Running vai_q_tensorflow2 - 2.0 English

Vitis AI User Guide (UG1414)

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2.0 English

The TensorFlow2 quantizer supports two different approaches to quantize a deep learning model:

Post-training quantization (PTQ)
PTQ is a technique to convert a pre-trained float model into a quantized model with little degradation in model accuracy. A representative dataset is needed to run a few batches of inference on the float model to obtain the distributions of the activations. This is also called quantize calibration.
Quantization aware training (QAT)
QAT models the quantization errors in both the forward and backward passes during model quantization. For QAT, starting from a float-point pre-trained model with good accuracy is recommended over starting from scratch.