Computer Vision & Physical AI Glossary

Data & Curation

Most model problems are data problems. Definitions for the datasets, embeddings, quality signals, and curation methods that decide what is worth labeling and training on in the first place.

Data Curation
Deliberately choosing which data to keep, label, and train on, so better data drives model gains.
Dataset
An organized collection of examples, labels, and metadata used to train and evaluate a model.
Image Blurriness
A per-image score for how out-of-focus or motion-blurred an image is, used as a curation signal.
Image Embeddings
A vector representation of an image's content, used to search, cluster, and curate data.
MCAP
An open-source, serialization-agnostic container file format for storing timestamped, multimodal robotics data, and the default recording format for ROS 2.

Annotation & labeling

Annotation is where model quality is won or lost. Definitions for the label types, formats, and quality measures that turn raw data into training data, from bounding boxes to ground truth.

Evaluation & Metrics

A model is only as trustworthy as the way you measure it. Definitions for the metrics and methods that tell you whether a model works, and where a strong headline number can still hide failure.

Models & Architectures

The building blocks that learn from your data. Definitions for the networks, foundation models, and algorithms behind modern visual AI, and what each one is actually good for.

Visual & Physical AI glossary

Data & Curation

Annotation & labeling

Evaluation & Metrics

Models & Architectures