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[DOCS] Improve NNCF workflow images (#19040)

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* Update DEVELOPMENT_FLOW_V3_crunch.svg

* Update DEVELOPMENT_FLOW_V3_crunch.svg

* update

* Update DEVELOPMENT_FLOW_V3_crunch.svg

* Update DEVELOPMENT_FLOW_V3_crunch.svg

* Update DEVELOPMENT_FLOW_V3_crunch.svg

* Update docs/optimization_guide/model_optimization_guide.md
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Sebastian Golebiewski 2023-08-31 14:00:31 +02:00 committed by GitHub
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docs/_static/images/DEVELOPMENT_FLOW_V3_crunch.svg vendored
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docs/optimization_guide/model_optimization_guide.md
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@ -18,7 +18,9 @@ Model optimization is an optional offline step of improving the final model perf
.. note:: OpenVINO also supports optimized models (for example, quantized) from source frameworks such as PyTorch, TensorFlow, and ONNX (in Q/DQ; Quantize/DeQuantize format). No special steps are required in this case and optimized models can be converted to the OpenVINO Intermediate Representation format (IR) right away.
Post-training Quantization is the fastest way to optimize a model and should be applied first, but it is limited in terms of achievable accuracy-performance trade-off. In case of poor accuracy or performance after Post-training Quantization, Training-time Optimization can be used as an option.
Post-training Quantization is the fastest way to optimize a model and should be applied first, but it is limited in terms of achievable accuracy-performance trade-off. The recommended approach to obtain OpenVINO quantized model is to convert a model from original framework to ``ov.Model`` and ensure that the model works correctly in OpenVINO, for example, by calculating the model metrics. Then, ``ov.Model`` can be used as input for the ``nncf.quantize()`` method to get the quantized model (see the diagram below).
In case of unsatisfactory accuracy or performance after Post-training Quantization, Training-time Optimization can be used as an option.
.. image:: _static/images/DEVELOPMENT_FLOW_V3_crunch.svg