An overview of Segments.ai interfaces

Data Annotation Platform
for Surveying and Mapping

Label images or point clouds with ML-powered tools

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Trusted by ML teams from...
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Powerful interfaces for every task

Image Segmentation
Semantic, instance, and panoptic segmentation
ML-powered labeling tools: Superpixels and Autosegment
Image Vector Labeling
Bounding boxes, polygons, polylines, and keypoints
Label videos with interpolation
3D Point Cloud Segmentation
Semantic, instance, and panoptic segmentation
Label point cloud sequences
Sensor fusion: view calibrated camera images
3D Point Cloud Cuboids
Annotate and track objects in sequences
Keyframe interpolation
Gina Stavropoulou
Gina Stavropoulou
Computer Vision Engineer at Spotr

At Spotr, data labeling forms the basis of all our ML pipelines and Segments.ai 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 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 annotating your data today

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