Classification Application

Classification

A Classification application identifies, or tags, media items with the single most prominent subject from a list of output subjects of interest, to the exclusion of the other subjects. The list of subjects must be mutually exclusive and exhaustive. In other words, precisely one of the subjects must apply to every media item presented to a classification app. The maximum number of output subjects supported is 20.

Creating a Classification Application

Classification applications require the following fields:

1. An application name, e.g. 'Security Cam People Classifier'.

2. A list, output_subjects, of subjects to classify the input media, e.g., 'security alert', ' employee', and 'visitor'.

3. An optional list, input_subjects, of one or more subjects to store input media. It can be output subjects from other Cogniac applications.

To view additional, optional application creation fields and their default values, see Applications - Create.

You can also view the entire flow on the Classification Creation → [Here].

Classification Subjects and Feedback

Classification applications select a single output subject in each input media item. When providing application feedback, selecting the subject that appears most prominently or is best represented in the media item is best practice. To attain an accurate and stable application, continue to upload new media to the application's input subjects and provide feedback on the application's subject assertions. Monitor the application's model performance statistics: Precision, Recall, and F1 scores until sufficient accuracy is reached.

Feedback Context

It is best practice to provide feedback in a manner that matches the feedback of a reasonable human expert in the application's domain. Providing highly contextual feedback for a classification application that will be used to process out-of-context images may result in lower model performance.
For example, while providing feedback to a classification application trained to identify if an image contains a particular person or subject, it is not recommended to provide positive feedback for blurry images or images where the individual providing feedback is aware the subject is in the image but is not visible without that knowledge.

The semantics of a classification application always require the selection of precisely one output subject. If, for some reason, there are media items presented to the application with none of the desired subjects or multiple of the desired subjects, an additional class or classes can be added to deal with these exceptional cases.

For example, if no subject of interest appears in some media items, an output subject representing 'None' or 'No Cats or Dogs' can be added. Similarly, if some media items contain multiple subjects, you can add a subject like "Multiple Cats or Dogs" to classify these images. This strategy works best if these cases are relatively rare. If you expect to have many images with no subjects or multiple subjects, then the 'Detection' application type is probably a better choice.