#CustomVision – Es el momento de mover los proyectos de Custom Vision a #Azure!

Buenas !

Durante las ultimas semanas he escrito mucho sobre Custom Vision, ejemplos sobre como exportar modelos a formato ONNX o a imágenes para Docker; y luego utilizar estos modelos en apps de Consola, o en UWP Apps, inclusive con Docker en una Raspberry Pi. A este post lo tengo en borrador desde hace un tiempo, por lo que lo mejor sera que lo publique lo antes posible.

Si eres usuario de CustomVision.ai, seguramente has visto el siguiente mensaje cuando accedes al portal. El mismo nos avisa que el servicio dejara de estar disponible en modo preview / test el día 2019-03-19. Esto implica que si quieres seguir utilizando CV, debes mover tus proyectos a Azure.

Custom Vision moved to Azure

Una opción puede ser crear nuevamente los proyectos de CV, cargar las imágenes y hacer todo el proceso de etiquetado y entrenamiento desde cero. Esa opción es valida. Sin embargo, los nuevos proyectos tendrán nuevos IDs y también nuevas URLs para acceder a los HTTP EndPoints de los mismos. La otra opción es [mover a Azure] los proyectos de CV.

Lo primero que debemos hacer es crear un Custom Vision resource en una suscripción de Azure. Si conoces Azure estos son 2 clics, y muy fáciles.

azure custom vision resource

Podemos seguir utilizando un plan Free, con los siguientes parámetros:

  • Up to 2 projects
  • Limit of 5000 training images
  • 2 transactions per seconds
  • Limit of 10000 predictions per month

Custom Vision Azure Prices

Una vez creado el resource en Azure, debemos volver al portal de CustomVision.ai, seleccionar el proyecto que queremos migrar y en la sección Settings veremos una opcion [Move to Azure] en la esquina izquierda inferior.

Custom Vision move to Azure button

Teniendo en cuenta que solo podemos usar Proyectos de CV en una única región, por ahora, tendremos que completar los datos para mover el proyecto. Y listo! El proyecto de CV esta migrado a Azure 😀

Custom Vision move to Azure only in South Central

Happy Coding!

Greetings @ Toronto

El Bruno

Resources

  1. Object recognition with Custom Vision and ONNX in Windows applications using WinML
  2. Object recognition with Custom Vision and ONNX in Windows applications using WinML
  3. Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, drawing frames
  4. Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, calculate FPS
  5. Can’t install Docker on Windows 10 Home, need Pro or Enterprise
  6. Running a Custom Vision project in a local Docker Container
  7. Analyzing images in a Console App using a Custom Vision project in a Docker Container
  8. Analyzing images using PostMan from a Custom Vision project hosted in a Docker Container
  9. Building the CustomVision.ai project in Docker in a RaspberryPi
  10. Container dies immediately upon successful start in a RaspberryPi. Of course, it’s all about TensorFlow dependencies
  11. About ports, IPs and more to access a container hosted in a Raspberry Pi
  12. Average response times using a CustomVision.ai docker container in a RaspberryPi and a PC

Windows 10 and YOLOV2 for Object Detection Series

 

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#CustomVision – It’s time to move your Custom Vision projects to #Azure!

Hi !

I’ve been writing a lot about Custom Vision, and how use and export CV models to ONNX or docker images to be used later in different types of scenarios. I got this post in draft mode, so it’s time to publish it.

If you are using CustomVision.ai, you probably notice the warning message about the service being moved from a preview / test stage on 2019-03-19. That’s mean that you need to move your CV projects to a valid Azure account if you want to use them.

Custom Vision moved to Azure

You may want to create and train again some cv projects, however you will get new project ids, new urls and you need to tag again all the images.

The 1st action here, is to create a Custom Vision resource in a valid Azure account. That’s a 2 click tutorial and it’s also very easy.

azure custom vision resource

There is also the option to continue working in a free mode scenario with the following parameters in the Free Instance:

  • Up to 2 projects
  • Limit of 5000 training images
  • 2 transactions per seconds
  • Limit of 10000 predictions per month

Custom Vision Azure Prices

Now we can go back to the Custom Vision.ai portal and select the project we want to migrate to Azure. In the Settings section, at the bottom left corner we have the [Move to Azure] option.

Custom Vision move to Azure button

Here we need to select the specific values of the resource we created before and that’s it! The Custom Vision project now is fully migrated to Azure 😀

Custom Vision move to Azure only in South Central

Happy Coding!

Greetings @ Toronto

El Bruno

Resources

  1. Object recognition with Custom Vision and ONNX in Windows applications using WinML
  2. Object recognition with Custom Vision and ONNX in Windows applications using WinML
  3. Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, drawing frames
  4. Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, calculate FPS
  5. Can’t install Docker on Windows 10 Home, need Pro or Enterprise
  6. Running a Custom Vision project in a local Docker Container
  7. Analyzing images in a Console App using a Custom Vision project in a Docker Container
  8. Analyzing images using PostMan from a Custom Vision project hosted in a Docker Container
  9. Building the CustomVision.ai project in Docker in a RaspberryPi
  10. Container dies immediately upon successful start in a RaspberryPi. Of course, it’s all about TensorFlow dependencies
  11. About ports, IPs and more to access a container hosted in a Raspberry Pi
  12. Average response times using a CustomVision.ai docker container in a RaspberryPi and a PC

Windows 10 and YOLOV2 for Object Detection Series