EduTech Main File.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install ultralytics\n",
        "!pip install roboflow\n",
        "!pip install wandb\n",
        "!pip install huggingface_hub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4IbVyatAx5S4",
        "outputId": "79fb6433-0098-4a6a-8134-983955800305"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.3.18-py3-none-any.whl.metadata (34 kB)\n",
            "Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.26.4)\n",
            "Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.7.1)\n",
            "Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.10.0.84)\n",
            "Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (10.4.0)\n",
            "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (6.0.2)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.32.3)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.13.1)\n",
            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.4.1+cu121)\n",
            "Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.19.1+cu121)\n",
            "Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.66.5)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ultralytics) (5.9.5)\n",
            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.0.0)\n",
            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.2.2)\n",
            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.13.2)\n",
            "Collecting ultralytics-thop>=2.0.0 (from ultralytics)\n",
            "  Downloading ultralytics_thop-2.0.9-py3-none-any.whl.metadata (9.3 kB)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.54.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (24.1)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2024.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2024.8.30)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.16.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (1.13.3)\n",
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2024.6.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (3.0.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n",
            "Downloading ultralytics-8.3.18-py3-none-any.whl (876 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m876.6/876.6 kB\u001b[0m \u001b[31m36.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading ultralytics_thop-2.0.9-py3-none-any.whl (26 kB)\n",
            "Installing collected packages: ultralytics-thop, ultralytics\n",
            "Successfully installed ultralytics-8.3.18 ultralytics-thop-2.0.9\n",
            "Collecting roboflow\n",
            "  Downloading roboflow-1.1.48-py3-none-any.whl.metadata (9.7 kB)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from roboflow) (2024.8.30)\n",
            "Collecting idna==3.7 (from roboflow)\n",
            "  Downloading idna-3.7-py3-none-any.whl.metadata (9.9 kB)\n",
            "Requirement already satisfied: cycler in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.12.1)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.4.7)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from roboflow) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.26.4)\n",
            "Requirement already satisfied: opencv-python-headless==4.10.0.84 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.10.0.84)\n",
            "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from roboflow) (10.4.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.8.2)\n",
            "Collecting python-dotenv (from roboflow)\n",
            "  Downloading python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.32.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.16.0)\n",
            "Requirement already satisfied: urllib3>=1.26.6 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.2.3)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.66.5)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (6.0.2)\n",
            "Collecting requests-toolbelt (from roboflow)\n",
            "  Downloading requests_toolbelt-1.0.0-py2.py3-none-any.whl.metadata (14 kB)\n",
            "Collecting filetype (from roboflow)\n",
            "  Downloading filetype-1.2.0-py2.py3-none-any.whl.metadata (6.5 kB)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (1.3.0)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (4.54.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (24.1)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (3.2.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->roboflow) (3.4.0)\n",
            "Downloading roboflow-1.1.48-py3-none-any.whl (80 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.3/80.3 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading idna-3.7-py3-none-any.whl (66 kB)\n",
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            "\u001b[?25hDownloading filetype-1.2.0-py2.py3-none-any.whl (19 kB)\n",
            "Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: filetype, python-dotenv, idna, requests-toolbelt, roboflow\n",
            "  Attempting uninstall: idna\n",
            "    Found existing installation: idna 3.10\n",
            "    Uninstalling idna-3.10:\n",
            "      Successfully uninstalled idna-3.10\n",
            "Successfully installed filetype-1.2.0 idna-3.7 python-dotenv-1.0.1 requests-toolbelt-1.0.0 roboflow-1.1.48\n",
            "Requirement already satisfied: wandb in /usr/local/lib/python3.10/dist-packages (0.18.3)\n",
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            "Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (0.4.0)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2024.8.30)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from gitdb<5,>=4.0.1->gitpython!=3.1.29,>=1.0.0->wandb) (5.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "id": "IY0KJ_5ulgBn",
        "outputId": "e4e32bc1-64f5-4ef1-f977-16c8aa5783b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Destination directory: /content/datasets\n",
            "Processing sea-ezx3q...\n",
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Attempting to download version 44 of sea-ezx3q\n",
            "Successfully downloaded sea-ezx3q to /content/datasets/sea-ezx3q\n",
            "Contents of /content/datasets/sea-ezx3q: ['test', 'README.dataset.txt', 'valid', 'data.yaml', 'train', 'README.roboflow.txt']\n",
            "--------------------------------------------------\n",
            "Processing marine-debris-vb2qm...\n",
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Attempting to download version 16 of marine-debris-vb2qm\n",
            "Successfully downloaded marine-debris-vb2qm to /content/datasets/marine-debris-vb2qm\n",
            "Contents of /content/datasets/marine-debris-vb2qm: ['test', 'README.dataset.txt', 'valid', 'data.yaml', 'train', 'README.roboflow.txt']\n",
            "--------------------------------------------------\n",
            "Processing new-taco...\n",
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Attempting to download version 3 of new-taco\n",
            "Successfully downloaded new-taco to /content/datasets/new-taco\n",
            "Contents of /content/datasets/new-taco: ['test', 'README.dataset.txt', 'valid', 'data.yaml', 'train', 'README.roboflow.txt']\n",
            "--------------------------------------------------\n",
            "Processing -kg...\n",
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Attempting to download version 5 of -kg\n",
            "Successfully downloaded -kg to /content/datasets/-kg\n",
            "Contents of /content/datasets/-kg: ['test', 'README.dataset.txt', 'valid', 'data.yaml', 'train', 'README.roboflow.txt']\n",
            "--------------------------------------------------\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "too many values to unpack (expected 3)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-9b56a5db0bf0>\u001b[0m in \u001b[0;36m<cell line: 47>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdest_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mworkspace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdatasets_info\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Processing {dataset_name}...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 3)"
          ]
        }
      ],
      "source": [
        "from roboflow import Roboflow\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "rf = Roboflow(api_key=\"TZCi2SCltMQkbaVAtOqT\")\n",
        "\n",
        "# List of datasets to download (workspace, dataset, version)\n",
        "datasets_info = [\n",
        "    (\"hongmo\", \"sea-ezx3q\", 44),\n",
        "    (\"kim-borma\", \"marine-debris-vb2qm\", 16),\n",
        "    (\"myfyp\", \"new-taco\", 3),\n",
        "    (\"anas-fd5bu\", \"-kg\", 5),\n",
        "    (\"trash-detection-eka7g\", \"aeriel-trash-detection\", 5),\n",
        "    (\"fyp-bfx3h\", \"yolov8-trash-detections\", 6),\n",
        "    (\"taco-ihjgk\", \"yolov8-trash-detections-kgnug\", 11),\n",
        "]\n",
        "\n",
        "\"\"\"\n",
        "    (\"trash-ai-27vay\", \"ultimate-rqkdd\", 1),\n",
        "    (\"iqram\", \"trashdetectionv8\", 2),\n",
        "    (\"smart-india-hackathon-2023\", \"garbage_best\", 1),\n",
        "    # New datasets (replace VERSION with the correct version number)\n",
        "    (\"kim-borma\", \"conttttt\", 1),\n",
        "    (\"adamson-university-rkm6w\", \"manila-bay\", 3),\n",
        "    (\"davids-workspace\", \"garbage-detection-mnj17\", 1),\n",
        "    (\"majasworkspace\", \"p_eine_klasse\", 1),\n",
        "    (\"ukm-wcn\", \"ml2-wcn-ukm\", 23),\n",
        "    (\"vit-akped\", \"tacodetecron-2\", 1),\n",
        "    (\"k2k\", \"imported-f38bi\", 4),\n",
        "    (\"python-project-5oizd\", \"trash-detection-mzljs\", 7),\n",
        "    (\"project-mqoot\", \"plastic-bag-detection\", 1),\n",
        "    (\"trash-dataset-for-oriented-bounded-box\", \"trash-detection-1fjjc\", 14),\n",
        "    (\"nam-nhat\", \"trash-dvdrr\", 5),\n",
        "    (\"litter-beach-detection\", \"beach-garbage\", 2),\n",
        "    (\"dechets-qlhbp\", \"tacoo-cj6rf\", 1),\n",
        "    (\"cscsi218\", \"garbageclassification-47d7t\", 1),\n",
        "    (\"hust-xz9js\", \"trashbot-wahhb\", 1),\n",
        "    (\"furqan-sayyed-veuct\", \"taco-ppujr\", 1),\n",
        "    (\"abhijeet-beedikar-pbc0x\", \"garbage-classification-taco\", 1)\n",
        "\"\"\"\n",
        "\n",
        "dest_dir = os.path.abspath(\"datasets\")\n",
        "print(f\"Destination directory: {dest_dir}\")\n",
        "\n",
        "if not os.path.exists(dest_dir):\n",
        "    os.makedirs(dest_dir)\n",
        "\n",
        "for (workspace, dataset_name, version) in datasets_info:\n",
        "    print(f\"Processing {dataset_name}...\")\n",
        "    try:\n",
        "        project = rf.workspace(workspace).project(dataset_name)\n",
        "        print(f\"Attempting to download version {version} of {dataset_name}\")\n",
        "\n",
        "        # Specify the download location\n",
        "        download_dir = os.path.join(dest_dir, dataset_name)\n",
        "\n",
        "        # Download the dataset to the specified location\n",
        "        dataset = project.version(version).download(\"yolov11\", location=download_dir)\n",
        "        print(f\"Successfully downloaded {dataset_name} to {dataset.location}\")\n",
        "\n",
        "        # List the contents of the downloaded directory\n",
        "        if os.path.exists(dataset.location):\n",
        "            print(f\"Contents of {dataset.location}: {os.listdir(dataset.location)}\")\n",
        "        else:\n",
        "            print(f\"Warning: Downloaded directory not found at {dataset.location}\")\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error processing {dataset_name}: {str(e)}\")\n",
        "    print(\"-\" * 50)\n",
        "\n",
        "print(\"Download process completed. Please check the following directory for your files:\")\n",
        "print(dest_dir)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "import yaml\n",
        "import glob\n",
        "\n",
        "def merge_datasets(datasets_dir, combined_dataset_dir):\n",
        "    # Create directories for the combined dataset\n",
        "    os.makedirs(combined_dataset_dir, exist_ok=True)\n",
        "    splits = ['train', 'valid', 'test']\n",
        "    for split in splits:\n",
        "        os.makedirs(os.path.join(combined_dataset_dir, 'images', split), exist_ok=True)\n",
        "        os.makedirs(os.path.join(combined_dataset_dir, 'labels', split), exist_ok=True)\n",
        "\n",
        "    combined_classes = []\n",
        "    class_mappings = {}\n",
        "\n",
        "    # First pass: Collect all class names and build combined_classes list\n",
        "    for dataset_name in os.listdir(datasets_dir):\n",
        "        dataset_path = os.path.join(datasets_dir, dataset_name)\n",
        "        if not os.path.isdir(dataset_path):\n",
        "            continue\n",
        "\n",
        "        data_yaml_path = os.path.join(dataset_path, 'data.yaml')\n",
        "        if not os.path.exists(data_yaml_path):\n",
        "            print(f\"data.yaml not found in {dataset_path}. Skipping this dataset.\")\n",
        "            continue\n",
        "\n",
        "        with open(data_yaml_path, 'r') as f:\n",
        "            data_yaml = yaml.safe_load(f)\n",
        "\n",
        "        dataset_classes = data_yaml.get('names', [])\n",
        "        if not dataset_classes:\n",
        "            print(f\"No 'names' field found in {data_yaml_path}. Skipping this dataset.\")\n",
        "            continue\n",
        "\n",
        "        # Update combined_classes and create class mappings for this dataset\n",
        "        mapping = {}\n",
        "        for idx, class_name in enumerate(dataset_classes):\n",
        "            if class_name not in combined_classes:\n",
        "                combined_classes.append(class_name)\n",
        "            mapping[idx] = combined_classes.index(class_name)\n",
        "        class_mappings[dataset_name] = mapping\n",
        "\n",
        "    print(f\"Combined classes: {combined_classes}\")\n",
        "\n",
        "    # Second pass: Copy images and labels, remap class IDs\n",
        "    for dataset_name in os.listdir(datasets_dir):\n",
        "        dataset_path = os.path.join(datasets_dir, dataset_name)\n",
        "        if not os.path.isdir(dataset_path):\n",
        "            continue\n",
        "\n",
        "        mapping = class_mappings.get(dataset_name)\n",
        "        if mapping is None:\n",
        "            continue  # Skip datasets that were not included in combined_classes\n",
        "\n",
        "        for split in ['train', 'valid', 'test']:\n",
        "            split_dir = os.path.join(dataset_path, split)\n",
        "            if not os.path.exists(split_dir):\n",
        "                print(f\"Split '{split}' not found in {dataset_name}. Skipping.\")\n",
        "                continue\n",
        "\n",
        "            # Check if images and labels are under 'images' and 'labels' directories\n",
        "            images_dir = os.path.join(split_dir, 'images')\n",
        "            labels_dir = os.path.join(split_dir, 'labels')\n",
        "            if not os.path.exists(images_dir) or not os.path.exists(labels_dir):\n",
        "                # Maybe images and labels are directly under split_dir\n",
        "                images_dir = split_dir\n",
        "                labels_dir = split_dir\n",
        "                # Check if there are images and labels in split_dir\n",
        "                image_files = glob.glob(os.path.join(images_dir, '*.jpg')) + \\\n",
        "                              glob.glob(os.path.join(images_dir, '*.jpeg')) + \\\n",
        "                              glob.glob(os.path.join(images_dir, '*.png')) + \\\n",
        "                              glob.glob(os.path.join(images_dir, '*.bmp')) + \\\n",
        "                              glob.glob(os.path.join(images_dir, '*.tif')) + \\\n",
        "                              glob.glob(os.path.join(images_dir, '*.tiff'))\n",
        "                label_files = glob.glob(os.path.join(labels_dir, '*.txt'))\n",
        "                if len(image_files) == 0:\n",
        "                    print(f\"No images found in {images_dir}. Skipping split '{split}' in dataset '{dataset_name}'.\")\n",
        "                    continue\n",
        "            else:\n",
        "                # Images and labels are under 'images' and 'labels' directories\n",
        "                image_files = glob.glob(os.path.join(images_dir, '*.*'))\n",
        "                label_files = glob.glob(os.path.join(labels_dir, '*.txt'))\n",
        "\n",
        "            for image_file in image_files:\n",
        "                image_filename = os.path.basename(image_file)\n",
        "                image_name, image_ext = os.path.splitext(image_filename)\n",
        "\n",
        "                # Corresponding label file\n",
        "                label_file = os.path.join(labels_dir, f\"{image_name}.txt\")\n",
        "                if not os.path.exists(label_file):\n",
        "                    print(f\"Label file {label_file} not found. Creating empty label.\")\n",
        "                    label_lines = []\n",
        "                else:\n",
        "                    with open(label_file, 'r') as lf:\n",
        "                        label_lines = lf.readlines()\n",
        "\n",
        "                # Remap class IDs in label file\n",
        "                remapped_labels = []\n",
        "                for line in label_lines:\n",
        "                    parts = line.strip().split()\n",
        "                    if len(parts) < 5:\n",
        "                        print(f\"Invalid label format in {label_file}. Skipping line.\")\n",
        "                        continue\n",
        "                    try:\n",
        "                        class_id = int(parts[0])\n",
        "                        new_class_id = mapping.get(class_id)\n",
        "                        if new_class_id is None:\n",
        "                            print(f\"Class ID {class_id} not found in mapping for {dataset_name}. Skipping line.\")\n",
        "                            continue\n",
        "                        parts[0] = str(new_class_id)\n",
        "                        remapped_labels.append(' '.join(parts))\n",
        "                    except ValueError:\n",
        "                        print(f\"Invalid class ID in {label_file}. Skipping line.\")\n",
        "                        continue\n",
        "\n",
        "                # Copy image\n",
        "                dest_image_name = f\"{dataset_name}_{image_filename}\"\n",
        "                target_split = split  # Use the same split name\n",
        "                dest_image_path = os.path.join(combined_dataset_dir, 'images', target_split, dest_image_name)\n",
        "                shutil.copy(image_file, dest_image_path)\n",
        "\n",
        "                # Write remapped label\n",
        "                dest_label_name = f\"{dataset_name}_{image_name}.txt\"\n",
        "                dest_label_path = os.path.join(combined_dataset_dir, 'labels', target_split, dest_label_name)\n",
        "                with open(dest_label_path, 'w') as lf:\n",
        "                    lf.write('\\n'.join(remapped_labels))\n",
        "\n",
        "    # Create combined data.yaml\n",
        "    combined_data = {\n",
        "        'path': os.path.abspath(combined_dataset_dir),\n",
        "        'train': os.path.join('images', 'train'),\n",
        "        'val': os.path.join('images', 'valid'),  # Use 'valid' as validation set\n",
        "        'names': combined_classes\n",
        "    }\n",
        "    with open(os.path.join(combined_dataset_dir, 'data.yaml'), 'w') as f:\n",
        "        yaml.dump(combined_data, f)\n",
        "\n",
        "    print(\"Dataset merging complete.\")\n",
        "\n",
        "# Usage\n",
        "datasets_dir = 'datasets'  # Directory containing individual datasets\n",
        "combined_dataset_dir = 'combined_dataset'  # Directory to store the combined dataset\n",
        "\n",
        "merge_datasets(datasets_dir, combined_dataset_dir)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01G3t6pRx__z",
        "outputId": "7b92789a-4831-49cd-b47c-abeb4f884066"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Combined classes: ['Aluminium foil', 'Bottle', 'Bottle cap', 'Can', 'Carton', 'Cigarette', 'Glass bottle', 'Lid', 'Metal', 'Metal cap', 'Other litter', 'Other plastic', 'Paper', 'Plastic bag', 'Plastic buoy', 'Plastic vessels', 'Pop tab', 'Straw', 'Styrofoam cup', 'Styrofoam piece', 'Styrofoam_Buoy', 'Wrapper', 'Glass', 'Net', 'PET_Bottle', 'Plastic_Buoy', 'Plastic_Buoy_China', 'Plastic_ETC', 'Rope', 'Styrofoam_Box', 'Styrofoam_Piece', 'PET', 'Styrofoam', 'cardboard', 'fabrics', 'glass', 'metal', 'other', 'plastic', 'wood']\n",
            "Dataset merging complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import yaml\n",
        "import os\n",
        "from collections import defaultdict\n",
        "\n",
        "def consolidate_classes(combined_dataset_dir):\n",
        "    data_yaml_path = os.path.join(combined_dataset_dir, 'data.yaml')\n",
        "\n",
        "    # Load the original data.yaml\n",
        "    with open(data_yaml_path, 'r') as f:\n",
        "        data = yaml.safe_load(f)\n",
        "\n",
        "    # Define class mappings (adjust this based on your specific classes)\n",
        "    class_mappings = {\n",
        "        'Glass': ['Glass', 'glass', 'Glass bottle', 'glass bottle'],\n",
        "        'Metal': ['Metal', 'metal', 'Can', 'can', 'aluminum', 'Aluminium foil', 'Pop tab', 'pop tab'],\n",
        "        'Plastic': ['Plastic', 'plastic', 'PET', 'PET_Bottle', 'Plastic_ETC', 'plastic bottle', 'plastic container',\n",
        "                    'Plastic bag', 'plastic bag', 'Plastic_Buoy', 'Plastic_Buoy_China', 'Bottle', 'Plastic vessels'],\n",
        "        'Styrofoam': ['Styrofoam', 'Styrofoam_Box', 'Styrofoam_Buoy', 'Styrofoam_Piece', 'styrofoam', 'Styrofoam cup'],\n",
        "        'Paper': ['Paper', 'paper', 'cardboard', 'cardboard boxes and cartons', 'Carton'],\n",
        "        'Rope': ['Rope', 'Net'],\n",
        "        'Food Waste': ['food - others', 'Decomposable'],\n",
        "        'Other': ['Other litter', 'other', 'Non-decomposable', 'Non-decomposable-', 'utensils and straw', 'Straw',\n",
        "                  'fabrics', 'wood', 'Cigarette', 'Wrapper', 'Lid', 'cap or lid', 'Bottle cap', 'plastic bottle cap']\n",
        "    }\n",
        "\n",
        "    # Create reverse mapping\n",
        "    reverse_mapping = {}\n",
        "    for new_class, old_classes in class_mappings.items():\n",
        "        for old_class in old_classes:\n",
        "            reverse_mapping[old_class.lower()] = new_class\n",
        "\n",
        "    # Function to update labels in a file\n",
        "    def update_labels(file_path):\n",
        "        with open(file_path, 'r') as f:\n",
        "            lines = f.readlines()\n",
        "\n",
        "        new_lines = []\n",
        "        for line in lines:\n",
        "            parts = line.strip().split()\n",
        "            if parts:\n",
        "                old_class = int(parts[0])\n",
        "                if old_class < len(data['names']):\n",
        "                    old_class_name = data['names'][old_class].lower()\n",
        "                    new_class_name = reverse_mapping.get(old_class_name, 'Other')\n",
        "                    new_class_id = list(class_mappings.keys()).index(new_class_name)\n",
        "                    new_lines.append(f\"{new_class_id} {' '.join(parts[1:])}\\n\")\n",
        "\n",
        "        with open(file_path, 'w') as f:\n",
        "            f.writelines(new_lines)\n",
        "\n",
        "    # Update all label files\n",
        "    for split in ['train', 'valid', 'test']:\n",
        "        label_dir = os.path.join(combined_dataset_dir, 'labels', split)\n",
        "        for filename in os.listdir(label_dir):\n",
        "            if filename.endswith('.txt'):\n",
        "                update_labels(os.path.join(label_dir, filename))\n",
        "\n",
        "    # Update data.yaml with new classes\n",
        "    data['names'] = list(class_mappings.keys())\n",
        "\n",
        "    # Save the updated data.yaml\n",
        "    with open(data_yaml_path, 'w') as f:\n",
        "        yaml.dump(data, f)\n",
        "\n",
        "    print(f\"Updated data.yaml saved to {data_yaml_path}\")\n",
        "    print(f\"New classes: {data['names']}\")\n",
        "\n",
        "# Usage\n",
        "datasets_dir = 'datasets'  # Directory containing individual datasets\n",
        "combined_dataset_dir = 'combined_dataset'  # Directory storing the combined dataset\n",
        "\n",
        "# First, run your existing merge_datasets function\n",
        "merge_datasets(datasets_dir, combined_dataset_dir)\n",
        "\n",
        "# Then, run the class consolidation\n",
        "consolidate_classes(combined_dataset_dir)\n",
        "\n",
        "# Now you can proceed with your YOLO training using the consolidated dataset"
      ],
      "metadata": {
        "id": "SPV8HME_ZjaS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import wandb\n",
        "from wandb.integration.ultralytics import add_wandb_callback\n",
        "import torch\n",
        "from ultralytics import YOLO\n",
        "from huggingface_hub import HfApi, Repository, login\n",
        "import shutil\n",
        "from datetime import datetime\n",
        "import sys\n",
        "from pathlib import Path\n",
        "\n",
        "# Hugging Face settings\n",
        "HF_REPO = \"EduTech-YOLOv11\"\n",
        "HF_USERNAME = \"shng2025\"  # Your Hugging Face username\n",
        "\n",
        "def ensure_hf_login():\n",
        "    \"\"\"Ensure user is logged in to Hugging Face\"\"\"\n",
        "    try:\n",
        "        api = HfApi()\n",
        "        # This will raise an error if not logged in\n",
        "        api.whoami()\n",
        "        print(\"Already logged in to Hugging Face\")\n",
        "    except Exception:\n",
        "        print(\"Please log in to Hugging Face\")\n",
        "        login()\n",
        "\n",
        "def setup_hf_repo(branch_name):\n",
        "    \"\"\"Setup or connect to Hugging Face repository\"\"\"\n",
        "    api = HfApi()\n",
        "    repo_id = f\"{HF_USERNAME}/{HF_REPO}\"\n",
        "\n",
        "    # Clone repository if it doesn't exist locally\n",
        "    if not os.path.exists(HF_REPO):\n",
        "        print(f\"Cloning repository {repo_id}...\")\n",
        "        Repository(\n",
        "            local_dir=HF_REPO,\n",
        "            clone_from=repo_id,\n",
        "        )\n",
        "\n",
        "    repo = Repository(\n",
        "        local_dir=HF_REPO,\n",
        "    )\n",
        "\n",
        "    # Create and checkout new branch or checkout existing branch\n",
        "    try:\n",
        "        repo.git_checkout(branch_name, create_branch_ok=True)\n",
        "        print(f\"Switched to branch: {branch_name}\")\n",
        "    except Exception as e:\n",
        "        print(f\"Error creating/checking out branch: {e}\")\n",
        "        sys.exit(1)\n",
        "\n",
        "    return repo\n",
        "\n",
        "def save_checkpoint(model, epoch, repo, branch_name):\n",
        "    \"\"\"Save checkpoint to Hugging Face repository\"\"\"\n",
        "    checkpoint_dir = os.path.join(HF_REPO, \"checkpoints\", f\"epoch_{epoch}\")\n",
        "    os.makedirs(checkpoint_dir, exist_ok=True)\n",
        "\n",
        "    # Save model state\n",
        "    checkpoint_path = os.path.join(checkpoint_dir, f\"model_epoch_{epoch}.pt\")\n",
        "    torch.save({\n",
        "        'epoch': epoch,\n",
        "        'model_state_dict': model.model.state_dict(),\n",
        "        'optimizer_state_dict': model.optimizer.state_dict() if hasattr(model, 'optimizer') else None,\n",
        "    }, checkpoint_path)\n",
        "\n",
        "    # Also save to root directory for latest version\n",
        "    latest_path = os.path.join(HF_REPO, \"latest_model.pt\")\n",
        "    shutil.copy(checkpoint_path, latest_path)\n",
        "\n",
        "    # Commit and push to Hugging Face\n",
        "    repo.git_add(auto_lfs_track=True)\n",
        "    repo.git_commit(f\"Checkpoint at epoch {epoch}\")\n",
        "    repo.git_push(upstream=\"origin\", branch=branch_name)\n",
        "    print(f\"Checkpoint saved and pushed to branch {branch_name}\")\n",
        "\n",
        "def get_available_branches():\n",
        "    \"\"\"Get list of available branches from Hugging Face repo\"\"\"\n",
        "    api = HfApi()\n",
        "    repo_id = f\"{HF_USERNAME}/{HF_REPO}\"\n",
        "    try:\n",
        "        branches = api.list_repo_refs(repo_id).branches\n",
        "        return [b.name for b in branches]\n",
        "    except Exception as e:\n",
        "        print(f\"Error fetching branches: {e}\")\n",
        "        return []\n",
        "\n",
        "def find_latest_checkpoint(repo_path, branch_name):\n",
        "    \"\"\"Find the latest checkpoint in the repository\"\"\"\n",
        "    checkpoint_dir = os.path.join(repo_path, \"checkpoints\")\n",
        "    if not os.path.exists(checkpoint_dir):\n",
        "        return None, 0\n",
        "\n",
        "    epochs = []\n",
        "    for d in os.listdir(checkpoint_dir):\n",
        "        if d.startswith(\"epoch_\"):\n",
        "            try:\n",
        "                epoch_num = int(d.split(\"_\")[1])\n",
        "                epochs.append(epoch_num)\n",
        "            except ValueError:\n",
        "                continue\n",
        "\n",
        "    if not epochs:\n",
        "        return None, 0\n",
        "\n",
        "    latest_epoch = max(epochs)\n",
        "    return os.path.join(checkpoint_dir, f\"epoch_{latest_epoch}\", f\"model_epoch_{latest_epoch}.pt\"), latest_epoch\n",
        "\n",
        "def main():\n",
        "    # Ensure logged in to Hugging Face\n",
        "    ensure_hf_login()\n",
        "\n",
        "    # Ask user whether to continue existing run or start new one\n",
        "    print(\"\\nAvailable options:\")\n",
        "    print(\"1. Start new training run\")\n",
        "    print(\"2. Continue existing run\")\n",
        "    choice = input(\"Enter your choice (1/2): \")\n",
        "\n",
        "    if choice == \"2\":\n",
        "        branches = get_available_branches()\n",
        "        if not branches:\n",
        "            print(\"No existing branches found. Starting new run.\")\n",
        "            choice = \"1\"\n",
        "        else:\n",
        "            print(\"\\nAvailable branches:\")\n",
        "            for i, branch in enumerate(branches, 1):\n",
        "                print(f\"{i}. {branch}\")\n",
        "            branch_idx = int(input(\"\\nSelect branch number: \")) - 1\n",
        "            branch_name = branches[branch_idx]\n",
        "            run_id = branch_name  # Use branch name as wandb run ID\n",
        "\n",
        "    if choice == \"1\":\n",
        "        # Generate new run name for both wandb and branch\n",
        "        timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
        "        run_id = f\"yolov11_run_{timestamp}\"\n",
        "        branch_name = run_id\n",
        "\n",
        "    # Initialize wandb\n",
        "    wandb.init(\n",
        "        project=\"Edutech\",\n",
        "        id=run_id if choice == \"2\" else None,\n",
        "        resume=\"must\" if choice == \"2\" else None,\n",
        "        config={\n",
        "            'epochs': 100,\n",
        "            'batch_size': 16,\n",
        "            'learning_rate': 0.001,\n",
        "            'fp16': True,\n",
        "            'compile': True,\n",
        "        }\n",
        "    )\n",
        "\n",
        "    # Setup HuggingFace repository and branch\n",
        "    repo = setup_hf_repo(branch_name)\n",
        "\n",
        "    # Load model\n",
        "    model = YOLO('yolo11x.pt')\n",
        "\n",
        "    # Enable torch.compile if available\n",
        "    if torch.__version__ >= \"2\" and wandb.config.compile:\n",
        "        print(\"Compiling model with torch.compile()...\")\n",
        "        model.model = torch.compile(model.model)\n",
        "\n",
        "    # Add WandB callback\n",
        "    add_wandb_callback(model)\n",
        "\n",
        "    # Load checkpoint if continuing existing run\n",
        "    start_epoch = 0\n",
        "    if choice == \"2\":\n",
        "        checkpoint_path, start_epoch = find_latest_checkpoint(HF_REPO, branch_name)\n",
        "        if checkpoint_path and os.path.exists(checkpoint_path):\n",
        "            checkpoint = torch.load(checkpoint_path)\n",
        "            model.model.load_state_dict(checkpoint['model_state_dict'])\n",
        "            if checkpoint['optimizer_state_dict']:\n",
        "                model.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
        "            print(f\"Resuming from epoch {start_epoch}\")\n",
        "\n",
        "    # Training loop\n",
        "    for epoch in range(start_epoch, 100, 5):  # Train in 5-epoch intervals\n",
        "        print(f\"\\nStarting training from epoch {epoch} to {epoch + 5}\")\n",
        "        model.train(\n",
        "            data='combined_dataset/data.yaml',\n",
        "            epochs=5,\n",
        "            imgsz=640,\n",
        "            batch=16,\n",
        "            workers=8,\n",
        "            exist_ok=True,\n",
        "            device='0',\n",
        "            project=wandb.run.project,\n",
        "            verbose=True,\n",
        "            amp=True,\n",
        "            resume=True if epoch > start_epoch else False,\n",
        "        )\n",
        "\n",
        "        # Save checkpoint\n",
        "        save_checkpoint(model, epoch + 5, repo, branch_name)\n",
        "        print(f\"Checkpoint saved for epoch {epoch + 5}\")\n",
        "\n",
        "    wandb.finish()\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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            "396d3194b1a54e7ea16058f6fa2df0f6",
            "fbf3ea9239904664961bf28539130015"
          ]
        },
        "id": "pljuUYoix_8Y",
        "outputId": "4f3c2f57-6ab4-4f40-eb08-db12741f2709"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating new Ultralytics Settings v0.0.6 file ✅ \n",
            "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n",
            "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m This integration is tested and supported for ultralytics v8.0.238 and below.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m             Please report any issues to https://github.com/wandb/wandb/issues with the tag `yolov8`.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.18.3"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20241021_164043-qv9gydoe</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/marlborough-college-malaysia/Edutech/runs/qv9gydoe' target=\"_blank\">ethereal-sound-16</a></strong> to <a href='https://wandb.ai/marlborough-college-malaysia/Edutech' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/marlborough-college-malaysia/Edutech' target=\"_blank\">https://wandb.ai/marlborough-college-malaysia/Edutech</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/marlborough-college-malaysia/Edutech/runs/qv9gydoe' target=\"_blank\">https://wandb.ai/marlborough-college-malaysia/Edutech/runs/qv9gydoe</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt to 'yolo11x.pt'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 109M/109M [00:00<00:00, 240MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using data.yaml at combined_dataset/data.yaml\n",
            "Ultralytics 8.3.18 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (NVIDIA L4, 22700MiB)\n",
            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolo11x.pt, data=combined_dataset/data.yaml, epochs=5, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=0, workers=8, project=Edutech, name=train, exist_ok=True, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=Edutech/train\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 755k/755k [00:00<00:00, 101MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overriding model.yaml nc=80 with nc=40\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      2784  ultralytics.nn.modules.conv.Conv             [3, 96, 3, 2]                 \n",
            "  1                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               \n",
            "  2                  -1  2    389760  ultralytics.nn.modules.block.C3k2            [192, 384, 2, True, 0.25]     \n",
            "  3                  -1  1   1327872  ultralytics.nn.modules.conv.Conv             [384, 384, 3, 2]              \n",
            "  4                  -1  2   1553664  ultralytics.nn.modules.block.C3k2            [384, 768, 2, True, 0.25]     \n",
            "  5                  -1  1   5309952  ultralytics.nn.modules.conv.Conv             [768, 768, 3, 2]              \n",
            "  6                  -1  2   5022720  ultralytics.nn.modules.block.C3k2            [768, 768, 2, True]           \n",
            "  7                  -1  1   5309952  ultralytics.nn.modules.conv.Conv             [768, 768, 3, 2]              \n",
            "  8                  -1  2   5022720  ultralytics.nn.modules.block.C3k2            [768, 768, 2, True]           \n",
            "  9                  -1  1   1476864  ultralytics.nn.modules.block.SPPF            [768, 768, 5]                 \n",
            " 10                  -1  2   3264768  ultralytics.nn.modules.block.C2PSA           [768, 768, 2]                 \n",
            " 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 13                  -1  2   5612544  ultralytics.nn.modules.block.C3k2            [1536, 768, 2, True]          \n",
            " 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 16                  -1  2   1700352  ultralytics.nn.modules.block.C3k2            [1536, 384, 2, True]          \n",
            " 17                  -1  1   1327872  ultralytics.nn.modules.conv.Conv             [384, 384, 3, 2]              \n",
            " 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 19                  -1  2   5317632  ultralytics.nn.modules.block.C3k2            [1152, 768, 2, True]          \n",
            " 20                  -1  1   5309952  ultralytics.nn.modules.conv.Conv             [768, 768, 3, 2]              \n",
            " 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 22                  -1  2   5612544  ultralytics.nn.modules.block.C3k2            [1536, 768, 2, True]          \n",
            " 23        [16, 19, 22]  1   3191752  ultralytics.nn.modules.head.Detect           [40, [384, 768, 768]]         \n",
            "YOLO11x summary: 631 layers, 56,919,976 parameters, 56,919,960 gradients, 195.7 GFLOPs\n",
            "\n",
            "Transferred 1009/1015 items from pretrained weights\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir Edutech/train', view at http://localhost:6006/\n",
            "Freezing layer 'model.23.dfl.conv.weight'\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLO11n...\n",
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5.35M/5.35M [00:00<00:00, 274MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/combined_dataset/labels/train... 32090 images, 416 backgrounds, 0 corrupt: 100%|██████████| 32090/32090 [00:27<00:00, 1160.03it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/combined_dataset/images/train/-kg_frame_3069_patch_0_0_jpg.rf.11338fd82fcb4c2d2976c7d82b39339d.jpg: 1 duplicate labels removed\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/combined_dataset/images/train/-kg_renamed_DJI_0471_tile_3_7_jpg.rf.987a8f6df1d8eb7780053f92d712e45e.jpg: 1 duplicate labels removed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/combined_dataset/labels/train.cache\n",
            "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 12132, len(boxes) = 110005. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of Albumentations is available: 1.4.18 (you have 1.4.15). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/combined_dataset/labels/valid... 6258 images, 135 backgrounds, 0 corrupt: 100%|██████████| 6258/6258 [00:05<00:00, 1056.34it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/combined_dataset/labels/valid.cache\n",
            "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 3174, len(boxes) = 21792. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n",
            "Plotting labels to Edutech/train/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000227, momentum=0.9) with parameter groups 167 weight(decay=0.0), 174 weight(decay=0.0005), 173 bias(decay=0.0)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
            "Image sizes 640 train, 640 val\n",
            "Using 8 dataloader workers\n",
            "Logging results to \u001b[1mEdutech/train\u001b[0m\n",
            "Starting training for 5 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "        1/5      16.6G      1.204       2.14      1.272         48        640: 100%|██████████| 2006/2006 [25:24<00:00,  1.32it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 196/196 [01:59<00:00,  1.64it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       6258      21792      0.371      0.394      0.323      0.234\n",
            "Ultralytics 8.3.18 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (NVIDIA L4, 22700MiB)\n",
            "YOLO11x summary (fused): 464 layers, 56,919,424 parameters, 0 gradients, 194.9 GFLOPs\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating Visualizations for batch-1/1:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "290bb9982f984665ab14c7fe58b3334c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating Visualizations for batch-1/1:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9fd7ebff66c142f89fa97d4f1ddc64ee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "        2/5        17G      1.154       1.58      1.255         37        640: 100%|██████████| 2006/2006 [24:52<00:00,  1.34it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 196/196 [01:57<00:00,  1.67it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       6258      21792      0.471      0.424      0.384      0.284\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating Visualizations for batch-1/1:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "925b6d2743ea48e9894c3281f769f94b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating Visualizations for batch-1/1:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b9cc9053f7434debb81689908548e573"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "        3/5      16.9G      1.114      1.431      1.236         41        640: 100%|██████████| 2006/2006 [24:38<00:00,  1.36it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 196/196 [01:57<00:00,  1.67it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       6258      21792      0.449      0.447      0.418      0.315\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating Visualizations for batch-1/1:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7fe1955983974a93bfe3f39c82d56f5c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating Visualizations for batch-1/1:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "225cec8f2d4d495a9258a1d343710ac7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "        4/5      16.9G      1.057      1.272      1.202         69        640: 100%|██████████| 2006/2006 [24:33<00:00,  1.36it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 196/196 [01:56<00:00,  1.68it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       6258      21792      0.524      0.498      0.469      0.359\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating Visualizations for batch-1/1:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dab55731a72f46b383aa510e324ded96"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating Visualizations for batch-1/1:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21a9a7bf316b4e9a80acb4552b7a2c8b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "        5/5      16.9G      1.092      1.258      1.218         96        640:   1%|          | 17/2006 [00:12<24:41,  1.34it/s]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Run inference using the best model\n",
        "results = model.predict(\n",
        "    source=os.path.join(combined_dataset_dir, 'images/val'),\n",
        "    save=True,\n",
        "    project='runs/predict',\n",
        "    name='yolo11x_predict',\n",
        "    exist_ok=True\n",
        ")\n"
      ],
      "metadata": {
        "id": "cuxYBebkx_wj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "import os\n",
        "\n",
        "def visualize_predictions(result_dir):\n",
        "    image_paths = glob.glob(os.path.join(result_dir, '*.jpg'))\n",
        "    num_images = min(4, len(image_paths))\n",
        "\n",
        "    plt.figure(figsize=(15, 12))\n",
        "    for i, image_path in enumerate(image_paths[:num_images]):\n",
        "        image = plt.imread(image_path)\n",
        "        plt.subplot(2, 2, i + 1)\n",
        "        plt.imshow(image)\n",
        "        plt.axis('off')\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "visualize_predictions('runs/predict/yolo11x_predict')\n"
      ],
      "metadata": {
        "id": "87daXwzIyLnd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zOU1jsLvyLkv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "21RTNNTlyLiA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VFCIHyOGyLfV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}