Trafficsigns_yolov5.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mehdi-touil/traffic-sign-fpga/blob/main/Trafficsigns_yolov5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjfVd3ew1rf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6c531f89-efc6-45e8-8a88-042195d9bcff"
      },
      "source": [
        "!python -m pip install --upgrade pip\n",
        "\n",
        "!pip install tensorflow==2.3.1\n",
        "\n",
        "!pip install tensorboard==2.4.1\n",
        "!pip install torch"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (23.1.2)\n",
            "Collecting pip\n",
            "  Downloading pip-23.2.1-py3-none-any.whl (2.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pip\n",
            "  Attempting uninstall: pip\n",
            "    Found existing installation: pip 23.1.2\n",
            "    Uninstalling pip-23.1.2:\n",
            "      Successfully uninstalled pip-23.1.2\n",
            "Successfully installed pip-23.2.1\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement tensorflow==2.3.1 (from versions: 2.8.0rc0, 2.8.0rc1, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0rc0, 2.9.0rc1, 2.9.0rc2, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0rc0, 2.10.0rc1, 2.10.0rc2, 2.10.0rc3, 2.10.0, 2.10.1, 2.11.0rc0, 2.11.0rc1, 2.11.0rc2, 2.11.0, 2.11.1, 2.12.0rc0, 2.12.0rc1, 2.12.0, 2.12.1, 2.13.0rc0, 2.13.0rc1, 2.13.0rc2, 2.13.0)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for tensorflow==2.3.1\u001b[0m\u001b[31m\n",
            "\u001b[0mCollecting tensorboard==2.4.1\n",
            "  Downloading tensorboard-2.4.1-py3-none-any.whl (10.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.6/10.6 MB\u001b[0m \u001b[31m40.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.10/dist-packages (from tensorboard==2.4.1) (1.4.0)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard==2.4.1) (1.56.2)\n",
            "Collecting google-auth<2,>=1.6.3 (from tensorboard==2.4.1)\n",
            "  Downloading google_auth-1.35.0-py2.py3-none-any.whl (152 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m152.9/152.9 kB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting google-auth-oauthlib<0.5,>=0.4.1 (from tensorboard==2.4.1)\n",
            "  Downloading google_auth_oauthlib-0.4.6-py2.py3-none-any.whl (18 kB)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard==2.4.1) (3.4.4)\n",
            "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard==2.4.1) (1.22.4)\n",
            "Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard==2.4.1) (3.20.3)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard==2.4.1) (2.27.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard==2.4.1) (67.7.2)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard==2.4.1) (1.16.0)\n",
            "Collecting tensorboard-plugin-wit>=1.6.0 (from tensorboard==2.4.1)\n",
            "  Downloading tensorboard_plugin_wit-1.8.1-py3-none-any.whl (781 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m781.3/781.3 kB\u001b[0m \u001b[31m40.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.10/dist-packages (from tensorboard==2.4.1) (2.3.6)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.10/dist-packages (from tensorboard==2.4.1) (0.41.0)\n",
            "Collecting cachetools<5.0,>=2.0.0 (from google-auth<2,>=1.6.3->tensorboard==2.4.1)\n",
            "  Downloading cachetools-4.2.4-py3-none-any.whl (10 kB)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<2,>=1.6.3->tensorboard==2.4.1) (0.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<2,>=1.6.3->tensorboard==2.4.1) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard==2.4.1) (1.3.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard==2.4.1) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard==2.4.1) (2023.7.22)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard==2.4.1) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard==2.4.1) (3.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=0.11.15->tensorboard==2.4.1) (2.1.3)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard==2.4.1) (0.5.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard==2.4.1) (3.2.2)\n",
            "Installing collected packages: tensorboard-plugin-wit, cachetools, google-auth, google-auth-oauthlib, tensorboard\n",
            "  Attempting uninstall: cachetools\n",
            "    Found existing installation: cachetools 5.3.1\n",
            "    Uninstalling cachetools-5.3.1:\n",
            "      Successfully uninstalled cachetools-5.3.1\n",
            "  Attempting uninstall: google-auth\n",
            "    Found existing installation: google-auth 2.17.3\n",
            "    Uninstalling google-auth-2.17.3:\n",
            "      Successfully uninstalled google-auth-2.17.3\n",
            "  Attempting uninstall: google-auth-oauthlib\n",
            "    Found existing installation: google-auth-oauthlib 1.0.0\n",
            "    Uninstalling google-auth-oauthlib-1.0.0:\n",
            "      Successfully uninstalled google-auth-oauthlib-1.0.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.12.3\n",
            "    Uninstalling tensorboard-2.12.3:\n",
            "      Successfully uninstalled tensorboard-2.12.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-api-core 2.11.1 requires google-auth<3.0.dev0,>=2.14.1, but you have google-auth 1.35.0 which is incompatible.\n",
            "google-colab 1.0.0 requires google-auth==2.17.3, but you have google-auth 1.35.0 which is incompatible.\n",
            "tensorflow 2.12.0 requires tensorboard<2.13,>=2.12, but you have tensorboard 2.4.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed cachetools-4.2.4 google-auth-1.35.0 google-auth-oauthlib-0.4.6 tensorboard-2.4.1 tensorboard-plugin-wit-1.8.1\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "google"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.7.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (16.0.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B83FCoCdJ5P5"
      },
      "source": [
        "import torch"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XKx1E9YLNkyf"
      },
      "source": [
        "from IPython.display import Image"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4azAZGfRKOwg"
      },
      "source": [
        "from google.colab import drive"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GPHqbMDeKVXE",
        "outputId": "ef22e267-0a94-4c3b-a03a-ef53f26660b7"
      },
      "source": [
        "drive.mount('/content/drive')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!cd drive/MyDrive && mkdir image_detection"
      ],
      "metadata": {
        "id": "Ed5Db6CLKemu"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tzushGErKfpa",
        "outputId": "25ef5476-1a4d-4093-8807-f3540a3019fb"
      },
      "source": [
        "%cd '/content/drive/MyDrive/image_detection'"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/image_detection\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7T7wvYMuKpTD",
        "outputId": "1d8cf5ca-2970-467f-d5de-3d020141abfa"
      },
      "source": [
        "!git clone https://github.com/ultralytics/yolov5"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'yolov5'...\n",
            "remote: Enumerating objects: 15874, done.\u001b[K\n",
            "remote: Counting objects: 100% (105/105), done.\u001b[K\n",
            "remote: Compressing objects: 100% (90/90), done.\u001b[K\n",
            "remote: Total 15874 (delta 46), reused 45 (delta 15), pack-reused 15769\u001b[K\n",
            "Receiving objects: 100% (15874/15874), 14.63 MiB | 4.41 MiB/s, done.\n",
            "Resolving deltas: 100% (10860/10860), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tM7bK2d1KkXD",
        "outputId": "2f12c0df-cc48-4da7-e5d6-8ac0e7e7e324"
      },
      "source": [
        "%cd '/content/drive/MyDrive/image_detection/yolov5'"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/image_detection/yolov5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ST74PCP1LplS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6906e81e-1760-40bf-8afb-960ea1487c94"
      },
      "source": [
        "!pip install -r requirements.txt"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting gitpython>=3.1.30 (from -r requirements.txt (line 5))\n",
            "  Obtaining dependency information for gitpython>=3.1.30 from https://files.pythonhosted.org/packages/67/50/742c2fb60989b76ccf7302c7b1d9e26505d7054c24f08cc7ec187faaaea7/GitPython-3.1.32-py3-none-any.whl.metadata\n",
            "  Downloading GitPython-3.1.32-py3-none-any.whl.metadata (10.0 kB)\n",
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            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 11)) (6.0.1)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 12)) (2.27.1)\n",
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            "Collecting thop>=0.1.1 (from -r requirements.txt (line 14))\n",
            "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: torch>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 15)) (2.0.1+cu118)\n",
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            "Collecting ultralytics>=8.0.147 (from -r requirements.txt (line 18))\n",
            "  Obtaining dependency information for ultralytics>=8.0.147 from https://files.pythonhosted.org/packages/10/b8/8523918453e1717582029d2beceae0d160a952d99548af0c9029fd392c24/ultralytics-8.0.149-py3-none-any.whl.metadata\n",
            "  Downloading ultralytics-8.0.149-py3-none-any.whl.metadata (28 kB)\n",
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            "Collecting gitdb<5,>=4.0.1 (from gitpython>=3.1.30->-r requirements.txt (line 5))\n",
            "  Downloading gitdb-4.0.10-py3-none-any.whl (62 kB)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->-r requirements.txt (line 27)) (2022.7.1)\n",
            "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython>=3.1.30->-r requirements.txt (line 5))\n",
            "  Downloading smmap-5.0.0-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3->-r requirements.txt (line 6)) (1.16.0)\n",
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            "Downloading GitPython-3.1.32-py3-none-any.whl (188 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m188.5/188.5 kB\u001b[0m \u001b[31m21.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading ultralytics-8.0.149-py3-none-any.whl (614 kB)\n",
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            "\u001b[?25hInstalling collected packages: smmap, gitdb, gitpython, ultralytics, thop\n",
            "Successfully installed gitdb-4.0.10 gitpython-3.1.32 smmap-5.0.0 thop-0.1.1.post2209072238 ultralytics-8.0.149\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd '/content/drive/My Drive/image_detection/image_yolov5/dataset'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P742tm5LN1EB",
        "outputId": "a1c800c1-6ac9-41fe-afcc-c93b80211115"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/image_detection/image_yolov5/dataset\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "WlFGpmRRPtwH",
        "outputId": "65af6fbd-b188-45f7-cf08-ba821a0abb89"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-b06eb717-a90f-464a-9b0d-4bb18943d27d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-b06eb717-a90f-464a-9b0d-4bb18943d27d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"elmehditouil\",\"key\":\"90b558c770bb671006945ff92b20e72b\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir ~/.kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z0EG6YG-P4x-",
        "outputId": "31e1b784-4a3e-4a17-88ec-4dfff47193f0"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! cp kaggle.json ~/.kaggle/\n"
      ],
      "metadata": {
        "id": "9uuHVlvNP-un"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "64xgOJvVQB0V"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! kaggle datasets list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYB_wuswQIFk",
        "outputId": "fe98583d-7982-4751-8f8f-6e06dc515b38"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ref                                                       title                                             size  lastUpdated          downloadCount  voteCount  usabilityRating  \n",
            "--------------------------------------------------------  -----------------------------------------------  -----  -------------------  -------------  ---------  ---------------  \n",
            "nelgiriyewithana/countries-of-the-world-2023              Global Country Information Dataset 2023           23KB  2023-07-08 20:37:33           8615        337  1.0              \n",
            "juhibhojani/house-price                                   House Price                                        7MB  2023-08-02 16:51:21            893         35  0.9411765        \n",
            "arnavsmayan/netflix-userbase-dataset                      Netflix Userbase Dataset                          25KB  2023-07-04 07:38:41          10192        181  1.0              \n",
            "alphiree/cardiovascular-diseases-risk-prediction-dataset  Cardiovascular Diseases Risk Prediction Dataset    5MB  2023-07-03 12:12:19           7921        280  1.0              \n",
            "nelgiriyewithana/global-youtube-statistics-2023           Global YouTube Statistics 2023                    60KB  2023-07-28 15:36:38           1415         69  1.0              \n",
            "abhijitdahatonde/zomato-restaurants-dataset               Zomato Restaurants Dataset                       181KB  2023-07-20 13:11:17            829         29  1.0              \n",
            "abhijitdahatonde/fifa-world-cup-all-dataset               FIFA World Cup All Dataset                       192KB  2023-07-31 02:22:58            448         26  0.9411765        \n",
            "kapturovalexander/bank-credit-scoring                     Bank credit scoring                               65KB  2023-08-07 06:19:32            757         33  1.0              \n",
            "joebeachcapital/ransomware-attacks                        Ransomware Attacks                                34KB  2023-08-03 00:41:08            294         23  1.0              \n",
            "pavankrishnanarne/global-stock-market-2008-present        Global Stock Market (2008-2023)                    1MB  2023-07-30 06:17:44            514         23  0.9411765        \n",
            "alirezajavid1999/google-stock-2010-2023                   Google Stock (2010-2023)                          76KB  2023-07-29 23:57:16            652         29  1.0              \n",
            "sudheerp2147234/salary-dataset-based-on-country-and-race  Salary dataset based on country and race          49KB  2023-07-06 09:10:21           2360         49  1.0              \n",
            "abhijitdahatonde/real-world-smartphones-dataset           Real World Smartphone's Dataset                   17KB  2023-08-02 07:03:01            478         25  1.0              \n",
            "joebeachcapital/worlds-biggest-data-breaches-and-hacks    World's Biggest Data Breaches & Hacks             49KB  2023-08-01 04:31:23            387         32  1.0              \n",
            "joebeachcapital/top-10000-spotify-songs-1960-now          Top 10000 Songs on Spotify 1960-Now                2MB  2023-07-26 00:54:14           1241         49  1.0              \n",
            "mexwell/world-airports                                    ✈️ World Airports                                  5MB  2023-07-25 08:37:38            906         27  1.0              \n",
            "harishkumardatalab/housing-price-prediction               Housing Price Prediction                           5KB  2023-07-07 04:34:24           3250         68  1.0              \n",
            "nathaniellybrand/chicago-car-crash-dataset                Chicago Car Crash Dataset                        116MB  2023-07-31 19:58:33            352         25  1.0              \n",
            "bhanupratapbiswas/olympic-data                            Olympic Data 🥇 ⛳🥅🏋️‍♀️🚴‍♂️                         1MB  2023-07-20 09:30:19           1790         52  1.0              \n",
            "khushipitroda/top-250-indian-movies-imdb                  Top 250 Indian movies IMDB                        24KB  2023-08-02 04:37:17            358         30  1.0              \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KmEBnSNSQctu",
        "outputId": "a35e3679-5b6f-4f35-eace-7ccedeae1834"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/image_detection/image_yolov5/dataset\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d valentynsichkar/traffic-signs-dataset-in-yolo-format"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "juYXGiVZQRcA",
        "outputId": "00832df3-a724-4b15-a8b1-2c401dad952f"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading traffic-signs-dataset-in-yolo-format.zip to /content/drive/MyDrive/image_detection/image_yolov5/dataset\n",
            " 99% 248M/250M [00:14<00:00, 21.9MB/s]\n",
            "100% 250M/250M [00:14<00:00, 18.5MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip traffic-signs-dataset-in-yolo-format.zip -d ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AQMp7DxfRR4h",
        "outputId": "a4f3c634-e5f5-4807-d8e9-8023d2197f23"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  traffic-signs-dataset-in-yolo-format.zip\n",
            "  inflating: ./classes.names         \n",
            "  inflating: ./getting-full-path.py  \n",
            "  inflating: ./test.txt              \n",
            "  inflating: ./traffic-sign-to-test.mp4  \n",
            "  inflating: ./train.txt             \n",
            "  inflating: ./ts/ts/00000.jpg       \n",
            "  inflating: ./ts/ts/00000.txt       \n",
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            "  inflating: ./ts/ts/00009.jpg       \n",
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            "  inflating: ./ts/ts/00010.jpg       \n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ggMwNNQt2iK-"
      },
      "source": [
        "# Divide the dataset in train and validation folder."
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QFjcxfgKLuiF",
        "outputId": "adc93ad5-3330-4329-abe3-c5489f7036fe"
      },
      "source": [
        "import os\n",
        "from random import choice\n",
        "import shutil\n",
        "\n",
        "#arrays to store file names\n",
        "imgs =[]\n",
        "xmls =[]\n",
        "\n",
        "#setup dir names\n",
        "trainPath = '/content/drive/My Drive/image_detection/image_yolov5/images/train'\n",
        "valPath = '/content/drive/My Drive/image_detection/image_yolov5/images/validation'\n",
        "crsPath = '/content/drive/MyDrive/image_detection/image_yolov5/dataset/ts/ts' #dir where images and annotations stored\n",
        "\n",
        "#setup ratio (val ratio = rest of the files in origin dir after splitting into train and test)\n",
        "train_ratio = 0.8\n",
        "val_ratio = 0.2\n",
        "\n",
        "\n",
        "#total count of imgs\n",
        "totalImgCount = len(os.listdir(crsPath))/2\n",
        "\n",
        "#soring files to corresponding arrays\n",
        "for (dirname, dirs, files) in os.walk(crsPath):\n",
        "    for filename in files:\n",
        "        if filename.endswith('.txt'):\n",
        "            xmls.append(filename)\n",
        "        else:\n",
        "            imgs.append(filename)\n",
        "\n",
        "\n",
        "#counting range for cycles\n",
        "countForTrain = int(len(imgs)*train_ratio)\n",
        "countForVal = int(len(imgs)*val_ratio)\n",
        "print(\"training images are : \",countForTrain)\n",
        "print(\"Validation images are : \",countForVal)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training images are :  592\n",
            "Validation images are :  148\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "5xt0n5xlLys6",
        "outputId": "3b64164d-31cb-4585-8819-8374d39870a7"
      },
      "source": [
        "trainimagePath = '/content/drive/My Drive/image_detection/image_yolov5/images/train'\n",
        "trainlabelPath = '/content/drive/My Drive/image_detection/image_yolov5/labels/train'\n",
        "valimagePath = '/content/drive/My Drive/image_detection/image_yolov5/images/train'\n",
        "vallabelPath = '/content/drive/My Drive/image_detection/image_yolov5/labels/validation'\n",
        "#cycle for train dir\n",
        "for x in range(countForTrain):\n",
        "\n",
        "    fileJpg = choice(imgs) # get name of random image from origin dir\n",
        "    fileXml = fileJpg[:-4] +'.txt' # get name of corresponding annotation file\n",
        "\n",
        "    #move both files into train dir\n",
        "    #shutil.move(os.path.join(crsPath, fileJpg), os.path.join(trainimagePath, fileJpg))\n",
        "    #shutil.move(os.path.join(crsPath, fileXml), os.path.join(trainlabelPath, fileXml))\n",
        "    shutil.copy(os.path.join(crsPath, fileJpg), os.path.join(trainimagePath, fileJpg))\n",
        "    shutil.copy(os.path.join(crsPath, fileXml), os.path.join(trainlabelPath, fileXml))\n",
        "\n",
        "\n",
        "    #remove files from arrays\n",
        "    imgs.remove(fileJpg)\n",
        "    xmls.remove(fileXml)\n",
        "\n",
        "\n",
        "\n",
        "#cycle for test dir\n",
        "for x in range(countForVal):\n",
        "\n",
        "    fileJpg = choice(imgs) # get name of random image from origin dir\n",
        "    fileXml = fileJpg[:-4] +'.txt' # get name of corresponding annotation file\n",
        "\n",
        "    #move both files into train dir\n",
        "    #shutil.move(os.path.join(crsPath, fileJpg), os.path.join(valimagePath, fileJpg))\n",
        "    #shutil.move(os.path.join(crsPath, fileXml), os.path.join(vallabelPath, fileXml))\n",
        "    shutil.copy(os.path.join(crsPath, fileJpg), os.path.join(valimagePath, fileJpg))\n",
        "    shutil.copy(os.path.join(crsPath, fileXml), os.path.join(vallabelPath, fileXml))\n",
        "\n",
        "    #remove files from arrays\n",
        "    imgs.remove(fileJpg)\n",
        "    xmls.remove(fileXml)\n",
        "\n",
        "#rest of files will be validation files, so rename origin dir to val dir\n",
        "#os.rename(crsPath, valPath)\n",
        "shutil.move(crsPath, valPath)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/My Drive/image_detection/image_yolov5/images/validation/ts'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yBH-shYl255f"
      },
      "source": [
        "Create dataset.yaml\n",
        "move the dataset’s dataset.yaml file to the /yolov5/data directory.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd '/content/drive/MyDrive/image_detection/yolov5'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9nUxK2X9UgYy",
        "outputId": "bfdaaa77-f8a6-4539-ce0b-11b229ba19bd"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/image_detection/yolov5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XDwn6gW9L21d",
        "outputId": "dbeb635d-2bdb-4abd-a3f1-6c444ac995b2"
      },
      "source": [
        "!python train.py --img 415 --batch 16 --epochs 30 --data dataset.yaml --weights yolov5s.pt --cache"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=dataset.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=30, batch_size=16, imgsz=415, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
            "YOLOv5 🚀 v7.0-203-g0897415 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=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=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, 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\n",
            "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
            "100% 755k/755k [00:00<00:00, 123MB/s]\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n",
            "100% 14.1M/14.1M [00:01<00:00, 12.1MB/s]\n",
            "\n",
            "Overriding model.yaml nc=80 with nc=4\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
            "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
            "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
            "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
            "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
            "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
            "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
            "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
            " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
            " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
            " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
            " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
            " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
            " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
            " 24      [17, 20, 23]  1     24273  models.yolo.Detect                      [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
            "Model summary: 214 layers, 7030417 parameters, 7030417 gradients, 16.0 GFLOPs\n",
            "\n",
            "Transferred 343/349 items from yolov5s.pt\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "WARNING ⚠️ --img-size 415 must be multiple of max stride 32, updating to 416\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\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[1mtrain: \u001b[0mScanning /content/drive/MyDrive/image_detection/image_yolov5/labels/train... 592 images, 148 backgrounds, 0 corrupt: 100% 740/740 [00:03<00:00, 197.30it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/drive/MyDrive/image_detection/image_yolov5/images/train/00340.jpg: 1 duplicate labels removed\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/drive/MyDrive/image_detection/image_yolov5/labels/train.cache\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.2GB ram): 100% 740/740 [00:14<00:00, 50.19it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/image_detection/image_yolov5/labels/validation/ts... 0 images, 741 backgrounds, 0 corrupt: 100% 741/741 [00:05<00:00, 137.51it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ No labels found in /content/drive/MyDrive/image_detection/image_yolov5/labels/validation/ts.cache. See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/drive/MyDrive/image_detection/image_yolov5/labels/validation/ts.cache\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.2GB ram): 100% 741/741 [00:23<00:00, 31.80it/s]\n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.24 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Plotting labels to runs/train/exp2/labels.jpg... \n",
            "Image sizes 416 train, 416 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/train/exp2\u001b[0m\n",
            "Starting training for 30 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       0/29      1.56G     0.1143     0.0104    0.03963         11        416: 100% 47/47 [00:13<00:00,  3.48it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00,  9.16it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       1/29      1.58G     0.1013    0.01019    0.03462         13        416: 100% 47/47 [00:07<00:00,  6.40it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:03<00:00,  7.53it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       2/29      1.58G    0.07491    0.01121    0.03199          4        416: 100% 47/47 [00:05<00:00,  7.97it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00,  9.87it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       3/29      1.58G    0.06813     0.0108    0.03067          8        416: 100% 47/47 [00:08<00:00,  5.54it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00, 10.75it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       4/29      1.58G    0.05983    0.01001    0.03135          8        416: 100% 47/47 [00:06<00:00,  7.16it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:03<00:00,  6.55it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       5/29      1.58G    0.05855   0.009369    0.03086          8        416: 100% 47/47 [00:05<00:00,  7.87it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00,  9.73it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       6/29      1.58G    0.05148   0.008402    0.02875         17        416: 100% 47/47 [00:08<00:00,  5.45it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00, 10.95it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       7/29      1.58G    0.04959   0.008163    0.02793         14        416: 100% 47/47 [00:06<00:00,  7.39it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:03<00:00,  7.01it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       8/29      1.58G    0.04968   0.007939    0.02619          8        416: 100% 47/47 [00:06<00:00,  7.78it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00, 10.26it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       9/29      1.58G    0.04755   0.007669    0.02442         14        416: 100% 47/47 [00:08<00:00,  5.40it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00, 10.97it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      10/29      1.58G    0.04523    0.00744    0.02213         13        416: 100% 47/47 [00:06<00:00,  7.44it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:03<00:00,  6.77it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      11/29      1.58G    0.04073   0.007066    0.01923          1        416: 100% 47/47 [00:05<00:00,  7.85it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00,  9.82it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      12/29      1.58G    0.04039   0.007233    0.01929          6        416: 100% 47/47 [00:09<00:00,  5.22it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00, 11.40it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      13/29      1.58G    0.03953   0.007159    0.01913         13        416: 100% 47/47 [00:06<00:00,  7.04it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:03<00:00,  7.58it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      14/29      1.58G    0.04033   0.007476    0.01892         13        416: 100% 47/47 [00:05<00:00,  7.89it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00,  9.86it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      15/29      1.58G    0.04067   0.006921    0.01434          8        416: 100% 47/47 [00:08<00:00,  5.50it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00, 11.35it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      16/29      1.58G    0.03798   0.006504    0.01458          4        416: 100% 47/47 [00:06<00:00,  7.53it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:03<00:00,  6.69it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      17/29      1.58G    0.03634    0.00627    0.01282          9        416: 100% 47/47 [00:05<00:00,  7.99it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00,  9.81it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      18/29      1.58G    0.03604   0.006797    0.01325         12        416: 100% 47/47 [00:08<00:00,  5.50it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00, 10.47it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      19/29      1.58G     0.0347   0.006489    0.01317          5        416: 100% 47/47 [00:06<00:00,  7.22it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:03<00:00,  6.91it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      20/29      1.58G    0.03457   0.006443    0.01363          2        416: 100% 47/47 [00:05<00:00,  7.91it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00,  9.92it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      21/29      1.58G    0.03263   0.006442    0.01226         13        416: 100% 47/47 [00:08<00:00,  5.47it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00, 11.24it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      22/29      1.58G    0.03177   0.006393    0.01167          9        416: 100% 47/47 [00:06<00:00,  7.68it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:03<00:00,  7.09it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      23/29      1.58G    0.02991   0.006159    0.00935          8        416: 100% 47/47 [00:05<00:00,  7.91it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00, 10.25it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      24/29      1.58G     0.0293   0.005894   0.009789          4        416: 100% 47/47 [00:08<00:00,  5.36it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00, 11.67it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      25/29      1.58G    0.02881   0.006149   0.009728         23        416: 100% 47/47 [00:05<00:00,  7.90it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:03<00:00,  7.32it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      26/29      1.58G    0.02851   0.006003   0.009319          4        416: 100% 47/47 [00:06<00:00,  7.49it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00,  9.76it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      27/29      1.58G     0.0263   0.006225   0.008747          3        416: 100% 47/47 [00:08<00:00,  5.37it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00, 11.71it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      28/29      1.58G    0.02314   0.005759   0.007075         10        416: 100% 47/47 [00:05<00:00,  7.97it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:03<00:00,  7.91it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      29/29      1.58G    0.02642   0.006081   0.008573         12        416: 100% 47/47 [00:07<00:00,  6.40it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:02<00:00, 11.36it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "\n",
            "30 epochs completed in 0.090 hours.\n",
            "Optimizer stripped from runs/train/exp2/weights/last.pt, 14.3MB\n",
            "Optimizer stripped from runs/train/exp2/weights/best.pt, 14.3MB\n",
            "\n",
            "Validating runs/train/exp2/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 24/24 [00:04<00:00,  5.76it/s]\n",
            "                   all        741          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in val set, can not compute metrics without labels\n",
            "Results saved to \u001b[1mruns/train/exp2\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YSveyyJH3elW"
      },
      "source": [
        "At the end of the training, two files should be saved in yolov5/runs/train/exp/weights: last.pt and best.pt. We’ll use best.pt.\n",
        "Using TensorBoard explore the metrics recorded during training,"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "u2_RVmmOL6v3",
        "outputId": "7f02fa67-57c6-4bd4-b7af-6d5f28e82b9c"
      },
      "source": [
        "%load_ext tensorboard\n",
        "%tensorboard --logdir runs"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "ERROR: Failed to launch TensorBoard (exited with 1).\n",
              "Contents of stderr:\n",
              "Traceback (most recent call last):\n",
              "  File \"/usr/local/bin/tensorboard\", line 5, in <module>\n",
              "    from tensorboard.main import run_main\n",
              "  File \"/usr/local/lib/python3.10/dist-packages/tensorboard/main.py\", line 43, in <module>\n",
              "    from tensorboard import default\n",
              "  File \"/usr/local/lib/python3.10/dist-packages/tensorboard/default.py\", line 36, in <module>\n",
              "    from tensorboard.plugins.audio import audio_plugin\n",
              "  File \"/usr/local/lib/python3.10/dist-packages/tensorboard/plugins/audio/audio_plugin.py\", line 25, in <module>\n",
              "    from tensorboard import plugin_util\n",
              "  File \"/usr/local/lib/python3.10/dist-packages/tensorboard/plugin_util.py\", line 23, in <module>\n",
              "    from tensorboard._vendor.bleach.sanitizer import Cleaner\n",
              "  File \"/usr/local/lib/python3.10/dist-packages/tensorboard/_vendor/bleach/__init__.py\", line 5, in <module>\n",
              "    from tensorboard._vendor.bleach.linkifier import (\n",
              "  File \"/usr/local/lib/python3.10/dist-packages/tensorboard/_vendor/bleach/linkifier.py\", line 4, in <module>\n",
              "    from tensorboard._vendor import html5lib\n",
              "  File \"/usr/local/lib/python3.10/dist-packages/tensorboard/_vendor/html5lib/__init__.py\", line 25, in <module>\n",
              "    from .html5parser import HTMLParser, parse, parseFragment\n",
              "  File \"/usr/local/lib/python3.10/dist-packages/tensorboard/_vendor/html5lib/html5parser.py\", line 8, in <module>\n",
              "    from . import _tokenizer\n",
              "  File \"/usr/local/lib/python3.10/dist-packages/tensorboard/_vendor/html5lib/_tokenizer.py\", line 16, in <module>\n",
              "    from ._trie import Trie\n",
              "  File \"/usr/local/lib/python3.10/dist-packages/tensorboard/_vendor/html5lib/_trie/__init__.py\", line 3, in <module>\n",
              "    from .py import Trie as PyTrie\n",
              "  File \"/usr/local/lib/python3.10/dist-packages/tensorboard/_vendor/html5lib/_trie/py.py\", line 6, in <module>\n",
              "    from ._base import Trie as ABCTrie\n",
              "  File \"/usr/local/lib/python3.10/dist-packages/tensorboard/_vendor/html5lib/_trie/_base.py\", line 3, in <module>\n",
              "    from collections import Mapping\n",
              "ImportError: cannot import name 'Mapping' from 'collections' (/usr/lib/python3.10/collections/__init__.py)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cb1vMxTuZoVd"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "8k_LHEVaZowA",
        "outputId": "32b9153e-1d9c-415f-9024-8d2af2347666"
      },
      "source": [
        "Image(filename='runs/train/exp/train_batch0.jpg', width=416)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-57-61251f0e75d1>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'runs/train/exp/train_batch0.jpg'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m416\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata)\u001b[0m\n\u001b[1;32m   1229\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretina\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mretina\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1230\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munconfined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munconfined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1231\u001b[0;31m         super(Image, self).__init__(data=data, url=url, filename=filename, \n\u001b[0m\u001b[1;32m   1232\u001b[0m                 metadata=metadata)\n\u001b[1;32m   1233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, metadata)\u001b[0m\n\u001b[1;32m    635\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 637\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\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    638\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mreload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1261\u001b[0m         \u001b[0;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1262\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1263\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\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   1264\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretina\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1265\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_retina_shape\u001b[0m\u001b[0;34m(\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/IPython/core/display.py\u001b[0m in \u001b[0;36mreload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    660\u001b[0m         \u001b[0;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 662\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_flags\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\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    663\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    664\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'runs/train/exp/train_batch0.jpg'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0t18icdHZ_9E"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9rCpw5sVaCWn"
      },
      "source": [
        "Image(filename='runs/train/exp/val_batch0_pred.jpg', width=416)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lj7eNnFR32rY"
      },
      "source": [
        "Implementing the detect.py script with the best.pt weights and image dimensions of 416x416 pixels. The results will be saved to runs/detect/exp. To display the results, run the following code:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jYAbfxVDL_ty",
        "outputId": "523f9d81-1e95-4a44-fc8a-6a5f9d434671"
      },
      "source": [
        " !python detect.py --source runs/train/exp/test --weights runs/train/exp/weights/best.pt --conf 0.25"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['runs/train/exp/weights/best.pt'], source=runs/train/exp/test, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
            "YOLOv5 🚀 v7.0-203-g0897415 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/MyDrive/image_detection/yolov5/detect.py\", line 262, in <module>\n",
            "    main(opt)\n",
            "  File \"/content/drive/MyDrive/image_detection/yolov5/detect.py\", line 257, in main\n",
            "    run(**vars(opt))\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\", line 115, in decorate_context\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/content/drive/MyDrive/image_detection/yolov5/detect.py\", line 99, in run\n",
            "    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)\n",
            "  File \"/content/drive/MyDrive/image_detection/yolov5/models/common.py\", line 344, in __init__\n",
            "    model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)\n",
            "  File \"/content/drive/MyDrive/image_detection/yolov5/models/experimental.py\", line 79, in attempt_load\n",
            "    ckpt = torch.load(attempt_download(w), map_location='cpu')  # load\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/serialization.py\", line 791, in load\n",
            "    with _open_file_like(f, 'rb') as opened_file:\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/serialization.py\", line 271, in _open_file_like\n",
            "    return _open_file(name_or_buffer, mode)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/serialization.py\", line 252, in __init__\n",
            "    super().__init__(open(name, mode))\n",
            "FileNotFoundError: [Errno 2] No such file or directory: 'runs/train/exp/weights/best.pt'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "AK09_yK0MG8n",
        "outputId": "5563c83e-5bef-46a3-bd0a-4b7815092b59"
      },
      "source": [
        "Image(filename='runs/detect/exp/00019.jpg', width=416)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-59-99266a5f66b8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'runs/detect/exp/00019.jpg'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m416\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata)\u001b[0m\n\u001b[1;32m   1229\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretina\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mretina\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1230\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munconfined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munconfined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1231\u001b[0;31m         super(Image, self).__init__(data=data, url=url, filename=filename, \n\u001b[0m\u001b[1;32m   1232\u001b[0m                 metadata=metadata)\n\u001b[1;32m   1233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, metadata)\u001b[0m\n\u001b[1;32m    635\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 637\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\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    638\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mreload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1261\u001b[0m         \u001b[0;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1262\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1263\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\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   1264\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretina\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1265\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_retina_shape\u001b[0m\u001b[0;34m(\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/IPython/core/display.py\u001b[0m in \u001b[0;36mreload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    660\u001b[0m         \u001b[0;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 662\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_flags\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\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    663\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    664\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'runs/detect/exp/00019.jpg'"
          ]
        }
      ]
    }
  ]
}