#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

• Introduction to YoloV2 for object detection
• Create a basic Windows10 App and use YoloV2 in the camera for object detection
• Transform YoloV2 output analysis to C# classes and display them in frames
• Resize YoloV2 output to support multiple formats and process and display frames per second
• How to convert Tiny-YoloV3 model in CoreML format to ONNX and use it in a Windows 10 App
• Updated demo using Tiny YOLO V2 1.2, Windows 10 and YOLOV2 for Object Detection Series
• Alternatives to Yolo for object detection in ONNX format