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[POT] Minor correction about OpenSource (#9625)

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* Update README.md

* Update README.md
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Maksim Proshin
2022-01-13 20:10:34 +03:00
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tools/pot/README.md
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@@ -19,12 +19,13 @@ features:
* Symmetric and asymmetric quantization schemes. For details, see the [Quantization](openvino/tools/pot/algorithms/quantization/README.md) section.
* Per-channel quantization for Convolutional and Fully-Connected layers.
The tool is aimed to fully automate the model transformation process without a need to change the model on the user's side.
The POT is available only in the Intel® distribution of OpenVINO™ toolkit and is not opensourced. For details
about the low-precision flow in OpenVINO™, see the [Low Precision Optimization Guide](docs/LowPrecisionOptimizationGuide.md).
The tool is aimed to fully automate the model transformation process without a need to change the model on the user's side. For details about
the low-precision flow in OpenVINO™, see the [Low Precision Optimization Guide](docs/LowPrecisionOptimizationGuide.md).
For benchmarking results collected for the models optimized with POT tool, see [INT8 vs FP32 Comparison on Select Networks and Platforms](@ref openvino_docs_performance_int8_vs_fp32).
POT is opensourced on GitHub as a part of https://github.com/openvinotoolkit/openvino.
Further documentation presumes that you are familiar with the basic Deep Learning concepts, such as model inference,
dataset preparation, model optimization, as well as with the OpenVINO™ toolkit and its components such
as [Model Optimizer](@ref openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide)