Automated Data Labeling

Data is growing rapidly, while the human capacity is fixed or barely growing. Labeling datasets manually is resource-intensive and requires significant investment in annotators. Yet, as humans, we are error-prone and long, repetitive tasks lead to errors. Manual labeling often takes weeks or months, making it slow to scale and create large datasets.

AI-assisted labeling tools decrease label creation time by 75% while auto annotation helps you cut human costs by 50%. Auto annotation workflows combined with unsupervised or semi supervised data engines speed model development by up to 100x. Manage large datasets with fully integrated vector search, and an enterprise-grade AI platform.

Build AI datasets with confidence

Clarifai was built to simplify how developers and teams create, share, and run AI at scale.

Ingest individual inputs or archives

Auto label as you ingest data with customizable ontologies. Complete support for imagery, video and text formats.

Index your inputs for search

Auto-index your input data using customizable embedding vectors for semantic similarity search and easier dataset management.

Manage datasets for labeling

Create, version and manage your datasets for data labeling, model training and evaluation. Export your data via SDK.

Faster supervised labeling with AI Assist

Standardize across popular models and frameworks to foster collaborate and innovate

Resource Monitoring

Automate your data labeling

Automated labeling with AI models including uploaded or custom-trained to your task

Complexity

One-click custom model training

Standardize across popular models and frameworks to foster collaborate and innovate

Complexity

Powerful semi-supervised auto annotation

Standardize across popular models and frameworks to foster collaborate and innovate

Complexity

Support advanced hierarchical ontologies

Customizable labeling ontologies, including advanced hierarchical ontologies for complex use cases.

75%

Reduction in per-label time for humans

“AI Assist and Auto Annotation helped us accelerate our model development using the same techniques that the best Silicon Valley companies are using.”

Supported Types

Data labeling types

Single label classification

Assign a single, specific label to an image or text input.

Multi-label classification

Associate an image or text input with multiple labels simultaneously to build robust models that accurately classify inputs with multiple labels.

Bounding boxes for object detection

Localize and identify objects within an image by labeling 2d bounding boxes. Object detection models power many computer vision applications that rely on precise localization and classification of objects within images.

Polygons for segmentation masks

Polygon labels are used in computer vision to provide precise pixel-level annotations of objects within images. Power custom semantic and instance segmentation models.

Video track labeling

Annotate objects or regions of interest within a video sequence over multiple frames. Follow objects across frames using AI-powered tracks.