#CustomVision – Label Images with suggested tags. Cool update for my #MSIgnite sessions

Buy Me A Coffee

Hi!

I’m updating some of my demos for Microsoft Ignite and I found an amazing new feature in Custom Vision: Suggested Tags (see references). This feature is super useful in scenarios for automatic detection, like the parking lot demo. I’ll use the official documentation to describe this feature

When you tag images for a Custom Vision model, the service uses the latest trained iteration of the model to predict the labels of untagged images. It then shows these predictions as suggested tags, based on the selected confidence threshold and prediction uncertainty. You can then either confirm or change the suggestions, speeding up the process of manually tagging the images for training.

Label images faster with suggested tags

And, as usual, let’s use 2 images to describe this. Once I add a new image to my Custom Vision project, I can start to select object and tag them. However, if I already trained my project, I will also see the [Suggested object on] option.

Custom Vision select objects and apply tags

With the default threshold value of 66%, the auto label feature does not detect any area. However, if I low the level, in example to 28%, it will automatically detect one of the parking slots: Slot 3. Once I’m happy with the suggested objects, I can confirm these objects and that’s it! Super easy.

Custom Vision enable suggested objects and low threshold

This feature is amazing, and I’m looking forward to using it in real projects to see how much time saves in image labeling scenarios.

Bonus: Below you can see the before and after of the demo project. My daughter also decorated the new parking lot box, with some IronMan content. I’ll need to figure out how to connect this with my session speech!

Happy coding!

Greetings @ Toronto

El Bruno

References

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#CustomVision – Sort and manage Json response estimation in a single line of code

Buy Me A Coffee

Hi!

On my Custom Vision samples, I usually send an image to a CustomVision.ai HTTP Endpoint, and I process the Json result. The results are very easy to understand, however, I created a C# converter class to help with the Custom Vision results.

To create this class I navigate: http://json2csharp.com/, and paste a sample result and make some changes on the result. The output and useful class is this one:

It’s a very simple class, and the best way to describe it, is to show an usage scenario

The main remarks points are

  • Lines 1 to 9, open a local file, create a HTTP client and make the HTTP Post request
  • Line 12, convert the json response (string) to a C# object and then get the best prediction
  • Where the best prediction is a single Linq code sorting the predictions by probability and selecting the 1t one.

Easy and amazing!

Happy Coding!

Greetings @ Burlington

El Bruno

References

#Event – Materials and Resources used during my #CustomVision and #AI session at #CDC2019

Hi!

Drafting these in the airplane, and also drafting a bigger post about the full and amazing experience at the Caribbean Developer Conference. So, I’ll start with the usual slides and materials, and also use this post later as reference for the full experience

Slides

Code

https://github.com/elbruno/events/tree/master/2019%2010%2004%20CDC

Links

Tweets

Greetings @ Toronto

El Bruno

References

My posts on Raspberry Pi

#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

#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