Learn how to classify images into categories like animals, landscapes, and objects using AI.
vlm-1
can intelligently classify images based on their content, composition, and visual characteristics. This enables robust classification of images into various categories, even when they come in different styles, lighting conditions, or perspectives.
For example, below is a diagram showing how an image can be classified into different types, and how each type can have its own custom post-processing logic.
vlm-1
can be used to automatically analyze and categorize television content. In this example, we’ll use vlm-1
to classify TV screenshots and frames into categories like news broadcasts, entertainment shows, commercials, and other programming types. This classification enables automated content monitoring, ad detection, and intelligent media archiving by identifying the type of TV content being shown.
Example image that needs classification.
vlm-1
can automatically classify images based on their content and visual characteristics, providing both a classification and a rationale for its decision. First, let’s create a custom schema that will be used to classify the images.
vlm-1
to classify images according to this schema. The classification will be validated against the schema you defined, ensuring that it conforms to the expected structure and types. First, let’s look at an example of how to classify a single image.
rationale
: A detailed explanation of why it classified the image as a news, based on visual features and content. This allows the developer or user to introspect on the classification and make any necessary adjustments downstream to the model.image_type
: The correct image classification type, in this case news
.confidence
: A qualitative confidence level of “high”, indicating strong certainty in the classification based on the clear presence of financial market data and a news presenter.