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Project Overview

Welcome to the Convolutional Neural Networks (CNN) project. In this project, we will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, our algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

Sample Output

Along with exploring state-of-the-art CNN models for classification and localization, we will make important design decisions about the user experience for the app.

Project Instructions

Instructions

  1. Clone the repository and navigate to the downloaded folder.

    	git clone https://github.com/Kabongosalomon/CNN-Project-Dog-Breed-Classifier.git
	cd CNN-Project-Dog-Breed-Classifier

  2. Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. The dogImages/ folder should contain 133 folders, each corresponding to a different dog breed.

  3. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder.

  4. Make sure you have already installed the necessary Python packages according to the README in the program repository.

  5. Open a terminal window and navigate to the project folder. Open the notebook and follow the instructions.

    	jupyter notebook dog_app.ipynb