#Event – I’ll be at the Caribbean Developer Conference on October ! #CDC2019

Hi !

Wow, I’ll completely amazed because I’ll have the chance to share some Machine Learning, Custom Vision and other experiences in the Caribbean Developer Conference in October.

Caribbean Developer Conference

This event is great and as usual, the list of speakers is AMAZING!

I’ll share more details later, and in the meantime, if you want to know more, the 2018 video recap is a great way to

Happy Coding!

Greetings @ Toronto

El Bruno

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#Event – Resources for the sessions about #DeepLearning and #CustomVision at the @ChicagoCodeCamp

Hi!

Another post-event post, this time with a big thanks to the team behind one of the most amazing event I’ve been this year: Chicago CodeCamp.

I had the chance to meet a lot of amazing people, to learn a lot during the sessions and also to visit the great city of Chicago.

As usual, now it’s time to share slides, code and more.

Deep Learning for Everyone? Challenge Accepted!

Let’s start with the Deep Learning resources


Demos Source Code: https://github.com/elbruno/events/tree/master/2019%2005%2011%20Chicago%20CodeCamp%20Deep%20Learning

Session: How a PoC at home can scale to Enterprise Level using Custom Vision APIs

And also the [How a PoC at home can scale to Enterprise Level using Custom Vision APIs] resources

Demos Source Code: https://github.com/elbruno/events/tree/master/2019%2005%2011%20Chicago%20CodeCamp%20CustomVision

And finally, some Machine Learning.Net, Deep Learning and Custom Vision resources:

My posts on Custom Vision and ONNX

  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

See you next one in Chicago for some Deep Learning fun!

Happy coding!

Greetings @ Toronto

El Bruno

#AI – Exporting #CustomVision projects to #docker for #RaspberryPi, the extra 2 steps

Hi !

I wrote several posts on how to create a image analysis solution using CustomVision.ai and how to export and use the project in a Raspberry Pi with Docker.

In my posts, I created a custom docker file for RPI using as a base the Linux one, from CustomVision.ai.

There is a new feature in Custom Vision, which allows us to directly export the docker image for Raspberry Pi.

This is amazing, and because I’m going to use it on Chicago CodeCamp, I decided to test it. And, of course, I got a couple of ugly errors when I try to build my image in the device.

Once I edit and read the docker file content, I realized that I need to disable the CROSS-BUILD option

And that’s it, now I’m waiting for the image to finish and I’ll be ready to test it!

Happy coding!

Greetings @ Toronto

El Bruno

My Posts

  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

#Event – See you @ChicagoCodeCamp on May 11, 2019 for some Deep Learning and Custom Vision experiences

Hi !

I’m very lucky to be at the next Chicago CodeCamp with another session around Custom Vision:

How a PoC at home can scale to Enterprise Level using Custom Vision APIs

It all started with a DIY project to use Computer Vision for security cameras at home. A custom Machine Learning model is the core component used to analyze pictures to detect people, animals and more in a house environment. The AI processing is performed at the edge, in dedicated hardware and the collected information is stored in the cloud. The same idea can be applied to several CCTV scenarios, like parking lots, train stations, malls and more. However, moving this into enterprise scale brings a set of challenges, which are going to be described and explained in this session.

More Information: https://www.chicagocodecamp.com/ and remember that we will be also talking about Deep Learning.

Greetings @ Toronto

El Bruno

#Event – Materiales utilizados durante #GlobalAINight con los amigos de @metrotorontoUG

 

Buenas !

Después de una noche genial con los amigos de Metro Toronto UG, llega el momento de compartir los materiales que utilice durante la sesión. La idea inicial era hablar un poco de Azure Notebooks, y de alguna manera terminamos hablando también de Cognitive Services y Custom Vision, fue genial!

Para comenzar, los 15 min con el video de la Keynote:

Mis Slides

Y algunos de los links que utilicé durante la sesión

My posts on Custom Vision and ONNX

  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

Saludos @ Toronto

El Bruno

#Event – Resources used during the #GlobalAINight @metrotorontoUG

 

Hi  !

After an amazing event with my friends from Metro Toronto UG, it’s time to share some resources. It was initially supposed to be focused only on Azure Notebooks, but somehow we spend a lot of time talking about Cognitive Services and Custom Vision, that was great!

Let’s start with the 15 min Keynote video:

My Slides

And some interesting online resources

My posts on Custom Vision and ONNX

  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

Greetings @ Toronto

El Bruno

#AI – Los proyectos de CustomVision.ai se pueden exportar para ser utilizados con el Vision AI Developer Kit

Buenas !

Estaba planeando escribir un par de posts sobre las características de Inteligencia Artificial en la suite de Microsoft, cuando comprobé esta característica disponible en CustomVision.ai.

Custom Vision export to Vision AI Dev Kit.jpg

El año pasado, Microsoft lanzó un programa llamado [Vision AI Developer Kit for IoT Solution Makers]

Integrado con Azure IoT Edge y trabajando con el servicio de aprendizaje automático de Microsoft Azure (versión preliminar pública), este kit de inicio de Azure IoT permite a los desarrolladores crear una solución de IA de visión y ejecutar sus modelos de IA en el dispositivo.

vision ai dev kit camera.png

El dispositivo utiliza la plataforma de inteligencia de Qualcomm Vision para la aceleración de hardware del modelo de AI para ofrecer un rendimiento de inferencia superior. Y está diseñado específicamente para implementar modelos de AI creados con Azure Machine learning con Azure IoT Edge.

Después de leer un poco me di cuenta, que también se puede desplegar en esta cámara, Modelos ONNX de la galería de Azure AI, modelos de Azure ML y, por supuesto, modelos personalizados creados con CustomVision.ai. Todo es compatible y administrado con Azure IoT Edge.

Por lo tanto, ahora es el momento de comprobar mis fechas de entrega para ver cuánto tiempo tengo que esperar a que llegue mi dispositivo y empezar a comprobar la opción de exportación disponible en el portal CustomVision.ai!

Happy Coding!

Saludos @ Toronto

El Bruno

References

My posts on Custom Visopn

  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

 

#AI – CustomVision.ai project now can export directly toVision AI Developer Kit

Hi !

I was planning to write a couple of posts about Artificial Intelligence features in the Microsoft Suite, when I checked this feature available in CustomVision.ai.

Custom Vision export to Vision AI Dev Kit.jpg

Last year, Microsoft released a program named [Vision AI Developer Kit for IoT Solution Makers]

Integrated with Azure IoT Edge and working with the Microsoft Azure Machine Learning service (public preview), this Azure IoT Starter kit enables developers to build vision AI solution and run their AI models on the device.

vision ai dev kit camera.png

The device uses the Qualcomm vision intelligence platform for hardware acceleration of the AI model to deliver superior inferencing performance. And is specifically designed to deploy AI models built using Azure Machine Learning with Azure IoT Edge.

I just realize that you can also deploy to this camera, ONNX models from Azure AI Gallery, Azure ML models and of course, custom models created using CustomVision.ai. It’s all supported and managed using Azure IoT Edge.

So, now it’s time to check my delivery dates to see how much time I need to wait for my device to arrive and start to check the export option available in the CustomVision.ai portal!

Happy Coding!

Greetings @ Burlington

El Bruno

References

My posts on Custom Visopn

  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

 

#AI – Mis posts sobre CustomVision.ai, exportando y utilizando ONNX, Docker, en PC, RaspberryPi, MacOS y más !

Buenas !

Ahora que tengo una pausa entre eventos en Canada y USA, y ya he escrito varios posts al respecto, es el tiempo ideal para compilar y compartir los posts que he escrito sobre CustomVision.ai. Sobre como crear un proyecto de reconocimiento de objectos, como utilizar el mismo en modo web, invocando un HTTP Endpoint desde una app de consola. Y también desde aplicaciones en Windows 10 exportando el proyecto a formato ONNX y utilizando Windows ML. Finalmente, un par de post donde explico como utilizar CV.ai con docker en PC, Mac y Raspberry Pi.

  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

Greetings @ Burlington

El Bruno

#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