program.