The training data platform for AI teams
A machine learning model is only as good as its training data. Labelbox is an end-to-end platform to create and manage high-quality training data all in one place, while supporting your production pipeline with powerful APIs.
Powerful image labeling tool for image classification, object detection and segmentation
When every pixel matters, you need accurate and intuitive image segmentation tools. Customize the tools to support your specific use case, including instances, custom attributes and much more.
- Bounding box
- Points & lines
- Polygons
- Instance segmentation toolkit (pen & superpixels)
- Supports complex ontologies with nested classifications
- Support for tiled imagery (slippy maps)
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Performant video labeling editor for cutting-edge computer vision
Label directly on the video up to 30 FPS with frame level. Additionally, Labelbox provides per frame label feature analytics enabling you to create better models faster.
Sign up for betaCreating training data for natural language intelligence has never been easier
Label text strings, conversations, paragraphs, and documents with fast & customizable classification and named entity recognition tools.
- Text classification
- Named entity recognition (beta)
- Supports complex ontologies with nested classifications
Reduce human labeling costs by 80% with model-based pre-labeling and active learning
Labelbox provide tools and workflows to chose the right data to label as well as pre-label the data with your models.
Pre-labeling
Seed un-labeled data with model predictions as a starting point for the annotation process. Use your own model by integrating it with the Labelbox Prediction API
Active learning
Always label the most important data with API driven labeling queue prioritization. This technique can greatly improve labeling productivity. Combine this with model-predicted labels as a starting point for the annotation process to see the largest improvements.
Automated & dynamic task distribution to internal and external labeling teams
Label data with internal and external teams simultaneously. Review annotations collaboratively. Keep track of activity and progress.
Role-based access controls
Manage access to data and projects for your internal team members. Ensure access controls when working with a labeling service.
Labeler performance
Stay in the loop on internal and external labeler productivity and work quality.
External labeling workforce
Scale your labeling project with one-click outsourcing to one or multiple labeling services.
Learn about labeling service
Bringing powerful analytics and insights around training data earlier
Training data
Knowing feature counts and object analytics for your training data means informed decision making about the state of your model capabilities today and how to improve them. Labelbox puts all of this information and more at your fingertips.
Human performance
Get accurate insights into human labeling performance. Labelbox provides the ultimate transparency and standardization across internal and external labeling teams.
Unified infrastructure to create, edit, store and manage company wide training data.
Search, browse and curate all of your training data in one place. Investigate bad or inconsistent labels. Improve your training data collaboratively.
Construct your machine learning pipeline with powerful APIs
Stream data into Labelbox and push labeled data into training environments. Connect your ML models to supercharge labeling productivity and orchestrate active learning. Labelbox is API-first so you can use it as infrastructure to scale up.
import Labelbox
image_url = 'https://labelbox.com/tesla_model3.jpg'
datasets = Labelbox.datasets()
Labelbox.add_row_to_dataset({
'dataset_id': datasets[0]['id'],
'data': image_url
})
import Labelbox
projects = Labelbox.projects()
images = Labelbox.dataRows()
Labelbox.create_label({
'project_id': projects[0]['id'],
'label': 'damaged',
'image_id': images[0]['id']
})
import Labelbox
projects = Labelbox.projects()
images = Labelbox.dataRows()
models = Labelbox.predictionModels()
Labelbox.create_prediction({
'label': 'damaged',
'project_id': projects[0]['id'],
'image_id': images[0]['id'],
'model_id': models[0]['id']
})
import Labelbox
projects = Labelbox.projects()
labels = Labelbox.exportLabels({
'project_id': projects[0]['id']
})