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tutorials/notebooks/example_quick_start_torchvision.ipynb

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+      "source": [
+        "# Quick Start\n",
+        "\n",
+        "[Run this tutorial in Google Colab](https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/example_quick_start_torchvision.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9bc664bf",
+      "metadata": {},
+      "source": [
+        "## Overview\n",
+        "This tutorial shows how to use \"quick-start\" with a pre-trained model from the torchvision library.\n",
+        "\n",
+        "The following steps will be covered:\n",
+        "\n",
+        "* **Setup the environment**: install MCT and add tutorials to PYTHONPATH\n",
+        "* **Download and organize the imagenet dataset**\n",
+        "* **Run quick_start on your model**\n",
+        "\n",
+        "**Note**: The following code will run faster on a GPU."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d0e81b09e6d30873",
+      "metadata": {
+        "collapsed": false,
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+      },
+      "source": [
+        "## Setup\n",
+        "\n",
+        "Install and import relevant packages"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "eda6ab0d8f0b6b56",
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+      "source": [
+        "In order to use quick-start you'll need the [MCT tutorial folder](https://github.com/sony/model_optimization/tree/main/tutorials), so we'll clone the MCT repository to a local folder and use that code.\n",
+        "\n",
+        "  **It's important to note that we use the most up-to-date MCT code available.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ca9a743c0e7ba067",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
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+        "outputId": "10c7246b-8b31-4c44-9a45-04d4d3a2958b"
+      },
+      "outputs": [],
+      "source": [
+        "!git clone https://github.com/sony/model_optimization.git local_mct\n",
+        "!pip install -r ./local_mct/requirements.txt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "PfJ3_AyieBL0",
+      "metadata": {
+        "id": "PfJ3_AyieBL0"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.environ['PYTHONPATH'] = './local_mct/'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ufTwrlH-eCyB",
+      "metadata": {
+        "id": "ufTwrlH-eCyB"
+      },
+      "source": [
+        "## Dataset\n",
+        "Download the imagenet validation dataset.\n",
+        "\n",
+        "Use torchvision.datasets.ImageNet to create the dataset in the correct structure.\n",
+        "\n",
+        "**Note**: We use validation for time convinience since the training split is too big. Typically, the validation samples should only be used for testing."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cj6WQGrLvkvx",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cj6WQGrLvkvx",
+        "outputId": "46efb6a0-abe3-48d9-ce89-6890a566e6de"
+      },
+      "outputs": [],
+      "source": [
+        "!mkdir imagenet\n",
+        "!wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_devkit_t12.tar.gz\n",
+        "!mv ILSVRC2012_devkit_t12.tar.gz imagenet/\n",
+        "!wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\n",
+        "!mv ILSVRC2012_img_val.tar imagenet/"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "coxDAP4Av3ol",
+      "metadata": {
+        "id": "coxDAP4Av3ol"
+      },
+      "outputs": [],
+      "source": [
+        "import torchvision\n",
+        "ds = torchvision.datasets.ImageNet(root='./imagenet', split='val')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "gY2UsnzOfjtk",
+      "metadata": {
+        "id": "gY2UsnzOfjtk"
+      },
+      "source": [
+        "## Run Quick Start script on our model of choice\n",
+        "\n",
+        "Here we set the model name, model library, validation dataset path, and representative datasets path.\n",
+        " \n",
+        "The remaining arguments are left with their default settings. Please verify that the dataset paths are configured correctly."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ZJ8CR1FQRwg2",
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+        "outputId": "607f95e7-8c80-408f-ebe8-ace70f999e3e"
+      },
+      "outputs": [],
+      "source": [
+        "!python ./local_mct/tutorials/quick_start/main.py --model_name mobilenet_v2 --model_library torchvision --validation_dataset_folder ./imagenet/val --representative_dataset_folder ./imagenet/val"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d5d80935",
+      "metadata": {},
+      "source": [
+        "\n",
+        "Copyright 2024 Sony Semiconductor Israel, Inc. All rights reserved.\n",
+        "\n",
+        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "you may not use this file except in compliance with the License.\n",
+        "You may obtain a copy of the License at\n",
+        "\n",
+        "    http://www.apache.org/licenses/LICENSE-2.0\n",
+        "\n",
+        "Unless required by applicable law or agreed to in writing, software\n",
+        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "See the License for the specific language governing permissions and\n",
+        "limitations under the License.\n"
+      ]
+    }
+  ],
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+    "accelerator": "GPU",
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