Book Image

Intelligent Mobile Projects with TensorFlow

By : Jeff Tang
Book Image

Intelligent Mobile Projects with TensorFlow

By: Jeff Tang

Overview of this book

As a developer, you always need to keep an eye out and be ready for what will be trending soon, while also focusing on what's trending currently. So, what's better than learning about the integration of the best of both worlds, the present and the future? Artificial Intelligence (AI) is widely regarded as the next big thing after mobile, and Google's TensorFlow is the leading open source machine learning framework, the hottest branch of AI. This book covers more than 10 complete iOS, Android, and Raspberry Pi apps powered by TensorFlow and built from scratch, running all kinds of cool TensorFlow models offline on-device: from computer vision, speech and language processing to generative adversarial networks and AlphaZero-like deep reinforcement learning. You’ll learn how to use or retrain existing TensorFlow models, build your own models, and develop intelligent mobile apps running those TensorFlow models. You'll learn how to quickly build such apps with step-by-step tutorials and how to avoid many pitfalls in the process with lots of hard-earned troubleshooting tips.
Table of Contents (14 chapters)

Retraining SSD-MobileNet and Faster RCNN models

The pre-trained TensorFlow Object Detection models certainly work well for some problems. But sometimes, you may need to use your own annotated dataset (with bounding boxes around objects or parts of objects that are of particular interest to you) and retrain an existing model so it can more accurately detect a different set of object classes.

We’ll use the same Oxford-IIIT Pets dataset, as documented in the TensorFlow Object Detection API site, to retrain two existing models on your local machine, instead of using Google Cloud covered in the documentation. We’ll also add an explanation for each step when needed. The following is the step-by-step guide on how to retrain a TensorFlow object detection model using the Oxford Pets dataset:

  1. In a Terminal window, preferably on our GPU-powered Ubuntu to make the retraining...