#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

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#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

#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

#AI – My posts on CustomVision.ai, running on ONNX, Docker, on PC, RaspberryPi, MacOS and more !

Hi !

After the events in Canada and USA, and several posts, I think it’s time to make a recap of the posts I’ve wrote about CustomVision.ai and how I created a custom object recognition project. And later used this as a web HTTP Endpoint in a Console application. And also in Windows 10 with ONNX using Windows ML; and finally running the Object Recognition project inside a Container in Docker on PC, Mac and 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