Labelbox

Semantic Segmentation

Accurately label images at the pixel level, enabling precise training data for computer vision models that require detailed scene understanding. This feature allows for nuanced object identification and classification.

Object Detection

Identify and locate objects within images using bounding boxes, polygons, and other annotation tools. This is crucial for training models to recognize and track specific objects in various environments.

Collaborative Labeling

Enable multiple annotators to work on the same dataset simultaneously, improving efficiency and reducing labeling time. Real-time collaboration features ensure consistency and accuracy across the entire dataset.

Quality Control

Implement quality control workflows to ensure the accuracy and consistency of annotations. This includes review processes, consensus scoring, and automated quality checks.

Active Learning Integration

Prioritize the most informative data points for labeling, reducing the overall labeling effort and improving model performance. This feature helps teams focus on the data that will have the greatest impact on model accuracy.

Customizable Workflows

Tailor the labeling workflow to meet the specific requirements of your project. This includes defining custom annotation interfaces, setting up quality control rules, and integrating with existing data pipelines.

Data Management

Efficiently manage and organize your datasets, making it easy to track progress, identify bottlenecks, and ensure data quality. This feature provides a centralized repository for all your training data.