Prompt encoder

As explained in Deep Learning with Python, Third Edition

A model component that converts a prompt into an embedded vector representation.

A prompt encoder converts a segmentation prompt into an embedded vector that a mask decoder can combine with an image embedding. In Segment Anything, the prompt identifies the object to segment through either a point inside it or a box around it. section 11.3.2

A typical prompt-to-mask workflow is:

• Encode the input image into a compact image embedding. section 11.3.2
• Choose a point prompt or a box prompt and map it to a prompt embedding. section 11.3.2
• Pass both embeddings to the mask decoder to produce several possible object masks. section 11.3.2

Training prompt inputs

Sample an image and an object mask, then randomly choose a point inside the mask or a box surrounding it. Pair the image and prompt with the ground-truth mask. section 11.3.2

Figure 11.8 The Segment Anything high-level architecture overview

Use the architecture overview to see how the image encoder, prompt encoder, and mask decoder fit together. section 11.3.2

How Deep Learning with Python, Third Edition covers it

How deep it goes

Defines the component and data flow but defers its modeling techniques and implementation details.

Examples

Uses points inside object masks and boxes surrounding masks as concrete prompt forms.

What it assumes

Assumes familiarity with image embeddings while introducing point and box prompts as segmentation inputs.

What you can do after

Understand how prompts participate in a multi-component segmentation model and training example.

How it connects

Connects image embeddings, mask decoders, image segmentation, and ground-truth masks.

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Related concepts

• SAMpart of · SAM, or Segment Anything Model, is a pretrained promptable computer-vision model that generates segmentation masks for objects in images.
• BERTuses · BERT is a pretrained Transformer encoder language model that uses bidirectional context to produce contextual representations of text.
• embeddingproduces · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
• transformer architectureuses · A transformer architecture is a neural-network architecture that processes sequences or other structured inputs using attention mechanisms and related transformations.
• encodercontrasts with · An encoder is a component that transforms input data into an encoded representation for subsequent processing, reconstruction, comparison, or transmission.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with Python, Third Edition. The section links point to the parts of the book it draws on.