From 1d7fe3d6197a0bdd564b526a914530d1fb9bd112 Mon Sep 17 00:00:00 2001 From: FTC Engineering Date: Wed, 21 Oct 2020 18:14:48 -0400 Subject: [PATCH] Updated Using TensorFlow Lite for the Ultimate Goal Challenge (markdown) --- Using-TensorFlow-Lite-for-the-Ultimate-Goal-Challenge.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Using-TensorFlow-Lite-for-the-Ultimate-Goal-Challenge.md b/Using-TensorFlow-Lite-for-the-Ultimate-Goal-Challenge.md index a3df4c3..5d70a94 100644 --- a/Using-TensorFlow-Lite-for-the-Ultimate-Goal-Challenge.md +++ b/Using-TensorFlow-Lite-for-the-Ultimate-Goal-Challenge.md @@ -1,7 +1,7 @@ ### What is a TensorFlow Lite? [TensorFlow Lite](https://www.tensorflow.org/lite/) is a lightweight version of Google's [TensorFlow](https://www.tensorflow.org/) machine learning technology that is designed to run on mobile devices such as an Android smartphone. A _trained TensorFlow model_ was developed to recognize game elements for the 2020-2021 Ultimate Goal presented by QualComm challenge. This TensorFlow model has been integrated into the FIRST Tech Challenge Control System software and can be used to identify and track these game pieces during a match. -

[[/images/Blocks-Sample-TensorFlow-Object-Detection-Op-Mode/quadAndSingle.png]]
Create an Op Mode with ConceptTensorFlowObjectDetection as its template.

+

[[/images/Blocks-Sample-TensorFlow-Object-Detection-Op-Mode/quadAndSingle.png]]
This season's inference model can recognize and track a stack of four rings or a single ring.

For this season's challenge, the inference model was trained to recognize a single Ultimate Goal ring. The model was also trained to recognize a stack of four rings. This was done since it is easier to train a model to distinguish between a single and quadruple ring stack, rather than training a model that can reliably distinguish the individual rings of a multi-ring stack.