In this chapter, we are going to introduce you to an open source package called TensorFlowSharp. More specifically, we will be using the TensorFlow[1] Object Detection API, which is an open source framework built on top of TensorFlow, which makes it easy to construct, train, and deploy various forms of object detection models.
For those not familiar with TensorFlow, the following is an excerpt from the TensorFlow website[2]:
"TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (such as CPUs, GPUs, and TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google's AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains."
TensorFlowSharp provides .NET bindings to the TensorFlow library, which are published here in case you ever need them: https://github.com/tensorflow/tensorflow.
The topics included in this chapter are as follows:
- Working with Tensors
- TensorFlowSharp
- Developing your own TensorFlow application
- Detecting images
- Minimum score for object highlighting