Google_Colab_fine_tuning.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "K8ALX-1rH5fh",
        "outputId": "1f4a65b3-cbe9-493d-cf71-d5088833a584",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'yolov5'...\n",
            "remote: Enumerating objects: 16605, done.\u001b[K\n",
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            "remote: Total 16605 (delta 50), reused 94 (delta 45), pack-reused 16459\u001b[K\n",
            "Receiving objects: 100% (16605/16605), 15.34 MiB | 17.13 MiB/s, done.\n",
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            "/content/yolov5\n",
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            "Installing collected packages: smmap, requests, pillow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, gitdb, nvidia-cusolver-cu12, gitpython, ultralytics-thop, thop, ultralytics\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "  Attempting uninstall: pillow\n",
            "    Found existing installation: Pillow 9.4.0\n",
            "    Uninstalling Pillow-9.4.0:\n",
            "      Successfully uninstalled Pillow-9.4.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "imageio 2.31.6 requires pillow<10.1.0,>=8.3.2, but you have pillow 10.3.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed gitdb-4.0.11 gitpython-3.1.43 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 pillow-10.3.0 requests-2.32.3 smmap-5.0.1 thop-0.1.1.post2209072238 ultralytics-8.2.32 ultralytics-thop-0.2.8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              },
              "id": "7048d69d54824ded8731069c9c59f9e4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting pillow!=8.3.*,>=5.3.0 (from torchvision)\n",
            "  Downloading Pillow-10.0.1-cp310-cp310-manylinux_2_28_x86_64.whl (3.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations) (3.5.0)\n",
            "Installing collected packages: pillow\n",
            "  Attempting uninstall: pillow\n",
            "    Found existing installation: pillow 10.3.0\n",
            "    Uninstalling pillow-10.3.0:\n",
            "      Successfully uninstalled pillow-10.3.0\n",
            "Successfully installed pillow-10.0.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              },
              "id": "b0fee06d357a4c59b592b93cc52ebb9c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Clone YOLOv5 repository and navigate into it\n",
        "!git clone https://github.com/ultralytics/yolov5\n",
        "%cd yolov5\n",
        "\n",
        "# Install dependencies\n",
        "!pip install -r requirements.txt\n",
        "!pip install albumentations torch torchvision\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure the data.yaml file is correctly configured\n",
        "dataset_location = '/content/football-players-detection-1'\n",
        "!cat {dataset_location}/data.yaml\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3xfqo7mqH-wX",
        "outputId": "f43c8b00-e4ae-426d-bfc3-53b347a3dffc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cat: /content/football-players-detection-1/data.yaml: No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ultralytics\n",
        "!pip install roboflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Bi3IxRscImll",
        "outputId": "bd0106b5-aaa3-4f24-a7bb-53080d3c80f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.2.32-py3-none-any.whl (780 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m780.6/780.6 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (6.0.1)\n",
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            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.11.4)\n",
            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.3.0+cu121)\n",
            "Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.18.0+cu121)\n",
            "Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.66.4)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ultralytics) (5.9.5)\n",
            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.0.0)\n",
            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.0.3)\n",
            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.13.1)\n",
            "Collecting ultralytics-thop>=0.2.5 (from ultralytics)\n",
            "  Downloading ultralytics_thop-0.2.8-py3-none-any.whl (25 kB)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.53.0)\n",
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            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (24.1)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2024.1)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.7)\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2.3.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.8.0->ultralytics)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
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            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)\n",
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, ultralytics-thop, ultralytics\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 ultralytics-8.2.32 ultralytics-thop-0.2.8\n",
            "Collecting roboflow\n",
            "  Downloading roboflow-1.1.32-py3-none-any.whl (75 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting certifi==2023.7.22 (from roboflow)\n",
            "  Downloading certifi-2023.7.22-py3-none-any.whl (158 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.3/158.3 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting chardet==4.0.0 (from roboflow)\n",
            "  Downloading chardet-4.0.0-py2.py3-none-any.whl (178 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m178.7/178.7 kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting cycler==0.10.0 (from roboflow)\n",
            "  Downloading cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
            "Collecting idna==2.10 (from roboflow)\n",
            "  Downloading idna-2.10-py2.py3-none-any.whl (58 kB)\n",
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            "Collecting opencv-python-headless==4.8.0.74 (from roboflow)\n",
            "  Downloading opencv_python_headless-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (49.1 MB)\n",
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            "Collecting python-dotenv (from roboflow)\n",
            "  Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n",
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            "Collecting requests-toolbelt (from roboflow)\n",
            "  Downloading requests_toolbelt-1.0.0-py2.py3-none-any.whl (54 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting python-magic (from roboflow)\n",
            "  Downloading python_magic-0.4.27-py2.py3-none-any.whl (13 kB)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (1.2.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (4.53.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (24.1)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (3.1.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->roboflow) (3.3.2)\n",
            "\u001b[33mWARNING: The candidate selected for download or install is a yanked version: 'opencv-python-headless' candidate (version 4.8.0.74 at https://files.pythonhosted.org/packages/76/02/f128517f3ade4bb5f71e2afd8461dba70e3f466ce745fa1fd1fade9ad1b7/opencv_python_headless-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (from https://pypi.org/simple/opencv-python-headless/) (requires-python:>=3.6))\n",
            "Reason for being yanked: deprecated, use 4.8.0.76\u001b[0m\u001b[33m\n",
            "\u001b[0mInstalling collected packages: python-magic, python-dotenv, opencv-python-headless, idna, cycler, chardet, certifi, requests-toolbelt, roboflow\n",
            "  Attempting uninstall: opencv-python-headless\n",
            "    Found existing installation: opencv-python-headless 4.10.0.82\n",
            "    Uninstalling opencv-python-headless-4.10.0.82:\n",
            "      Successfully uninstalled opencv-python-headless-4.10.0.82\n",
            "  Attempting uninstall: idna\n",
            "    Found existing installation: idna 3.7\n",
            "    Uninstalling idna-3.7:\n",
            "      Successfully uninstalled idna-3.7\n",
            "  Attempting uninstall: cycler\n",
            "    Found existing installation: cycler 0.12.1\n",
            "    Uninstalling cycler-0.12.1:\n",
            "      Successfully uninstalled cycler-0.12.1\n",
            "  Attempting uninstall: chardet\n",
            "    Found existing installation: chardet 5.2.0\n",
            "    Uninstalling chardet-5.2.0:\n",
            "      Successfully uninstalled chardet-5.2.0\n",
            "  Attempting uninstall: certifi\n",
            "    Found existing installation: certifi 2024.6.2\n",
            "    Uninstalling certifi-2024.6.2:\n",
            "      Successfully uninstalled certifi-2024.6.2\n",
            "Successfully installed certifi-2023.7.22 chardet-4.0.0 cycler-0.10.0 idna-2.10 opencv-python-headless-4.8.0.74 python-dotenv-1.0.1 python-magic-0.4.27 requests-toolbelt-1.0.0 roboflow-1.1.32\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "certifi",
                  "cycler"
                ]
              },
              "id": "f7c04ae10f814266a63fee98431a4cf5"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"EvTR3mXlUyMGP21qjaTZ\")\n",
        "project = rf.workspace(\"roboflow-jvuqo\").project(\"football-players-detection-3zvbc\")\n",
        "version = project.version(1)\n",
        "dataset = version.download(\"yolov5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0YjYnYIIrzJ",
        "outputId": "420ca34e-dbc2-49bf-dc7d-ebf9250f64ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading Dataset Version Zip in football-players-detection-1 to yolov5pytorch:: 100%|██████████| 148663/148663 [00:05<00:00, 28721.77it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Extracting Dataset Version Zip to football-players-detection-1 in yolov5pytorch:: 100%|██████████| 1338/1338 [00:00<00:00, 2511.07it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.location"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "v4Xj7d95IzvM",
        "outputId": "f2736723-e451-4941-aa60-73d966675f12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/football-players-detection-1'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "\n",
        "shutil.move('football-players-detection-1/train',\n",
        "            'football-players-detection-1/football-players-detection-1/train'\n",
        "            )\n",
        "shutil.move('football-players-detection-1/test',\n",
        "            'football-players-detection-1/football-players-detection-1/test'\n",
        "            )\n",
        "shutil.move('football-players-detection-1/valid',\n",
        "            'football-players-detection-1/football-players-detection-1/valid'\n",
        "            )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "-2AZFtVgI4P7",
        "outputId": "16c5f999-eec8-42ef-ff0b-1f21cf033737"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'football-players-detection-1/football-players-detection-1/valid'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo task=detect mode=train model=yolov5xu.pt data={dataset.location}/data.yaml epochs=100 imgsz=640"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddS0MzgfI_Y8",
        "outputId": "87740a09-74b6-4813-a297-e2800659d016"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5xu.pt to 'yolov5xu.pt'...\n",
            "100% 186M/186M [00:00<00:00, 266MB/s]\n",
            "Ultralytics YOLOv8.2.32 🚀 Python-3.10.12 torch-2.3.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov5xu.pt, data=/content/football-players-detection-1/data.yaml, epochs=100, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, 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, 38.9MB/s]\n",
            "Overriding model.yaml nc=80 with nc=4\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      8800  ultralytics.nn.modules.conv.Conv             [3, 80, 6, 2, 2]              \n",
            "  1                  -1  1    115520  ultralytics.nn.modules.conv.Conv             [80, 160, 3, 2]               \n",
            "  2                  -1  4    309120  ultralytics.nn.modules.block.C3              [160, 160, 4]                 \n",
            "  3                  -1  1    461440  ultralytics.nn.modules.conv.Conv             [160, 320, 3, 2]              \n",
            "  4                  -1  8   2259200  ultralytics.nn.modules.block.C3              [320, 320, 8]                 \n",
            "  5                  -1  1   1844480  ultralytics.nn.modules.conv.Conv             [320, 640, 3, 2]              \n",
            "  6                  -1 12  13125120  ultralytics.nn.modules.block.C3              [640, 640, 12]                \n",
            "  7                  -1  1   7375360  ultralytics.nn.modules.conv.Conv             [640, 1280, 3, 2]             \n",
            "  8                  -1  4  19676160  ultralytics.nn.modules.block.C3              [1280, 1280, 4]               \n",
            "  9                  -1  1   4099840  ultralytics.nn.modules.block.SPPF            [1280, 1280, 5]               \n",
            " 10                  -1  1    820480  ultralytics.nn.modules.conv.Conv             [1280, 640, 1, 1]             \n",
            " 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 13                  -1  4   5332480  ultralytics.nn.modules.block.C3              [1280, 640, 4, False]         \n",
            " 14                  -1  1    205440  ultralytics.nn.modules.conv.Conv             [640, 320, 1, 1]              \n",
            " 15                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 16             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 17                  -1  4   1335040  ultralytics.nn.modules.block.C3              [640, 320, 4, False]          \n",
            " 18                  -1  1    922240  ultralytics.nn.modules.conv.Conv             [320, 320, 3, 2]              \n",
            " 19            [-1, 14]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 20                  -1  4   4922880  ultralytics.nn.modules.block.C3              [640, 640, 4, False]          \n",
            " 21                  -1  1   3687680  ultralytics.nn.modules.conv.Conv             [640, 640, 3, 2]              \n",
            " 22            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 23                  -1  4  19676160  ultralytics.nn.modules.block.C3              [1280, 1280, 4, False]        \n",
            " 24        [17, 20, 23]  1  11025820  ultralytics.nn.modules.head.Detect           [4, [320, 640, 1280]]         \n",
            "YOLOv5x summary: 493 layers, 97203260 parameters, 97203244 gradients, 246.9 GFLOPs\n",
            "\n",
            "Transferred 817/823 items from pretrained weights\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
            "Freezing layer 'model.24.dfl.conv.weight'\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt to 'yolov8n.pt'...\n",
            "100% 6.23M/6.23M [00:00<00:00, 180MB/s]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
            "  return F.conv2d(input, weight, bias, self.stride,\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/football-players-detection-1/football-players-detection-1/train/labels... 612 images, 0 backgrounds, 0 corrupt: 100% 612/612 [00:00<00:00, 1768.04it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/football-players-detection-1/football-players-detection-1/train/labels.cache\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",
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/football-players-detection-1/football-players-detection-1/valid/labels... 38 images, 0 backgrounds, 0 corrupt: 100% 38/38 [00:00<00:00, 1036.71it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/football-players-detection-1/football-players-detection-1/valid/labels.cache\n",
            "Plotting labels to runs/detect/train/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.00125, momentum=0.9) with parameter groups 135 weight(decay=0.0), 142 weight(decay=0.0005), 141 bias(decay=0.0)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\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 100 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "  0% 0/39 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:744: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
            "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
            "      1/100      15.4G      1.444      4.342     0.8766        609        640:  18% 7/39 [00:16<01:15,  2.36s/it]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/bin/yolo\", line 8, in <module>\n",
            "    sys.exit(entrypoint())\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/cfg/__init__.py\", line 591, in entrypoint\n",
            "    getattr(model, mode)(**overrides)  # default args from model\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/engine/model.py\", line 674, in train\n",
            "    self.trainer.train()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/engine/trainer.py\", line 199, in train\n",
            "    self._do_train(world_size)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/engine/trainer.py\", line 376, in _do_train\n",
            "    self.loss, self.loss_items = self.model(batch)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
            "    return self._call_impl(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
            "    return forward_call(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/nn/tasks.py\", line 88, in forward\n",
            "    return self.loss(x, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/nn/tasks.py\", line 267, in loss\n",
            "    return self.criterion(preds, batch)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/utils/loss.py\", line 222, in __call__\n",
            "    _, target_bboxes, target_scores, fg_mask, _ = self.assigner(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1532, in _wrapped_call_impl\n",
            "    return self._call_impl(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\", line 1541, in _call_impl\n",
            "    return forward_call(*args, **kwargs)\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 \"/usr/local/lib/python3.10/dist-packages/ultralytics/utils/tal.py\", line 72, in forward\n",
            "    mask_pos, align_metric, overlaps = self.get_pos_mask(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/utils/tal.py\", line 92, in get_pos_mask\n",
            "    mask_in_gts = self.select_candidates_in_gts(anc_points, gt_bboxes)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ultralytics/utils/tal.py\", line 227, in select_candidates_in_gts\n",
            "    bbox_deltas = torch.cat((xy_centers[None] - lt, rb - xy_centers[None]), dim=2).view(bs, n_boxes, n_anchors, -1)\n",
            "torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 122.00 MiB. GPU \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Restart the runtime\n",
        "from IPython.display import clear_output\n",
        "clear_output(wait=True)\n",
        "%cd yolov5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBzEeLJfIIx_",
        "outputId": "46300662-ba78-43e0-a40a-3db17c1f06b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python train_spawn.py --img 640 --batch 16 --epochs 100 --data {dataset_location}/data.yaml --cfg yolov5x.yaml --weights yolov5x.pt --name yolov5x_results --workers 0 --device 0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UAoFy_M_IO2X",
        "outputId": "cd1a7160-0409-4b52-d829-c34451f76e31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024-06-14 19:09:40.395783: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-06-14 19:09:40.395866: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-06-14 19:09:40.548225: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cF2LB9PtJoyu",
        "outputId": "2351b9eb-ca3b-424f-9596-ff4d45584eb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DZ3zxaKEKI9R",
        "outputId": "774cb25f-6253-47bf-820c-acb53f4a7798"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "football-players-detection-1  runs  sample_data  train_spawn.py  yolov5  yolov5xu.pt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo task=detect mode=train model=yolov5xu.pt data=/content/football-players-detection-1/data.yaml epochs=100 imgsz=640 batch=8"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2gL5kkYKMRR",
        "outputId": "178df939-22ef-4047-8661-8a986d969117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics YOLOv8.2.32 🚀 Python-3.10.12 torch-2.3.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov5xu.pt, data=/content/football-players-detection-1/data.yaml, epochs=100, time=None, patience=100, batch=8, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train3, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train3\n",
            "Overriding model.yaml nc=80 with nc=4\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      8800  ultralytics.nn.modules.conv.Conv             [3, 80, 6, 2, 2]              \n",
            "  1                  -1  1    115520  ultralytics.nn.modules.conv.Conv             [80, 160, 3, 2]               \n",
            "  2                  -1  4    309120  ultralytics.nn.modules.block.C3              [160, 160, 4]                 \n",
            "  3                  -1  1    461440  ultralytics.nn.modules.conv.Conv             [160, 320, 3, 2]              \n",
            "  4                  -1  8   2259200  ultralytics.nn.modules.block.C3              [320, 320, 8]                 \n",
            "  5                  -1  1   1844480  ultralytics.nn.modules.conv.Conv             [320, 640, 3, 2]              \n",
            "  6                  -1 12  13125120  ultralytics.nn.modules.block.C3              [640, 640, 12]                \n",
            "  7                  -1  1   7375360  ultralytics.nn.modules.conv.Conv             [640, 1280, 3, 2]             \n",
            "  8                  -1  4  19676160  ultralytics.nn.modules.block.C3              [1280, 1280, 4]               \n",
            "  9                  -1  1   4099840  ultralytics.nn.modules.block.SPPF            [1280, 1280, 5]               \n",
            " 10                  -1  1    820480  ultralytics.nn.modules.conv.Conv             [1280, 640, 1, 1]             \n",
            " 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 13                  -1  4   5332480  ultralytics.nn.modules.block.C3              [1280, 640, 4, False]         \n",
            " 14                  -1  1    205440  ultralytics.nn.modules.conv.Conv             [640, 320, 1, 1]              \n",
            " 15                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 16             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 17                  -1  4   1335040  ultralytics.nn.modules.block.C3              [640, 320, 4, False]          \n",
            " 18                  -1  1    922240  ultralytics.nn.modules.conv.Conv             [320, 320, 3, 2]              \n",
            " 19            [-1, 14]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 20                  -1  4   4922880  ultralytics.nn.modules.block.C3              [640, 640, 4, False]          \n",
            " 21                  -1  1   3687680  ultralytics.nn.modules.conv.Conv             [640, 640, 3, 2]              \n",
            " 22            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 23                  -1  4  19676160  ultralytics.nn.modules.block.C3              [1280, 1280, 4, False]        \n",
            " 24        [17, 20, 23]  1  11025820  ultralytics.nn.modules.head.Detect           [4, [320, 640, 1280]]         \n",
            "YOLOv5x summary: 493 layers, 97203260 parameters, 97203244 gradients, 246.9 GFLOPs\n",
            "\n",
            "Transferred 817/823 items from pretrained weights\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train3', view at http://localhost:6006/\n",
            "Freezing layer 'model.24.dfl.conv.weight'\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
            "  return F.conv2d(input, weight, bias, self.stride,\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/football-players-detection-1/football-players-detection-1/train/labels.cache... 612 images, 0 backgrounds, 0 corrupt: 100% 612/612 [00:00<?, ?it/s]\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",
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/football-players-detection-1/football-players-detection-1/valid/labels.cache... 38 images, 0 backgrounds, 0 corrupt: 100% 38/38 [00:00<?, ?it/s]\n",
            "Plotting labels to runs/detect/train3/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.00125, momentum=0.9) with parameter groups 135 weight(decay=0.0), 142 weight(decay=0.0005), 141 bias(decay=0.0)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/train3\u001b[0m\n",
            "Starting training for 100 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "  0% 0/77 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:744: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
            "  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n",
            "      1/100      9.75G      1.201      1.174     0.8204        186        640: 100% 77/77 [00:59<00:00,  1.30it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:02<00:00,  1.15it/s]\n",
            "                   all         38        905      0.768      0.438      0.489      0.285\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      2/100      9.15G       1.21     0.7167     0.8163        239        640: 100% 77/77 [00:50<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.62it/s]\n",
            "                   all         38        905      0.587      0.555      0.542      0.313\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      3/100      9.19G      1.298     0.8152     0.8241        193        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.27it/s]\n",
            "                   all         38        905      0.712      0.375      0.446      0.258\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      4/100      9.17G      1.234     0.7178     0.8206         72        640: 100% 77/77 [00:51<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.37it/s]\n",
            "                   all         38        905      0.796      0.675      0.708      0.442\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      5/100      9.19G      1.201     0.6681     0.8144        182        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.81it/s]\n",
            "                   all         38        905      0.874      0.592      0.704       0.45\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      6/100      9.14G       1.13     0.5793     0.8058        163        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.75it/s]\n",
            "                   all         38        905       0.74      0.672      0.726      0.467\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      7/100      8.74G      1.085     0.5507     0.8029        108        640: 100% 77/77 [00:51<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.66it/s]\n",
            "                   all         38        905      0.902      0.675      0.763      0.475\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      8/100      9.16G      1.083     0.5471     0.8024        235        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.84it/s]\n",
            "                   all         38        905      0.812      0.684      0.735      0.476\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      9/100      9.14G      1.079     0.5526     0.8013        148        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.82it/s]\n",
            "                   all         38        905      0.719      0.635      0.644      0.368\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     10/100      9.11G      1.022     0.5128     0.7974        196        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.47it/s]\n",
            "                   all         38        905      0.849      0.626      0.729      0.477\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     11/100      9.11G     0.9835     0.4955     0.7966        142        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.56it/s]\n",
            "                   all         38        905      0.931      0.703      0.794      0.524\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     12/100      9.16G      1.011     0.5038     0.7971        131        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.75it/s]\n",
            "                   all         38        905      0.819      0.688      0.768       0.51\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     13/100      9.15G      0.982     0.4812     0.7945        152        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.78it/s]\n",
            "                   all         38        905      0.817      0.637      0.712      0.456\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     14/100      9.09G     0.9994     0.4828     0.7936        114        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.45it/s]\n",
            "                   all         38        905      0.865      0.713      0.794      0.541\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     15/100      8.76G      1.013     0.4923     0.7943        122        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.71it/s]\n",
            "                   all         38        905      0.837      0.733      0.761      0.505\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     16/100      9.16G      1.086     0.5515     0.7985        225        640: 100% 77/77 [00:50<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.83it/s]\n",
            "                   all         38        905      0.825      0.712      0.764      0.452\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     17/100      9.11G     0.9985     0.4967     0.7938        143        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.61it/s]\n",
            "                   all         38        905      0.871      0.754      0.807      0.528\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     18/100      9.16G     0.9682     0.4675     0.7921        207        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.68it/s]\n",
            "                   all         38        905       0.92       0.74        0.8      0.494\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     19/100      9.17G     0.9466      0.464     0.7936        131        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.74it/s]\n",
            "                   all         38        905      0.866        0.7      0.743      0.495\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     20/100      9.14G     0.9259     0.4641     0.7925        127        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.16it/s]\n",
            "                   all         38        905      0.916      0.734      0.781       0.52\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     21/100      9.14G     0.9645      0.473     0.7921        217        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.49it/s]\n",
            "                   all         38        905      0.893      0.647      0.755      0.497\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     22/100      9.17G     0.9305      0.452     0.7897        230        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.53it/s]\n",
            "                   all         38        905      0.868      0.758      0.795      0.539\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     23/100      8.73G     0.9267     0.4475     0.7899        157        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.86it/s]\n",
            "                   all         38        905      0.835      0.769      0.804      0.544\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     24/100       9.1G     0.8851     0.4336     0.7891        136        640: 100% 77/77 [00:51<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.87it/s]\n",
            "                   all         38        905      0.871      0.742      0.786      0.528\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     25/100      9.17G     0.8918     0.4358     0.7874        262        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.45it/s]\n",
            "                   all         38        905      0.864      0.725        0.8      0.549\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     26/100      9.16G     0.9147     0.4485     0.7908        166        640: 100% 77/77 [00:50<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.88it/s]\n",
            "                   all         38        905      0.896      0.726      0.798      0.533\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     27/100      9.16G     0.9325     0.4587     0.7886        111        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.61it/s]\n",
            "                   all         38        905      0.851      0.736      0.774      0.499\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     28/100      9.19G      0.895     0.4356     0.7892        228        640: 100% 77/77 [00:50<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.82it/s]\n",
            "                   all         38        905      0.933      0.714      0.818      0.541\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     29/100      9.14G     0.8851     0.4279     0.7877        201        640: 100% 77/77 [00:50<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.66it/s]\n",
            "                   all         38        905      0.874        0.7      0.785      0.532\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     30/100      9.11G     0.9046     0.4354     0.7876        136        640: 100% 77/77 [00:51<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.69it/s]\n",
            "                   all         38        905      0.888      0.759      0.798      0.544\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     31/100      8.77G     0.9034     0.4368      0.788        102        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.69it/s]\n",
            "                   all         38        905      0.919      0.745      0.799      0.556\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     32/100      9.12G     0.9111     0.4372     0.7889        131        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.02it/s]\n",
            "                   all         38        905      0.884      0.697      0.791      0.549\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     33/100      9.12G     0.8943      0.421     0.7883        241        640: 100% 77/77 [00:51<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.73it/s]\n",
            "                   all         38        905      0.884      0.749      0.796      0.543\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     34/100      9.08G       0.87     0.4053     0.7879        124        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.90it/s]\n",
            "                   all         38        905      0.866      0.761      0.812       0.54\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     35/100      9.15G     0.8519     0.4043     0.7852        200        640: 100% 77/77 [00:51<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.78it/s]\n",
            "                   all         38        905      0.846      0.739       0.79      0.533\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     36/100      9.14G     0.8516     0.4077     0.7859        115        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.73it/s]\n",
            "                   all         38        905      0.894      0.708      0.774      0.519\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     37/100      9.15G     0.8465     0.4101     0.7858        131        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.49it/s]\n",
            "                   all         38        905      0.818      0.732      0.776      0.546\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     38/100      9.16G     0.8648     0.4149     0.7864        106        640: 100% 77/77 [00:51<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.91it/s]\n",
            "                   all         38        905       0.88      0.742      0.804      0.546\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     39/100      8.74G     0.8501     0.4045     0.7859        122        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.34it/s]\n",
            "                   all         38        905      0.934      0.734      0.818      0.549\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     40/100      9.14G     0.8471      0.402     0.7843        103        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.49it/s]\n",
            "                   all         38        905      0.874      0.766      0.812      0.569\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     41/100      9.13G     0.8489     0.4046     0.7843        193        640: 100% 77/77 [00:50<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.40it/s]\n",
            "                   all         38        905      0.872      0.755      0.809       0.57\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     42/100      9.14G     0.8525     0.4037     0.7839        213        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.81it/s]\n",
            "                   all         38        905      0.934      0.705      0.812      0.564\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     43/100      9.07G     0.8515     0.3988     0.7831        108        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.31it/s]\n",
            "                   all         38        905      0.941      0.752      0.822       0.57\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     44/100      9.15G     0.8685     0.4076     0.7833        120        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.80it/s]\n",
            "                   all         38        905      0.935      0.737      0.829      0.562\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     45/100      9.16G     0.8585     0.4035     0.7844        282        640: 100% 77/77 [00:50<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.40it/s]\n",
            "                   all         38        905      0.932      0.762      0.828      0.574\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     46/100      9.15G     0.8137     0.3864     0.7833        191        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.73it/s]\n",
            "                   all         38        905      0.868      0.746      0.807      0.549\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     47/100      8.76G     0.8158     0.3809     0.7811        205        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.25it/s]\n",
            "                   all         38        905      0.921      0.695       0.81      0.569\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     48/100      9.12G     0.8019     0.3766     0.7827        109        640: 100% 77/77 [00:51<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.62it/s]\n",
            "                   all         38        905      0.881      0.747      0.824      0.566\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     49/100      9.14G     0.8187     0.3826     0.7806        164        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.99it/s]\n",
            "                   all         38        905      0.942      0.739      0.824      0.571\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     50/100       9.1G     0.8118     0.3786      0.783        138        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.71it/s]\n",
            "                   all         38        905      0.896      0.766      0.829      0.577\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     51/100      9.11G     0.7885     0.3696     0.7817        180        640: 100% 77/77 [00:50<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.84it/s]\n",
            "                   all         38        905       0.94      0.725      0.825      0.563\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     52/100      9.14G     0.8008     0.3803     0.7811         94        640: 100% 77/77 [00:49<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.27it/s]\n",
            "                   all         38        905      0.915       0.75      0.825      0.565\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     53/100      9.16G     0.7964     0.3735     0.7825        183        640: 100% 77/77 [00:50<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.44it/s]\n",
            "                   all         38        905      0.926      0.747      0.827      0.566\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     54/100      9.19G     0.8177     0.3767     0.7831        245        640: 100% 77/77 [00:50<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.78it/s]\n",
            "                   all         38        905      0.907      0.754      0.819      0.558\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     55/100      8.73G     0.7757     0.3626     0.7793         80        640: 100% 77/77 [00:51<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.61it/s]\n",
            "                   all         38        905      0.945      0.739      0.836      0.569\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     56/100      9.14G     0.7943     0.3733     0.7819        221        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.56it/s]\n",
            "                   all         38        905       0.95      0.726      0.822      0.565\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     57/100      9.18G     0.7749     0.3654      0.781        196        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.30it/s]\n",
            "                   all         38        905      0.882      0.764       0.82      0.578\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     58/100      9.17G     0.7744     0.3667     0.7791        185        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.48it/s]\n",
            "                   all         38        905      0.914      0.735      0.818      0.559\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     59/100      9.13G     0.7811     0.3673     0.7801         93        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.86it/s]\n",
            "                   all         38        905      0.887      0.755      0.815      0.566\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     60/100      9.19G     0.7874     0.3725     0.7796        160        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.81it/s]\n",
            "                   all         38        905       0.94       0.73      0.833       0.57\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     61/100      9.11G     0.7824     0.3707     0.7785        116        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.59it/s]\n",
            "                   all         38        905      0.883      0.769      0.832      0.585\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     62/100      9.14G     0.7548     0.3548     0.7776        140        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.75it/s]\n",
            "                   all         38        905      0.889      0.768      0.832      0.564\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     63/100      8.79G     0.7604     0.3693     0.7818        186        640: 100% 77/77 [00:51<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.85it/s]\n",
            "                   all         38        905       0.91      0.764      0.818      0.561\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     64/100      9.12G     0.7751     0.3658     0.7809        267        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.98it/s]\n",
            "                   all         38        905      0.933      0.755       0.84      0.575\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     65/100      9.19G     0.7433     0.3533     0.7766        159        640: 100% 77/77 [00:51<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.79it/s]\n",
            "                   all         38        905      0.946      0.777      0.833      0.591\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     66/100      9.11G     0.7615     0.3576     0.7772        158        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.46it/s]\n",
            "                   all         38        905      0.918      0.779      0.841      0.601\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     67/100      9.13G     0.7511     0.3549     0.7782        226        640: 100% 77/77 [00:51<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.63it/s]\n",
            "                   all         38        905      0.924       0.77       0.84        0.6\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     68/100      9.15G     0.7358     0.3461     0.7805        199        640: 100% 77/77 [00:49<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.63it/s]\n",
            "                   all         38        905      0.938       0.78      0.836      0.589\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     69/100      9.16G     0.7465     0.3496     0.7778        136        640: 100% 77/77 [00:50<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  4.01it/s]\n",
            "                   all         38        905      0.905      0.779      0.837      0.585\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     70/100      9.18G      0.781     0.3607     0.7781        168        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.78it/s]\n",
            "                   all         38        905      0.893      0.746      0.821      0.589\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     71/100      8.73G     0.7441     0.3471     0.7753        160        640: 100% 77/77 [00:51<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.41it/s]\n",
            "                   all         38        905      0.921      0.784      0.843      0.593\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     72/100      9.18G     0.7217     0.3403     0.7762        111        640: 100% 77/77 [00:50<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.94it/s]\n",
            "                   all         38        905      0.916      0.768      0.846      0.593\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     73/100      9.13G     0.7269     0.3404     0.7773        199        640: 100% 77/77 [00:50<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.68it/s]\n",
            "                   all         38        905      0.928      0.758      0.844      0.571\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     74/100      9.09G     0.7414     0.3524     0.7768        131        640: 100% 77/77 [00:50<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.44it/s]\n",
            "                   all         38        905      0.898      0.772      0.825      0.561\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     75/100      9.17G     0.7291     0.3465     0.7757        149        640: 100% 77/77 [00:50<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.92it/s]\n",
            "                   all         38        905      0.897      0.777      0.829      0.579\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     76/100      9.17G     0.7452     0.3469     0.7772        128        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.53it/s]\n",
            "                   all         38        905      0.949       0.76      0.825      0.576\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     77/100      9.11G     0.7168     0.3333     0.7754        168        640: 100% 77/77 [00:51<00:00,  1.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.51it/s]\n",
            "                   all         38        905      0.912      0.762      0.835      0.586\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     78/100      9.17G     0.6963     0.3287     0.7768        196        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.62it/s]\n",
            "                   all         38        905       0.93      0.759      0.846      0.592\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     79/100      8.78G     0.7167     0.3361     0.7742        180        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.41it/s]\n",
            "                   all         38        905      0.952      0.757      0.846      0.574\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     80/100      9.11G     0.7036     0.3345     0.7754         95        640: 100% 77/77 [00:51<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.61it/s]\n",
            "                   all         38        905      0.902      0.762      0.852      0.583\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     81/100      9.17G      0.708      0.333     0.7753        106        640: 100% 77/77 [00:51<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.09it/s]\n",
            "                   all         38        905      0.893       0.75      0.838      0.586\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     82/100      9.18G     0.6945      0.326     0.7755        171        640: 100% 77/77 [00:51<00:00,  1.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.41it/s]\n",
            "                   all         38        905      0.897      0.773      0.843      0.601\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     83/100      9.14G     0.6831     0.3237     0.7728        168        640: 100% 77/77 [00:50<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.79it/s]\n",
            "                   all         38        905      0.865      0.775      0.832      0.585\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     84/100      9.11G     0.6964     0.3267     0.7733        191        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.55it/s]\n",
            "                   all         38        905      0.871      0.771      0.834      0.586\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     85/100       9.1G      0.703     0.3276     0.7755        210        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  4.08it/s]\n",
            "                   all         38        905       0.91      0.749      0.842      0.591\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     86/100      9.12G     0.6951      0.324     0.7731        161        640: 100% 77/77 [00:51<00:00,  1.49it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.49it/s]\n",
            "                   all         38        905      0.918      0.764      0.842      0.598\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     87/100      8.77G     0.6587      0.315     0.7743        125        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.68it/s]\n",
            "                   all         38        905      0.914      0.759       0.84      0.594\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     88/100      9.14G      0.676     0.3168     0.7735        291        640: 100% 77/77 [00:50<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.67it/s]\n",
            "                   all         38        905      0.952      0.748      0.843      0.587\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     89/100      9.12G     0.6698     0.3168     0.7735        179        640: 100% 77/77 [00:51<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.42it/s]\n",
            "                   all         38        905      0.922       0.74       0.83      0.595\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     90/100      9.15G       0.67     0.3146     0.7722         78        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.46it/s]\n",
            "                   all         38        905      0.943      0.746      0.835      0.588\n",
            "Closing dataloader mosaic\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",
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     91/100         9G     0.6233     0.3075     0.7745         92        640: 100% 77/77 [00:55<00:00,  1.38it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.82it/s]\n",
            "                   all         38        905      0.929       0.74      0.834      0.593\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     92/100      9.01G     0.6175     0.3027      0.774         92        640: 100% 77/77 [00:49<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.60it/s]\n",
            "                   all         38        905      0.926      0.769      0.836      0.587\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     93/100      8.99G     0.6128     0.2998     0.7746         94        640: 100% 77/77 [00:50<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.75it/s]\n",
            "                   all         38        905      0.941      0.736      0.824      0.582\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     94/100      8.98G     0.6027     0.2945     0.7704         89        640: 100% 77/77 [00:50<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.09it/s]\n",
            "                   all         38        905      0.941       0.75      0.828      0.586\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     95/100      8.62G     0.6091     0.2964     0.7715         88        640: 100% 77/77 [00:49<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.66it/s]\n",
            "                   all         38        905      0.941      0.749      0.831      0.588\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     96/100      9.01G     0.5996     0.2944     0.7723         95        640: 100% 77/77 [00:51<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.35it/s]\n",
            "                   all         38        905      0.948       0.74      0.838      0.591\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     97/100      9.01G     0.5761     0.2856     0.7695         92        640: 100% 77/77 [00:49<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.51it/s]\n",
            "                   all         38        905      0.907      0.747      0.832      0.595\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     98/100      9.02G     0.5988     0.2918     0.7712         94        640: 100% 77/77 [00:50<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.65it/s]\n",
            "                   all         38        905      0.932      0.733      0.835      0.591\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "     99/100      8.99G     0.5884     0.2866     0.7714         93        640: 100% 77/77 [00:50<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.45it/s]\n",
            "                   all         38        905       0.94      0.745      0.836      0.597\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "    100/100         9G     0.5759     0.2824     0.7705         92        640: 100% 77/77 [00:50<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:00<00:00,  3.45it/s]\n",
            "                   all         38        905      0.969      0.749      0.837      0.597\n",
            "\n",
            "100 epochs completed in 1.868 hours.\n",
            "Optimizer stripped from runs/detect/train3/weights/last.pt, 195.0MB\n",
            "Optimizer stripped from runs/detect/train3/weights/best.pt, 195.0MB\n",
            "\n",
            "Validating runs/detect/train3/weights/best.pt...\n",
            "Ultralytics YOLOv8.2.32 🚀 Python-3.10.12 torch-2.3.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "YOLOv5x summary (fused): 358 layers, 97156460 parameters, 0 gradients, 246.0 GFLOPs\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:02<00:00,  1.20it/s]\n",
            "                   all         38        905      0.918      0.779      0.841      0.601\n",
            "                  ball         35         35          1      0.246      0.458      0.206\n",
            "            goalkeeper         27         27      0.857          1      0.976      0.777\n",
            "                player         38        754      0.963      0.959      0.983      0.781\n",
            "               referee         38         89      0.853       0.91      0.946      0.639\n",
            "Speed: 0.5ms preprocess, 18.1ms inference, 0.0ms loss, 4.6ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train3\u001b[0m\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/train\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p drive/MyDrive/Colab\\ Notebooks/football-players-detection-1/yolov5"
      ],
      "metadata": {
        "id": "VOerTMa4MjjY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp runs/detect/train3/weights/best.pt drive/MyDrive/Colab\\ Notebooks/football-players-detection-1/yolov5/best.pt\n"
      ],
      "metadata": {
        "id": "d3Ex7sDnr4Cx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp runs/detect/train3/weights/last.pt drive/MyDrive/Colab\\ Notebooks/football-players-detection-1/yolov5/last.pt"
      ],
      "metadata": {
        "id": "h1nBOSDNsn-r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9Y9ooRP3stlt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}