Core AI Data Terms
- Active learning
- A training strategy where uncertain or high-value examples are routed for human review so annotation effort is focused where it can improve model performance most.
- Annotation guideline
- A written instruction set that defines labels, edge cases, examples, counterexamples and acceptance rules for reviewers.
- Bounding box
- A rectangular annotation used to identify object location in images or video. See image annotation services.
- Classification
- Assigning a category or label to an item such as an image, document, utterance, product or content item.
- Computer vision
- AI systems that interpret visual data such as images, video, medical scans, retail shelves, roads, products and industrial scenes.
- Data curation
- Selecting, cleaning, organizing and preparing datasets so they represent the model objective and production environment.
- Data labeling
- Assigning structured labels to data so a machine learning model can learn from examples.
- Dataset validation
- Checking training data for label quality, completeness, consistency, class balance and suitability for model training or evaluation.
- Ground truth
- The validated reference label or answer used to train, test or evaluate an AI model.
- Human-in-the-loop
- A workflow where human reviewers guide, validate, audit or correct AI outputs. It is central to high-risk, ambiguous and quality-sensitive AI systems.
- Instance segmentation
- A pixel-level annotation method that separates each object instance in an image, even when objects belong to the same class.
- Keypoint annotation
- Marking specific points on an object or body, often used for pose estimation, product landmarking and visual measurement tasks.
- LiDAR annotation
- Labeling 3D point cloud data for autonomous vehicles, robotics, mapping and spatial AI. See LiDAR annotation services.
- Named entity recognition
- An NLP annotation method that identifies entities such as people, companies, products, dates, locations and domain-specific terms in text.
- OCR annotation
- Preparing text extraction, document labeling and field validation data for optical character recognition and document AI systems.
- Polygon annotation
- A visual annotation method that traces object boundaries more precisely than a bounding box.
- RLHF
- Reinforcement learning from human feedback, where human preferences or evaluations help improve model behavior, especially in LLM workflows.
- Semantic segmentation
- Pixel-level labeling where each pixel is assigned to a class such as road, vehicle, product, tumor, shelf or background.
- Synthetic data
- Artificially generated training data used to supplement real datasets, stress-test models or fill data gaps.
- Training dataset
- The collection of labeled examples used to train or fine-tune a machine learning model.
- Video annotation
- Labeling objects, actions, events and movements across frames. See video annotation services.
Related Services
Explore text annotation services, image segmentation services, data audit services and the AI data annotation blog.