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Connect TensorFlow with Label Studio

Overview

TensorFlow is an open source end-to-end machine learning framework focusing on deep neural network training and inference. Leverage TensorFlow by adding a machine learning backend to Label Studio.

Annotations from Label Studio can be used to train, retrain, or fine-tune models in TensorFlow, while TensorFlow models can be used to make labeling predictions on labeling tasks within Label Studio.

Benefits

Integrating TensorFlow with Label Studio provides the following benefits:

  • Quicker Prototyping: TensorFlow has a rich set of libraries and a well-documented interface for rapidly prototyping new models or modifying existing models.
  • Python-Centric: TensorFlow is Python-centric, making it easier to access and adopt across data scientists and ML research.
  • High availability of resources: With a widely-available tool, TensorFlow is widely-available and popular, bringing a wide range of resources to get started and debug your workflows.
  • Data Parallelism: TensorFlow brings a data parallelism feature, natively supporting an asynchronous execution of tasks — while possible in other tools, this is far easier to execute within PyTorch.
  • Human Expertise in Model Retraining: Label Studio brings expert human labeling into model tuning and retraining.
  • Speed Labeling through ML Automation: TensorFlow models can speed the labeling process by automatically annotating data, including confidence intervals that can flag complex tasks for human labeling.

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