Create a Custom Project

By Ashwin Venugopal -

 




Introduction

To develop a personalized Azure AI Vision model, you initially require an Azure AI Services resource (or an Azure AI Vision resource). After deploying that resource to your subscription, the next step is to establish a custom project.

Components Of a Custom Vision Project

The initial element of a tailored project is the dataset. This dataset comprises your assortment of images for training your model, along with the COCO file that outlines the labeling details associated with those images.

After defining your images and class labels, you can begin training your custom model. During the training process, you'll indicate the model type to be trained, the dataset to utilize, and your allocated training time budget. Once the model training has finished, you can assess its performance and utilize the model for making predictions.

In most cases, the steps you follow are:

  • Create your blob storage container and upload just the training images.

  • Create the dataset for your project, and connect it to your blob storage container. When creating your dataset, you define what type of project it is (image classification, object detection, or product recognition).

  • Label your data in your Azure Machine Learning Data Labeling Project, which creates the COCO file in your blob storage container.

  • Connect your completed COCO file for the labeled images to your dataset.

  • Train your custom model on the dataset and labels created.

  • Verify performance and iterate if the trained performance isn't meeting expectations.

Once you're happy with the performance, the model can be used in Vision Studio or in your own application.

COCO Files

A COCO file is a JSON file with a specific format that defines:

  • images: Defines the image location in blob storage, name, width, height, and ID.

  • annotations: Defines the classifications (or objects), including which category the image is classified as, the area, and the bounding box (if labeling for object detection).

  • categories: Defines the ID for the named label class.

In most cases, COCO files are created by labeling your training images in an Azure Machine Learning Data Labeling Project.

Creating Your Dataset

After you have photos in your blob storage container, you may use Vision Studio or the REST API to generate your training dataset.

You would choose your resource, generate your dataset, and navigate to the custom model tile if you were using Vision Studio. You may then upload an already-existing COCO file or launch or establish an Azure Machine Learning Data Labeling Project.

Instead of including the COCO file in the REST request, you can connect to your labeling project in Azure Machine Learning by using Vision Studio.

Conclusion

We have successfully learnt about components of a custom vision project.







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