Adding Quantizing with Accuracy Control using NNCF notebook (#19587)

Author: sgolebiewski-intel

docs/notebooks/001-hello-world-with-output.rst

@@ -1,7 +1,7 @@
 Hello Image Classification
 ==========================
 
-.. _top:
+
 
 This basic introduction to OpenVINO™ shows how to do inference with an
 image classification model.
@@ -15,6 +15,10 @@ created, refer to the `TensorFlow to
 OpenVINO <101-tensorflow-classification-to-openvino-with-output.html>`__
 tutorial. 
 
+
+
+.. _top:
+
 **Table of contents**:
 
 - `Imports <#imports>`__

docs/notebooks/003-hello-segmentation-with-output.rst

@@ -1,7 +1,7 @@
 Hello Image Segmentation
 ========================
 
-.. _top:
+
 
 A very basic introduction to using segmentation models with OpenVINO™.
 
@@ -12,6 +12,10 @@ Zoo <https://github.com/openvinotoolkit/open_model_zoo/>`__ is used.
 ADAS stands for Advanced Driver Assistance Services. The model
 recognizes four classes: background, road, curb and mark.
 
+
+
+.. _top:
+
 **Table of contents**:
 
 - `Imports <#imports>`__

docs/notebooks/004-hello-detection-with-output.rst

@@ -1,7 +1,7 @@
 Hello Object Detection
 ======================
 
-.. _top:
+
 
 A very basic introduction to using object detection models with
 OpenVINO™.
@@ -18,6 +18,10 @@ corner, ``(x_max, y_max)`` are the coordinates of the bottom right
 bounding box corner and ``conf`` is the confidence for the predicted
 class. 
 
+
+
+.. _top:
+
 **Table of contents**: 
 
 - `Imports <#imports>`__ 

docs/notebooks/101-tensorflow-classification-to-openvino-with-output.rst

@@ -1,7 +1,7 @@
 Convert a TensorFlow Model to OpenVINO™
 =======================================
 
-.. _top:
+
 
 | This short tutorial shows how to convert a TensorFlow
   `MobileNetV3 <https://docs.openvino.ai/2023.1/omz_models_model_mobilenet_v3_small_1_0_224_tf.html>`__
@@ -13,7 +13,11 @@ Convert a TensorFlow Model to OpenVINO™
   Runtime <https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_OV_Runtime_User_Guide.html>`__
   and do inference with a sample image.
 
-| **Table of contents**:
+
+
+| .. _top:
+
+**Table of contents**:
 
 - `Imports <#imports>`__
 - `Settings <#settings>`__

docs/notebooks/102-pytorch-onnx-to-openvino-with-output.rst

@@ -1,7 +1,7 @@
 Convert a PyTorch Model to ONNX and OpenVINO™ IR
 ================================================
 
-.. _top:
+
 
 This tutorial demonstrates step-by-step instructions on how to do
 inference on a PyTorch semantic segmentation model, using OpenVINO
@@ -35,6 +35,10 @@ plant, sheep, sofa, train, tv monitor**
 More information about the model is available in the `torchvision
 documentation <https://pytorch.org/vision/main/models/lraspp.html>`__
 
+
+
+.. _top:
+
 **Table of contents**: 
 
 - `Preparation <#preparation>`__

docs/notebooks/102-pytorch-to-openvino-with-output.rst

@@ -1,7 +1,7 @@
 Convert a PyTorch Model to OpenVINO™ IR
 =======================================
 
-.. _top:
+
 
 This tutorial demonstrates step-by-step instructions on how to do
 inference on a PyTorch classification model using OpenVINO Runtime.
@@ -31,6 +31,10 @@ but elevated to the design space level. The RegNet design space provides
 simple and fast networks that work well across a wide range of flop
 regimes. 
 
+
+
+.. _top:
+
 **Table of contents**:
 
 - `Prerequisites <#prerequisites>`__

docs/notebooks/103-paddle-to-openvino-classification-with-output.rst

@@ -1,7 +1,7 @@
 Convert a PaddlePaddle Model to OpenVINO™ IR
 ============================================
 
-.. _top:
+
 
 This notebook shows how to convert a MobileNetV3 model from
 `PaddleHub <https://github.com/PaddlePaddle/PaddleHub>`__, pre-trained
@@ -16,6 +16,10 @@ IR model.
 Source of the
 `model <https://www.paddlepaddle.org.cn/hubdetail?name=mobilenet_v3_large_imagenet_ssld&en_category=ImageClassification>`__.
 
+
+
+.. _top:
+
 **Table of contents**:
 
 - `Preparation <#preparation>`__

docs/notebooks/104-model-tools-with-output.rst

@@ -1,13 +1,15 @@
 Working with Open Model Zoo Models
 ==================================
 
-.. _top:
+
 
 This tutorial shows how to download a model from `Open Model
 Zoo <https://github.com/openvinotoolkit/open_model_zoo>`__, convert it
 to OpenVINO™ IR format, show information about the model, and benchmark
 the model. 
 
+.. _top:
+
 **Table of contents**:
 
 - `OpenVINO and Open Model Zoo Tools <#openvino-and-open-model-zoo-tools>`__

docs/notebooks/105-language-quantize-bert-with-output.rst

@@ -1,7 +1,7 @@
 Quantize NLP models with Post-Training Quantization ​in NNCF
 ============================================================
 
-.. _top:
+
 
 This tutorial demonstrates how to apply ``INT8`` quantization to the
 Natural Language Processing model known as
@@ -24,6 +24,10 @@ and datasets. It consists of the following steps:
 -  Compare the performance of the original, converted and quantized
    models.
 
+
+
+.. _top:
+
 **Table of contents**:
 
 - `Imports <#imports>`__

docs/notebooks/106-auto-device-with-output.rst

@@ -1,8 +1,6 @@
 Automatic Device Selection with OpenVINO™
 =========================================
 
-.. _top:
-
 The `Auto
 device <https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_supported_plugins_AUTO.html>`__
 (or AUTO in short) selects the most suitable device for inference by
@@ -32,6 +30,10 @@ first inference.
 
    auto
 
+
+
+.. _top:
+
 **Table of contents**:
 
 - `Import modules and create Core <#import-modules-and-create-core>`__