Data Annotation Platform
for Robot Perception

Build training data for autonomous robots and vehicles using ML-powered tools.

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Powering robotics innovators such as ...

Powerful interfaces for every task

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: view calibrated camera images
Annotate and track objects in sequences
Keyframe interpolation
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.

Why ML teams choose Segments.ai


  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

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.

1

Label images, point clouds, and text on Segments.ai

2

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

3

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

4

Use model predictions as prelabels to speed up labeling

Start building training data for your robots

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