Andres MiliotoSenior Computer Vision Engineer at Scythe Robotics
Author of SemanticKITTI and RangeNet++
Segments.ai's Superpixel tool for image segmentation boosts our labeling efficiency significantly. Their labeling/reviewing flows, Python SDK, and webhooks have been game changers for us. Their platform is now seamlessly integrated with our active learning pipeline.
Gina StavropoulouComputer 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!
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 )
Collaborate with your team or onboard an external workforce. Our management tools make it easy to build and review large datasets together.
Onboard an external workforce from our labeling service partners
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