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