Detect Objects In Images (Part 2)

By Ashwin Venugopal -

 






Train an Object Detector

You can either utilize the REST API or SDK to build code that carries out the training activities, or you can use the Azure AI Custom Vision portal to upload and label photos prior to training, assessing, testing, and releasing the model.

The labeling of the images with tags is the main distinction between training an object detection model and training an image classification model.

Object detection necessitates that each label have a tag and a region that specifies the bounding box for each object in an image, whereas image classification requires one or more tags that apply to the entire image. You can label your training photos using the graphical interface offered by the Azure AI Custom Vision portal.

Consider Options for Labeling Images

The Azure AI Custom Vision portal's interactive interface is the simplest way to identify photos for object detection. By sliding the bounding box to enclose the object you wish to label, you can modify or add tags to the automatically suggested regions that contain objects.

Additionally, you can train the model after labeling a first batch of photos. The portal's smart labeler tool, which can recommend both the regions and the classes of objects they contain, can be useful for subsequent labeling of new photos.

As an alternative, you can utilize a labeling tool, like the one included in the Microsoft Visual Object Tagging Tool (VOTT) or Azure Machine Learning Studio, to benefit from additional features, like delegating picture tagging responsibilities to other team members.

Bounding Box Measurement Units

If you choose to use a labeling tool other than the Azure AI Custom Vision portal, you may need to adjust the output to match the measurement units expected by the Azure AI Custom Vision API. Bounding boxes are defined by four values that represent the left (X) and top (Y) coordinates of the top left corner of the bounding box, and the width and height of the bounding box.

These values are expressed as proportional values relative to the source image size.

Conclusion

We have successfully learnt about object detection in images.

 






























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