Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you’d use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications.
In this project, I trained an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you’d train this classifier, then export it for use in your application. We’ll be using this dataset from Oxford of 102 flower categories, you can see a few examples.
I used the MobileNet pre-trained model from TensorFlow Hub to get the image features. Build and train a new feed-forward classifier using those features. Classification probability of some examples are shown below.
Purpose : Creating a image classifier model for mobile phone.
Contribution : Using MobileNet architecture, I obtained image classifier model.
Completion Date : 10.03.2020