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Create segmentation labels using a powerful labeling interface. Easy to set up and integrate into your workflow.
Label objects and regions with unseen speed and accuracy, assisted by our Superpixel and Autosegment technology.
Learn moreExport your labels as instance, semantic, or panoptic segmentation labels. Multiple common export formats supported.
Learn moreLabel training data faster by leveraging your model. Upload model predictions for new data and correct the predictions using smart labeling tools.
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
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