Hi !
I’ve been writing a lot about Custom Vision, and how use and export CV models to ONNX or docker images to be used later in different types of scenarios. I got this post in draft mode, so it’s time to publish it.
If you are using CustomVision.ai, you probably notice the warning message about the service being moved from a preview / test stage on 2019-03-19. That’s mean that you need to move your CV projects to a valid Azure account if you want to use them.
You may want to create and train again some cv projects, however you will get new project ids, new urls and you need to tag again all the images.
The 1st action here, is to create a Custom Vision resource in a valid Azure account. That’s a 2 click tutorial and it’s also very easy.
There is also the option to continue working in a free mode scenario with the following parameters in the Free Instance:
- Up to 2 projects
- Limit of 5000 training images
- 2 transactions per seconds
- Limit of 10000 predictions per month
Now we can go back to the Custom Vision.ai portal and select the project we want to migrate to Azure. In the Settings section, at the bottom left corner we have the [Move to Azure] option.
Here we need to select the specific values of the resource we created before and that’s it! The Custom Vision project now is fully migrated to Azure 😀
Happy Coding!
Greetings @ Toronto
El Bruno
Resources
- Object recognition with Custom Vision and ONNX in Windows applications using WinML
- Object recognition with Custom Vision and ONNX in Windows applications using WinML
- Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, drawing frames
- Object recognition with Custom Vision and ONNX in Windows applications using Windows ML, calculate FPS
- Can’t install Docker on Windows 10 Home, need Pro or Enterprise
- Running a Custom Vision project in a local Docker Container
- Analyzing images in a Console App using a Custom Vision project in a Docker Container
- Analyzing images using PostMan from a Custom Vision project hosted in a Docker Container
- Building the CustomVision.ai project in Docker in a RaspberryPi
- Container dies immediately upon successful start in a RaspberryPi. Of course, it’s all about TensorFlow dependencies
- About ports, IPs and more to access a container hosted in a Raspberry Pi
- 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
