Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, to include an image classifier in a smart phone app. That would be using 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, an image classifier is trained to recognize different species of flowers. Using something like this in a phone app that tells the name of the flower the camera is looking at. Here, this classifier is trained, then exported for use in the application. The dataset used this dataset from Oxford of 102 flower categories it is one of the tensorflow datsets.
The project is broken down into multiple steps:
- Load the image dataset and create a pipeline.
- Build and Train an image classifier on this dataset.
- Use your trained model to perform inference on flower images.
