prep_dataset_2_&_train.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Traffix-C23-PC636/scripts/blob/main/prep_dataset_2_%26_train.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qk7TZE-FvjcH"
      },
      "source": [
        "# ***Data is ready, run cell below to be directed to train cell***\n",
        "\n",
        "https://colab.research.google.com/drive/1Q_sKKTtf1UAo1BiQcJpsrim8Cvy9K4Dy#scrollTo=y3HfL-Y7u6oP&line=1&uniqifier=1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQmg1do-WUSF",
        "outputId": "f3b705d5-1486-4c0a-b255-57a1ae2ca517"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1zCkYY0otv8UUsHRZMyzh79JGNAWsDkR4\n",
            "To: /content/data_train.zip\n",
            " 62% 2.18G/3.53G [00:43<00:26, 50.4MB/s]Traceback (most recent call last):\n",
            "  File \"/usr/local/bin/gdown\", line 8, in <module>\n",
            "    sys.exit(main())\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gdown/cli.py\", line 151, in main\n",
            "    filename = download(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gdown/download.py\", line 274, in download\n",
            "    for chunk in res.iter_content(chunk_size=CHUNK_SIZE):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/requests/models.py\", line 760, in generate\n",
            "    for chunk in self.raw.stream(chunk_size, decode_content=True):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/urllib3/response.py\", line 628, in stream\n",
            "    data = self.read(amt=amt, decode_content=decode_content)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/urllib3/response.py\", line 567, in read\n",
            "    data = self._fp_read(amt) if not fp_closed else b\"\"\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/urllib3/response.py\", line 533, in _fp_read\n",
            "    return self._fp.read(amt) if amt is not None else self._fp.read()\n",
            "  File \"/usr/lib/python3.10/http/client.py\", line 466, in read\n",
            "    s = self.fp.read(amt)\n",
            "  File \"/usr/lib/python3.10/socket.py\", line 705, in readinto\n",
            "    return self._sock.recv_into(b)\n",
            "  File \"/usr/lib/python3.10/ssl.py\", line 1274, in recv_into\n",
            "    return self.read(nbytes, buffer)\n",
            "  File \"/usr/lib/python3.10/ssl.py\", line 1130, in read\n",
            "    return self._sslobj.read(len, buffer)\n",
            "KeyboardInterrupt\n",
            " 62% 2.20G/3.53G [00:43<00:26, 50.1MB/s]\n",
            "^C\n"
          ]
        }
      ],
      "source": [
        "!gdown --fuzzy https://drive.google.com/file/d/1zCkYY0otv8UUsHRZMyzh79JGNAWsDkR4/view?usp=share_link"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q9glu-yFWwdv",
        "outputId": "88952057-c9da-48cc-cbe2-d9adf85e87df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "unzip:  cannot find or open data_train.zip, data_train.zip.zip or data_train.zip.ZIP.\n"
          ]
        }
      ],
      "source": [
        "!unzip data_train.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VOKs_RI7XTLX"
      },
      "outputs": [],
      "source": [
        "import logging\n",
        "logging.getLogger().setLevel(logging.CRITICAL)\n",
        "\n",
        "!pip install pylabel\n",
        "from pylabel import importer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9XffdxnvXhXp",
        "outputId": "8c74ffc8-8559-48fb-db3a-e2df5b0af256"
      },
      "outputs": [
        {
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-6c3fe3cacf42>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m#Import the dataset into the pylable schema\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimporter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImportCoco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_to_annotations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath_to_images\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath_to_images\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"BCCD_coco\"\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      9\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pylabel/importer.py\u001b[0m in \u001b[0;36mImportCoco\u001b[0;34m(path, path_to_images, name, encoding)\u001b[0m\n\u001b[1;32m     55\u001b[0m         \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimporter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImportCoco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"coco_annotations.json\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \"\"\"\n\u001b[0;32m---> 57\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcocojson\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     58\u001b[0m         \u001b[0mannotations_json\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcocojson\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/datasets/COCO/my_custom_dataset_train/annotations/coco_annotation.json'"
          ]
        }
      ],
      "source": [
        "#Specify path to the coco.json file\n",
        "# path_to_annotations = \"data/BCCD_Dataset.json\"\n",
        "path_to_annotations = \"/content/datasets/COCO/my_custom_dataset_train/annotations/coco_annotation.json\"\n",
        "#Specify the path to the images (if they are in a different folder than the annotations)\n",
        "path_to_images = \"/content/datasets/COCO/my_custom_dataset_train/images\"\n",
        "\n",
        "#Import the dataset into the pylable schema \n",
        "dataset = importer.ImportCoco(path_to_annotations, path_to_images=path_to_images, name=\"BCCD_coco\")\n",
        "dataset.df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RuwbDqraX6kL"
      },
      "outputs": [],
      "source": [
        "print(f\"Number of images: {dataset.analyze.num_images}\")\n",
        "print(f\"Number of classes: {dataset.analyze.num_classes}\")\n",
        "print(f\"Classes:{dataset.analyze.classes}\")\n",
        "print(f\"Class counts:\\n{dataset.analyze.class_counts}\")\n",
        "print(f\"Path to annotations:\\n{dataset.path_to_annotations}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4LfyoOOAX9vj"
      },
      "outputs": [],
      "source": [
        "from IPython.display import display\n",
        "from matplotlib.pyplot import imshow\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "\n",
        "\n",
        "# from IPython.display import  display\n",
        "# from PIL import Image\n",
        "# display(dataset.visualize.ShowBoundingBoxes())\n",
        "# print(dataset)\n",
        "img = (dataset.visualize.ShowBoundingBoxes(391895))\n",
        "print(img)\n",
        "img.show\n",
        "imshow(np.asarray(img))\n",
        "display(img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iwSItxlpYC6O"
      },
      "outputs": [],
      "source": [
        "dataset.export.ExportToYoloV5()[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wP_04QopZs09"
      },
      "source": [
        "move COCO `images` folder to `training` folder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_YKlv7dHIaxv"
      },
      "outputs": [],
      "source": [
        "!mv /content/datasets/COCO/my_custom_dataset_train/images /content/training/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhb57CDDe-Fr"
      },
      "source": [
        "Preprocessing data train\n",
        "https://colab.research.google.com/drive/1ws71iim9Nm9QUabaZ9lRjYQsGQZtIlsb?usp=sharing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kX0J_mYDqEO-"
      },
      "source": [
        "Validation data preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hmlQK-H0fePO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c8e84ab-31cf-4703-de64-be54ae601ba1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1s6bsNvDW9aWTI8pfAdqIWKN8mw4gsaK7\n",
            "To: /content/data_val.zip\n",
            "100% 155M/155M [00:04<00:00, 37.1MB/s]\n"
          ]
        }
      ],
      "source": [
        "!gdown --fuzzy https://drive.google.com/file/d/1s6bsNvDW9aWTI8pfAdqIWKN8mw4gsaK7/view?usp=drive_link"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gDP0vKDVjsSm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac8af57b-b29d-4236-8fcc-bd3e95d30303"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  data_val.zip\n",
            "   creating: datasets/COCO/my_custom_dataset_val/\n",
            "   creating: datasets/COCO/my_custom_dataset_val/images/\n",
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            "   creating: datasets/COCO/my_custom_dataset_val/annotations/\n",
            "  inflating: datasets/COCO/my_custom_dataset_val/annotations/coco_annotation.json  \n"
          ]
        }
      ],
      "source": [
        "!unzip data_val.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-oOOSIEmir94"
      },
      "outputs": [],
      "source": [
        "#Specify path to the coco.json file\n",
        "# path_to_annotations = \"data/BCCD_Dataset.json\"\n",
        "path_to_annotations_val = \"/content/datasets/COCO/my_custom_dataset_val/annotations/coco_annotation.json\"\n",
        "#Specify the path to the images (if they are in a different folder than the annotations)\n",
        "path_to_images_val = \"/content/datasets/COCO/my_custom_dataset_val/images\"\n",
        "\n",
        "#Import the dataset into the pylable schema \n",
        "val = importer.ImportCoco(path_to_annotations_val, path_to_images_val, name=\"BCCD_coco_val\")\n",
        "val.df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9cSFWGhRjCZf"
      },
      "outputs": [],
      "source": [
        "print(f\"Number of images: {val.analyze.num_images}\")\n",
        "print(f\"Number of classes: {val.analyze.num_classes}\")\n",
        "print(f\"Classes:{val.analyze.classes}\")\n",
        "print(f\"Class counts:\\n{val.analyze.class_counts}\")\n",
        "print(f\"Path to annotations:\\n{val.path_to_annotations}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_wjiTVlqjCZg"
      },
      "outputs": [],
      "source": [
        "from IPython.display import display\n",
        "from matplotlib.pyplot import imshow\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "\n",
        "\n",
        "# from IPython.display import  display\n",
        "# from PIL import Image\n",
        "# display(dataset.visualize.ShowBoundingBoxes())\n",
        "# print(dataset)\n",
        "img = (val.visualize.ShowBoundingBoxes(724))\n",
        "print(img)\n",
        "img.show\n",
        "imshow(np.asarray(img))\n",
        "display(img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4pMVooWWic9y"
      },
      "outputs": [],
      "source": [
        "val.export.ExportToYoloV5()[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dJkwUwV9ik9S"
      },
      "outputs": [],
      "source": [
        "#open txt on labels to check,for this file should be 3 on the start of the only row\n",
        "with open('/content/training/labels/000000000724.txt', 'r') as file:\n",
        "    content = file.read()\n",
        "\n",
        "print(content)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t060ZPyXjyku"
      },
      "outputs": [],
      "source": [
        "# modify the id\n",
        "\n",
        "import os\n",
        "\n",
        "# Define the directory path where the text files are located\n",
        "directory = '/content/training/labels'\n",
        "\n",
        "# Define the numbers you want to replace and their replacements\n",
        "numbers_to_replace = {\n",
        "    \"2\": \"0\",\n",
        "    \"3\": \"1\",\n",
        "    \"4\": \"2\",\n",
        "    \"6\": \"3\",\n",
        "    \"8\": \"4\"\n",
        "}\n",
        "\n",
        "# Iterate over each file in the directory\n",
        "for filename in os.listdir(directory):\n",
        "    if filename.endswith('.txt'):\n",
        "        file_path = os.path.join(directory, filename)\n",
        "        \n",
        "        # Read the contents of the file\n",
        "        with open(file_path, 'r') as file:\n",
        "            file_contents = file.readlines()\n",
        "        \n",
        "        # Modify the contents of the file\n",
        "        modified_contents = []\n",
        "        for line in file_contents:\n",
        "            # Check if any of the numbers need to be replaced\n",
        "            for number, replacement in numbers_to_replace.items():\n",
        "                if line.startswith(number):\n",
        "                    # Replace the number at the start of the line\n",
        "                    modified_line = line.replace(number, replacement, 1)\n",
        "                    line = modified_line\n",
        "                    break\n",
        "            \n",
        "            modified_contents.append(line)\n",
        "        \n",
        "        # Write the modified contents back to the file\n",
        "        with open(file_path, 'w') as file:\n",
        "            file.writelines(modified_contents)\n",
        "\n",
        "print('Numbers replaced successfully in all files.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TRxGxslzj8uw"
      },
      "outputs": [],
      "source": [
        "#open txt on labels to check the modified labels, same file as opened earlier before we change the id (first number on the only row),\n",
        "# earlier the id is == 3 , now should be == 1\n",
        "with open('/content/training/labels/000000000724.txt', 'r') as file:\n",
        "    content = file.read()\n",
        "\n",
        "print(content)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W7654dXglga3"
      },
      "outputs": [],
      "source": [
        "!zip -r val_data.zip /content/training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cL3GC0gEmDu1"
      },
      "source": [
        "folder renamed to val_data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zPDREK2pG6TO"
      },
      "outputs": [],
      "source": [
        "!zip -r training_data.zip /content/training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n007bcPM1Ue2"
      },
      "outputs": [],
      "source": [
        "!cp training_data.zip /content/drive/MyDrive/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y3HfL-Y7u6oP"
      },
      "source": [
        "## **Data is ready, start here**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "83C-PzLkqsRT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf0a170e-fc10-401a-e59f-4bbd2e3003c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Sgsa79cZ04g",
        "outputId": "d77ae410-fb7a-45b0-bf1a-56b1c0e322fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1-3ytnjiGfC4X_FWYS1vmfX4LpnH3XQT2\n",
            "To: /content/Yolo_training_data_capstoneTRAFFIX.zip\n",
            "100% 3.68G/3.68G [00:52<00:00, 70.5MB/s]\n"
          ]
        }
      ],
      "source": [
        "# DATA TRAINING & LABELS FOR YOLO ready to train, (from custom_train_data and custom_val_data)\n",
        "\n",
        "!gdown --fuzzy https://drive.google.com/file/d/1-3ytnjiGfC4X_FWYS1vmfX4LpnH3XQT2/view?usp=share_link"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PSSe_KFpbdkE",
        "outputId": "0b3b1ea1-aad1-48da-d50c-3a3502e51d94"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!unzip Yolo_training_data_capstoneTRAFFIX.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GNrF0J2Ruy4V"
      },
      "outputs": [],
      "source": [
        "#Move training folder inside content folder, to colab main folder (inside /content)\n",
        "!mv /content/content/training /content/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xYez_VWBVAE2"
      },
      "source": [
        "Documentation https://docs.ultralytics.com/modes/train/#arguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BfpeOyQ0YPB_",
        "outputId": "f7c86a8a-316a-4036-941d-91ac891b1c37"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.0.112-py3-none-any.whl (593 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m593.1/593.1 kB\u001b[0m \u001b[31m26.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.2.2->ultralytics) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.7.0->ultralytics) (2.1.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.7.0->ultralytics) (1.3.0)\n",
            "Installing collected packages: ultralytics\n",
            "Successfully installed ultralytics-8.0.112\n"
          ]
        }
      ],
      "source": [
        "!pip install ultralytics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hsPckXacZdxU",
        "outputId": "ca72352e-10b2-41c4-d3e4-b4757f3d4674"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Ultralytics YOLOv8.0.112 🚀 Python-3.10.11 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=last.pt, data=./training/dataset.yaml, epochs=4, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=Adam, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=0, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/train\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
            "100%|██████████| 755k/755k [00:00<00:00, 121MB/s]\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
            "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
            "  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n",
            "  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
            "  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n",
            "  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
            "  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
            "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
            "  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n",
            "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  \n",
            " 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 \n",
            " 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n",
            " 22        [15, 18, 21]  1    752287  ultralytics.nn.modules.head.Detect           [5, [64, 128, 256]]           \n",
            "YOLOv8n summary: 225 layers, 3011823 parameters, 3011807 gradients\n",
            "\n",
            "Transferred 355/355 items from pretrained weights\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to yolov8n.pt...\n",
            "100%|██████████| 6.23M/6.23M [00:00<00:00, 218MB/s]\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m Adam(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/training/train_data/labels.cache... 19759 images, 0 backgrounds, 0 corrupt: 100%|██████████| 19759/19759 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/training/train_data/images/000000099844.jpg: 2 duplicate labels removed\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), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/training/val_data/labels.cache... 870 images, 0 backgrounds, 0 corrupt: 100%|██████████| 870/870 [00:00<?, ?it/s]\n",
            "Plotting labels to runs/detect/train/labels.jpg... \n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
            "Starting training for 4 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "        1/4      2.48G      1.336      1.514      1.401        103        640: 100%|██████████| 1235/1235 [08:01<00:00,  2.57it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:13<00:00,  2.03it/s]\n",
            "                   all        870       3319      0.584      0.384      0.443      0.269\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "        2/4      2.42G      1.363      1.576       1.42        147        640:  76%|███████▋  | 943/1235 [06:00<03:02,  1.60it/s]"
          ]
        }
      ],
      "source": [
        "from ultralytics import YOLO\n",
        "\n",
        "model = YOLO('last.pt')\n",
        "\n",
        "model.train(data='./training/dataset.yaml', epochs=4, imgsz=640, batch=16, optimizer='Adam')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f3Uk8UorhsSb"
      },
      "outputs": [],
      "source": [
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_MluVrLHaPA_"
      },
      "outputs": [],
      "source": [
        "!zip -r train_result_188epoch.zip ./runs/detect/train/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jGTvS0y13y3j"
      },
      "outputs": [],
      "source": [
        "!cp train_result_188epoch.zip /content/drive/MyDrive/"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}