Updated Using a TensorFlow Pretrained Model to Detect Everyday Objects (markdown)

Using-a-TensorFlow-Pretrained-Model-to-Detect-Everyday-Objects.md

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 ### Introduction
 Teams have the option of using a custom TensorFlow object detection model to detect objects other than the current season's game elements.  This tutorial demonstrates how use a pretrained model to look for and track everyday objects.  This particular tutorial uses the OnBot Java programming tool, but teams can also use Android Studio or the Blocks programming tool to implement this example.  This tutorial covers an advanced topic and assumes that the user has good programming knowledge and is familiar with Android devices and computer technology.
 
+<p align="center">[[/images/Using-a-TensorFlow-Pretrained-Model-to-Detect-Everyday-Objects/tfliteDemo.png]]<br/>TensorFlow can be used to recognize everyday objects like a clock, a cellphone or a keyboard.<p> 
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 This tutorial also assumes that you have already completed the steps in [a previous TensorFlow tutorial](Using-a-Custom-TensorFlow-Model-with-Java).  If you have not yet completed the steps in the [previous TensorFlow tutorial](Using-a-Custom-TensorFlow-Model-with-Java), then please do so before continuing with this tutorial.
 
 ### Downloading the Pretrained Model and Label Map