#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

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#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

#Event – Materiales utilizados en la sesión [Getting Started with Machine Learning.Net & Windows Machine Learning] con el grupo de usuarios GLUG

Buenas!

Es momento de otro post para compartir los materiales utilizados durante la sesión con el grupo de usuarios GLUGnet User Group for .NET, Web, Mobile, Database. Especial agradecimiento para Joe Kunk (@JoeKunk) y a los asistentes al webcast vía Google Hangouts. Por cierto, la sesión fue con los materiales actualizados a la version 0.10 de ML.NetÑ Windows ML and Machine Learning.Net.

Como siempre, aquí están las slides.

Source Code GitHub https://github.com/elbruno/events/tree/master/2019%2002%2021%20GLUG%20NetUG%20MLNet

Y algunos links que comenté durante la sesión:

Resources

Happy Coding!

Saludos @ Toronto

El Bruno

#Event – Resources for the session [Getting Started with Machine Learning.Net & Windows Machine Learning] on the GLUG .Net User Group

Hi!

Another post-event post, this time with a big thanks to Joe Kunk (@JoeKunk) and to all the members of the GLUGnet User Group for .NET, Web, Mobile, Database. We had an amazing time, via Google Hangouts in the session about Windows ML and Machine Learning.Net.

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

Source Code GitHub https://github.com/elbruno/events/tree/master/2019%2002%2021%20GLUG%20NetUG%20MLNet

And some Machine Learning.Net resources:

Resources

Greetings @ Toronto

El Bruno

#Event – Webinar: Getting Started with ML.NET and Windows Machine Learning, Today with the GLUG .Net User Group

giphy-downsized

Hi !

Another virtual event about Windows ML and Machine Learning .Net, and this time with my good friends from Okemos, MI. The event will be

Getting Started with ML.NET and Windows Machine Learning

One more time, we have new features and improvements on the platform since the previous event, so it’s time for me to review and prepare new content. In example, use TensorFlow and ML.Net !!!

Description  

Machine Learning has moved out of the lab and into production systems. Understanding how to work with this technology is one of the essential skills for developers today. In this session, you will learn the basics of machine learning, how to use existing models and services in your apps, and how to get started with creating your own simple models.

In other words, if you are a .NET developer, this session is for you! We will cover the basics of ML.NET, a complete machine learning framework to work with C#, F# or any other .NET Core language.

Online Register https://www.meetup.com/GLUGnet/events/qkmgpkyzdbcc/

Happy Coding!

Greetings @ Burlington

El Bruno

#Event – Resources used on my session at the largest Canada makeathon: @MakeUofT [How a PoC at home can scale to Enterprise Level using #CustomVision APIs]

2019 02 16 MakeUofT Custom Vision Bruno

Hi !

What an amazing time at the Canadian Largest Makeathon: MakeUofT (https://ieee.utoronto.ca/makeuoft/). The event, people and ideas are great. And now it’s time to share some of the materials used during my session

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.

These are the slides I’ve used

And the source code is available here

https://github.com/elbruno/events/tree/master/2019%2002%2016%20MakeUofT%20Custom%20Vision

In the source code you can find the console and Windows 10 app samples I’ve coded live and also the exported images of my custom vision demo project in windows, linux and raspberry pi flavors. The 3rd one is where I spent some time updating the original linux one to work on the small device.

And as usual a couple of interesting links

Greetings @ Toronto

El Bruno

#Event – 30 min de #MachineLearning.Net durante el evento en vivo @mvpdays (ahora mismo!)

aerial photo of mountain surrounded by fog
Photo by icon0.com on Pexels.com

Buenas !

en pocas horas dará comienzo un evento gratuito de formación: MVP Days.

Yo tuve la suerte de tener mis 30 minutos para hablar de Machine Learning.Net

Getting Started with Machine Learning.Net and Windows Machine Learning

Machine Learning has moved out of the lab and into production systems. Understanding how to work with this technology is one of the essential skills for developers today. In this session, you will learn the basics of machine learning, how to use existing models and services in your apps, and how to get started with creating your own simple models.
And if you are a .Net developer, we will cover the basis of Machine Learning.Net, a complete ML framework to work with C#, F# or any other .Net Core language.

Salvo yo, la agenda y speakers son geniales !

Happy Coding!

Saludos @ Toronto

El Bruno

#Event – 30 min of #MachineLearning.Net on the next @mvpdays on Jan 30!

aerial photo of mountain surrounded by fog
Photo by icon0.com on Pexels.com

Hi !

less than 24 hours for a full day of free training, take a look at MVP Days.

I was lucky enough to have my slot to share some experiences and a zero to one demo on Machine Learning.Net

Getting Started with Machine Learning.Net and Windows Machine Learning

Machine Learning has moved out of the lab and into production systems. Understanding how to work with this technology is one of the essential skills for developers today. In this session, you will learn the basics of machine learning, how to use existing models and services in your apps, and how to get started with creating your own simple models.
And if you are a .Net developer, we will cover the basis of Machine Learning.Net, a complete ML framework to work with C#, F# or any other .Net Core language.

You should really check the agenda, there are a lot of amazing topics and speakers !

Greetings @ Toronto

El Bruno

#Event – Resources used on my @CodeMash session [How a PoC at home can scale to Enterprise Level using #CustomVision APIs]

code mash bruno session

Hi !

What an amazing time at CodeMash (http://codemash.org/). This conference is one the best experiences I’ve had so far. I’ll write a more detailed post next week, this one is to share some of the materials used during my session

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.

These are the slides I’ve used

And the source code is available here

https://github.com/elbruno/events/tree/master/2019%2001%2010%20CodeMash%20CustomVision

In the source code you can find the console and Windows 10 app samples I’ve coded live and also the exported images of my custom vision demo project in windows, linux and raspberry pi flavors. The 3rd one is where I spent some time updating the original linux one to work on the small device.

And as usual a couple of interesting links

Greetings @ Sandusky, Ohio

El Bruno

#Event – Resources for the session [Getting Started with Machine Learning.Net & Windows Machine Learning] on the Tech Valley .NET UG @TUVG

Hi!

Another post-event post, this time with a big thanks to Chris Miller (@anotherlab) and to all the members of the Tech Valley .NET User Group. We had an amazing time, via Skype in the session about Windows ML and Machine Learning.Net.

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

Source Code GitHub https://github.com/elbruno/events/tree/master/2018%2012%2018%20TechValley%20NetUG%20MLNet

And some Machine Learning.Net resources:

Resources

Greetings @ Toronto

El Bruno