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Model

Model

Improve performance using foundation models

Data dictates model performance. Take advantage of foundation models using the Foundry to enrich your datasets and automate critical data tasks. Create high-quality data in order to diagnose, debug, and optimize your custom machine learning models.
Build robust models with powerful error analysis

Build robust models with powerful error analysis

Identifying and fixing model errors with improved training data is the key to building high-performing models. Conduct powerful error analysis to surface model errors, diagnose root causes, and fix them with targeted improvements to your training data. Collaboratively version, evaluate, and compare training data, hyperparameters, and models across iterations in a single place.

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Leverage your model to find and fix data quality issues

Leverage your model to find and fix data quality issues

Data quality issues can severely undermine your model’s performance. Use your model as a guide to find and fix labeling mistakes, unbalanced data classes, and poorly crafted data splits that can affect model performance.

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Optimize labeling budget

Optimize labeling budget

Not all data impacts model performance equally. Leverage your data distribution, model predictions, model confidence scores, and similarity search to curate high-impact unlabeled data that will boost your model performance. 

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DataStore
DataStore
DataStore
Customization

Plug into powerful data-centric workflows

Simplify your data-to-model pipeline without friction. Seamlessly integrate Labelbox with your existing machine learning tech stack using our Python SDK. Labelbox Model works with any model training and inference framework, major cloud providers (AWS, Azure, GCS), and any data lake (Databricks, Snowflake).

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Supported annotations

Image
  1. Bounding box
  2. Segmentation
  3. Polygon
  4. Polyline
  5. Point
  6. Classification
Video
  1. Classification
Geospatial / Tiled imagery
  1. Bounding box
  2. Polygon
  3. Polyline
  4. Classification
Text
  1. Named-entity recognition
  2. Classification
Documents
  1. Classification
Audio
  1. Classification

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