VisionFeaturePrint_Screen is a feature extractor model provided by Apple, primarily used in Create ML and Core ML for image classification and other vision-related machine learning tasks.

It is a pre-trained deep learning model optimized for extracting high-level features from images. Instead of training an image classifier from scratch, developers can use VisionFeaturePrint_Screen to generate a feature representation of an image.

These features can then be used in tasks like:

• Image classification

• Object detection

• Similarity search (e.g., finding visually similar images)

• Content-based image retrieval

This model takes an input image and processes it through a deep neural network. It transforms images into numerical vectors (feature embeddings) that represent essential visual patterns. These embeddings are then used as input for a classifier, such as logistic regression or decision trees, to make predictions.

Why Use VisionFeaturePrint_Screen?

This model is pre-trained & optimized for Apple devices, reducing training time. It works efficiently on-device, ensuring fast inference and privacy (no need to send images to a server). It improves accuracy by leveraging Apple’s state-of-the-art image recognition models.

When training an image classifier in Create ML, we can select VisionFeaturePrint_Screen as the feature extractor. This allows our model to learn patterns from images more efficiently without requiring massive amounts of training data.

Training Dataset for VisionFeaturePrint_Screen

The VisionFeaturePrint_Screen model is trained on a large-scale dataset of diverse images, including natural scenes, objects, textures, and various image categories. Apple has not publicly disclosed the exact dataset used, but it is likely based on a combination of:

• Common objects (e.g., everyday items, animals, buildings, vehicles)

• Natural scenes (e.g., landscapes, indoor and outdoor environments)

• Textures and patterns (e.g., fabrics, materials, abstract textures)

• Faces and human-related features (potentially for facial recognition tasks)

Since VisionFeaturePrint_Screen is a feature extractor, it doesn’t classify images directly. Instead, it produces feature embeddings that represent an image’s visual characteristics, which can then be used for classification, similarity search, and other tasks.

Conclusion

In this chapter, we learned about the VisionFeaturePrint_Screen model, which is used for feature extraction in image-based model training within Create ML and Core ML.