Running vai_q_tensorflow2 - 3.5 English

Vitis AI User Guide (UG1414)

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3.5 English

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

Post-training quantization (PTQ)
PTQ is a technique to convert a pre-trained floating-point model into a quantized model with little degradation in model accuracy. To perform PTQ, a representative dataset is required to run a few batches of inference on the floating-point model, which helps obtain the distributions of the activations. This process is also known as quantize calibration.
Quantization aware training (QAT)
QAT models the quantization error in both the forward and backward passes during model quantization. When using QAT, it is recommended to begin with a floating-point pre-trained model that already exhibits good accuracy rather than starting from scratch.