openvino/docs/optimization_guide/model_optimization_guide.md

Model Optimization Guide

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pot_README docs_nncf_introduction

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Model optimization assumes applying transformations to the model and relevant data flow to improve the inference performance. These transformations are basically offline and can require the availability of training and validation data. It includes such methods as quantization, pruning, preprocessing optimization, etc. OpenVINO provides several tools to optimize models at different steps of model development:

• Post-training Optimization tool (POT) is designed to optimize the inference of deep learning models by applying post-training methods that do not require model retraining or fine-tuning, like post-training quantization.

• Neural Network Compression Framework (NNCF) provides a suite of advanced algorithms for Neural Networks inference optimization with minimal accuracy drop, for example, quantization, pruning algorithms.

• Model Optimizer implements optimization to a model, most of them added by default, but you can configure mean/scale values, batch size, RGB vs BGR input channels, and other parameters to speed-up preprocess of a model (Embedding Preprocessing Computation)

Detailed workflow:

To understand which development optimization tool you need, refer to the diagram:

POT is the easiest way to get optimized models, and usually takes several minutes depending on the model size and used HW. NNCF can be considered as an alternative or addition when the first one does not give accurate results.

See also

• Deployment optimization