In this project you will use a created image classifier to identify dog breeds.
Using an image classification application using a deep learning model called a convolutional neural network (often abbreviated as CNN). CNNs work particularly well for detecting features in images like colors, textures, and edges; then using these features to identify objects in the images. You'll use a CNN that has already learned the features from a giant dataset of 1.2 million images called ImageNet. There are different types of CNNs that have different structures (architectures) that work better or worse depending on your criteria. With this project you'll explore the three different architectures (AlexNet, VGG, and ResNet) and determine which is best for your application.
We have provided you with a classifier function in classifier.py that will allow you to use these CNNs to classify your images. The test_classifier.py file contains an example program that demonstrates how to use the classifier function.
. Correctly identify which pet images are of dogs (even if breed is misclassified) and which pet images aren't of dogs.
. Correctly classify the breed of dog, for the images that are of dogs.
. Determine which CNN model architecture (ResNet, AlexNet, or VGG), "best" achieve the objectives 1 and 2.
. Consider the time resources required to best achieve objectives 1 and 2, and determine if an alternative solution would have given a "good enough" result, given the amount of time each of the algorithms take to run.