[Deprecated] Quantizing Models with Accuracy Control


Post-training Optimization Tool is deprecated since OpenVINO 2023.0. Neural Network Compression Framework (NNCF) is recommended for the post-training quantization instead.

The Accuracy-aware Quantization algorithm allows performing quantization while maintaining accuracy within a pre-defined range. Note that it should be used only if the Default Quantization introduces a significant accuracy degradation. The reason for it not being the primary choice is its potential for performance degradation, due to some layers getting reverted to the original precision.

To proceed with this article, make sure you have read how to use Default Quantization.


The Accuracy-aware Quantization algorithm’s behavior is different for the GNA target_device. In this case, it searches for the best configuration and selects between INT8 and INT16 precisions for the weights of each layer. The algorithm works for the performance preset only. It is not useful for the accuracy preset, since the whole model is already in INT16 precision.

A script for Accuracy-aware Quantization includes four steps:

  1. Prepare data and dataset interface.

  2. Define accuracy metric.

  3. Select quantization parameters.

  4. Define and run the quantization process.

Prepare data and dataset interface

This step is the same as in Default Quantization. The only difference is that __getitem__() should return (data, annotation) or (data, annotation, metadata). The annotation is required and its format should correspond to the expectations of the Metric class. The metadata is an optional field that can be used to store additional information required for post-processing.

Define accuracy metric

To control accuracy during optimization, the openvino.tools.pot.Metric interface should be implemented. Each implementation should override the following properties and methods:


  • value - returns the accuracy metric value for the last model output in a format of Dict[str, numpy.array].

  • avg_value - returns the average accuracy metric over collected model results in a format of Dict[str, numpy.array].

  • higher_better if a higher value of the metric corresponds to better performance, returns True , otherwise, False. The default implementation returns True.


  • update(output, annotation) - calculates and updates the accuracy metric value, using the last model output and annotation. The model output and annotation should be passed in this method. It should also contain the model-specific post-processing in case the model returns the raw output.

  • reset() - resets collected accuracy metric.

  • get_attributes() - returns a dictionary of metric attributes:

    {metric_name: {attribute_name: value}}

    Required attributes:

    • direction - (higher-better or higher-worse) a string parameter defining whether the metric value should be increased in accuracy-aware algorithms.

    • type - a string representation of a metric type. For example, “accuracy” or “mean_iou”.

Below is an example of the accuracy top-1 metric implementation with POT API: ```python from openvino.tools.pot import Metric

class Accuracy(Metric):

# Required methods def __init__(self, top_k=1):

super().__init__() self._top_k = top_k self._name = ‘accuracy@top{}’.format(self._top_k) self._matches = [] # container of the results

@property def value(self):

“”” Returns accuracy metric value for all model outputs. “”” return {self._name: self._matches[-1]}

@property def avg_value(self):

“”” Returns accuracy metric value for all model outputs. “”” return {self._name: np.ravel(self._matches).mean()}

def update(self, output, target):

“”” Updates prediction matches. :param output: model output :param target: annotations “”” if len(output) > 1:

raise Exception(‘The accuracy metric cannot be calculated ‘

‘for a model with multiple outputs’)

if isinstance(target, dict):

target = list(target.values())

predictions = np.argsort(output[0], axis=1)[:, -self._top_k:] match = [float(t in predictions[i]) for i, t in enumerate(target)]


def reset(self):

“”” Resets collected matches “”” self._matches = []

def get_attributes(self):

“”” Returns a dictionary of metric attributes {metric_name: {attribute_name: value}}. Required attributes: ‘direction’: ‘higher-better’ or ‘higher-worse’

‘type’: metric type

“”” return {self._name: {‘direction’: ‘higher-better’,

‘type’: ‘accuracy’}}


An instance of the Metric implementation should be passed to IEEngine object responsible for model inference.

metric = Accuracy()
engine = IEEngine(config=engine_config, data_loader=data_loader, metric=metric)

Select quantization parameters

Accuracy-aware Quantization uses the Default Quantization algorithm at the initialization step in such an order that all its parameters are also valid and can be specified. The only parameter required exclusively by Accuracy-aware Quantization is:

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

Run quantization

The example code below shows a basic quantization workflow with accuracy control. UserDataLoader() is a placeholder for the implementation of DataLoader.

from openvino.tools.pot import IEEngine
from openvino.tools.pot import load_model, save_model
from openvino.tools.pot import compress_model_weights
from openvino.tools.pot import create_pipeline

# Model config specifies the model name and paths to model .xml and .bin file
model_config = Dict(
        "model_name": "model",
        "model": path_to_xml,
        "weights": path_to_bin,

# Engine config
engine_config = Dict({"device": "CPU"})

algorithms = [
        "name": "AccuracyAwareQuantization",
        "params": {
            "target_device": "ANY",
            "stat_subset_size": 300,
            'maximal_drop': 0.02

# Step 1: Implement and create user's data loader.
data_loader = UserDataLoader()

# Step 2: Implement and create user's data loader.
metric = Accuracy()

# Step 3: Load the model.
model = load_model(model_config=model_config)

# Step 4: Initialize the engine for metric calculation and statistics collection.
engine = IEEngine(config=engine_config, data_loader=data_loader, metric=metric)

# Step 5: Create a pipeline of compression algorithms and run it.
pipeline = create_pipeline(algorithms, engine)
compressed_model = pipeline.run(model=model)

# Step 6 (Optional): Compress model weights to quantized precision
#                    to reduce the size of the final .bin file.

# Step 7: Save the compressed model to the desired path.
# Set save_path to the directory where the model should be saved.
compressed_model_paths = save_model(

# Step 8 (Optional): Evaluate the compressed model. Print the results.
metric_results = pipeline.evaluate(compressed_model)

It is worth noting that now the evaluate method that can compute accuracy on demand is also available in the Pipeline object.

In case when Accuracy-aware Quantization does not allow achieving the desired accuracy-performance trade-off, it is recommended to try Quantization-aware Training from NNCF.