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Avoid html in navigation for table of content (openvinotoolkit#1277)
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‎.ci/spellcheck/.pyspelling.wordlist.txt

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Unet
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Unimodal
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unsqueeze
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Uparrow
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Upcroft
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upsample
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‎notebooks/001-hello-world/001-hello-world.ipynb

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"\n",
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"A pre-trained [MobileNetV3 model](https://docs.openvino.ai/2023.0/omz_models_model_mobilenet_v3_small_1_0_224_tf.html) from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used in this tutorial. For more information about how OpenVINO IR models are created, refer to the [TensorFlow to OpenVINO](../101-tensorflow-classification-to-openvino/101-tensorflow-classification-to-openvino.ipynb) tutorial.",
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"\n",
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"<a id=\"0\"></a>\n",
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"Table of content:\n",
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"- [Imports](#1)\n",
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"- [Download the Model and data samples](#2)\n",
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"- [Select inference device](#3)\n",
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"- [Load the Model](#4)\n",
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"- [Load an Image](#5)\n",
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"- [Do Inference](#6)\n"
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"#### Table of content:",
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"- [Imports](#Imports-Uparrow)\n",
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"- [Download the Model and data samples](#Download-the-Model-and-data-samples-Uparrow)\n",
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"- [Select inference device](#Select-inference-device-Uparrow)\n",
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"- [Load the Model](#Load-the-Model-Uparrow)\n",
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"- [Load an Image](#Load-an-Image-Uparrow)\n",
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"- [Do Inference](#Do-Inference-Uparrow)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e4c8cbe5",
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"metadata": {},
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"source": [
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"<a id=\"1\"></a>\n",
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"## Imports [&#8657;](#0)\n"
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"## Imports [$\\Uparrow$](#Table-of-content:)\n"
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]
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},
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{
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"id": "fb4afb34",
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"metadata": {},
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"source": [
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"<a id=\"2\"></a>\n",
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"## Download the Model and data samples [&#8657;](#0)\n"
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"## Download the Model and data samples [$\\Uparrow$](#Table-of-content:)\n"
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]
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},
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{
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"id": "5d2cf255-ac39-4656-b742-ec12520f423b",
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"metadata": {},
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"source": [
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"<a id=\"3\"></a>\n",
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"## Select inference device [&#8657;](#0)\n",
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"## Select inference device [$\\Uparrow$](#Table-of-content:)\n",
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"\n",
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"select device from dropdown list for running inference using OpenVINO"
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]
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"id": "55e49ae7",
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"metadata": {},
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"source": [
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"<a id=\"4\"></a>\n",
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"## Load the Model [&#8657;](#0)\n"
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"## Load the Model [$\\Uparrow$](#Table-of-content:)\n"
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]
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},
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{
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"id": "a19fc080",
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"metadata": {},
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"source": [
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"<a id=\"5\"></a>\n",
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"## Load an Image [&#8657;](#0)\n"
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"## Load an Image [$\\Uparrow$](#Table-of-content:)\n"
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]
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},
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"id": "6be327b6",
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"metadata": {},
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"source": [
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"<a id=\"6\"></a>\n",
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"## Do Inference [&#8657;](#0)\n"
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"## Do Inference [$\\Uparrow$](#Table-of-content:)\n"
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]
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},
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{

‎notebooks/003-hello-segmentation/003-hello-segmentation.ipynb

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"\n",
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"In this tutorial, a pre-trained [road-segmentation-adas-0001](https://docs.openvino.ai/2023.0/omz_models_model_road_segmentation_adas_0001.html) model from the [Open Model 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.",
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"\n",
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"<a id=\"0\"></a>\n",
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"Table of content:\n",
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"- [Imports](#1)\n",
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"- [Download model weights](#2)\n",
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"- [Select inference device](#3)\n",
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"- [Load the Model](#4)\n",
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"- [Load an Image](#5)\n",
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"- [Do Inference](#6)\n",
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"- [Prepare Data for Visualization](#7)\n",
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"- [Visualize data](#8)\n"
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"#### Table of content:",
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"- [Imports](#Imports-Uparrow)\n",
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"- [Download model weights](#Download-model-weights-Uparrow)\n",
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"- [Select inference device](#Select-inference-device-Uparrow)\n",
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"- [Load the Model](#Load-the-Model-Uparrow)\n",
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"- [Load an Image](#Load-an-Image-Uparrow)\n",
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"- [Do Inference](#Do-Inference-Uparrow)\n",
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"- [Prepare Data for Visualization](#Prepare-Data-for-Visualization-Uparrow)\n",
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"- [Visualize data](#Visualize-data-Uparrow)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e2f2f808",
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"metadata": {},
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"source": [
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"<a id=\"1\"></a>\n",
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"## Imports [&#8657;](#0)\n"
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"## Imports [$\\Uparrow$](#Table-of-content:)\n"
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]
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},
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{
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"id": "7f2028e9",
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"metadata": {},
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"source": [
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"<a id=\"2\"></a>\n",
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"## Download model weights [&#8657;](#0)\n"
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"## Download model weights [$\\Uparrow$](#Table-of-content:)\n"
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]
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},
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{
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"id": "4e1083df-a497-4a19-91d2-20915c18954b",
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"metadata": {},
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"source": [
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"<a id=\"3\"></a>\n",
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"## Select inference device [&#8657;](#0)\n",
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"## Select inference device [$\\Uparrow$](#Table-of-content:)\n",
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"\n",
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"select device from dropdown list for running inference using OpenVINO"
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]
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"id": "2a340210",
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"metadata": {},
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"source": [
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"## Load the Model [&#8657;](#0)\n"
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"## Load the Model [$\\Uparrow$](#Table-of-content:)\n"
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]
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},
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"id": "c18397f8",
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"metadata": {},
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"source": [
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"<a id=\"5\"></a>\n",
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"## Load an Image [&#8657;](#0)\n",
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"## Load an Image [$\\Uparrow$](#Table-of-content:)\n",
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"A sample image from the [Mapillary Vistas](https://www.mapillary.com/dataset/vistas) dataset is provided. "
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]
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},
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"id": "df0e7703",
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"metadata": {},
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"source": [
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"<a id=\"6\"></a>\n",
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"## Do Inference [&#8657;](#0)\n"
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"## Do Inference [$\\Uparrow$](#Table-of-content:)\n"
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]
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},
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"id": "9503a83b",
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"metadata": {},
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"source": [
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"<a id=\"7\"></a>\n",
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"## Prepare Data for Visualization [&#8657;](#0)\n"
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"## Prepare Data for Visualization [$\\Uparrow$](#Table-of-content:)\n"
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]
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},
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"id": "49d16e71",
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"metadata": {},
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"source": [
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"<a id=\"8\"></a>\n",
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"## Visualize data [&#8657;](#0)\n"
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"## Visualize data [$\\Uparrow$](#Table-of-content:)\n"
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]
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},
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{

‎notebooks/004-hello-detection/004-hello-detection.ipynb

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"The [horizontal-text-detection-0001](https://docs.openvino.ai/2023.0/omz_models_model_horizontal_text_detection_0001.html) model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used. It detects horizontal text in images and returns a blob of data in the shape of `[100, 5]`. Each detected text box is stored in the `[x_min, y_min, x_max, y_max, conf]` format, where the\n",
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"`(x_min, y_min)` are the coordinates of the top left bounding box corner, `(x_max, y_max)` are the coordinates of the bottom right bounding box corner and `conf` is the confidence for the predicted class.",
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"\n",
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"<a id=\"0\"></a>\n",
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"Table of content:\n",
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"- [Imports](#1)\n",
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"- [Download model weights](#2)\n",
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"- [Select inference device](#3)\n",
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"- [Load the Model](#4)\n",
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"- [Load an Image](#5)\n",
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"- [Do Inference](#6)\n",
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"- [Visualize Results](#7)\n"
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"#### Table of content:",
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"- [Imports](#Imports-Uparrow)\n",
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"- [Download model weights](#Download-model-weights-Uparrow)\n",
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"- [Select inference device](#Select-inference-device-Uparrow)\n",
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"- [Load the Model](#Load-the-Model-Uparrow)\n",
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"- [Load an Image](#Load-an-Image-Uparrow)\n",
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"- [Do Inference](#Do-Inference-Uparrow)\n",
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"- [Visualize Results](#Visualize-Results-Uparrow)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "740bfdd8",
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"metadata": {},
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"source": [
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"<a id=\"1\"></a>\n",
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"## Imports [&#8657;](#0)\n"
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"## Imports [$\\Uparrow$](#Table-of-content:)\n"
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]
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},
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"id": "4bd379ea",
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"metadata": {},
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"source": [
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"<a id=\"2\"></a>\n",
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"## Download model weights [&#8657;](#0)\n"
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"## Download model weights [$\\Uparrow$](#Table-of-content:)\n"
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]
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"id": "151c5b81-2cf9-41a7-95ec-8170a33de965",
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"metadata": {},
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"## Select inference device [&#8657;](#0)\n",
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"## Select inference device [$\\Uparrow$](#Table-of-content:)\n",
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"\n",
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"select device from dropdown list for running inference using OpenVINO"
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]
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"id": "85b48949",
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"metadata": {},
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"source": [
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"## Load the Model [&#8657;](#0)\n"
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"## Load the Model [$\\Uparrow$](#Table-of-content:)\n"
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"id": "705ce668",
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"metadata": {},
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"source": [
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"## Load an Image [&#8657;](#0)\n"
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"## Load an Image [$\\Uparrow$](#Table-of-content:)\n"
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"id": "f9fcaba9",
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"metadata": {},
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"source": [
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"## Do Inference [&#8657;](#0)\n"
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"## Do Inference [$\\Uparrow$](#Table-of-content:)\n"
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]
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"id": "09dfac5d",
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"metadata": {},
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"source": [
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"## Visualize Results [&#8657;](#0)\n"
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"## Visualize Results [$\\Uparrow$](#Table-of-content:)\n"
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]
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},
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{

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