02_training_colab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w7RcR7XbJuM5"
   },
   "source": [
    "## My computer's hardware confuiguration is not good enough to train the yolo model that's why I have switched to google colab for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vKQ9Uq8xENo6",
    "outputId": "efd56183-bac0-47d7-f0bf-b71cf886ba8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting ultralytics\n",
      "  Downloading ultralytics-8.0.48-py3-none-any.whl (303 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m303.4/303.4 KB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (4.64.1)\n",
      "Requirement already satisfied: tensorboard>=2.4.1 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (2.11.2)\n",
      "Requirement already satisfied: torch>=1.7.0 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (1.13.1+cu116)\n",
      "Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (4.6.0.66)\n",
      "Requirement already satisfied: torchvision>=0.8.1 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (0.14.1+cu116)\n",
      "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (1.22.4)\n",
      "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (1.3.5)\n",
      "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (8.4.0)\n",
      "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (2.25.1)\n",
      "Requirement already satisfied: certifi>=2022.12.7 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (2022.12.7)\n",
      "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (6.0)\n",
      "Collecting thop>=0.1.1\n",
      "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
      "Requirement already satisfied: psutil in /usr/local/lib/python3.8/dist-packages (from ultralytics) (5.4.8)\n",
      "Collecting sentry-sdk\n",
      "  Downloading sentry_sdk-1.16.0-py2.py3-none-any.whl (184 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m184.3/184.3 KB\u001b[0m \u001b[31m25.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (1.7.3)\n",
      "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (0.11.2)\n",
      "Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.8/dist-packages (from ultralytics) (3.5.3)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->ultralytics) (3.0.9)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->ultralytics) (0.11.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->ultralytics) (2.8.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->ultralytics) (4.38.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->ultralytics) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=3.2.2->ultralytics) (23.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=1.1.4->ultralytics) (2022.7.1)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.23.0->ultralytics) (1.24.3)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.23.0->ultralytics) (2.10)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.23.0->ultralytics) (4.0.0)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (1.0.1)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (0.4.6)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (57.4.0)\n",
      "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (1.51.3)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (2.16.1)\n",
      "Requirement already satisfied: protobuf<4,>=3.9.2 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (3.19.6)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (1.8.1)\n",
      "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (0.38.4)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (0.6.1)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (3.4.1)\n",
      "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.8/dist-packages (from tensorboard>=2.4.1->ultralytics) (1.4.0)\n",
      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch>=1.7.0->ultralytics) (4.5.0)\n",
      "Collecting urllib3<1.27,>=1.21.1\n",
      "  Downloading urllib3-1.26.14-py2.py3-none-any.whl (140 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m891.1 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics) (0.2.8)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics) (4.9)\n",
      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics) (5.3.0)\n",
      "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.8/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics) (1.15.0)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.8/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->ultralytics) (1.3.1)\n",
      "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.8/dist-packages (from markdown>=2.6.8->tensorboard>=2.4.1->ultralytics) (6.0.0)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard>=2.4.1->ultralytics) (3.14.0)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.8/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.4.1->ultralytics) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->ultralytics) (3.2.2)\n",
      "Installing collected packages: urllib3, thop, sentry-sdk, ultralytics\n",
      "  Attempting uninstall: urllib3\n",
      "    Found existing installation: urllib3 1.24.3\n",
      "    Uninstalling urllib3-1.24.3:\n",
      "      Successfully uninstalled urllib3-1.24.3\n",
      "Successfully installed sentry-sdk-1.16.0 thop-0.1.1.post2209072238 ultralytics-8.0.48 urllib3-1.26.14\n"
     ]
    }
   ],
   "source": [
    "# installing ultralytics\n",
    "!pip install ultralytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NHzYsyKgEYX3",
    "outputId": "0c9d6afa-8302-4a80-c0e4-fe0eaec9e4a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://download.pytorch.org/whl/cu116\n",
      "Requirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.1+cu116)\n",
      "Requirement already satisfied: torchvision in /usr/local/lib/python3.8/dist-packages (0.14.1+cu116)\n",
      "Requirement already satisfied: torchaudio in /usr/local/lib/python3.8/dist-packages (0.13.1+cu116)\n",
      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.5.0)\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.8/dist-packages (from torchvision) (8.4.0)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from torchvision) (2.25.1)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from torchvision) (1.22.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (2022.12.7)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (2.10)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (4.0.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (1.26.14)\n"
     ]
    }
   ],
   "source": [
    "# installing ultralytics on installs cpu version of pytorch\n",
    "# so lets install cuda version \n",
    "!pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Up6ALX5rKi-g"
   },
   "source": [
    "#### Data folder straucture same as local:-"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mP8O2D4EKw3s"
   },
   "source": [
    "![image.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "N.B. I kept the data folder outside github repo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-RtKMLlXH_aY"
   },
   "source": [
    "## Remaining cells will be same as our local training notebook (02_training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "wMxTJCzpEkyl"
   },
   "outputs": [],
   "source": [
    "from ultralytics import YOLO\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "VvOybqlLEssq",
    "outputId": "a74e5184-52c6-4c4d-b23b-6cf703240272"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'1.13.1+cu116'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M_4Ln9I1Etj8",
    "outputId": "d052e083-660b-459f-ac29-bb1c8d074721"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 66,
     "referenced_widgets": [
      "eca7d8573ab24fc5bd8d4afa508d93e3",
      "9bd505a1f80d4f6ba81c8916c219819c",
      "5d4b27244e2046bb89b2e236b085d4d7",
      "d9b5f48362d94cbfbcd26eda68e06927",
      "06b41e68377c4ab4bbfa6d39c1c38489",
      "90cdd507a9b54f76a9d4dc5c1d78c48f",
      "ca0e49bcaf8c417493e7ab915e2e0b13",
      "96834794a1444cec9c81ded79f3e4bb5",
      "28c49d222d1d4ac1b50d245750ee42c1",
      "9be25013ffb74c84ab1e4702eebc9236",
      "ff3a433bd6ce4b99a9a3dede19d06520"
     ]
    },
    "id": "CRZlPMw9EvB_",
    "outputId": "c9dbb8e0-f69a-42a2-d4f6-3189995383aa"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to yolov8n.pt...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eca7d8573ab24fc5bd8d4afa508d93e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/6.23M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = YOLO(\"yolov8n.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "QN5Xer_3FJvF"
   },
   "outputs": [],
   "source": [
    "model._check_is_pytorch_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "yCl3cZ8vFMW6"
   },
   "outputs": [],
   "source": [
    "data_yaml_path = \"/content/data/data.yaml\""
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inside data.yaml\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QZo6TwJZJKPL"
   },
   "outputs": [],
   "source": [
    "# this is for the reference during actual traing, we will use only those which are needed\n",
    "\n",
    "# model.train(data = path_to_yaml,\n",
    "#             epochs=100,\n",
    "#             patience=50, # early stopping if no improvement observed\n",
    "#             batch=16, # batch size\n",
    "#             imgsz=640, # image size \n",
    "#             save=True, # save traing check points and predict results\n",
    "#             save_period=-1, # Save checkpoint every x epochs (disabled if < 1)\n",
    "#             cache=False, # True/ram, disk or False. Use cache for data loading\n",
    "#             device=null, # device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu\n",
    "#             workers=8, # number of parallel threads\n",
    "#             project=null, # project name\n",
    "#             name=null, # experiment name\n",
    "#             exist_ok=False, # whether to overwrite existing experiment\n",
    "#             pretrained=False, # whether to use a pretrained model\n",
    "#             optimizer='SGD', # optimizer to use, choice=['SGD', 'Adam', 'AdamW', 'RMSProp']\n",
    "#             verbose=False, # print stuffs during training\n",
    "#             seed=0, # seed\n",
    "#             deterministic=False, # whether to enable deterministic mode\n",
    "#            )"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "task=detect, mode=train, model=E:\\Work\\Data_Science\\Projects\\Custom_Object_Detection\\pretrained_models\\yolov8n.pt, data=E:\\Work\\Data_Science\\Projects\\Custom_Object_Detection\\Custom_Object_Detection_YOLOv8\\data.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=cpu, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, min_memory=False, overlap_mask=True, mask_ratio=4, dropout=False, val=True, split=val, save_json=False, save_hybrid=False, conf=0.001, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=ultralytics/assets/, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.001, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs\\detect\\train22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gSBtJ2kRFncz",
    "outputId": "3157d70e-5407-4aa4-8902-52ad37997bdf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.0.48 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15102MiB)\n",
      "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=/content/data/data.yaml, epochs=100, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=0, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, min_memory=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/train3\n",
      "\n",
      "                   from  n    params  module                                       arguments                     \n",
      "  0                  -1  1       464  ultralytics.nn.modules.Conv                  [3, 16, 3, 2]                 \n",
      "  1                  -1  1      4672  ultralytics.nn.modules.Conv                  [16, 32, 3, 2]                \n",
      "  2                  -1  1      7360  ultralytics.nn.modules.C2f                   [32, 32, 1, True]             \n",
      "  3                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                \n",
      "  4                  -1  2     49664  ultralytics.nn.modules.C2f                   [64, 64, 2, True]             \n",
      "  5                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               \n",
      "  6                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           \n",
      "  7                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              \n",
      "  8                  -1  1    460288  ultralytics.nn.modules.C2f                   [256, 256, 1, True]           \n",
      "  9                  -1  1    164608  ultralytics.nn.modules.SPPF                  [256, 256, 5]                 \n",
      " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
      " 12                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 \n",
      " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
      " 15                  -1  1     37248  ultralytics.nn.modules.C2f                   [192, 64, 1]                  \n",
      " 16                  -1  1     36992  ultralytics.nn.modules.Conv                  [64, 64, 3, 2]                \n",
      " 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
      " 18                  -1  1    123648  ultralytics.nn.modules.C2f                   [192, 128, 1]                 \n",
      " 19                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              \n",
      " 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
      " 21                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 \n",
      " 22        [15, 18, 21]  1    752092  ultralytics.nn.modules.Detect                [4, [64, 128, 256]]           \n",
      "Model summary: 225 layers, 3011628 parameters, 3011612 gradients, 8.2 GFLOPs\n",
      "\n",
      "Transferred 355/355 items from pretrained weights\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/data/training_demo/train/labels.cache... 24 images, 0 backgrounds, 0 corrupt: 100%|██████████| 24/24 [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",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/data/training_demo/test/labels.cache... 8 images, 0 backgrounds, 0 corrupt: 100%|██████████| 8/8 [00:00<?, ?it/s]\n",
      "Plotting labels to runs/detect/train3/labels.jpg... \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",
      "      1/100      2.73G      1.573      3.685      1.595        152        640: 100%|██████████| 2/2 [00:04<00:00,  2.25s/it]\n",
      "/usr/local/lib/python3.8/dist-packages/torch/optim/lr_scheduler.py:138: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
      "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:01<00:00,  1.42s/it]\n",
      "                   all          8         98    0.00931      0.428     0.0625     0.0293\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      2/100      3.31G      1.533      3.654      1.529        190        640: 100%|██████████| 2/2 [00:05<00:00,  2.59s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.17it/s]\n",
      "                   all          8         98     0.0103      0.437     0.0516     0.0254\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      3/100      3.32G      1.521      3.674       1.56        194        640: 100%|██████████| 2/2 [00:03<00:00,  1.67s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.73it/s]\n",
      "                   all          8         98     0.0104      0.442     0.0436     0.0211\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      4/100      3.32G      1.461      3.666      1.504        204        640: 100%|██████████| 2/2 [00:03<00:00,  1.70s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.39it/s]\n",
      "                   all          8         98     0.0114      0.446      0.033      0.014\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      5/100      3.32G      1.387      3.574      1.481        157        640: 100%|██████████| 2/2 [00:03<00:00,  1.64s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.35it/s]\n",
      "                   all          8         98     0.0146      0.498     0.0293     0.0117\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      6/100      3.32G      1.168      3.452      1.297        176        640: 100%|██████████| 2/2 [00:04<00:00,  2.01s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.69it/s]\n",
      "                   all          8         98     0.0152       0.53     0.0401     0.0173\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      7/100      3.32G      1.157       3.42      1.246        182        640: 100%|██████████| 2/2 [00:03<00:00,  1.67s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.31it/s]\n",
      "                   all          8         98     0.0172      0.563     0.0413     0.0205\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      8/100      3.32G      1.091      3.309      1.205        160        640: 100%|██████████| 2/2 [00:03<00:00,  1.55s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.49it/s]\n",
      "                   all          8         98     0.0189      0.618     0.0693     0.0335\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      9/100      3.32G      1.047       3.23      1.172        178        640: 100%|██████████| 2/2 [00:04<00:00,  2.29s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.50it/s]\n",
      "                   all          8         98     0.0201      0.627     0.0863     0.0409\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     10/100      3.32G      1.016      3.101       1.17        188        640: 100%|██████████| 2/2 [00:03<00:00,  1.86s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.32it/s]\n",
      "                   all          8         98     0.0198      0.649      0.103     0.0464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     11/100      3.32G     0.9218      2.952      1.133        161        640: 100%|██████████| 2/2 [00:03<00:00,  1.74s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.80it/s]\n",
      "                   all          8         98     0.0218      0.663      0.127      0.064\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     12/100      3.32G     0.9805       2.94      1.149        186        640: 100%|██████████| 2/2 [00:03<00:00,  1.94s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.26it/s]\n",
      "                   all          8         98     0.0231      0.687      0.197     0.0938\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     13/100      3.32G     0.9954      2.821      1.128        213        640: 100%|██████████| 2/2 [00:03<00:00,  1.62s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.40it/s]\n",
      "                   all          8         98     0.0292      0.722      0.233      0.148\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     14/100      3.32G     0.9308      2.705      1.143        135        640: 100%|██████████| 2/2 [00:04<00:00,  2.22s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.64it/s]\n",
      "                   all          8         98     0.0315      0.731       0.31       0.22\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     15/100      3.32G     0.9309       2.51      1.117        201        640: 100%|██████████| 2/2 [00:03<00:00,  1.66s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.33it/s]\n",
      "                   all          8         98     0.0379      0.811      0.365       0.24\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     16/100      3.32G     0.9363      2.516      1.096        183        640: 100%|██████████| 2/2 [00:03<00:00,  1.64s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.17it/s]\n",
      "                   all          8         98      0.037      0.811      0.388      0.264\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     17/100      3.32G     0.9627      2.423      1.111        200        640: 100%|██████████| 2/2 [00:04<00:00,  2.39s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.27it/s]\n",
      "                   all          8         98      0.039      0.844      0.446      0.303\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     18/100      3.32G     0.8366      2.167       1.05        172        640: 100%|██████████| 2/2 [00:03<00:00,  1.64s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.23it/s]\n",
      "                   all          8         98      0.953      0.139      0.473       0.32\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     19/100      3.93G     0.8651      2.077      1.059        176        640: 100%|██████████| 2/2 [00:03<00:00,  1.65s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.98it/s]\n",
      "                   all          8         98      0.971       0.14        0.5      0.331\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     20/100      4.54G      0.903      2.274      1.043        220        640: 100%|██████████| 2/2 [00:03<00:00,  1.98s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.14it/s]\n",
      "                   all          8         98      0.966      0.147      0.507      0.342\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     21/100      4.54G     0.8514      1.993      1.061        147        640: 100%|██████████| 2/2 [00:03<00:00,  1.53s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.06it/s]\n",
      "                   all          8         98      0.974      0.162      0.591      0.414\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     22/100      4.54G      0.847      1.899      1.029        164        640: 100%|██████████| 2/2 [00:03<00:00,  1.73s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.59it/s]\n",
      "                   all          8         98      0.973      0.156      0.569      0.382\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     23/100      4.54G     0.8524      1.819      1.051        152        640: 100%|██████████| 2/2 [00:03<00:00,  1.61s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.24it/s]\n",
      "                   all          8         98      0.952      0.181      0.613      0.456\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     24/100      4.54G     0.8212        1.6      1.033        172        640: 100%|██████████| 2/2 [00:03<00:00,  1.61s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.26it/s]\n",
      "                   all          8         98      0.821      0.203      0.611      0.488\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     25/100      4.54G     0.8481      1.816      1.025        149        640: 100%|██████████| 2/2 [00:04<00:00,  2.43s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.21it/s]\n",
      "                   all          8         98      0.813      0.223      0.617      0.451\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     26/100      4.54G     0.8567      1.573     0.9915        202        640: 100%|██████████| 2/2 [00:03<00:00,  1.51s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.25it/s]\n",
      "                   all          8         98      0.783      0.234      0.632      0.433\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     27/100      4.54G     0.8452       1.58      1.021        174        640: 100%|██████████| 2/2 [00:03<00:00,  1.64s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.25it/s]\n",
      "                   all          8         98      0.755      0.239      0.634      0.469\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     28/100      4.54G     0.8264      1.523      1.021        161        640: 100%|██████████| 2/2 [00:04<00:00,  2.20s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.02it/s]\n",
      "                   all          8         98      0.755      0.239      0.634      0.469\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     29/100      4.54G      0.844      1.579     0.9912        217        640: 100%|██████████| 2/2 [00:03<00:00,  1.57s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.54it/s]\n",
      "                   all          8         98      0.753      0.383      0.634      0.455\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     30/100      4.54G     0.7863      1.434      1.023        141        640: 100%|██████████| 2/2 [00:03<00:00,  1.82s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.56it/s]\n",
      "                   all          8         98       0.72      0.504       0.65      0.478\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     31/100      4.54G     0.8336      1.458      1.041        157        640: 100%|██████████| 2/2 [00:03<00:00,  1.84s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.24it/s]\n",
      "                   all          8         98       0.72      0.504       0.65      0.478\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     32/100      4.54G     0.8392      1.381      1.017        135        640: 100%|██████████| 2/2 [00:03<00:00,  1.66s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.21it/s]\n",
      "                   all          8         98      0.679      0.555      0.643       0.46\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     33/100      4.54G     0.8514      1.456      1.033        251        640: 100%|██████████| 2/2 [00:03<00:00,  1.90s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.59it/s]\n",
      "                   all          8         98      0.721      0.651      0.678      0.497\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     34/100      4.54G     0.8811      1.429      1.001        214        640: 100%|██████████| 2/2 [00:03<00:00,  1.56s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.26it/s]\n",
      "                   all          8         98      0.721      0.651      0.678      0.497\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     35/100      4.54G     0.8574       1.25      1.002        178        640: 100%|██████████| 2/2 [00:03<00:00,  1.58s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.15it/s]\n",
      "                   all          8         98      0.649      0.654      0.672      0.485\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     36/100      4.54G      0.863      1.238      1.011        184        640: 100%|██████████| 2/2 [00:04<00:00,  2.11s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.24it/s]\n",
      "                   all          8         98      0.669      0.646      0.694      0.498\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     37/100      4.54G     0.7629      1.143     0.9942        189        640: 100%|██████████| 2/2 [00:03<00:00,  1.55s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.19it/s]\n",
      "                   all          8         98      0.669      0.646      0.694      0.498\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     38/100      4.54G      0.815      1.146     0.9745        189        640: 100%|██████████| 2/2 [00:03<00:00,  1.63s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.21it/s]\n",
      "                   all          8         98      0.688       0.71      0.735      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     39/100      4.54G     0.8576      1.252      1.057        127        640: 100%|██████████| 2/2 [00:04<00:00,  2.35s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.24it/s]\n",
      "                   all          8         98      0.655      0.751      0.733      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     40/100      4.54G     0.8218       1.14     0.9752        176        640: 100%|██████████| 2/2 [00:03<00:00,  1.62s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.26it/s]\n",
      "                   all          8         98      0.655      0.751      0.733      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     41/100      4.54G     0.7686      1.005     0.9759        181        640: 100%|██████████| 2/2 [00:03<00:00,  1.54s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.56it/s]\n",
      "                   all          8         98      0.697      0.718      0.749      0.512\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     42/100      4.54G     0.8482      0.994       1.01        171        640: 100%|██████████| 2/2 [00:03<00:00,  1.85s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.20it/s]\n",
      "                   all          8         98      0.654      0.718      0.757      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     43/100      4.54G     0.8102      1.091     0.9879        174        640: 100%|██████████| 2/2 [00:03<00:00,  1.55s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.30it/s]\n",
      "                   all          8         98      0.654      0.718      0.757      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     44/100      4.54G     0.7928      1.052      1.015        165        640: 100%|██████████| 2/2 [00:04<00:00,  2.31s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.53it/s]\n",
      "                   all          8         98      0.733      0.657      0.772      0.561\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     45/100      4.54G     0.7766      1.023     0.9807        176        640: 100%|██████████| 2/2 [00:03<00:00,  1.60s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.20it/s]\n",
      "                   all          8         98      0.733      0.657      0.772      0.561\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     46/100      4.54G     0.7866      1.031     0.9856        183        640: 100%|██████████| 2/2 [00:03<00:00,  1.56s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.21it/s]\n",
      "                   all          8         98      0.692      0.667      0.789      0.563\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     47/100      4.54G     0.8238     0.9993     0.9624        199        640: 100%|██████████| 2/2 [00:04<00:00,  2.29s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.97it/s]\n",
      "                   all          8         98      0.692      0.667      0.789      0.563\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     48/100      4.54G     0.8571      1.054      1.011        215        640: 100%|██████████| 2/2 [00:03<00:00,  1.66s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.09it/s]\n",
      "                   all          8         98      0.795      0.646       0.81      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     49/100      4.54G     0.7782      1.041      1.001        204        640: 100%|██████████| 2/2 [00:03<00:00,  1.55s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.09it/s]\n",
      "                   all          8         98      0.795      0.646       0.81      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     50/100      4.54G     0.7892      1.011     0.9884        134        640: 100%|██████████| 2/2 [00:04<00:00,  2.00s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.25it/s]\n",
      "                   all          8         98      0.566      0.545      0.643       0.42\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     51/100      4.54G      0.806      0.995     0.9694        216        640: 100%|██████████| 2/2 [00:03<00:00,  1.65s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.94it/s]\n",
      "                   all          8         98      0.566      0.545      0.643       0.42\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     52/100      4.54G      0.757     0.9749       0.98        156        640: 100%|██████████| 2/2 [00:03<00:00,  1.80s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.52it/s]\n",
      "                   all          8         98      0.801      0.668      0.812      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     53/100      4.54G     0.7616     0.9789      0.991        195        640: 100%|██████████| 2/2 [00:03<00:00,  1.67s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.22it/s]\n",
      "                   all          8         98      0.801      0.668      0.812      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     54/100      4.54G     0.7364     0.9164     0.9706        173        640: 100%|██████████| 2/2 [00:03<00:00,  1.59s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.30it/s]\n",
      "                   all          8         98      0.785      0.686       0.81      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     55/100      4.54G     0.7771       0.94     0.9834        163        640: 100%|██████████| 2/2 [00:04<00:00,  2.12s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.93it/s]\n",
      "                   all          8         98      0.785      0.686       0.81      0.596\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     56/100      4.54G     0.7807     0.9659     0.9656        238        640: 100%|██████████| 2/2 [00:03<00:00,  1.62s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.31it/s]\n",
      "                   all          8         98      0.646      0.699      0.729       0.46\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     57/100      4.54G     0.7691     0.8993     0.9719        178        640: 100%|██████████| 2/2 [00:03<00:00,  1.64s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.32it/s]\n",
      "                   all          8         98      0.646      0.699      0.729       0.46\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     58/100      4.54G     0.8382     0.9526     0.9835        230        640: 100%|██████████| 2/2 [00:04<00:00,  2.34s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.34it/s]\n",
      "                   all          8         98      0.727      0.761      0.804      0.615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     59/100      4.54G     0.6998     0.8805     0.9551        194        640: 100%|██████████| 2/2 [00:03<00:00,  1.51s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.10it/s]\n",
      "                   all          8         98      0.727      0.761      0.804      0.615\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     60/100      4.54G     0.7333     0.8837     0.9952        168        640: 100%|██████████| 2/2 [00:03<00:00,  1.56s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.25it/s]\n",
      "                   all          8         98      0.762      0.747      0.792      0.603\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     61/100      4.54G     0.7179     0.8529     0.9687        203        640: 100%|██████████| 2/2 [00:04<00:00,  2.05s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.21it/s]\n",
      "                   all          8         98      0.762      0.747      0.792      0.603\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     62/100      4.54G     0.7382     0.8681     0.9725        156        640: 100%|██████████| 2/2 [00:03<00:00,  1.55s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.29it/s]\n",
      "                   all          8         98      0.796      0.752      0.838       0.67\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     63/100      4.54G      0.757     0.8817     0.9845        180        640: 100%|██████████| 2/2 [00:03<00:00,  1.67s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.64it/s]\n",
      "                   all          8         98      0.796      0.752      0.838       0.67\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     64/100      4.54G     0.7399     0.8962     0.9794        168        640: 100%|██████████| 2/2 [00:03<00:00,  1.69s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.37it/s]\n",
      "                   all          8         98      0.681      0.769      0.778      0.597\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     65/100      4.54G     0.7406     0.8791     0.9631        205        640: 100%|██████████| 2/2 [00:03<00:00,  1.57s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.37it/s]\n",
      "                   all          8         98      0.681      0.769      0.778      0.597\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     66/100      4.54G      0.749     0.8542     0.9422        200        640: 100%|██████████| 2/2 [00:03<00:00,  1.75s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.60it/s]\n",
      "                   all          8         98      0.697      0.751      0.755      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     67/100      4.54G     0.7358     0.8803     0.9565        163        640: 100%|██████████| 2/2 [00:02<00:00,  1.47s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.24it/s]\n",
      "                   all          8         98      0.697      0.751      0.755      0.579\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     68/100      4.54G     0.7124     0.8672     0.9559        147        640: 100%|██████████| 2/2 [00:03<00:00,  1.63s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.18it/s]\n",
      "                   all          8         98       0.74      0.777      0.791      0.587\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     69/100      4.54G     0.7257     0.8885     0.9668        142        640: 100%|██████████| 2/2 [00:04<00:00,  2.02s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.64it/s]\n",
      "                   all          8         98       0.74      0.777      0.791      0.587\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     70/100      4.54G      0.717     0.8315     0.9462        214        640: 100%|██████████| 2/2 [00:03<00:00,  1.60s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.28it/s]\n",
      "                   all          8         98      0.819       0.73      0.812      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     71/100      4.54G       0.69     0.8394     0.9416        208        640: 100%|██████████| 2/2 [00:03<00:00,  1.61s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.26it/s]\n",
      "                   all          8         98      0.819       0.73      0.812      0.639\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     72/100      4.54G     0.7057     0.8338     0.9494        185        640: 100%|██████████| 2/2 [00:04<00:00,  2.31s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.18it/s]\n",
      "                   all          8         98      0.729      0.782      0.826      0.657\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     73/100      4.54G     0.7292     0.8392     0.9538        153        640: 100%|██████████| 2/2 [00:03<00:00,  1.57s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.16it/s]\n",
      "                   all          8         98      0.729      0.782      0.826      0.657\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     74/100      4.54G     0.7131     0.8406     0.9851        130        640: 100%|██████████| 2/2 [00:03<00:00,  1.52s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.24it/s]\n",
      "                   all          8         98      0.796      0.788      0.821      0.657\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     75/100      4.54G     0.6798     0.7482      0.954        155        640: 100%|██████████| 2/2 [00:04<00:00,  2.17s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.20it/s]\n",
      "                   all          8         98      0.796      0.788      0.821      0.657\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     76/100      4.54G     0.6617     0.7855     0.9377        156        640: 100%|██████████| 2/2 [00:03<00:00,  1.56s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.29it/s]\n",
      "                   all          8         98      0.709      0.821      0.828      0.662\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     77/100      4.54G     0.6928     0.7989     0.9302        181        640: 100%|██████████| 2/2 [00:03<00:00,  1.59s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.36it/s]\n",
      "                   all          8         98      0.709      0.821      0.828      0.662\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     78/100      4.54G     0.6907     0.8295     0.9388        160        640: 100%|██████████| 2/2 [00:04<00:00,  2.04s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.24it/s]\n",
      "                   all          8         98      0.711      0.846      0.835      0.632\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     79/100      4.54G      0.675     0.7737     0.9338        231        640: 100%|██████████| 2/2 [00:03<00:00,  1.55s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.15it/s]\n",
      "                   all          8         98      0.711      0.846      0.835      0.632\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     80/100      4.54G     0.7083     0.7997     0.9448        194        640: 100%|██████████| 2/2 [00:03<00:00,  1.69s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.46it/s]\n",
      "                   all          8         98      0.744      0.847      0.844      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     81/100      4.54G     0.6138      0.721     0.9061        174        640: 100%|██████████| 2/2 [00:03<00:00,  1.72s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.21it/s]\n",
      "                   all          8         98      0.744      0.847      0.844      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     82/100      4.54G     0.7269     0.8755      1.029        121        640: 100%|██████████| 2/2 [00:03<00:00,  1.52s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.23it/s]\n",
      "                   all          8         98      0.712      0.871      0.859      0.685\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     83/100      4.54G     0.6574     0.8943      0.961        112        640: 100%|██████████| 2/2 [00:03<00:00,  1.85s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.58it/s]\n",
      "                   all          8         98      0.712      0.871      0.859      0.685\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     84/100      4.54G     0.6926     0.8198     0.9292        252        640: 100%|██████████| 2/2 [00:04<00:00,  2.41s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.05it/s]\n",
      "                   all          8         98      0.722      0.868      0.863      0.681\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     85/100      4.54G     0.6731     0.7677     0.9193        199        640: 100%|██████████| 2/2 [00:03<00:00,  1.63s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.87it/s]\n",
      "                   all          8         98      0.722      0.868      0.863      0.681\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     86/100      4.54G     0.6845     0.8086     0.9361        217        640: 100%|██████████| 2/2 [00:04<00:00,  2.07s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.18it/s]\n",
      "                   all          8         98      0.762      0.857      0.872      0.673\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     87/100      4.54G     0.6408     0.7283     0.9613        135        640: 100%|██████████| 2/2 [00:03<00:00,  1.68s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.17it/s]\n",
      "                   all          8         98      0.762      0.857      0.872      0.673\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     88/100      4.54G     0.6778     0.8026     0.9256        182        640: 100%|██████████| 2/2 [00:03<00:00,  1.57s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.54it/s]\n",
      "                   all          8         98      0.756      0.875      0.871      0.687\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     89/100      4.54G      0.623     0.7372     0.9323        189        640: 100%|██████████| 2/2 [00:03<00:00,  1.72s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.21it/s]\n",
      "                   all          8         98      0.756      0.875      0.871      0.687\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     90/100      4.54G     0.6623     0.7777     0.9491        175        640: 100%|██████████| 2/2 [00:03<00:00,  1.55s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.18it/s]\n",
      "                   all          8         98      0.784      0.866      0.879      0.711\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",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     91/100      4.54G     0.7242      1.197     0.9455        112        640: 100%|██████████| 2/2 [00:03<00:00,  1.66s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:02<00:00,  2.13s/it]\n",
      "                   all          8         98      0.784      0.866      0.879      0.711\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     92/100      4.54G     0.7021      1.082     0.9785        103        640: 100%|██████████| 2/2 [00:02<00:00,  1.26s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.05it/s]\n",
      "                   all          8         98      0.811      0.854      0.883      0.717\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     93/100      4.54G     0.6655       1.19     0.9377         66        640: 100%|██████████| 2/2 [00:01<00:00,  1.29it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.28it/s]\n",
      "                   all          8         98      0.811      0.854      0.883      0.717\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     94/100      4.54G      0.683      1.137      0.948         89        640: 100%|██████████| 2/2 [00:01<00:00,  1.33it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.47it/s]\n",
      "                   all          8         98      0.797       0.86      0.879      0.717\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     95/100      4.54G     0.7055       1.12      0.984         80        640: 100%|██████████| 2/2 [00:01<00:00,  1.01it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.20it/s]\n",
      "                   all          8         98      0.797       0.86      0.879      0.717\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     96/100      4.54G     0.6106     0.9342     0.9015         79        640: 100%|██████████| 2/2 [00:01<00:00,  1.35it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.17it/s]\n",
      "                   all          8         98      0.886      0.781      0.881      0.711\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     97/100      4.54G     0.6156     0.8913      0.925         99        640: 100%|██████████| 2/2 [00:01<00:00,  1.36it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.25it/s]\n",
      "                   all          8         98      0.886      0.781      0.881      0.711\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     98/100      4.54G     0.6133     0.8868      0.905         95        640: 100%|██████████| 2/2 [00:01<00:00,  1.34it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.20it/s]\n",
      "                   all          8         98      0.812      0.807      0.868      0.701\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     99/100      4.54G     0.5909     0.8769     0.9318         86        640: 100%|██████████| 2/2 [00:01<00:00,  1.08it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.53it/s]\n",
      "                   all          8         98      0.812      0.807      0.868      0.701\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    100/100      4.54G      0.643     0.8963     0.9243        104        640: 100%|██████████| 2/2 [00:01<00:00,  1.22it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.69it/s]\n",
      "                   all          8         98      0.884      0.793      0.865      0.698\n",
      "\n",
      "100 epochs completed in 0.131 hours.\n",
      "Optimizer stripped from runs/detect/train3/weights/last.pt, 6.2MB\n",
      "Optimizer stripped from runs/detect/train3/weights/best.pt, 6.2MB\n",
      "\n",
      "Validating runs/detect/train3/weights/best.pt...\n",
      "Ultralytics YOLOv8.0.48 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15102MiB)\n",
      "Model summary (fused): 168 layers, 3006428 parameters, 0 gradients, 8.1 GFLOPs\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.69it/s]\n",
      "                   all          8         98       0.81      0.855      0.886      0.719\n",
      "                 sec_1          8         26      0.937      0.692      0.881      0.637\n",
      "                 sec_2          8         10      0.681      0.857      0.832      0.733\n",
      "                 sec_3          8         54      0.783       0.87      0.836      0.658\n",
      "                 sec_4          8          8       0.84          1      0.995      0.847\n",
      "Speed: 0.2ms preprocess, 3.4ms inference, 0.0ms loss, 5.7ms postprocess per image\n",
      "Results saved to \u001b[1mruns/detect/train3\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "model.train(data = data_yaml_path, \n",
    "            epochs=100,\n",
    "            imgsz=640,\n",
    "            device=0\n",
    "           )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I will download the entire output "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "06b41e68377c4ab4bbfa6d39c1c38489": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "28c49d222d1d4ac1b50d245750ee42c1": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "ProgressStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "ProgressStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "bar_color": null,
      "description_width": ""
     }
    },
    "5d4b27244e2046bb89b2e236b085d4d7": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "FloatProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "FloatProgressModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "success",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_96834794a1444cec9c81ded79f3e4bb5",
      "max": 6534387,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_28c49d222d1d4ac1b50d245750ee42c1",
      "value": 6534387
     }
    },
    "90cdd507a9b54f76a9d4dc5c1d78c48f": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "96834794a1444cec9c81ded79f3e4bb5": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "9bd505a1f80d4f6ba81c8916c219819c": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_90cdd507a9b54f76a9d4dc5c1d78c48f",
      "placeholder": "​",
      "style": "IPY_MODEL_ca0e49bcaf8c417493e7ab915e2e0b13",
      "value": "100%"
     }
    },
    "9be25013ffb74c84ab1e4702eebc9236": {
     "model_module": "@jupyter-widgets/base",
     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
      "grid_area": null,
      "grid_auto_columns": null,
      "grid_auto_flow": null,
      "grid_auto_rows": null,
      "grid_column": null,
      "grid_gap": null,
      "grid_row": null,
      "grid_template_areas": null,
      "grid_template_columns": null,
      "grid_template_rows": null,
      "height": null,
      "justify_content": null,
      "justify_items": null,
      "left": null,
      "margin": null,
      "max_height": null,
      "max_width": null,
      "min_height": null,
      "min_width": null,
      "object_fit": null,
      "object_position": null,
      "order": null,
      "overflow": null,
      "overflow_x": null,
      "overflow_y": null,
      "padding": null,
      "right": null,
      "top": null,
      "visibility": null,
      "width": null
     }
    },
    "ca0e49bcaf8c417493e7ab915e2e0b13": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    },
    "d9b5f48362d94cbfbcd26eda68e06927": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HTMLModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HTMLModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HTMLView",
      "description": "",
      "description_tooltip": null,
      "layout": "IPY_MODEL_9be25013ffb74c84ab1e4702eebc9236",
      "placeholder": "​",
      "style": "IPY_MODEL_ff3a433bd6ce4b99a9a3dede19d06520",
      "value": " 6.23M/6.23M [00:00&lt;00:00, 117MB/s]"
     }
    },
    "eca7d8573ab24fc5bd8d4afa508d93e3": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "HBoxModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "HBoxModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "HBoxView",
      "box_style": "",
      "children": [
       "IPY_MODEL_9bd505a1f80d4f6ba81c8916c219819c",
       "IPY_MODEL_5d4b27244e2046bb89b2e236b085d4d7",
       "IPY_MODEL_d9b5f48362d94cbfbcd26eda68e06927"
      ],
      "layout": "IPY_MODEL_06b41e68377c4ab4bbfa6d39c1c38489"
     }
    },
    "ff3a433bd6ce4b99a9a3dede19d06520": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "DescriptionStyleModel",
     "state": {
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
      "_model_name": "DescriptionStyleModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "StyleView",
      "description_width": ""
     }
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}