First draft of TFOD entry page, updated for Freight Frenzy. Not linked yet, still private.

Using-TensorFlow-in-Freight-Frenzy.md

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+### What is TensorFlow?
+FTC teams can use [TensorFlow Lite](https://www.tensorflow.org/lite/), 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 2021-2022 Freight Frenzy challenge.
+
+_<p align="center">[[https://raw.githubusercontent.com/wiki/WestsideRobotics/FTC-training/Images/010-TFOD-Cube-Duck-crop-2.png]]<br/>
+This season's TFOD model can recognize Freight elements._
+<br/>
+
+TensorFlow Object Detection (TFOD) has been integrated into the FTC control system software, to identify and track these game pieces during a match.  The FTC software (SDK version 7.0) contains TFOD Sample Op Modes that can recognize the Freight elements Duck, Box (or Cube), and Cargo (or Ball).
+
+Also, FTC teams can soon use a new tool to train their own TFOD models, to recognize their custom Team Shipping Element (TSE) and/or to improve recognition of Freight elements.  This training could take into account certain conditions of distance, angle, lighting and background.
+
+This new tool, FTC Machine Learning Toolchain, was [announced 10/7/2021](http://firsttechchallenge.blogspot.com/2021/10/new-machine-learning-tool-beta-testing.html) for upcoming beta testing by interested FTC teams.
+
+### How Might a Team Use TensorFlow in Freight Frenzy?
+
+For this season's challenge, during the pre-Match stage a single die is rolled and the field is randomized.
+
+
+_<p align="center">[[https://raw.githubusercontent.com/wiki/WestsideRobotics/FTC-training/Images/020-TFOD-Barcode.png]]<br/>
+Randomization_
+<br/>
+
+At the beginning of the match's Autonomous period, a robot can use TensorFlow to "look" at the **Barcode** area and determine whether the Duck or optional Team Shipping Element (TSE) is in position 1, 2 or 3.  This indicates the preferred scoring level on the **Alliance Shipping Hub**.  A bonus is available for using the TSE instead of a Duck.
+
+_<p align="center">[[https://raw.githubusercontent.com/wiki/WestsideRobotics/FTC-training/Images/030-TFOD-levels.png]]<br/>
+Alliance Shipping Hub_
+<br/>
+
+### Important Note on Phone Compatibility
+
+TensorFlow Lite runs on Android 6.0 (Marshmallow) or higher, a requirement met by all currently allowed FTC devices.  If you are a Blocks programmer using an older/disallowed Android device that is not running Marshmallow or higher, TFOD Blocks will automatically be missing from the Blocks toolbox or design palette.
+
+### Sample Op Modes
+
+The FTC software (SDK version 7.0 and higher) contains sample Blocks and Java op modes that demonstrate TensorFlow **recognition** of Freight elements Duck, Box (cube) and Cargo (ball).  The sample op modes also show **where** in the camera's field of view a detected object is located.
+
+Click on the following links to learn more about these sample Op Modes.
+
+* [Blocks TensorFlow Object Detection Example](https://github.com/FIRST-Tech-Challenge/FtcRobotController/wiki/Blocks-Sample-Op-Mode-for-TensorFlow-Object-Detection)
+* [Java TensorFlow Object Detection Example](https://github.com/FIRST-Tech-Challenge/FtcRobotController/wiki/Java-Sample-Op-Mode-for-TensorFlow-Object-Detection)
+
+### Using a Custom Inference Model
+
+Teams have the option of using a custom inference model with the FIRST Tech Challenge software.  As in the past, some teams may want to use the [TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) to create an enhanced model of the Freight elements or TSE, or to create a custom model to detect other entirely different objects.  Other teams might also want to use an available pre-trained model to build a robot that can detect common everyday objects (for demo or outreach purposes, for example).
+
+The FTC software includes sample op modes (Blocks and Java versions) that demonstrate how to use a **custom inference model**:
+
+* [Using a Custom TensorFlow Model with Blocks](https://github.com/FIRST-Tech-Challenge/FtcRobotController/wiki/Using-a-Custom-TensorFlow-Model-with-Blocks)
+* [Using a Custom TensorFlow Model with Java](https://github.com/FIRST-Tech-Challenge/FtcRobotController/wiki/Using-a-Custom-TensorFlow-Model-with-Java)
+
+These tutorials use examples from a previous FTC season (Skystone), but the process remains generally valid for Freight Frenzy.
+
+As noted above, soon FTC teams will have a streamlined tool for training their own TFOD models.  Watch for announcements regarding the FTC Machine Learning Toolchain, already scheduled for beta testing.
+
+### Detecting Everyday Objects
+
+You can use a pretrained TensorFlow Lite model to detect **everyday objects**, such as a clock, person, computer mouse, or cell phone.  The following advanced tutorial shows how you can use a free, pretrained model to recognize numerous everyday objects.
+
+* [Using a TensorFlow Pretrained Model to Detect Everyday Objects](https://github.com/FIRST-Tech-Challenge/FtcRobotController/wiki/Using-a-TensorFlow-Pretrained-Model-to-Detect-Everyday-Objects)
+
+_<p align="center">[[https://raw.githubusercontent.com/wiki/FIRST-Tech-Challenge/FtcRobotController/images/Using-a-TensorFlow-Pretrained-Model-to-Detect-Everyday-Objects/tfliteDemo.png]]<br/>TensorFlow can recognize everyday objects._
+<br/>
+ 
+_<p align="right">updated 10/21/21</p>_