2D & 3D data labeling
for robotics and autonomous vehicles

The platform for fast and accurate multi-sensor data labeling.
Label in-house or with an external workforce.

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Label all your sensor data
in one place

Intuitive labeling interfaces for images, videos, and 3D point clouds (lidar and RGBD). Obtain segmentation labels, vector labels, and more.

Label sequences of data fast with interpolation and ML assistance

Sensor fusion: visualize and label multiple modalities in the same interface

Consistent object IDs across time and modalities

Label merged 3D point clouds of unlimited size

Image Segmentation

Semantic, instance, and panoptic segmentation
ML-powered labeling tools: Superpixels and Autosegment

Image Vector Labeling

Bounding boxes
Polygons
Polylines
Keypoints

Point Cloud Segmentation

Semantic segmentation
Instance segmentation
Panoptic segmentation

Point Cloud Vector Labeling

Cuboids / 3D bounding boxes
3D keypoints
Polygons and polylines
Model predictions vs. corrected labels

Get annotations faster

Our labeling interfaces are set up to label fast and precise. Powerful ML assistance lets you label faster and reduce costs.

Pre-label your data with model-assisted labeling using your own ML models or pre-trained models

Speed up image labeling with our ML-powered Superpixel and Autosegment tools

Maximize efficiency by customizing any hotkey

Label 3D sequences faster with batch mode and merged point cloud view

Set up automated workflows

Integrate data labeling into your existing ML pipelines and workflows using our simple yet powerful Python SDK.

Upload data, download labels, and manage datasets programmatically

Automatically trigger actions using webhooks

Connect your cloud provider (AWS, Google Cloud, Azure)

Export to popular ML frameworks (PyTorch, TensorFlow, Hugging Face 🤗)


    from segments import SegmentsClient

    client = SegmentsClient("api_key")

    dataset = client.add_dataset(
      "dataset_name", 
      task_type, 
      task_attributes
    )

    sample = client.add_sample(
      "user/dataset_name", 
      "sample_name", 
      attributes
    )
                  
TensorFlow, AWS, Hugging Face, and PyTorch logo
Model predictions vs. corrected labels

Label in-house or outsource

Onboard your own workforce or use one of our workforce partners. Our management tools make it easy to label and review large datasets together.

Give collaborators access to specific datasets and choose their permissions

Onboard an external workforce from our labeling service partners

Set up a reviewing step and communicate with labelers via issues

Track labeling performance through metrics and custom dashboards

Andres Milioto
Andres Milioto
Senior Computer Vision Engineer at Scythe Robotics
Author of SemanticKITTI and RangeNet++

Segments.ai's Superpixel tool for image segmentation boosts our labeling efficiency significantly. Their labeling/reviewing flows, Python SDK, and webhooks have been game changers for us. Their platform is now seamlessly integrated with our active learning pipeline.

About Segments.ai

Segments.ai was founded in 2020 by Otto Debals and Bert De Brabandere. During their PhDs in machine learning, they saw a need for a tool to label computer vision data faster.

In 2021, Segments.ai became the first Belgian startup to participate in Y Combinator, and raised a seed round soon after to expand its product and team. The team then built new interfaces and workflows to help customers with all their labeling needs.

Now, Segments.ai is providing a data labeling backbone to help robotics and AV companies build better datasets.

An overview of Segments.ai interfaces

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Start building better datasets today

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