AI Data Annotation

Data Annotation

The process of labeling data like text or images so AI can understand it.

Data Annotator

A person who labels data to train AI models.

Ground Truth

The absolute correct and accurate data used as a target for AI training.

Label

A tag or note added to a piece of data to describe what it is.

Dataset

A collection of related data used to train or test AI.

Training Data

The initial data used to teach an AI model how to make predictions.

Testing Data

Data used to check how accurate the AI model is after training.

Validation Data

Data used to fine-tune an AI model's settings during training.

Bounding Box

A simple rectangle drawn around an object in an image to isolate it.

Polygon Annotation

Drawing a detailed shape with many sides around an irregular object in an image.

Semantic Segmentation

Coloring every single pixel in an image based on what category it belongs to.

Instance Segmentation

Coloring every individual object in an image separately even if they are the same type.

Keypoint Annotation

Placing dots on specific parts of an object like human joints or facial features.

Polyline Annotation

Drawing lines to mark boundaries like roads

Cuboid Annotation

Drawing a 3D box around an object in a 2D image to show its depth.

Image Classification

Assigning a single label or category to an entire image.

Object Detection

Finding and labeling specific objects inside an image using boxes.

Text Annotation

Labeling words

Named Entity Recognition (NER)

Identifying and labeling proper nouns like names

Sentiment Analysis

Labeling text to show if the mood is positive

Intent Classification

Labeling text to figure out what the user is trying to achieve.

Audio Transcription

Converting spoken words from an audio file into written text.

Audio Annotation

Tagging specific sounds or speaker changes in an audio recording.

Video Annotation

Labeling objects or actions frame-by-frame across a video clip.

LiDAR Annotation

Labeling 3D point clouds captured by laser sensors

Point Cloud

A collection of 3D data points in space captured by lasers or scanners.

Metadata

Extra background information about a piece of data

Raw Data

Unprocessed and unlabeled data straight from the source.

Clean Data

Data that is corrected

Noisy Data

Data that contains a lot of useless

Data Bias

When a dataset favors certain groups or outcomes

Data Diversity

Including a wide variety of different examples in a dataset to make AI fair.

Data Augmentation

Creating new data from existing data by making small changes like rotating images.

Synthetic Data

Fake data generated by a computer that mimics real-world data.

Data Privacy

Protecting personal or sensitive information in a dataset from being leaked.

Anonymization

Removing personal details like names or faces from data to protect privacy.

Masking

Hiding specific parts of data

Data Security

Measures taken to protect data from unauthorized access or theft.

Gold Standard

A perfectly labeled dataset used to judge the quality of other annotations.

Inter-Annotator Agreement (IAA)

A score showing how much different annotators agree on the same labels.

Quality Assurance (QA)

The process of double-checking labeled data to catch and fix mistakes.

Quality Control (QC)

The system of testing data to ensure it meets the required standards.

Accuracy

How close the AI's prediction or the annotator's label is to the truth.

Precision

How consistent and exact the labels or predictions are.

Recall

The ability of an AI or annotator to find all the relevant examples in a dataset.

F1 Score

A single score that balances both precision and recall to measure quality.

Edge Case

An unusual or rare situation in data that is difficult for AI to understand.

Outlier

A piece of data that is very different from all the other data.

Ambiguity

When data can be interpreted in more than one way

Taxonomy

A structured list of categories or labels used for an annotation project.

Ontology

A detailed system showing how different labels and concepts relate to each other.

Active Learning

A process where the AI asks humans to label only the hardest data points.

Human-in-the-Loop (HITL)

A system where humans constantly guide

Machine Learning (ML)

A type of AI that learns patterns from data without being explicitly programmed.

Deep Learning

A advanced type of AI that uses structures mimicking the human brain to learn.

Computer Vision (CV)

The field of AI that helps computers see and understand images or videos.

Natural Language Processing (NLP)

The field of AI that helps computers understand human speech and text.

Automated Labeling

Using an AI model to label data automatically instead of humans.

Pre-labeling

Using an AI to make a first guess at labels

Re-labeling

Labeling the same data again to fix errors or update categories.

Crowdsourcing

Sending data annotation tasks to a large public network of online workers.

In-house Annotation

Having a dedicated team within a company do the data labeling.

Outsourcing

Hiring a third-party company to handle data annotation tasks.

Data Pipeline

The step-by-step journey data takes from being collected to being fed into AI.

Labeling Tool

The software or application that annotators use to tag data.

Bounding Box Fatigue

Tiredness felt by annotators from drawing thousands of boxes all day.

Pixel-Perfect

Annotation that is incredibly precise

Occlusion

When an object in an image is partially hidden behind something else.

Truncation

When an object is cut off by the edge of an image frame.

Class Imbalance

When one label appears way more often than other labels in a dataset.

Overfitting

When an AI learns training data too perfectly but fails on new data.

Underfitting

When an AI is too simple to learn the patterns in the training data.

Target

The final answer or label that the AI is trying to predict.

Tokenization

Splitting a sentence into individual words or smaller pieces for text AI.

Part-of-Speech (POS) Tagging

Labeling words in a sentence as nouns

Coreference Resolution

Labeling which words refer to the same thing

Text Classification

Assigning a category or topic to a piece of text.

Entity Linking

Connecting a labeled word in a text to a specific concept in a database.

Semantic Annotation

Adding meaning or context to text beyond just the literal words.

Phonetic Transcription

Writing down spoken words exactly how they sound using special symbols.

Diarization

Identifying and separating different speakers in an audio recording.

Frame Rate

The number of images shown per second in a video clip.

Keyframe

An important frame in a video that marks the start or end of an action.

Interpolation

An automated tool feature that guesses and fills in labels between video frames.

Localization

Finding where an object is located within an image or space.

Classification

Grouping data points into pre-defined categories.

Regression

Predicting a continuous numerical value instead of a category.

Supervised Learning

Training an AI using data that is already labeled by humans.

Unsupervised Learning

Training an AI on unlabeled data so it has to find patterns on its own.

Semi-Supervised Learning

Training an AI using a small amount of labeled data and a lot of unlabeled data.

Data Sourcing

The process of finding

Data Curation

Organizing

Data Governance

Rules and standards for managing data security

Data Ingestion

The process of importing large amounts of data into a storage system.

Data Warehouse

A central digital storage system used to hold huge amounts of structured data.

Scrubbing

The act of removing bad

Data Drift

When real-world data changes over time

Model Drift

The drop in an AI's performance as real-world data changes over time.

Consensus

A quality check method where multiple annotators must agree on a label for it to count.