Tiny_model_training.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets==2.11.0\n",
        "\n",
        "!pip install pyarrow==15.0.0\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qtMQmFvWp10R",
        "outputId": "4554a95e-a3cb-489e-e9f1-a2154f7082fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets==2.11.0\n",
            "  Downloading datasets-2.11.0-py3-none-any.whl.metadata (20 kB)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets==2.11.0) (1.26.4)\n",
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            "Collecting dill<0.3.7,>=0.3.0 (from datasets==2.11.0)\n",
            "  Downloading dill-0.3.6-py3-none-any.whl.metadata (9.8 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets==2.11.0) (2.2.2)\n",
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            "Collecting xxhash (from datasets==2.11.0)\n",
            "  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting multiprocess (from datasets==2.11.0)\n",
            "  Downloading multiprocess-0.70.17-py310-none-any.whl.metadata (7.2 kB)\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets==2.11.0) (24.1)\n",
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            "  Downloading responses-0.18.0-py3-none-any.whl.metadata (29 kB)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets==2.11.0) (4.12.2)\n",
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            "INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.\n",
            "Collecting multiprocess (from datasets==2.11.0)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
            "  Downloading multiprocess-0.70.15-py310-none-any.whl.metadata (7.2 kB)\n",
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            "\u001b[?25hDownloading dill-0.3.6-py3-none-any.whl (110 kB)\n",
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            "\u001b[?25hDownloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m19.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: xxhash, dill, responses, multiprocess, datasets\n",
            "Successfully installed datasets-2.11.0 dill-0.3.6 multiprocess-0.70.14 responses-0.18.0 xxhash-3.5.0\n",
            "Collecting pyarrow==15.0.0\n",
            "  Downloading pyarrow-15.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.0 kB)\n",
            "Requirement already satisfied: numpy<2,>=1.16.6 in /usr/local/lib/python3.10/dist-packages (from pyarrow==15.0.0) (1.26.4)\n",
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            "\u001b[?25hInstalling collected packages: pyarrow\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 16.1.0\n",
            "    Uninstalling pyarrow-16.1.0:\n",
            "      Successfully uninstalled pyarrow-16.1.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.6.1 requires pyarrow<16.2.0a0,>=16.1.0, but you have pyarrow 15.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed pyarrow-15.0.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pyarrow"
                ]
              },
              "id": "4fb69bf2c7214a4c81bf3d313529c553"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Import required Dependencies for a Computer Vision Project"
      ],
      "metadata": {
        "id": "J5GR-MxNiv7E"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PJwyjSxIh4Dj"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "import os\n",
        "import glob\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from PIL import Image\n",
        "from transformers import ViTFeatureExtractor, ViTForImageClassification\n",
        "import requests\n",
        "from transformers import AutoImageProcessor\n",
        "import pandas as pd\n",
        "from torch.utils.data import Dataset\n",
        "import matplotlib.pyplot as plt\n",
        "from datasets import Dataset\n",
        "from datasets import load_metric\n",
        "from transformers import AutoImageProcessor, TrainingArguments, Trainer,AutoModelForImageClassification\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "8133b512e05241c7836ded7db99115ac",
            "fce4d814c30c4e40a21692e8ad5a3435",
            "945c1e7c3343476d922256e78c0e1195",
            "9c4d614fdc6a4f7a878f5c23f28a3708",
            "e965fab33d8a45acb6abdae7bd9ba7bc",
            "d510dd1637804f739de9f8c1e9ff7424",
            "13ba2a983b6d41a8a1c48d2dce6aebf3",
            "6d933f2e819445d98fe50ed36e4702b8",
            "f9708423d5c24a32be217eb00f3c3b39",
            "816efcd4d62b4490953a22f84b52e18b",
            "48341eba81274a10b26f22ebcb3d130e"
          ]
        },
        "id": "8UGse36eLeeb",
        "outputId": "8f0c54b6-4a72-4bef-c70d-23e8dcb210bb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-21f3ecd7ee32>:1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  metric = load_metric(\"accuracy\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.65k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8133b512e05241c7836ded7db99115ac"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "metric = load_metric(\"accuracy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Connecte Google Colab à Google Drive"
      ],
      "metadata": {
        "id": "0uxbLoVqjhQV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AnDkl0IljBqu",
        "outputId": "ebd61705-bd0a-4af1-bf7d-bd35129d3fea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def unzip_file(zip_path, extract_to):\n",
        "    \"\"\"\n",
        "    Unzip a zip file and maintain its directory structure.\n",
        "\n",
        "    Args:\n",
        "    zip_path (str): The path to the zip file.\n",
        "    extract_to (str): The directory to extract the files into.\n",
        "    \"\"\"\n",
        "    with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
        "        # Extract all the files in the specified directory\n",
        "        zip_ref.extractall(extract_to)\n",
        "        print(f\"Files extracted to {extract_to}\")\n",
        "\n",
        "zip_file_path_train = '/content/gdrive/MyDrive/eya/Financial_Data.zip'\n",
        "zip_file_path_test = '/content/gdrive/MyDrive/eya/Financial_data_test.zip'\n",
        "\n",
        "destination_path = '/content/'\n",
        "unzip_file(zip_file_path_train, destination_path)\n",
        "unzip_file(zip_file_path_test , destination_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJkG-kIdjsjw",
        "outputId": "9cb02681-0aca-45c6-e93c-1cbedf023650"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files extracted to /content/\n",
            "Files extracted to /content/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the folder paths for training and test images\n",
        "folder_path_train = '/content/Financial Data'\n",
        "folder_path_test = '/content/Financial_data_test'\n",
        "\n",
        "# Count the total number of .jpeg images in the training folder\n",
        "total_images_train = len(glob.glob(os.path.join(folder_path_train, '**/*.[jJ][pP][eE][gG]'), recursive=True))\n",
        "\n",
        "# Count the total number of .jpeg images in the test folder\n",
        "total_images_test = len(glob.glob(os.path.join(folder_path_test, '**/*.[jJ][pP][eE][gG]'), recursive=True))\n",
        "\n",
        "# Print the total number of .jpeg images in the train and test datasets\n",
        "print(f\"Total number of .jpeg images_train: {total_images_train}\")\n",
        "print(f\"Total number of .jpeg images_test: {total_images_test}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWX2U0yMlFMy",
        "outputId": "09d2726f-9220-4680-873b-129969ec0720"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of .jpeg images_train: 47996\n",
            "Total number of .jpeg images_test: 39996\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_full_img_class_df(base_path):\n",
        "    \"\"\"\n",
        "    Create a DataFrame with image paths and their corresponding labels by traversing the directory structure.\n",
        "\n",
        "    Args:\n",
        "    base_path (str): The path to the directory containing subdirectories of images.\n",
        "\n",
        "    Returns:\n",
        "    DataFrame: A DataFrame with columns 'img' for image paths and 'label' for their corresponding labels.\n",
        "    \"\"\"\n",
        "    data = pd.DataFrame()\n",
        "    img_rows = []\n",
        "    label_rows = []\n",
        "\n",
        "    # List all subdirectories (each representing a label)\n",
        "    for label in sorted(os.listdir(base_path)):\n",
        "        label_path = os.path.join(base_path, label)\n",
        "        if os.path.isdir(label_path):\n",
        "            # List all image files in the subdirectory\n",
        "            for img_file in sorted(os.listdir(label_path)):\n",
        "                if img_file.endswith(('.jpg', '.jpeg', '.png')):\n",
        "                    img_path = os.path.join(label_path, img_file)\n",
        "                    img_rows.append(img_path)\n",
        "                    label_rows.append(label)\n",
        "\n",
        "    data['img'] = img_rows\n",
        "    data['label'] = label_rows\n",
        "\n",
        "\n",
        "    return data\n",
        "\n",
        "\n",
        "\n",
        "# Create the DataFrame\n",
        "df_train = create_full_img_class_df(folder_path_train)\n",
        "df_test= create_full_img_class_df(folder_path_test)\n",
        "df_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rqN3LQYMlnJ2",
        "outputId": "d13018ea-194d-4b84-c3b5-bd9ce3b0fe63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 img          label\n",
              "0  /content/Financial Data/advertisement/00000004...  advertisement\n",
              "1  /content/Financial Data/advertisement/00000016...  advertisement\n",
              "2  /content/Financial Data/advertisement/00000016...  advertisement\n",
              "3  /content/Financial Data/advertisement/00000016...  advertisement\n",
              "4  /content/Financial Data/advertisement/00000018...  advertisement"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-78215d32-b3e9-49d5-8773-3880f73cfd28\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>img</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/Financial Data/advertisement/00000004...</td>\n",
              "      <td>advertisement</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/Financial Data/advertisement/00000016...</td>\n",
              "      <td>advertisement</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/Financial Data/advertisement/00000016...</td>\n",
              "      <td>advertisement</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/Financial Data/advertisement/00000016...</td>\n",
              "      <td>advertisement</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/Financial Data/advertisement/00000018...</td>\n",
              "      <td>advertisement</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78215d32-b3e9-49d5-8773-3880f73cfd28')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-78215d32-b3e9-49d5-8773-3880f73cfd28 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-78215d32-b3e9-49d5-8773-3880f73cfd28');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6439ae29-45aa-4193-8fbe-15fa8a8b76c7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6439ae29-45aa-4193-8fbe-15fa8a8b76c7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6439ae29-45aa-4193-8fbe-15fa8a8b76c7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train",
              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 47996,\n  \"fields\": [\n    {\n      \"column\": \"img\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 47996,\n        \"samples\": [\n          \"/content/Financial Data/questionnaire/505666865_505666868.jpeg\",\n          \"/content/Financial Data/scientific publication/10354971_10354980.jpeg\",\n          \"/content/Financial Data/scientific report/2501483788_2501483818.jpeg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"advertisement\",\n          \"budget\",\n          \"handwritten\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preparation\n",
        "\n",
        "## Split the data into training and validation sets\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "j9Luf28umis4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_val, y_train, y_val = train_test_split(df_train['img'], df_train['label'], test_size = 0.2, stratify = df_train['label'])\n",
        "# Display the DataFrame\n",
        "print(X_train.shape[0])\n",
        "print(X_val.shape[0])\n",
        "print(df_test.shape[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qkNlvjA-ls5_",
        "outputId": "390c9cc0-9192-4101-c5a9-6ea508246a0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "38396\n",
            "9600\n",
            "39996\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Converting the labels to numeric format"
      ],
      "metadata": {
        "id": "7YODQJSGoOdN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Find all unique labels in y_train\n",
        "unique_labels_train = y_train.unique()\n",
        "# Create a single mapping dictionary based on the training labels\n",
        "label2id = {label: i for i, label in enumerate(unique_labels_train)}\n",
        "# Create the inverse mapping dictionary from id to label\n",
        "id2label = {i: label for label, i in label2id.items()}\n",
        "# Convert the labels in y_train and y_val to indices\n",
        "y_train_mapped = y_train.map(label2id)\n",
        "y_val_mapped = y_val.map(label2id)\n",
        "y_test = df_test['label'].str.replace('_', ' ') # Replace underscores with spaces in the test labels to match the format of training and validation labels\n",
        "# Some categories in the test set contain underscores, which do not exist in the training/validation labels.\n",
        "# This replacement ensures consistent labels across all sets and allows correct label mapping.\n",
        "y_test_mapped = y_test.map(label2id)\n",
        "# Print label2id,id2label\n",
        "print('label2id :', label2id)\n",
        "print('id2label :', id2label)\n",
        "print('y_train_mapped :', y_train_mapped)\n",
        "print('y_val_mapped :', y_val_mapped)\n",
        "print('y_test_mapped :', y_test_mapped)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AH293qk0m4Ay",
        "outputId": "c1ba1f5d-4c7f-4a22-db82-01daf69dd940"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label2id : {'budget': 0, 'presentation': 1, 'questionnaire': 2, 'resume': 3, 'handwritten': 4, 'invoice': 5, 'scientific publication': 6, 'advertisement': 7, 'letter': 8, 'file folder': 9, 'specification': 10, 'news article': 11, 'memo': 12, 'scientific report': 13, 'form': 14, 'email': 15}\n",
            "id2label : {0: 'budget', 1: 'presentation', 2: 'questionnaire', 3: 'resume', 4: 'handwritten', 5: 'invoice', 6: 'scientific publication', 7: 'advertisement', 8: 'letter', 9: 'file folder', 10: 'specification', 11: 'news article', 12: 'memo', 13: 'scientific report', 14: 'form', 15: 'email'}\n",
            "y_train_mapped : 4662      0\n",
            "30047     1\n",
            "34864     2\n",
            "3192      0\n",
            "36220     3\n",
            "         ..\n",
            "32376     1\n",
            "7878     15\n",
            "43889    13\n",
            "46064    10\n",
            "94        7\n",
            "Name: label, Length: 38396, dtype: int64\n",
            "y_val_mapped : 17779     4\n",
            "43220    13\n",
            "26491    12\n",
            "6609     15\n",
            "29984    11\n",
            "         ..\n",
            "32055     1\n",
            "15471     4\n",
            "18352     5\n",
            "33839     2\n",
            "37982     3\n",
            "Name: label, Length: 9600, dtype: int64\n",
            "y_test_mapped : 0         7\n",
            "1         7\n",
            "2         7\n",
            "3         7\n",
            "4         7\n",
            "         ..\n",
            "39991    10\n",
            "39992    10\n",
            "39993    10\n",
            "39994    10\n",
            "39995    10\n",
            "Name: label, Length: 39996, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a DataFrame for the training set\n",
        "train_df = pd.DataFrame({\n",
        "    'img': X_train,\n",
        "    'label': y_train_mapped\n",
        "})\n",
        "# Create a DataFrame for the validation set\n",
        "val_df = pd.DataFrame({\n",
        "    'img': X_val,\n",
        "    'label': y_val_mapped\n",
        "})\n",
        "test_df = pd.DataFrame({\n",
        "    'img': df_test['img'],\n",
        "    'label': y_test_mapped\n",
        "})\n",
        "# Reset the index of train_df and val_df before converting them to Hugging Face datasets\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "val_df = val_df.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "7RdIsDA2u79a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Hugging Face datasets\n",
        "test_dataset = Dataset.from_pandas(test_df)\n",
        "train_dataset = Dataset.from_pandas(train_df)\n",
        "val_dataset = Dataset.from_pandas(val_df)\n",
        "print(test_dataset)\n",
        "print(train_dataset)\n",
        "print(val_dataset)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0x8Go6fjr4Bo",
        "outputId": "ee63aac1-3a85-4060-b1e2-1b92656d21a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['img', 'label'],\n",
            "    num_rows: 39996\n",
            "})\n",
            "Dataset({\n",
            "    features: ['img', 'label'],\n",
            "    num_rows: 38396\n",
            "})\n",
            "Dataset({\n",
            "    features: ['img', 'label'],\n",
            "    num_rows: 9600\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to display an image from the dataset\n",
        "def show_image_from_dataset(dataset, index):\n",
        "    # Retrieve the image path and label from the dataset\n",
        "    img_path = dataset['img'][index]\n",
        "    label = dataset['label'][index]\n",
        "\n",
        "    # Open the image using PIL\n",
        "    image = Image.open(img_path)\n",
        "\n",
        "    # Display the image with its label\n",
        "    plt.imshow(image)\n",
        "    plt.title(f\"Label: {label}\")\n",
        "    plt.axis('off')  # Hide axes for better visualization\n",
        "    plt.show()\n",
        "\n",
        "# Example: Display the first image in the train_dataset\n",
        "show_image_from_dataset(train_dataset, 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "HsNOHMlvxFsQ",
        "outputId": "1fad4390-b54c-4cf3-89ed-0ab37eb886ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data processing"
      ],
      "metadata": {
        "id": "DqVEqiUNmDap"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "image_processor  = AutoImageProcessor.from_pretrained(model_checkpoint)\n",
        "print(image_processor)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572,
          "referenced_widgets": [
            "8262c595d2d0457bbed5ab6a3f657cde",
            "0f149c4edbe54da5a92acda51e4a3961",
            "8ebbe44847ab4ab49c4d1770f2b4f556",
            "d07e040c98394d729822253428491de4",
            "4fef774ae0d74cfc943f97ddce73e7e9",
            "c8942cec8a4e432d8fc1d47f9d5df31b",
            "def2704ffaec44f8bdd0094437a6696b",
            "823cf3f3038e4a788304f211c2c8d256",
            "fc87a9f25a524dc8865ade394ba6ca73",
            "54fee51e081845868096e9c69f4418e6",
            "773cbd5fa7704d8fa8aaeb6199300fe8"
          ]
        },
        "id": "OsBP3qJ3yIlm",
        "outputId": "94ac6184-05ec-4ac1-aa30-314eab22cb5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/255 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8262c595d2d0457bbed5ab6a3f657cde"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ViTImageProcessor {\n",
            "  \"do_normalize\": true,\n",
            "  \"do_rescale\": true,\n",
            "  \"do_resize\": true,\n",
            "  \"image_mean\": [\n",
            "    0.485,\n",
            "    0.456,\n",
            "    0.406\n",
            "  ],\n",
            "  \"image_processor_type\": \"ViTImageProcessor\",\n",
            "  \"image_std\": [\n",
            "    0.229,\n",
            "    0.224,\n",
            "    0.225\n",
            "  ],\n",
            "  \"resample\": 3,\n",
            "  \"rescale_factor\": 0.00392156862745098,\n",
            "  \"size\": {\n",
            "    \"height\": 224,\n",
            "    \"width\": 224\n",
            "  }\n",
            "}\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.transforms import (\n",
        "    Compose,\n",
        "    Normalize,\n",
        "    Resize,\n",
        "    RandomHorizontalFlip,\n",
        "    RandomApply,\n",
        "    ColorJitter,\n",
        "    ToTensor,\n",
        ")\n",
        "\n",
        "# Extract mean and std for normalization\n",
        "normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)\n",
        "\n",
        "# Determine the image size based on the image processor\n",
        "if \"height\" in image_processor.size:\n",
        "    size = (image_processor.size[\"height\"], image_processor.size[\"width\"])\n",
        "    crop_size = size\n",
        "    max_size = None\n",
        "elif \"shortest_edge\" in image_processor.size:\n",
        "    size = image_processor.size[\"shortest_edge\"]\n",
        "    crop_size = (size, size)\n",
        "    max_size = image_processor.size.get(\"longest_edge\")\n",
        "\n",
        "# Data preprocessing (Reading -> Resizing -> Normalization)\n",
        "def preprocess(image):\n",
        "    return Compose([\n",
        "        Resize(size),  # Resize the image based on the size from the image processor\n",
        "        ToTensor(),  # Convert to tensor\n",
        "        normalize,  # Normalize the image\n",
        "    ])(image)\n",
        "\n",
        "# Data augmentation (random brightness, contrast, flip)\n",
        "def augment(image):\n",
        "    return Compose([\n",
        "        RandomApply([ColorJitter(brightness=0.2, contrast=(0.2, 0.4))], p=1.0),  # Random brightness & contrast\n",
        "        RandomHorizontalFlip(p=0.5),  # Randomly flip horizontally\n",
        "    ])(image)\n",
        "\n",
        "# Complete preprocessing + augmentation for training\n",
        "def preprocess_train(image):\n",
        "    img = preprocess(image)  # Preprocess the image (resize, normalize)\n",
        "    img = augment(img)  # Apply data augmentation\n",
        "    return img\n",
        "\n",
        "# Preprocessing for validation (no augmentation)\n",
        "def preprocess_val(image):\n",
        "    img = preprocess(image)  # Only preprocess (resize, normalize)\n",
        "    return img\n",
        "\n",
        "# Example functions for batch processing\n",
        "def preprocess_train_batch(example_batch):\n",
        "    # Open the image from the file path and apply the transformations\n",
        "    example_batch[\"pixel_values\"] = [\n",
        "        preprocess_train(Image.open(image_path).convert(\"RGB\")) for image_path in example_batch[\"img\"]\n",
        "    ]\n",
        "    return example_batch\n",
        "\n",
        "def preprocess_val_batch(example_batch):\n",
        "    # Open the image from the file path and apply the transformations (only preprocessing, no augmentation)\n",
        "    example_batch[\"pixel_values\"] = [\n",
        "        preprocess_val(Image.open(image_path).convert(\"RGB\")) for image_path in example_batch[\"img\"]\n",
        "    ]\n",
        "    return example_batch"
      ],
      "metadata": {
        "id": "7WjSHJCF5D-r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset.set_transform(preprocess_train_batch)\n",
        "val_dataset.set_transform(preprocess_val_batch)"
      ],
      "metadata": {
        "id": "kjBBbAUf6QF5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_sz5-aa0FaBY",
        "outputId": "d24b7a3c-76d1-4522-93bc-eca53474d939"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'img': '/content/Financial Data/scientific publication/50682664-2669.jpeg',\n",
              " 'label': 0,\n",
              " 'pixel_values': tensor([[[0.8794, 0.8794, 0.8794,  ..., 0.8794, 0.8794, 0.8794],\n",
              "          [0.8794, 0.8794, 0.8794,  ..., 0.8794, 0.8794, 0.8794],\n",
              "          [0.8794, 0.8794, 0.8794,  ..., 0.8794, 0.8794, 0.8794],\n",
              "          ...,\n",
              "          [0.8794, 0.8794, 0.8794,  ..., 0.8794, 0.8794, 0.8794],\n",
              "          [0.8794, 0.8794, 0.8794,  ..., 0.8794, 0.8794, 0.8794],\n",
              "          [0.8794, 0.8794, 0.8794,  ..., 0.8794, 0.8794, 0.8794]],\n",
              " \n",
              "         [[0.8794, 0.8794, 0.8794,  ..., 0.8794, 0.8794, 0.8794],\n",
              "          [0.8794, 0.8794, 0.8794,  ..., 0.8794, 0.8794, 0.8794],\n",
              "          [0.8794, 0.8794, 0.8794,  ..., 0.8794, 0.8794, 0.8794],\n",
              "          ...,\n",
              "          [0.8794, 0.8794, 0.8794,  ..., 0.8794, 0.8794, 0.8794],\n",
              "          [0.8794, 0.8794, 0.8794,  ..., 0.8794, 0.8794, 0.8794],\n",
              "          [0.8794, 0.8794, 0.8794,  ..., 0.8794, 0.8794, 0.8794]],\n",
              " \n",
              "         [[0.4968, 0.4968, 0.4968,  ..., 0.4968, 0.4968, 0.4968],\n",
              "          [0.4968, 0.4968, 0.4968,  ..., 0.4968, 0.4968, 0.4968],\n",
              "          [0.4968, 0.4968, 0.4968,  ..., 0.4968, 0.4968, 0.4968],\n",
              "          ...,\n",
              "          [0.4968, 0.4968, 0.4968,  ..., 0.4968, 0.4968, 0.4968],\n",
              "          [0.4968, 0.4968, 0.4968,  ..., 0.4968, 0.4968, 0.4968],\n",
              "          [0.4968, 0.4968, 0.4968,  ..., 0.4968, 0.4968, 0.4968]]])}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model creation"
      ],
      "metadata": {
        "id": "K7sCzZLYmW7v"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210,
          "referenced_widgets": [
            "f8b2d9a3143543118fdc2104b1faf561",
            "cdef9e31a1724ec5ae53586293fefcef",
            "4e21651ddbe6410e83bd1d72c640f6fc",
            "834df39c6dd343c1ba84222a18857ce5",
            "fd1743fccfd040d1abf7edee36a803ec",
            "d16a7acfea6b42668ce821b2aa041abd",
            "611de3a4d30942a6b2cdc9617000d610",
            "ba714be372264d33b81ae7a7e26f52f8",
            "1e6e9634f4fd43ae930bb47292f4350f",
            "7409edbc1697432682b88f3ea3662a14",
            "d2a3ccfcd1194f7392317784bd67834f",
            "9331ac7528b140719768609480a5e465",
            "1ae9cc0cb61a47adaeb369ff12f33b52",
            "7a84815f4be34f989aefdb428a967d15",
            "833c6c1616bc4900a5db445e99a8e614",
            "06ed3ee262734ab9b728f906fed95797",
            "14e1b18a6b064868a914d91c25036e59",
            "ad7f885e2b4b4ef5b6eee17a592dc445",
            "b67609b010dd4eda81a63a97e6fe9c51",
            "8816a055aa364c4aa2f9f1ee05c40d8a",
            "b342473c3e4c4b3e964981359beffcef",
            "fc70992247a94c7bbd93be056ffa7659"
          ]
        },
        "id": "X9DDujL0q1ac",
        "outputId": "86b75b62-570d-49c6-c563-cc0fa245ea38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py:469: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/71.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8b2d9a3143543118fdc2104b1faf561"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/113M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9331ac7528b140719768609480a5e465"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of SwinForImageClassification were not initialized from the model checkpoint at microsoft/swin-tiny-patch4-window7-224 and are newly initialized because the shapes did not match:\n",
            "- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([16]) in the model instantiated\n",
            "- classifier.weight: found shape torch.Size([1000, 768]) in the checkpoint and torch.Size([16, 768]) in the model instantiated\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "\n",
        "model_checkpoint = \"microsoft/swin-tiny-patch4-window7-224\" # pre-trained model to use as the base for fine-tuning(swin Transformer)\n",
        "batch_size = 32\n",
        "hf_token = \"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\" # Use your Hugging Face token here\n",
        "model = AutoModelForImageClassification.from_pretrained(\n",
        "    model_checkpoint,\n",
        "    label2id=label2id,\n",
        "    id2label=id2label,\n",
        "    ignore_mismatched_sizes = True,use_auth_token=hf_token\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the number of parameters in the model\n",
        "num_params = sum(p.numel() for p in model.parameters())\n",
        "print(f\"Number of parameters in the model: {num_params}\")\n",
        "# print(model)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WEXd2J7J8GZ1",
        "outputId": "8c332343-0af7-4f9d-bb21-5319f9f7984b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of parameters in the model: 27531658\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "\n",
        "\n",
        "args = TrainingArguments(\n",
        "    f\"{model_name}-finetuned-eurosat\",\n",
        "    remove_unused_columns=False,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=3e-3,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    gradient_accumulation_steps=4,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=10,\n",
        "    warmup_ratio=0.1,\n",
        "    logging_steps=10,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"accuracy\",\n",
        "    push_to_hub=False,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZtzC3yBL838l",
        "outputId": "95d5c9f4-40ca-46be-d551-ad9489a8edf2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AdamW, Trainer\n",
        "\n",
        "# Créez l'optimiseur AdamW avec les paramètres personnalisés, mais sans redéfinir le learning_rate\n",
        "optimizer = AdamW(\n",
        "    model.parameters(),\n",
        "    betas=(0.9, 0.999),  # Les betas spécifiés\n",
        "    weight_decay=0.3  # Décroissance des poids\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aj3AvVJm7Hew",
        "outputId": "a82ffa31-f71c-4fbd-f9ee-2f652299d6c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EVWfiBuv2uCS"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    \"\"\"Computes accuracy on a batch of predictions\"\"\"\n",
        "    predictions = np.argmax(eval_pred.predictions, axis=1)\n",
        "    return metric.compute(predictions=predictions, references=eval_pred.label_ids)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=val_dataset,\n",
        "    optimizers=(optimizer, None),\n",
        "    compute_metrics=compute_metrics\n",
        ")\n"
      ],
      "metadata": {
        "id": "s8Z5zBca8_Wm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training and validation phases"
      ],
      "metadata": {
        "id": "gXGRia0MmiFA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_results = trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "rdSTMTcCE1rN",
        "outputId": "a908bf23-8572-4c42-8777-a86640a3cf50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3000' max='3000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3000/3000 3:02:07, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.519100</td>\n",
              "      <td>1.364347</td>\n",
              "      <td>0.570521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.315700</td>\n",
              "      <td>1.168040</td>\n",
              "      <td>0.631563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.117700</td>\n",
              "      <td>0.948624</td>\n",
              "      <td>0.710938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.990800</td>\n",
              "      <td>0.855208</td>\n",
              "      <td>0.738646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.934100</td>\n",
              "      <td>0.788841</td>\n",
              "      <td>0.759375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.801600</td>\n",
              "      <td>0.724298</td>\n",
              "      <td>0.781563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.710900</td>\n",
              "      <td>0.673874</td>\n",
              "      <td>0.795104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.632400</td>\n",
              "      <td>0.655025</td>\n",
              "      <td>0.800417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.577000</td>\n",
              "      <td>0.617979</td>\n",
              "      <td>0.823021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.477100</td>\n",
              "      <td>0.593846</td>\n",
              "      <td>0.827083</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metrics = trainer.evaluate()\n",
        "# Log metrics for evaluation\n",
        "trainer.log_metrics(\"eval\", metrics)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "LFjciQ6rKLfU",
        "outputId": "fc7f28aa-57d1-4543-cce7-25d07ef52c3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='300' max='300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [300/300 02:21]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "***** eval metrics *****\n",
            "  epoch                   =       10.0\n",
            "  eval_accuracy           =     0.8271\n",
            "  eval_loss               =     0.5938\n",
            "  eval_runtime            = 0:02:22.60\n",
            "  eval_samples_per_second =     67.319\n",
            "  eval_steps_per_second   =      2.104\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test Phase"
      ],
      "metadata": {
        "id": "A0HS7j3emt-s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset.set_transform(preprocess_val_batch)\n",
        "print(test_dataset[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BwSUi3-cN3AX",
        "outputId": "74959fd6-d617-43b4-c274-9eb7864b9193"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'img': '/content/Financial_data_test/advertisement/0000001863.jpeg', 'label': 4, 'pixel_values': tensor([[[ 2.2147,  2.2147,  2.2147,  ...,  2.2147,  2.2147,  2.2147],\n",
            "         [ 2.2147,  2.2147,  2.2147,  ...,  2.2147,  2.2147,  2.2147],\n",
            "         [ 2.2147,  2.2147,  2.2147,  ...,  2.2147,  2.2147,  2.2147],\n",
            "         ...,\n",
            "         [ 2.2147,  2.2147,  2.2147,  ...,  2.2147,  2.2147,  2.2147],\n",
            "         [ 2.2147,  2.2147,  2.2147,  ...,  2.2147,  2.2147,  2.2147],\n",
            "         [ 2.2147,  2.2147,  2.2147,  ...,  2.2147,  2.2147,  2.2147]],\n",
            "\n",
            "        [[ 1.9909,  1.9909,  1.9909,  ...,  1.9909,  1.9909,  1.9909],\n",
            "         [ 1.9909,  1.9909,  1.9909,  ...,  1.9909,  1.9909,  1.9909],\n",
            "         [ 1.9909,  1.9909,  1.9909,  ...,  1.9909,  1.9909,  1.9909],\n",
            "         ...,\n",
            "         [ 1.9909,  1.9909,  1.9909,  ...,  1.9909,  1.9909,  1.9909],\n",
            "         [ 1.9909,  1.9909,  1.9909,  ...,  1.9909,  1.9909,  1.9909],\n",
            "         [ 1.9909,  1.9909,  1.9909,  ...,  1.9909,  1.9909,  1.9909]],\n",
            "\n",
            "        [[-1.1770, -1.1770, -1.1770,  ..., -1.1770, -1.1770, -1.1770],\n",
            "         [-1.1770, -1.1770, -1.1770,  ..., -1.1770, -1.1770, -1.1770],\n",
            "         [-1.1770, -1.1770, -1.1770,  ..., -1.1770, -1.1770, -1.1770],\n",
            "         ...,\n",
            "         [-1.1770, -1.1770, -1.1770,  ..., -1.1770, -1.1770, -1.1770],\n",
            "         [-1.1770, -1.1770, -1.1770,  ..., -1.1770, -1.1770, -1.1770],\n",
            "         [-1.1770, -1.1770, -1.1770,  ..., -1.1770, -1.1770, -1.1770]]])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation on the test dataset (using eval_dataset)\n",
        "test_metrics = trainer.evaluate(eval_dataset=test_dataset)\n",
        "\n",
        "# Show evaluation metrics\n",
        "trainer.log_metrics(\"test\", test_metrics)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "M_yzkuNGbKEg",
        "outputId": "dab1bea5-22fc-4dfc-995f-da5d37a28acf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1819' max='300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [300/300 15:29]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "***** test metrics *****\n",
            "  epoch                   =       10.0\n",
            "  eval_accuracy           =     0.8273\n",
            "  eval_loss               =     0.5967\n",
            "  eval_runtime            = 0:10:07.57\n",
            "  eval_samples_per_second =     65.829\n",
            "  eval_steps_per_second   =      2.057\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "predictions = trainer.predict(test_dataset=test_dataset)\n",
        "\n",
        "# Obtenez les prédictions finales (classe avec le score le plus élevé)\n",
        "predicted_classes = np.argmax(predictions.predictions, axis=-1)\n",
        "\n",
        "# Les labels réels\n",
        "true_labels = predictions.label_ids\n",
        "\n",
        "# Comparer les prédictions aux labels réels\n",
        "correct_predictions = (predicted_classes == true_labels)\n",
        "\n",
        "# Afficher combien de prédictions sont correctes\n",
        "print(f\"Nombre de prédictions correctes: {np.sum(correct_predictions)} sur {len(true_labels)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "7GiRtNt5f8hv",
        "outputId": "ffc50cde-2723-4183-8df7-7a90a00e6529"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nombre de prédictions correctes: 33088 sur 39996\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Labels réels et classes prédites du dataset de test\n",
        "print(classification_report(predictions.label_ids, predicted_classes))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4lLkxTAUjbwI",
        "outputId": "b629c76c-b4a5-40ef-8eb3-61dc28c0ec9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.88      0.88      2569\n",
            "           1       0.63      0.72      0.67      2506\n",
            "           2       0.68      0.76      0.72      2489\n",
            "           3       0.91      0.81      0.86      2463\n",
            "           4       0.86      0.88      0.87      2515\n",
            "           5       0.86      0.77      0.81      2477\n",
            "           6       0.88      0.86      0.87      2472\n",
            "           7       0.90      0.86      0.88      2492\n",
            "           8       0.85      0.94      0.89      2527\n",
            "           9       0.85      0.82      0.83      2464\n",
            "          10       0.62      0.67      0.64      2498\n",
            "          11       0.91      0.90      0.91      2536\n",
            "          12       0.77      0.72      0.74      2435\n",
            "          13       0.96      0.97      0.97      2516\n",
            "          14       0.93      0.92      0.92      2532\n",
            "          15       0.80      0.75      0.78      2505\n",
            "\n",
            "    accuracy                           0.83     39996\n",
            "   macro avg       0.83      0.83      0.83     39996\n",
            "weighted avg       0.83      0.83      0.83     39996\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save model"
      ],
      "metadata": {
        "id": "cK6Emm6im1TJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the directory in your Google Drive where you want to save the model\n",
        "save_path = '/content/gdrive/MyDrive/eya/trained_model_10epoch/'\n",
        "\n",
        "# Save the trained model\n",
        "trainer.save_model(save_path)\n",
        "\n",
        "# Save the training state (optional, for resuming training later)\n",
        "trainer.state.save_to_json(save_path + 'trainer_state.json')"
      ],
      "metadata": {
        "id": "gvJ8DRo2mSdi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the path where your model is saved in Google Drive\n",
        "model_path = '/content/gdrive/MyDrive/eya/MODEL_stream/'\n",
        "\n",
        "# Load the model\n",
        "model = AutoModelForImageClassification.from_pretrained(model_path)\n",
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdkyHCxmNWbe",
        "outputId": "98c2e00a-91bb-49de-d822-f5a1698e41d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SwinForImageClassification(\n",
            "  (swin): SwinModel(\n",
            "    (embeddings): SwinEmbeddings(\n",
            "      (patch_embeddings): SwinPatchEmbeddings(\n",
            "        (projection): Conv2d(3, 96, kernel_size=(4, 4), stride=(4, 4))\n",
            "      )\n",
            "      (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "      (dropout): Dropout(p=0.0, inplace=False)\n",
            "    )\n",
            "    (encoder): SwinEncoder(\n",
            "      (layers): ModuleList(\n",
            "        (0): SwinStage(\n",
            "          (blocks): ModuleList(\n",
            "            (0-1): 2 x SwinLayer(\n",
            "              (layernorm_before): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "              (attention): SwinAttention(\n",
            "                (self): SwinSelfAttention(\n",
            "                  (query): Linear(in_features=96, out_features=96, bias=True)\n",
            "                  (key): Linear(in_features=96, out_features=96, bias=True)\n",
            "                  (value): Linear(in_features=96, out_features=96, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "                (output): SwinSelfOutput(\n",
            "                  (dense): Linear(in_features=96, out_features=96, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (drop_path): SwinDropPath(p=0.1)\n",
            "              (layernorm_after): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
            "              (intermediate): SwinIntermediate(\n",
            "                (dense): Linear(in_features=96, out_features=384, bias=True)\n",
            "                (intermediate_act_fn): GELUActivation()\n",
            "              )\n",
            "              (output): SwinOutput(\n",
            "                (dense): Linear(in_features=384, out_features=96, bias=True)\n",
            "                (dropout): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (downsample): SwinPatchMerging(\n",
            "            (reduction): Linear(in_features=384, out_features=192, bias=False)\n",
            "            (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "          )\n",
            "        )\n",
            "        (1): SwinStage(\n",
            "          (blocks): ModuleList(\n",
            "            (0-1): 2 x SwinLayer(\n",
            "              (layernorm_before): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "              (attention): SwinAttention(\n",
            "                (self): SwinSelfAttention(\n",
            "                  (query): Linear(in_features=192, out_features=192, bias=True)\n",
            "                  (key): Linear(in_features=192, out_features=192, bias=True)\n",
            "                  (value): Linear(in_features=192, out_features=192, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "                (output): SwinSelfOutput(\n",
            "                  (dense): Linear(in_features=192, out_features=192, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (drop_path): SwinDropPath(p=0.1)\n",
            "              (layernorm_after): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
            "              (intermediate): SwinIntermediate(\n",
            "                (dense): Linear(in_features=192, out_features=768, bias=True)\n",
            "                (intermediate_act_fn): GELUActivation()\n",
            "              )\n",
            "              (output): SwinOutput(\n",
            "                (dense): Linear(in_features=768, out_features=192, bias=True)\n",
            "                (dropout): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (downsample): SwinPatchMerging(\n",
            "            (reduction): Linear(in_features=768, out_features=384, bias=False)\n",
            "            (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "          )\n",
            "        )\n",
            "        (2): SwinStage(\n",
            "          (blocks): ModuleList(\n",
            "            (0-5): 6 x SwinLayer(\n",
            "              (layernorm_before): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "              (attention): SwinAttention(\n",
            "                (self): SwinSelfAttention(\n",
            "                  (query): Linear(in_features=384, out_features=384, bias=True)\n",
            "                  (key): Linear(in_features=384, out_features=384, bias=True)\n",
            "                  (value): Linear(in_features=384, out_features=384, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "                (output): SwinSelfOutput(\n",
            "                  (dense): Linear(in_features=384, out_features=384, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (drop_path): SwinDropPath(p=0.1)\n",
            "              (layernorm_after): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
            "              (intermediate): SwinIntermediate(\n",
            "                (dense): Linear(in_features=384, out_features=1536, bias=True)\n",
            "                (intermediate_act_fn): GELUActivation()\n",
            "              )\n",
            "              (output): SwinOutput(\n",
            "                (dense): Linear(in_features=1536, out_features=384, bias=True)\n",
            "                (dropout): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "          (downsample): SwinPatchMerging(\n",
            "            (reduction): Linear(in_features=1536, out_features=768, bias=False)\n",
            "            (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
            "          )\n",
            "        )\n",
            "        (3): SwinStage(\n",
            "          (blocks): ModuleList(\n",
            "            (0-1): 2 x SwinLayer(\n",
            "              (layernorm_before): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "              (attention): SwinAttention(\n",
            "                (self): SwinSelfAttention(\n",
            "                  (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "                  (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "                  (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "                (output): SwinSelfOutput(\n",
            "                  (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "                  (dropout): Dropout(p=0.0, inplace=False)\n",
            "                )\n",
            "              )\n",
            "              (drop_path): SwinDropPath(p=0.1)\n",
            "              (layernorm_after): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "              (intermediate): SwinIntermediate(\n",
            "                (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "                (intermediate_act_fn): GELUActivation()\n",
            "              )\n",
            "              (output): SwinOutput(\n",
            "                (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "                (dropout): Dropout(p=0.0, inplace=False)\n",
            "              )\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
            "    (pooler): AdaptiveAvgPool1d(output_size=1)\n",
            "  )\n",
            "  (classifier): Linear(in_features=768, out_features=16, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Example of use"
      ],
      "metadata": {
        "id": "vnCVZiyom_Kn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "# Path to the individual image\n",
        "path = '/content/Financial_data_test/invoice/2028719701_2028719702.jpeg'\n",
        "image = Image.open(path)\n",
        "image\n",
        "\n",
        "# Apply the same preprocessing as in preprocess_val_batch\n",
        "# Ensure that preprocess_val performs the same transformations applied to the entire dataset\n",
        "pixel_values = preprocess_val(image.convert(\"RGB\"))  # Use preprocess_val here\n",
        "\n",
        "# Add a batch dimension so the tensor shape is [1, num_channels, height, width]\n",
        "pixel_values = pixel_values.unsqueeze(0)\n",
        "\n",
        "# Forward pass to obtain predictions\n",
        "with torch.no_grad():\n",
        "    outputs = model(pixel_values=pixel_values)\n",
        "    logits = outputs.logits\n",
        "    predicted_class_idx = logits.argmax(-1).item()\n",
        "\n",
        "# Display the predicted class\n",
        "print(\"Predicted class:\", model.config.id2label[predicted_class_idx])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FT4HRhYgkycE",
        "outputId": "210af7a6-0e80-442d-f622-18aea0c401a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classe prédite : invoice\n"
          ]
        }
      ]
    }
  ]
}