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Annotate lidar and other point cloud data with intuitive labeling interfaces.
Easy to set up and integrate into your workflow.
Label sequences of point clouds, and optionally define the ego pose for each frame. Use keyframe interpolation to speed up bounding box labeling.
Learn moreLabel training data faster by leveraging your model. Upload model predictions for new data and correct the predictions in the labeling interface.
Learn more
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
)
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)
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.
Implement active learning by using your model predictions to find failure cases and to speed up labeling.
Label images, point clouds, and text on Segments.ai
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
Try Segments.ai free for 14 days. No credit card required.
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