# AccuracyAwareQuantization Parameters¶

## Introduction¶

Accuracy-aware Quantization algorithm is aimed at accurate quantization and allows the model’s accuracy to stay within the pre-defined range. This may cause a degradation in performance in comparison to Default Quantization algorithm because some layers can be reverted back to the original precision.

## Parameters¶

Since the Default Quantization algorithm is used as an initialization, all its parameters are also valid and can be specified. Here is an example of the definition of the Accuracy-aware Quantization method and its parameters:

{
"name": "AccuracyAwareQuantization", // the name of optimization algorithm
"params": {
...
}
}

Below are the descriptions of AccuracyAwareQuantization-specific parameters:

• "ranking_subset_size" - size of a subset that is used to rank layers by their contribution to the accuracy drop. Default value is 300, and more samples it has the better ranking, potentially.

• "max_iter_num" - the maximum number of iterations of the algorithm. In other words, the maximum number of layers that may be reverted back to floating-point precision. By default, it is limited by the overall number of quantized layers.

• "maximal_drop" - the maximum accuracy drop which has to be achieved after the quantization. The default value is 0.01 (1%).

• "drop_type" - a drop type of the accuracy metric:

• "absolute" - the (default) absolute drop with respect to the results of the full-precision model.

• "relative" - relative to the results of the full-precision model.

• "use_prev_if_drop_increase" - the use of network snapshot from the previous iteration when a drop increases. The default value is True.

• "base_algorithm" - name of the algorithm that is used to quantize a model at the beginning. The default value is “DefaultQuantization”.

• "convert_to_mixed_preset" - set to convert the model to “mixed” mode if the accuracy criteria for the model quantized with “performance” preset are not satisfied. This option can help to reduce number of layers that are reverted to floating-point precision. Keep in mind that this is an experimental feature.

• "metrics" - an optional list of metrics that are taken into account during optimization. It consists of tuples with the following parameters:

• "name" - name of the metric to optimize.

• "baseline_value" - (optional parameter) a baseline metric value of the original model. The validations on The validation will be initiated entirely in the beginning if nothing specified.

• "metric_subset_ratio" - a part of the validation set that is used to compare original full-precision and fully quantized models when creating a ranking subset in case of predefined metric values of the original model. The default value is 0.5.

• "tune_hyperparams" - enables tuning of quantization parameters as a preliminary step before reverting layers back to the floating-point precision. It can bring an additional boost in performance and accuracy, at the cost of increased overall quantization time. The default value is False.