Doggo v1.0

The purpose of this project is to build an image classifier that accurately identifies pictures as containing a dog or not. Initially this was our goal, but as our classifier got more accurate in predicting pictures containing dogs, we decided to have it classify other animals as well. We are now able to run datasets of different animals through our model to train and test, then see if it can accurately predict a picture’s subject. Once this process is finished, we are able to save the model and pick back up where we left off next time we decide to work with it. Our average accuracy is currently at around 87% but requires more training as we input more diverse animals (other than just dog and cat).

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

What things you need to install the software and how to install them

Python 3.6 https://www.python.org/downloads/

Anaconda 5+ https://www.anaconda.com/download

conda update -n base conda

Keras https://keras.io/#installation

conda install -c conda-forge keras

Installing

The below steps will allow you to download this code and run it locally.

1. Clone git repository

   https://github.com/SirMrMistery/Doggo.git

2. Open Anaconda application

3. Open Spyder

4. Click File -> Open and open P2.py

Running tests

1. Click the play button at the top of Spyder to run P2.py

2. In the console space, type the number of the picture you would like

   the image classifier to run against

3. After seeing the prediction, enter in the console “0” to run more

   tests or anything else to stop execution

4. Repeat Steps 2 and 3 with different numbers of pictures to test the

   classifier against different pictures

Built With

• Python - programming language used

• Keras - API used for machine learning and Artificial intelligence

• Anaconda/Spyder - Environment used to code

• Github - collaboration space to push/pull code as a team

Authors

• Kevin Moore

• Cameron House

• Ricardo Lesmes-Navarro

• Fabian Desoto

• Joseph Price

• Felix Benavides

Acknowledgements

• We were inspired by

  https://becominghuman.ai/building-an-image-classifier-using-deep-learning-in-python-totally-from-a-beginners-perspective-be8dbaf22dd8 to create this image classifier. We reused several pieces of the code and modified other pieces to combine for a specialized image classifier that is now the final product we have today.

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