From 2c4dbf6c9d16da2ac6f9772e032fffb0ea076aa0 Mon Sep 17 00:00:00 2001 From: Jim Bennett Date: Tue, 6 Jul 2021 08:07:43 -0700 Subject: [PATCH] Spelling fixes (#164) * Adding content * Update en.json * Update README.md * Update TRANSLATIONS.md * Adding lesson tempolates * Fixing code files with each others code in * Update README.md * Adding lesson 16 * Adding virtual camera * Adding Wio Terminal camera capture * Adding wio terminal code * Adding SBC classification to lesson 16 * Adding challenge, review and assignment * Adding images and using new Azure icons * Update README.md * Update iot-reference-architecture.png * Adding structure for JulyOT links * Removing icons * Sketchnotes! * Create lesson-1.png * Starting on lesson 18 * Updated sketch * Adding virtual distance sensor * Adding Wio Terminal image classification * Update README.md * Adding structure for project 6 and wio terminal distance sensor * Adding some of the smart timer stuff * Updating sketchnotes * Adding virtual device speech to text * Adding chapter 21 * Language tweaks * Lesson 22 stuff * Update en.json * Bumping seeed libraries * Adding functions lab to lesson 22 * Almost done with LUIS * Update README.md * Reverting sunlight sensor change Fixes #88 * Structure * Adding speech to text lab for Pi * Adding virtual device text to speech lab * Finishing lesson 23 * Clarifying privacy Fixes #99 * Update README.md * Update hardware.md * Update README.md * Fixing some code samples that were wrong * Adding more on translation * Adding more on translator * Update README.md * Update README.md * Adding public access to the container * First part of retail object detection * More on stock lesson * Tweaks to maps lesson * Update README.md * Update pi-sensor.md * IoT Edge install stuffs * Notes on consumer groups and not running the event monitor at the same time * Assignment for object detector * Memory notes for speech to text * Migrating LUIS to an HTTP trigger * Adding Wio Terminal speech to text * Changing smart timer to functions from hub * Changing a param to body to avoid URL encoding * Update README.md * Tweaks before IoT Show * Adding sketchnote links * Adding object detection labs * Adding more on object detection * More on stock detection * Finishing stock counting * Tidying stuff * Adding wio purchase link * Updating Seeed logo * Update pi-proximity.md * Fix clean up link Fixes #145 * Moving attributions to a separate file * First draft of edge classifier * Adding extras * Moving folder * Adding lesson 11 questions * Image improvements * More image tweaks * Adding lesson 12 quiz * Quiz for lesson 13 * Adding quiz for lesson 14 * Lesson 15 and 16 quiz * Update README.md --- 5-retail/lessons/1-train-stock-detector/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/5-retail/lessons/1-train-stock-detector/README.md b/5-retail/lessons/1-train-stock-detector/README.md index e9dbdcb..8cfacfa 100644 --- a/5-retail/lessons/1-train-stock-detector/README.md +++ b/5-retail/lessons/1-train-stock-detector/README.md @@ -105,7 +105,7 @@ You can train an object detector using Custom Vision, in a similar way to how yo Call your project `stock-detector`. - When you create your project, make sure to use the `stock-detector-training` resource you created earlier. Use *Object Detection* project type, and the *Products on Shelves* domain. + When you create your project, make sure to use the `stock-detector-training` resource you created earlier. Use the *Object Detection* project type, and the *Products on Shelves* domain. ![The settings for the custom vision project with the name set to fruit-quality-detector, no description, the resource set to fruit-quality-detector-training, the project type set to classification, the classification types set to multi class and the domains set to food](../../../images/custom-vision-create-object-detector-project.png) @@ -135,7 +135,7 @@ To train your model you will need a set of images containing the objects you wan ![Tagging some tomato paste](../../../images/object-detector-tag-tomato-paste.png) - > 💁 If you have more than 15 images for each object, you can train after 15 then use the **Suggested tags** feature. This will use the trained model to detect the objecs in the untagged image. You can then confirm the detected objects, or reject and re-draw the bounding boxes. This can save a *lot* of time. + > 💁 If you have more than 15 images for each object, you can train after 15 then use the **Suggested tags** feature. This will use the trained model to detect the objects in the untagged image. You can then confirm the detected objects, or reject and re-draw the bounding boxes. This can save a *lot* of time. 1. Follow the [Train the detector section of the Build an object detector quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/get-started-build-detector?WT.mc_id=academic-17441-jabenn#train-the-detector) to train the object detector on your tagged images.