onnxruntime/docs/tutorials/inferencing/tensorflow.md

title	nav_order	grand_parent	parent
Accelerate TensorFlow	3	Tutorials	Inferencing

Accelerate TensorFlow model inferencing

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ONNX Runtime can accelerate inferencing times for TensorFlow, TFLite, and Keras models.

Contents

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Get Started

• End to end: Run TensorFlow models in ONNX Runtime

Export model to ONNX

TensorFlow

These examples use the TensorFlow-ONNX converter, which supports TensorFlow 1, 2, Keras, and TFLite model formats.

• TensorFlow: Object detection (efficentdet)
• TensorFlow: Object detection (SSD Mobilenet)
• TensorFlow: Image classification (efficientnet-edge)
• TensorFlow: Image classification (efficientnet-lite)
• TensorFlow: Natural Language Processing (BERT)

TFLite

• TFLite: Image classifciation (mobiledet)

Keras

Keras models can be converted using either the tensorflow-onnx or Keras-ONNX converter. The TensorFlow-ONNX converter supports newer opsets with more active support.

• tf2onnx: Image classification (Resnet 50)
• keras2onnx: Image classification (efficientnet)
• keras2onnx: Image classification (Densenet)
• keras2onnx: Natural Language Processing (BERT)
• keras2onnx: Handwritten Digit Recognition (MNIST)

Accelerate TensorFlow model inferencing

• Accelerate BERT model on CPU
• Accelerate BERT model on GPU