In this project, we develop an algorithm that takes in an image and identifies if contains a dog or a human. If it does contain either a dog or a human, the algorithm will classify the dog's breed or the dog breed that closely resembles that human. This problem falls under the popular category of computer vision. To solve these problems we will be using machine learning methods. We will look at a couple of different methods for each problem and identify which is better. We will also focus on the dog breed classifier and use off-the-shelf tools for the human and dog detectors.

In this article, we will learn how to classify images based on fine details of images using a stacked pre-trained model to get maximum accuracy in TensorFlow. Hey folks, I hope you have done some image classification using pre-trained TensorFlow or TensorFlowor other CNN pre-trained models and might have some idea about how we classify images, but when it comes to classifying finely detailed objects (dog breed, cat breed, leaves diseases) this method doesn't give us a good result, in this case, we would prefer model stacking to capture most of the details. Let's get straight to the technicalities of it. In our dataset, we have 120 dog breeds and we will have to classify them using a stacked pre-trained model (TensorFlow, Densenet121) which is trained on Imagenet. We will stack bottleneck features extracted by these models for greater accuracy that will depend on the models we are stacking together.

Dogs are man's best friend and they deserve to be identified correctly. In pursuit of differentiating a Husky (Go Dawgs!) from an Alaskan Malamute, let's learn how to use transfer learning to classify dog breeds. Find the entire Jupyter Notebook on my GitHub. NOTE: This project/article is based off of Udacity's skeleton Dog Breed Classifier project as part of the AIND program with certain modifications. As always with most of my technical posts, we need to make sure we have the data we want to work with.