Deep Learning

Creating an image classifier

Objectives

Imagine an iphone app that tells you the name of the flower your camera is looking at.
In practice, we train an image classifier to recognize different species of flowers, then we export for use use in an application.
Once the project completed in a jupyter notebook, we create a standalone command line application that can be trained on any set of labeled images.

Results

We use the trained model to predict this image

Here is the result:

We use matplotlib to plot the probabilities for the top 5 classes as a bar graph, along with the input image.

Performance metrics

The Accuracy of the network on the test images is 86%

Model and classifier choices

To build and train a classifier, we use one of the pretrained models from torchvision.models: VGG16, to get the image features. We build and train a new feed-forward classfier using those features.

Install

This project requires Python 3.x and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• PyTorch

You will also need to have software installed to run and execute an iPython Notebook

We recommend to install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.

Code

The code is provided in the image_classifier.ipynb notebook file. The code included in visuals.py is meant to provide the visualizations created in the notebook.

Run

In a terminal or command window, navigate to the top-level project directory classify_images_with_PyTorch/ (that contains this README) and run one of the following commands:

ipython notebook image_classifier.ipynb

or

jupyter notebook image_classifier.ipynb

This will open the iPython Notebook software and project file in your browser.

Data

We use this dataset of 102 flower categories.