Segments.ai makes it easy to manage, label and improve datasets.
Discover the different interfaces and features yourself.
The Cityscapes dataset focuses on semantic understanding of urban street scenes.
The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection.
SemanticKITTI is a dataset of LiDAR sequences for semantic scene understanding.
The nuScenes dataset is a large-scale autonomous driving dataset with 3D cuboid annotations.
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.
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 🤗)
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.
Implement active learning by using your model predictions to find failure cases and to speed up labeling.
Label images and point clouds 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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