From 5abbd9409f89a9e72778be29c4e7af5d4ddfeb3a Mon Sep 17 00:00:00 2001 From: Westside Robotics Date: Fri, 19 Nov 2021 08:45:42 -0800 Subject: [PATCH] Add multiple links to FTC Machine Learning toolchain --- FTC-Webcam-Controls.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/FTC-Webcam-Controls.md b/FTC-Webcam-Controls.md index 4ab022f..4d7152b 100644 --- a/FTC-Webcam-Controls.md +++ b/FTC-Webcam-Controls.md @@ -125,7 +125,7 @@ Full details are described in the [GainControl Javadoc](https://javadoc.io/stati We interrupt this tutorial to demonstrate the two webcam interfaces described so far: ExposureControl and GainControl. -These 2 examples assume you are already using TensorFlow Object Detection (TFOD) in the Freight Frenzy game. Namely you have a TFOD model and OpMode that are working reasonably well. The model may have been supplied with the FTC SDK, or created with the [FTC Machine Learning toolchain](https://ftc-ml.firstinspires.org/). +These 2 examples assume you are already using TensorFlow Object Detection (TFOD) in the Freight Frenzy game. Namely you have a TFOD model and OpMode that are working reasonably well. The model may have been supplied with the FTC SDK, or created with the **FTC Machine Learning toolchain** [[forum]](https://community.ftclive.org/) [[guide]](https://github.com/FIRST-Tech-Challenge/fmltc/blob/main/doc/usage.md) [[source]](https://github.com/FIRST-Tech-Challenge/fmltc). Here we will discuss only the Duck game element. **Can the exposure and/or gain controls improve the chance of a fast, accurate TFOD detection?**