Simplify multi-sensor data labeling

A biker and traffic segmented semantically

Image segmentation

Cityscapes

The Cityscapes dataset focuses on semantic understanding of urban street scenes.

View dataset Label image

People carrying canoes with bounding box labels

Image bounding boxes

VOC

The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection.

View dataset Label image

Point cloud with cars segmented semantically

Point cloud segmentation

SemanticKITTI

SemanticKITTI is a dataset of LiDAR sequences for semantic scene understanding.

View dataset Label sequence

Point cloud cuboids

nuScenes

The nuScenes dataset is a large-scale autonomous driving dataset with 3D cuboid annotations.

View dataset Label sequence

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