Vector labeling interface

Image vector labeling tool
for robotics and autonomous vehicles

The platform to create fast and accurate vector annotations.
Support for bounding boxes, polygons, polylines, and keypoints.
Label in-house or with an external workforce.

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Bounding box, polygon, polyline, and keypoint

Accurate labels for your use case

Bounding boxes

Label videos with interpolation supports labeling sequences of images. Use keyframe interpolation to speed up bounding box labeling.

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Bounding box keyframe interpolation
Model predictions vs. corrected labels

Model-assisted labeling

Label training data faster by leveraging your model. Upload model predictions for new data and correct the predictions in the labeling interface.

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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.

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 🤗)

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.

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 and point clouds 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

Start building better datasets today

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