An overview of interfaces

Build better datasets
to build better models

The platform for computer vision experts to manage, label and improve datasets.

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Trusted by ML teams from...
Semantic, instance, and panoptic segmentation
ML-powered labeling tools: Superpixels and Autosegment
Bounding boxes, polygons, polylines, and keypoints
Label videos with interpolation
Semantic, instance, and panoptic segmentation
Label point cloud sequences
Sensor fusion
Label point cloud sequences with interpolation
Sensor fusion
Label words and phrases with non-overlapping categories
Intuitive interface with customizable hotkeys
Label words and phrases with categories that can overlap
Intuitive interface with customizable hotkeys
Andres Milioto
Andres Milioto
Senior Computer Vision Engineer at Scythe Robotics
Author of SemanticKITTI and RangeNet++'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.

Gina Stavropoulou
Gina Stavropoulou
Computer Vision Engineer at Spotr

At Spotr, data labeling forms the basis of all our ML pipelines and has been instrumental to that end. Their Python SDK is well-documented and developer-friendly, and the support from their tech team has been phenomenal!

Why ML teams choose

  from segments import SegmentsClient

  client = SegmentsClient("api_key")

  dataset = client.add_dataset(

  sample = client.add_sample(
TensorFlow, AWS, Hugging Face, and PyTorch logo

Easy to set up and integrate

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

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

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

Manage your workforce

Collaborate with your team or onboard an external workforce. Our management tools make it easy to build 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.

Model predictions vs. corrected labels
Model predictions vs. corrected labels

Set up advanced workflows

Implement active learning by using your model predictions to find failure cases and to speed up labeling.


Label images, point clouds, and text on


Train a model and keep it on your side. Only upload model predictions


Compare predictions with ground-truth labels and use our search syntax to find failure cases


Use model predictions as prelabels to speed up labeling

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