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Contrib Add HTML Demo - Second Try (#528)
* HTML Demo initial upload * Updated UI code to run from Blob images * Final UI changes * Removed separate references * Added notebook for deploying to app service. Updated readmes for JupyterCode and root directory. * Notebook updates * Update blob storage links * Update readme.md * Renamed notebook 30 to 3. Added ../../.. to the path so it will work in its new directory. Removed the log stream commands, as they do not work on all plans. Fixed some typos throughout. * Updated notebooks to reflect new paths and names * update screenshot * Update readme.md * update jpgs screenshot * Update readme.md * Update readme.md * Update readme.md * Update readme.md * Update readme.md * Update readme.md * Update readme.md * Update readme.md * Update readme.md * Update readme.md * Update readme.md * Update readme.md * Update readme.md * Update readme.md * Updated readme * Update JupyterCode readme some more * Update readme * Minor tweaks to readmes * Added about tab * Update readme.md * Update readme.md * Update readme.md * Update readme.md * Fix broken link Co-authored-by: Charles Henneberger <cihen@bu.edu> Co-authored-by: primnp <51148602+primnp@users.noreply.github.com> Co-authored-by: Prim P <p.nuwapa@gmail.com> Co-authored-by: mullaa <48615903+mullaa@users.noreply.github.com>
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contrib/README.md

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@@ -14,5 +14,6 @@ Each project should live in its own subdirectory ```/contrib/<project>``` and co
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## Tools
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| Directory | Project description | Build status (optional) |
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|---|---|---|
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| [HTML Demo](html_demo) | These files provide an HTML web page that allows users to visualize the output of a deployed computer vision DNN model. Users can improve on and gain insights from their deployed model by uploading query/test images and examining the model results for correctness through the user interface. The interface includes sample query/test images for testing your own model and example output for 3 types of models: image classification, object detection, and image similarity. | |
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| [vm_builder](vm_builder) | This script helps users create a single Ubuntu Data Science Virtual Machine with a GPU with the computer vision recipes repo installed and ready to be used. If you find the script to be out-dated or not working, you can create the VM using the Azure portal or the Azure CLI tool with a few more steps. | |
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| [vmss_builder](vmss_builder) | This script helps you setup a cluster of virtual machines with the computer vision recipes repo pre-installed using VMSS. This cluster is designed to be temporal, ie to be spun up and torn down. Users for this cluster will be prescribed a username/password/ip. This setup can be used for hands-on / lab sessions when you need to prepare multiple VM environments for a short period.|

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