Dog-Breed Classifier - CNN Project

This project is a part of Udacity Deep Learning Nanodegree Program.

Project Overview

The aim of this project is to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. The code accepts any user-supplied image as input and performs the following tasks:

1. If a dog is detected in the image, the algorithm will return the predicted breed.

2. If supplied an image of a human, the code will identify the resembling dog breed.

   Convolutional Neural Network models are used to perform the above classifications.

   Technologies Used

3. Python (version 3.6.3)

4. Pytorch (version 0.4.0)

5. Jupyter Notebook

   Steps Involved

   1. Detect Human Faces: OpenCV’s implementation of Haar feature-based cascade classifiers is used to detect human faces in images.

   2. Detect Dogs: Pre-trained VGG-16 model is used to detect dogs.

   3. Create a CNN to Classify Dog Breeds (from Scratch): The CNN is built from scratch to perform the above classification.

   4. Create a CNN to Classify Dog Breeds (using Transfer Learning): Pre-trained VGG-19 model is used for transfer learning.