Voxel51 Features

Gives user a metric to determine the quality of data labelers, based on consistency scores, domain knowledge, dynamic ground truth, and more.

Ensures that labeling tasks are accurate through consensus, review, anomaly detection, and more.

Ensures the data is of a high quality as compared to benchmark.

Gives user the ability to review and edit labels.

Uses models to predict the correct label for a given input (image, video, audio, text, etc.).

Automatically route input to the optimal labeler or labeling service based on predicted speed and cost.

Has the ability to place imaginary boxes or polygons around objects or pixels in an image.

has the ability to detect objects within images.

Track unique object IDs across multiple video frames

Supports a range of different types of images (satelite, thermal cameras, etc.)

Gives user the ability to extract entities from text (such as locations and names).

Gives user the ability to tag text based on its sentiment.

Gives user the ability to label and verify text data in an image.

Allows the user to transcribe audio.

Gives user the ability to label emotions in recorded audio.

Enables smart selection of data for annotation to reduce overall training time and costs.

Allows for automatic retraining of models with newly annotated data for continuous improvement.

Facilitates the setup of an active learning process tailored to specific AI projects.

Allows users to establish a feedback loop between data annotation and model training.

Provides the ability to identify and address edge cases to enhance model robustness.

Enables efficient triaging of training data to identify which data points should be labeled next.

Streamlines the data labeling process with tools designed for efficiency and accuracy.

Automates the detection of anomalies and outliers in the training data for correction.

Offers tools to optimize the selection of data for labeling based on model uncertainty.

Provides actionable insights into data quality, enabling targeted improvements in data labeling.

Delivers in-depth insights into factors impacting model performance and suggests enhancements.

Enables model improvement at the lowest possible cost by focusing on the most impactful data.

Integrates the handling of edge cases into the model training loop for continuous performance enhancement.

Provides the ability to fine-tune models for increased accuracy and specialization for niche use cases.

Offers advanced tools to analyze label outliers and errors to inform further model training.