US-DATAData Annotation
Cabinet
Video classification helps AI systems understand video content, recognize events and account for context over time.
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Video classification assigns one or more predefined classes to an entire video, a segment or individual frames. Unlike detection and tracking, it focuses on the semantic content of the scene.
One label that reflects the primary content.
Separate classes for time intervals.
Labels for every frame or selected key frames.
One category from a defined set.
Several labels for one video or frame.
Classes and subclasses in a layered structure.
Frame classification identifies event boundaries, scene-state changes and brief events. Annotation frequency is selected for the task: every frame, at intervals or only key frames.
Datasets can include video-level labels, temporal segments, frame labels, multi-label annotations, scene attributes and action signals. We account for dependencies between frames.
We label whole videos, temporal segments and frames, and create binary, multi-class, multi-label and hierarchical datasets. Before launch, we agree the taxonomy, temporal boundaries and guidelines.
Inconsistent labels reduce accuracy, introduce bias and can make production behavior unreliable.
We create task-specific guidelines and validate every stage so the dataset matches the model architecture and business objective.
Examples of data annotation for machine learning
Full data preparation cycle from raw data to model-ready output
How does US-DATA deliver the results business needs?
We pay close attention to quality. Even the most accurate model will not perform well if the data is labeled with errors.
Our team works under a unified annotation rule system based on multi-level review and consistency control. Every process is adapted to client needs and the specifics of the ML model. The result is a clean dataset ready for training without additional rework.
We understand how data quality affects model training.
Annotation tailored to architecture and project goals.
From pilots to large-scale data volumes.
Control at every stage and transparent metrics.
We handle non-standard and challenging scenarios.
Data exported in the format you need.
Faster model training
Higher accuracy and robustness
Lower retraining costs
Production-ready datasets
Data security and compliance
Expandable sections with indicative cost tables.
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* This estimate is not a public offer. Final cost is determined after technical analysis and data review.
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Leave a request — we will assess the task and propose the right annotation approach and delivery format.
Video and frame classification identifies content type, events, actions and context in a video sequence. US-DATA provides Video Frame Classification, temporal-segment labels, multi-class, multi-label and hierarchical annotation.
We develop annotation guidelines, run pilot labeling and multi-level quality control, then deliver a consistent dataset in the format required by your ML pipeline.
The result is data that is ready to train, validate and deploy machine learning models in real operating conditions.
