Label all your sensor data - 2D & 3D - in one place

A biker and traffic segmented semantically

Image labeling interface

Label your camera images smart and efficiently with ML-assisted features and smart sequences.

Label your data with bounding boxes, segmentation, keypoints, polygons and polylines

Point cloud with cars segmented semantically

Label 3D Point Cloud of any size in one place

Discover the most intuitive platform for machine learning engineers to create smart workflows and annotate LiDAR and other point cloud data with powerful features.

Benefit from smart 3D labeling features

People carrying canoes with bounding box labels

The next-gen multi-sensor labeling platform

Uploading your 2D and 3D data, accelerate your labeling workflow, achieve greater efficiency and ensure unparalleled consistency.

Unlock the full potential of multi-sensor data

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
    )

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.

Set up advanced workflows

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

STEP 1

Label images and point clouds on Segments.ai

STEP 2

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

STEP 3

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

STEP 4

Use model predictions as prelabels to speed up labeling

Data labeling solutions