Classification models

Machine learning models that analyze the content of an image and assign it to predefined categories or labels

Classification models in DAM

Machine learning models, particularly classification models, play a crucial role in digital asset management. Classification models are algorithms that learn patterns and relationships in data to assign predefined categories or labels to new, unseen data. They can be used to categorize and organize assets, such as images, videos, or documents, based on their content or characteristics.

Classification models are typically trained on labeled datasets, where each data instance is associated with a known category or label. During the training process, the model learns to recognize patterns and features in the input data that differentiate one category from another. Once trained, the model can classify new, unseen data based on the learned patterns.

The following pages describe the models that are available in ASK Filerobot:

Automatic assignment of relevant tags and keywords to the input image

An ML model that detects logos of popular brands visible in the input image

An ML model that extracts the most prominent colors from an image

A combination of ML models that accurately detect human faces and predict crucial characteristics like facial landmarks, expression, ethnicity, age and gender

A system that detects human faces, extracts feature vectors and clusters similar faces to efficiently group images based on the individuals present in them

An ML model that detects and recognizes vehicle registration plate numbers

A machine learning algorithm that utilizes optical character recognition techniques to accurately identify and extract text from images or scanned documents

An ML model classifying an image as pure product image or application image, showing the product in use

An ML model that accurately classifies images, offering appropriate scene categories and attributes

An ML model that identifies sport activities depicted in images, empowering diverse industries with precise content organization and targeted applications

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