#Docker – Average response times using a CustomVision.ai docker container in a #RaspberryPi and a PC

Hi !

I was testing the performance of the same customvision.ai exported project, running in a docker container in standard PC and a Raspberry Pi. And, I’m really surprised and happy about the RPI times.

Let’s start with the times for a container running in a PC with the following specs

Note: I know this is very subjective, because there is more information needed for a deep study. Like SSDs, Windows 10 version, apps running and more. This is just for reference.

A sample process to analyze 20 images tooks 10.45 seconds.

The same process using a container in a Raspberry Pi took 70.46 seconds.

The average time are

• PC, 0.52 seconds
• Raspberry Pi, 3.52 seconds

Again, amazing times for a 30 dollars device!

Happy coding!

Greetings @ Toronto

El Bruno

References

• Custom Vision
• Docker Desktop
• Raspberry Pi

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

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