{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "09795e9d",
   "metadata": {},
   "source": [
    "# YOLOv5 для детекции автомобилей\n",
    "\n",
    "В работе рассматривается задача **детекции автомобилей на изображениях**: модель должна предсказать координаты ограничивающих рамок и класс объекта.\n",
    "\n",
    "Ниже сохранена логика исходного ноутбука, но доработаны:\n",
    "- описание архитектуры **YOLOv5** и показателей качества;\n",
    "- конфигурация **anchors**, подобранных по статистике реальных bbox датасета;\n",
    "- **learning rate** через отдельный `hyp`-файл;\n",
    "- полноценное обучение, валидация, расчёт метрик и автоматический вывод.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c579be5",
   "metadata": {},
   "source": [
    "## Кратко об устройстве YOLOv5\n",
    "\n",
    "**YOLOv5** — одностадийный детектор объектов. Его типовая структура включает:\n",
    "1. **Backbone** — извлечение признаков из изображения (CSP-подобные блоки и свёрточные слои).\n",
    "2. **Neck** — агрегация признаков разных масштабов (FPN/PAN-подобная схема).\n",
    "3. **Detection head** — предсказание bbox, objectness и классов на нескольких масштабах.\n",
    "\n",
    "В этой задаче используется одноклассовая детекция (`car`), поэтому важны:\n",
    "- **anchors** — типовые размеры рамок, от которых отталкивается head;\n",
    "- **learning rate** — влияет на устойчивость fine-tuning предобученной модели.\n",
    "\n",
    "Основные метрики:\n",
    "- **Precision**\n",
    "- **Recall**\n",
    "- **mAP@0.50**\n",
    "- **mAP@0.50:0.95**\n",
    "\n",
    "Для данной задачи ключевыми обычно считаются `mAP@0.50` и `mAP@0.50:0.95`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af58f49",
   "metadata": {},
   "source": [
    "## 1. Импорт библиотек\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75d4560e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import time\n",
    "import yaml\n",
    "import shutil as sh\n",
    "import random\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "from sklearn.cluster import KMeans\n",
    "from IPython.display import Image, display\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2962271e",
   "metadata": {},
   "source": [
    "## 2. Клонирование YOLOv5 и установка зависимостей\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5fe7cb19",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Ignoring invalid distribution ~ulsectl (/home/konnilol/.pyenv/versions/3.12.12/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~hirp (/home/konnilol/.pyenv/versions/3.12.12/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~ulsectl (/home/konnilol/.pyenv/versions/3.12.12/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~hirp (/home/konnilol/.pyenv/versions/3.12.12/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~ulsectl (/home/konnilol/.pyenv/versions/3.12.12/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~hirp (/home/konnilol/.pyenv/versions/3.12.12/lib/python3.12/site-packages)\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "set -e\n",
    "if [ ! -d yolov5 ]; then\n",
    "  git clone https://github.com/ultralytics/yolov5\n",
    "fi\n",
    "pip install -qr yolov5/requirements.txt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2045dc81",
   "metadata": {},
   "source": [
    "## 3. Загрузка и подготовка датасета\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cea95bc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATA_ROOT = /home/konnilol/Downloads/car-object-detection/data\n",
      "WORKDIR = /home/konnilol/Downloads/work_yolov5_car\n"
     ]
    },
    {
     "data": {
      "application/vnd.positron.dataexplorer+json": {
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       "shape": {
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        "rows": 5
       },
       "source": "pandas",
       "title": "pandas",
       "version": 1
      },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_id</th>\n",
       "      <th>xmin</th>\n",
       "      <th>ymin</th>\n",
       "      <th>xmax</th>\n",
       "      <th>ymax</th>\n",
       "      <th>x_center</th>\n",
       "      <th>y_center</th>\n",
       "      <th>w</th>\n",
       "      <th>h</th>\n",
       "      <th>classes</th>\n",
       "      <th>x_center_norm</th>\n",
       "      <th>y_center_norm</th>\n",
       "      <th>w_norm</th>\n",
       "      <th>h_norm</th>\n",
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       "      <th>0</th>\n",
       "      <td>vid_4_1000</td>\n",
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       "      <td>187.035071</td>\n",
       "      <td>327.727931</td>\n",
       "      <td>223.225547</td>\n",
       "      <td>304.493488</td>\n",
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       "      <td>46.468886</td>\n",
       "      <td>36.190476</td>\n",
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       "      <td>0.450434</td>\n",
       "      <td>0.539817</td>\n",
       "      <td>0.068741</td>\n",
       "      <td>0.095238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>vid_4_10000</td>\n",
       "      <td>15.163531</td>\n",
       "      <td>187.035071</td>\n",
       "      <td>120.329957</td>\n",
       "      <td>236.430180</td>\n",
       "      <td>67.746744</td>\n",
       "      <td>211.732625</td>\n",
       "      <td>105.166425</td>\n",
       "      <td>49.395109</td>\n",
       "      <td>0</td>\n",
       "      <td>0.100217</td>\n",
       "      <td>0.557191</td>\n",
       "      <td>0.155572</td>\n",
       "      <td>0.129987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vid_4_10040</td>\n",
       "      <td>239.192475</td>\n",
       "      <td>176.764801</td>\n",
       "      <td>361.968162</td>\n",
       "      <td>236.430180</td>\n",
       "      <td>300.580318</td>\n",
       "      <td>206.597490</td>\n",
       "      <td>122.775687</td>\n",
       "      <td>59.665380</td>\n",
       "      <td>0</td>\n",
       "      <td>0.444645</td>\n",
       "      <td>0.543678</td>\n",
       "      <td>0.181621</td>\n",
       "      <td>0.157014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vid_4_10020</td>\n",
       "      <td>496.483358</td>\n",
       "      <td>172.363256</td>\n",
       "      <td>630.020260</td>\n",
       "      <td>231.539575</td>\n",
       "      <td>563.251809</td>\n",
       "      <td>201.951416</td>\n",
       "      <td>133.536903</td>\n",
       "      <td>59.176319</td>\n",
       "      <td>0</td>\n",
       "      <td>0.833213</td>\n",
       "      <td>0.531451</td>\n",
       "      <td>0.197540</td>\n",
       "      <td>0.155727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>vid_4_10060</td>\n",
       "      <td>16.630970</td>\n",
       "      <td>186.546010</td>\n",
       "      <td>132.558611</td>\n",
       "      <td>238.386422</td>\n",
       "      <td>74.594790</td>\n",
       "      <td>212.466216</td>\n",
       "      <td>115.927641</td>\n",
       "      <td>51.840412</td>\n",
       "      <td>0</td>\n",
       "      <td>0.110347</td>\n",
       "      <td>0.559122</td>\n",
       "      <td>0.171491</td>\n",
       "      <td>0.136422</td>\n",
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      ],
      "text/plain": [
       "      image_id        xmin        ymin        xmax        ymax    x_center  \\\n",
       "0   vid_4_1000  281.259045  187.035071  327.727931  223.225547  304.493488   \n",
       "1  vid_4_10000   15.163531  187.035071  120.329957  236.430180   67.746744   \n",
       "2  vid_4_10040  239.192475  176.764801  361.968162  236.430180  300.580318   \n",
       "3  vid_4_10020  496.483358  172.363256  630.020260  231.539575  563.251809   \n",
       "4  vid_4_10060   16.630970  186.546010  132.558611  238.386422   74.594790   \n",
       "\n",
       "     y_center           w          h  classes  x_center_norm  y_center_norm  \\\n",
       "0  205.130309   46.468886  36.190476        0       0.450434       0.539817   \n",
       "1  211.732625  105.166425  49.395109        0       0.100217       0.557191   \n",
       "2  206.597490  122.775687  59.665380        0       0.444645       0.543678   \n",
       "3  201.951416  133.536903  59.176319        0       0.833213       0.531451   \n",
       "4  212.466216  115.927641  51.840412        0       0.110347       0.559122   \n",
       "\n",
       "     w_norm    h_norm  \n",
       "0  0.068741  0.095238  \n",
       "1  0.155572  0.129987  \n",
       "2  0.181621  0.157014  \n",
       "3  0.197540  0.155727  \n",
       "4  0.171491  0.136422  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMG_H, IMG_W = 380, 676\n",
    "\n",
    "# Локальные пути (не Kaggle)\n",
    "DATA_ROOT = Path('./car-object-detection/data').resolve()\n",
    "WORKDIR = Path('./work_yolov5_car').resolve()\n",
    "WORKDIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "CSV_PATH = DATA_ROOT / 'train_solution_bounding_boxes (1).csv'\n",
    "if not CSV_PATH.exists():\n",
    "    raise FileNotFoundError(f'Не найден CSV-файл: {CSV_PATH}')\n",
    "\n",
    "df = pd.read_csv(CSV_PATH)\n",
    "df = df.rename(columns={'image': 'image_id'})\n",
    "df['image_id'] = df['image_id'].str.replace('.jpg', '', regex=False)\n",
    "\n",
    "df['x_center'] = (df['xmin'] + df['xmax']) / 2\n",
    "df['y_center'] = (df['ymin'] + df['ymax']) / 2\n",
    "df['w'] = df['xmax'] - df['xmin']\n",
    "df['h'] = df['ymax'] - df['ymin']\n",
    "df['classes'] = 0\n",
    "\n",
    "# Нормировка для YOLO\n",
    "df['x_center_norm'] = df['x_center'] / IMG_W\n",
    "df['y_center_norm'] = df['y_center'] / IMG_H\n",
    "df['w_norm'] = df['w'] / IMG_W\n",
    "df['h_norm'] = df['h'] / IMG_H\n",
    "\n",
    "print('DATA_ROOT =', DATA_ROOT)\n",
    "print('WORKDIR =', WORKDIR)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "93d55a8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Число изображений: 355\n",
      "Число bbox: 559\n",
      "Средняя ширина bbox (px): 101.94\n",
      "Средняя высота bbox (px): 44.98\n"
     ]
    }
   ],
   "source": [
    "print('Число изображений:', df['image_id'].nunique())\n",
    "print('Число bbox:', len(df))\n",
    "print('Средняя ширина bbox (px):', round(df['w'].mean(), 2))\n",
    "print('Средняя высота bbox (px):', round(df['h'].mean(), 2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dbd1022",
   "metadata": {},
   "source": [
    "## 4. Визуализация примера\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3fed1128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.positron.dataexplorer+json": {
       "comm_id": "9786e1ac-9650-4475-8c80-6f1ae62444bf",
       "shape": {
        "columns": 4,
        "rows": 1
       },
       "source": "pandas",
       "title": "pandas",
       "version": 1
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>xmin</th>\n",
       "      <th>ymin</th>\n",
       "      <th>xmax</th>\n",
       "      <th>ymax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>149.678727</td>\n",
       "      <td>190.458494</td>\n",
       "      <td>268.541245</td>\n",
       "      <td>237.408301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          xmin        ymin        xmax        ymax\n",
       "11  149.678727  190.458494  268.541245  237.408301"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_image = random.choice(df['image_id'].unique().tolist())\n",
    "display(Image(filename=str(DATA_ROOT / 'training_images' / f'{sample_image}.jpg'), width=700))\n",
    "\n",
    "sample_boxes = df[df['image_id'] == sample_image][['xmin', 'ymin', 'xmax', 'ymax']]\n",
    "sample_boxes.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2556b196",
   "metadata": {},
   "source": [
    "## 5. Подготовка структуры датасета YOLOv5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4970b11f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 355/355 [00:00<00:00, 1990.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train images: 284\n",
      "Val images: 71\n",
      "base_dir = /home/konnilol/Downloads/work_yolov5_car/convertor/fold0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "index = list(df['image_id'].unique())\n",
    "random.Random(42).shuffle(index)\n",
    "\n",
    "source = 'training_images'\n",
    "fold = 0\n",
    "val_index = set(index[len(index) * fold // 5 : len(index) * (fold + 1) // 5])\n",
    "\n",
    "base_dir = WORKDIR / 'convertor' / f'fold{fold}'\n",
    "for split in ['train2017', 'val2017']:\n",
    "    (base_dir / 'labels' / split).mkdir(parents=True, exist_ok=True)\n",
    "    (base_dir / 'images' / split).mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "for name, mini in tqdm(df.groupby('image_id')):\n",
    "    split = 'val2017' if name in val_index else 'train2017'\n",
    "    label_path = base_dir / 'labels' / split / f'{name}.txt'\n",
    "    rows = mini[['classes', 'x_center_norm', 'y_center_norm', 'w_norm', 'h_norm']].astype(float).values\n",
    "    with open(label_path, 'w') as f:\n",
    "        for row in rows:\n",
    "            f.write(' '.join(map(str, row)) + '\\n')\n",
    "    src_img = DATA_ROOT / source / f'{name}.jpg'\n",
    "    dst_img = base_dir / 'images' / split / f'{name}.jpg'\n",
    "    if not src_img.exists():\n",
    "        raise FileNotFoundError(f'Не найдено изображение: {src_img}')\n",
    "    if not dst_img.exists():\n",
    "        sh.copy(src_img, dst_img)\n",
    "\n",
    "print('Train images:', len(list((base_dir / 'images' / 'train2017').glob('*.jpg'))))\n",
    "print('Val images:', len(list((base_dir / 'images' / 'val2017').glob('*.jpg'))))\n",
    "print('base_dir =', base_dir)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed68536",
   "metadata": {},
   "source": [
    "## 6. Подбор новых anchors\n",
    "\n",
    "Логика изменения anchors:\n",
    "- исходные COCO anchors не обязательно оптимальны для этого датасета;\n",
    "- здесь одна категория (`car`), а сами bbox имеют свою характерную геометрию;\n",
    "- поэтому anchors рассчитываются **по обучающим bbox** через `KMeans` на размерах рамок, пересчитанных к размеру `img=640`.\n",
    "\n",
    "Это даёт более согласованную начальную геометрию рамок для detection head.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e39789f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Новые anchors в формате YOLOv5:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[39, 49, 69, 55, 57, 72],\n",
       " [92, 67, 112, 78, 108, 111],\n",
       " [141, 96, 170, 123, 258, 183]]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMG_SIZE = 640\n",
    "\n",
    "train_df = df[~df['image_id'].isin(val_index)].copy()\n",
    "wh = train_df[['w_norm', 'h_norm']].values * IMG_SIZE\n",
    "wh = wh[(wh[:, 0] > 2) & (wh[:, 1] > 2)]\n",
    "\n",
    "kmeans = KMeans(n_clusters=9, random_state=42, n_init=20)\n",
    "kmeans.fit(wh)\n",
    "anchors = kmeans.cluster_centers_\n",
    "\n",
    "# Сортировка anchor-блоков по площади\n",
    "areas = anchors[:, 0] * anchors[:, 1]\n",
    "anchors = anchors[np.argsort(areas)]\n",
    "\n",
    "# 3 уровня детекции × 3 anchor на каждом уровне × 2 координаты (w, h)\n",
    "anchor_groups = np.round(anchors.reshape(3, 3, 2)).astype(int)\n",
    "\n",
    "# Для YOLOv5 anchors в YAML должны быть заданы как 3 списка по 6 чисел,\n",
    "# например: [[10,13, 16,30, 33,23], [...], [...]]\n",
    "anchor_groups_yolov5 = [[int(v) for pair in group for v in pair] for group in anchor_groups]\n",
    "\n",
    "print('Новые anchors в формате YOLOv5:')\n",
    "anchor_groups_yolov5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6a2def",
   "metadata": {},
   "source": [
    "## 7. Конфигурационные файлы: dataset, model, hyp\n",
    "\n",
    "Изменение learning rate:\n",
    "- в исходной конфигурации YOLOv5 базовый `lr0` ориентирован на стандартный сценарий;\n",
    "- для сравнительно небольшого одноклассового датасета и fine-tuning предобученной модели более безопасно использовать **меньший learning rate**;\n",
    "- здесь задаётся `lr0 = 0.003` вместо более агрессивного значения.\n",
    "\n",
    "Это снижает риск нестабильного обучения и переобучения на ранних эпохах.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "600667fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "path: /home/konnilol/Downloads/work_yolov5_car/convertor/fold0\n",
      "train: images/train2017\n",
      "val: images/val2017\n",
      "nc: 1\n",
      "names:\n",
      "- car\n",
      "\n"
     ]
    }
   ],
   "source": [
    "car_yaml = {\n",
    "    'path': str(base_dir),\n",
    "    'train': 'images/train2017',\n",
    "    'val': 'images/val2017',\n",
    "    'nc': 1,\n",
    "    'names': ['car']\n",
    "}\n",
    "\n",
    "car_yaml_path = WORKDIR / 'car.yaml'\n",
    "with open(car_yaml_path, 'w') as f:\n",
    "    yaml.safe_dump(car_yaml, f, sort_keys=False)\n",
    "\n",
    "print(car_yaml_path.read_text())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e5c6451",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nc: 1\n",
      "depth_multiple: 0.33\n",
      "width_multiple: 0.5\n",
      "anchors:\n",
      "- - 39\n",
      "  - 49\n",
      "  - 69\n",
      "  - 55\n",
      "  - 57\n",
      "  - 72\n",
      "- - 92\n",
      "  - 67\n",
      "  - 112\n",
      "  - 78\n",
      "  - 108\n",
      "  - 111\n",
      "- - 141\n",
      "  - 96\n",
      "  - 170\n",
      "  - 123\n",
      "  - 258\n",
      "  - 183\n",
      "backbone:\n",
      "- - -1\n",
      "  - 1\n",
      "  - Conv\n",
      "  - - 64\n",
      "    - 6\n",
      "    - 2\n",
      "    - 2\n",
      "- - -1\n",
      "  - 1\n",
      "  - Conv\n",
      "  - - 128\n",
      "    - 3\n",
      "    - 2\n",
      "- - -1\n",
      "  - 3\n",
      "  - C3\n",
      "  - - 128\n",
      "- - -1\n",
      "  - 1\n",
      "  - Conv\n",
      "  - - 256\n",
      "    - 3\n",
      "    - 2\n",
      "- - -1\n",
      "  - 6\n",
      "  - C3\n",
      "  - - 256\n",
      "- - -1\n",
      "  - 1\n",
      "  - Conv\n",
      "  - - 512\n",
      "    - 3\n",
      "    - 2\n",
      "- - -1\n",
      "  - 9\n",
      "  - C3\n",
      "  - - 512\n",
      "- - -1\n",
      "  - 1\n",
      "  - Conv\n",
      "  - - 1024\n",
      "    - 3\n",
      "    - 2\n",
      "- - -1\n",
      "  - 3\n",
      "  - C3\n",
      "  - - 1024\n",
      "- - -1\n",
      "  - 1\n",
      "  - SPPF\n",
      "  - - 1024\n",
      "    - 5\n",
      "head:\n",
      "- - -1\n",
      "  - 1\n",
      "  - Conv\n",
      "  - - 512\n",
      "    - 1\n",
      "    - 1\n",
      "- - -1\n",
      "  - 1\n",
      "  - nn.Upsample\n",
      "  - - None\n",
      "    - 2\n",
      "    - nearest\n",
      "- - - -1\n",
      "    - 6\n",
      "  - 1\n",
      "  - Concat\n",
      "  - - 1\n",
      "- - -1\n",
      "  - 3\n",
      "  - C3\n",
      "  - - 512\n",
      "    - false\n",
      "- - -1\n",
      "  - 1\n",
      "  - Conv\n",
      "  - - 256\n",
      "    - 1\n",
      "    - 1\n",
      "- - -1\n",
      "  - 1\n",
      "  - nn.Upsample\n",
      "  - - None\n",
      "    - 2\n",
      "    - nearest\n",
      "- - - -1\n",
      "    - 4\n",
      "  - 1\n",
      "  - Concat\n",
      "  - - 1\n",
      "- - -1\n",
      "  - 3\n",
      "  - C3\n",
      "  - - 256\n",
      "    - false\n",
      "- - -1\n",
      "  - 1\n",
      "  - Conv\n",
      "  - - 256\n",
      "    - 3\n",
      "    - 2\n",
      "- - - -1\n",
      "    - 14\n",
      "  - 1\n",
      "  - Concat\n",
      "  - - 1\n",
      "- - -1\n",
      "  - 3\n",
      "  - C3\n",
      "  - - 512\n",
      "    - false\n",
      "- - -1\n",
      "  - 1\n",
      "  - Conv\n",
      "  - - 512\n",
      "    - 3\n",
      "    - 2\n",
      "- - - -1\n",
      "    - \n"
     ]
    }
   ],
   "source": [
    "base_model_yaml = Path('yolov5/models/yolov5s.yaml')\n",
    "with open(base_model_yaml, 'r') as f:\n",
    "    model_cfg = yaml.safe_load(f)\n",
    "\n",
    "model_cfg['nc'] = 1\n",
    "model_cfg['anchors'] = anchor_groups_yolov5\n",
    "\n",
    "custom_model_yaml = WORKDIR / 'yolov5s_car_custom.yaml'\n",
    "with open(custom_model_yaml, 'w') as f:\n",
    "    yaml.safe_dump(model_cfg, f, sort_keys=False)\n",
    "\n",
    "print(custom_model_yaml.read_text()[:1200])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c8f0c31f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr0: 0.003\n",
      "lrf: 0.12\n",
      "momentum: 0.937\n",
      "weight_decay: 0.0005\n",
      "warmup_epochs: 3.0\n",
      "warmup_momentum: 0.8\n",
      "warmup_bias_lr: 0.1\n",
      "box: 0.05\n",
      "cls: 0.5\n",
      "cls_pw: 1.0\n",
      "obj: 1.0\n",
      "obj_pw: 1.0\n",
      "iou_t: 0.2\n",
      "anchor_t: 4.0\n",
      "fl_gamma: 0.0\n",
      "hsv_h: 0.015\n",
      "hsv_s: 0.7\n",
      "hsv_v: 0.4\n",
      "degrees: 0.0\n",
      "translate: 0.1\n",
      "scale: 0.5\n",
      "shear: 0.0\n",
      "perspective: 0.0\n",
      "flipud: 0.0\n",
      "fliplr: 0.5\n",
      "mosaic: 1.0\n",
      "mixup: 0.0\n",
      "copy_paste: 0.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Берём \"low\" конфиг и точечно меняем learning rate\n",
    "base_hyp_yaml = Path('yolov5/data/hyps/hyp.scratch-low.yaml')\n",
    "with open(base_hyp_yaml, 'r') as f:\n",
    "    hyp_cfg = yaml.safe_load(f)\n",
    "\n",
    "hyp_cfg['lr0'] = 0.003\n",
    "hyp_cfg['lrf'] = 0.12\n",
    "hyp_cfg['warmup_epochs'] = 3.0\n",
    "\n",
    "custom_hyp_yaml = WORKDIR / 'hyp_car_custom.yaml'\n",
    "with open(custom_hyp_yaml, 'w') as f:\n",
    "    yaml.safe_dump(hyp_cfg, f, sort_keys=False)\n",
    "\n",
    "print(custom_hyp_yaml.read_text())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23228660",
   "metadata": {},
   "source": [
    "## 8. Обучение модифицированной модели\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fbada365",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Команда обучения:\n",
      "/home/konnilol/.pyenv/versions/3.12.12/bin/python yolov5/train.py --img 640 --batch 16 --epochs 50 --data /home/konnilol/Downloads/work_yolov5_car/car.yaml --cfg /home/konnilol/Downloads/work_yolov5_car/yolov5s_car_custom.yaml --weights yolov5s.pt --hyp /home/konnilol/Downloads/work_yolov5_car/hyp_car_custom.yaml --project /home/konnilol/Downloads/work_yolov5_car/runs/train --name yolov5s_car_anchor_lr_tuned --workers 8 --cache\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=/home/konnilol/Downloads/work_yolov5_car/yolov5s_car_custom.yaml, data=/home/konnilol/Downloads/work_yolov5_car/car.yaml, hyp=/home/konnilol/Downloads/work_yolov5_car/hyp_car_custom.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=yolov5/data/hyps, resume_evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=/home/konnilol/Downloads/work_yolov5_car/runs/train, name=yolov5s_car_anchor_lr_tuned, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest, ndjson_console=False, ndjson_file=False\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
      "YOLOv5 🚀 v7.0-463-g88af13e3 Python-3.12.12 torch-2.10.0+cu128 CUDA:0 (NVIDIA GeForce RTX 3080, 9873MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.003, lrf=0.12, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir /home/konnilol/Downloads/work_yolov5_car/runs/train', view at http://localhost:6006/\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     16182  models.yolo.Detect                      [1, [[39, 49, 69, 55, 57, 72], [92, 67, 112, 78, 108, 111], [141, 96, 170, 123, 258, 183]], [128, 256, 512]]\n",
      "YOLOv5s_car_custom summary: 214 layers, 7022326 parameters, 7022326 gradients, 15.9 GFLOPs\n",
      "\n",
      "Transferred 342/349 items from yolov5s.pt\n",
      "/home/konnilol/Downloads/yolov5/models/common.py:899: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with amp.autocast(autocast):\n",
      "/home/konnilol/Downloads/yolov5/models/common.py:899: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with amp.autocast(autocast):\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.003) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/konnilol/Downloads/work_yolov5_car/convertor/fold0/labels/train2017.cache... 284 images, 0 backgrounds, 0 corrupt: 100%|██████████| 284/284 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.2GB ram): 100%|██████████| 284/284 [00:00<00:00, 4182.49it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /home/konnilol/Downloads/work_yolov5_car/convertor/fold0/labels/val2017.cache... 71 images, 0 backgrounds, 0 corrupt: 100%|██████████| 71/71 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.0GB ram): 100%|██████████| 71/71 [00:00<00:00, 1101.63it/s]\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m7.90 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to /home/konnilol/Downloads/work_yolov5_car/runs/train/yolov5s_car_anchor_lr_tuned/labels.jpg... \n",
      "/home/konnilol/Downloads/yolov5/train.py:357: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
      "  scaler = torch.cuda.amp.GradScaler(enabled=amp)\n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1m/home/konnilol/Downloads/work_yolov5_car/runs/train/yolov5s_car_anchor_lr_tuned\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.29G     0.1215    0.03545          0         38        640:   6%|▌         | 1/18 [00:01<00:29,  1.72s/it]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1215    0.03705          0         43        640:  11%|█         | 2/18 [00:01<00:12,  1.28it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1195    0.03707          0         32        640:  17%|█▋        | 3/18 [00:01<00:07,  2.12it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1189    0.03783          0         44        640:  17%|█▋        | 3/18 [00:02<00:07,  2.12it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1194     0.0385          0         57        640:  28%|██▊       | 5/18 [00:02<00:03,  3.90it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1195    0.03845          0         43        640:  28%|██▊       | 5/18 [00:02<00:03,  3.90it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1192    0.03805          0         33        640:  39%|███▉      | 7/18 [00:02<00:01,  5.53it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1189    0.03763          0         39        640:  39%|███▉      | 7/18 [00:02<00:01,  5.53it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1185    0.03745          0         39        640:  50%|█████     | 9/18 [00:02<00:01,  6.94it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1181    0.03739          0         42        640:  50%|█████     | 9/18 [00:02<00:01,  6.94it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G      0.118    0.03745          0         48        640:  61%|██████    | 11/18 [00:02<00:00,  8.08it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1175    0.03751          0         40        640:  61%|██████    | 11/18 [00:02<00:00,  8.08it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G      0.117     0.0375          0         41        640:  72%|███████▏  | 13/18 [00:02<00:00,  8.96it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1167    0.03733          0         43        640:  72%|███████▏  | 13/18 [00:02<00:00,  8.96it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1164    0.03744          0         55        640:  83%|████████▎ | 15/18 [00:03<00:00,  9.64it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G      0.116    0.03747          0         50        640:  83%|████████▎ | 15/18 [00:03<00:00,  9.64it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1154    0.03738          0         36        640:  94%|█████████▍| 17/18 [00:03<00:00, 10.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       0/49      3.34G     0.1149    0.03715          0         25        640: 100%|██████████| 18/18 [00:03<00:00,  5.45it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  3.16it/s]\n",
      "                   all         71        116      0.252      0.112      0.113     0.0231\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G      0.101    0.04114          0         40        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G     0.1011    0.04278          0         49        640:  11%|█         | 2/18 [00:00<00:01, 12.07it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G     0.1018    0.04304          0         55        640:  11%|█         | 2/18 [00:00<00:01, 12.07it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G     0.1021    0.04235          0         53        640:  22%|██▏       | 4/18 [00:00<00:01, 12.00it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G     0.1018    0.04105          0         43        640:  22%|██▏       | 4/18 [00:00<00:01, 12.00it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G     0.1013    0.04076          0         50        640:  33%|███▎      | 6/18 [00:00<00:01, 11.98it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G     0.1005    0.04017          0         40        640:  33%|███▎      | 6/18 [00:00<00:01, 11.98it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G    0.09968    0.03963          0         40        640:  44%|████▍     | 8/18 [00:00<00:00, 11.98it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G    0.09882    0.03938          0         37        640:  44%|████▍     | 8/18 [00:00<00:00, 11.98it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G    0.09816    0.03942          0         41        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.96it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G    0.09792     0.0394          0         47        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.96it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G    0.09694    0.04002          0         49        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.01it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G    0.09636    0.03996          0         45        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.01it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G    0.09585    0.03978          0         41        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.03it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G      0.095    0.04018          0         49        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.03it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G    0.09413    0.04038          0         45        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.99it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G    0.09342    0.04022          0         35        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.99it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       1/49      3.54G    0.09271    0.04013          0         26        640: 100%|██████████| 18/18 [00:01<00:00, 12.11it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  7.35it/s]\n",
      "                   all         71        116      0.486      0.784      0.624      0.179\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.08216    0.03903          0         38        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.08135    0.04209          0         55        640:  11%|█         | 2/18 [00:00<00:01, 12.12it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G     0.0824    0.04114          0         45        640:  11%|█         | 2/18 [00:00<00:01, 12.12it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.08141    0.04199          0         50        640:  22%|██▏       | 4/18 [00:00<00:01, 12.02it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.08193    0.04166          0         52        640:  22%|██▏       | 4/18 [00:00<00:01, 12.02it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.08103    0.04112          0         42        640:  33%|███▎      | 6/18 [00:00<00:00, 12.04it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.08053    0.04048          0         39        640:  33%|███▎      | 6/18 [00:00<00:00, 12.04it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.08005     0.0411          0         50        640:  44%|████▍     | 8/18 [00:00<00:00, 12.04it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.07955    0.04053          0         38        640:  44%|████▍     | 8/18 [00:00<00:00, 12.04it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.07847    0.04097          0         42        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.04it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.07808    0.04138          0         52        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.04it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.07758    0.04136          0         48        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.00it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G     0.0772    0.04202          0         60        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.00it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.07685    0.04216          0         54        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.98it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.07646     0.0419          0         41        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.98it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.07591    0.04176          0         39        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.02it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G    0.07543    0.04123          0         30        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.02it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       2/49      3.54G      0.075    0.04114          0         36        640: 100%|██████████| 18/18 [00:01<00:00, 12.06it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  8.59it/s]\n",
      "                   all         71        116      0.168      0.672      0.162     0.0454\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.07283    0.04427          0         60        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.07062    0.04208          0         46        640:  11%|█         | 2/18 [00:00<00:01, 12.65it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.07072    0.04067          0         42        640:  11%|█         | 2/18 [00:00<00:01, 12.65it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.06857    0.03954          0         41        640:  22%|██▏       | 4/18 [00:00<00:01, 12.27it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.06775    0.04082          0         52        640:  22%|██▏       | 4/18 [00:00<00:01, 12.27it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.06756     0.0407          0         45        640:  33%|███▎      | 6/18 [00:00<00:00, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.06839    0.03896          0         35        640:  33%|███▎      | 6/18 [00:00<00:00, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.06793     0.0394          0         49        640:  44%|████▍     | 8/18 [00:00<00:00, 12.42it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.06751    0.03932          0         43        640:  44%|████▍     | 8/18 [00:00<00:00, 12.42it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G     0.0672     0.0399          0         58        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.23it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.06715    0.03993          0         49        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.23it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.06679    0.04009          0         54        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.06653    0.03943          0         32        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.06629     0.0395          0         50        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.06616    0.03988          0         59        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G      0.066    0.03946          0         44        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.06584     0.0395          0         52        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       3/49      3.54G    0.06579    0.03939          0         36        640: 100%|██████████| 18/18 [00:01<00:00, 12.34it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  8.69it/s]\n",
      "                   all         71        116      0.311      0.733      0.302     0.0881\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06416    0.03753          0         51        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06309    0.03485          0         38        640:  11%|█         | 2/18 [00:00<00:01, 12.75it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06444    0.03511          0         48        640:  11%|█         | 2/18 [00:00<00:01, 12.75it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06392    0.03468          0         46        640:  22%|██▏       | 4/18 [00:00<00:01, 12.35it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06373    0.03483          0         45        640:  22%|██▏       | 4/18 [00:00<00:01, 12.35it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G     0.0633    0.03456          0         46        640:  33%|███▎      | 6/18 [00:00<00:00, 12.15it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06318    0.03554          0         59        640:  33%|███▎      | 6/18 [00:00<00:00, 12.15it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06301    0.03496          0         38        640:  44%|████▍     | 8/18 [00:00<00:00, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06299    0.03516          0         51        640:  44%|████▍     | 8/18 [00:00<00:00, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06258    0.03496          0         43        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.10it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06255    0.03544          0         62        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.10it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06242    0.03529          0         50        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.04it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06232    0.03483          0         42        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.04it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06233     0.0347          0         50        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06222    0.03444          0         44        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06209    0.03412          0         38        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06203    0.03433          0         57        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       4/49      3.54G    0.06221    0.03415          0         35        640: 100%|██████████| 18/18 [00:01<00:00, 12.34it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  8.50it/s]\n",
      "                   all         71        116      0.495      0.629      0.513      0.159\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.05842     0.0293          0         42        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06204    0.03487          0         69        640:  11%|█         | 2/18 [00:00<00:01, 11.89it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06187    0.03198          0         36        640:  11%|█         | 2/18 [00:00<00:01, 11.89it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06151    0.03168          0         52        640:  22%|██▏       | 4/18 [00:00<00:01, 12.21it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06173    0.03092          0         46        640:  22%|██▏       | 4/18 [00:00<00:01, 12.21it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06137    0.03043          0         39        640:  33%|███▎      | 6/18 [00:00<00:00, 12.09it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06171    0.03009          0         46        640:  33%|███▎      | 6/18 [00:00<00:00, 12.09it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06194    0.02996          0         51        640:  44%|████▍     | 8/18 [00:00<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06223    0.02932          0         37        640:  44%|████▍     | 8/18 [00:00<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06214    0.02936          0         45        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06175    0.02916          0         39        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06135     0.0294          0         51        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06143    0.02939          0         48        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06122    0.02935          0         44        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06129    0.02938          0         49        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06164    0.02924          0         56        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06157     0.0292          0         53        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       5/49      3.54G    0.06135    0.02904          0         29        640: 100%|██████████| 18/18 [00:01<00:00, 12.28it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  8.32it/s]\n",
      "                   all         71        116      0.315      0.698      0.337      0.126\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06105    0.02615          0         43        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06102    0.02705          0         58        640:  11%|█         | 2/18 [00:00<00:01, 12.72it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G     0.0631    0.02657          0         44        640:  11%|█         | 2/18 [00:00<00:01, 12.72it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06354    0.02653          0         46        640:  22%|██▏       | 4/18 [00:00<00:01, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06328    0.02618          0         39        640:  22%|██▏       | 4/18 [00:00<00:01, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G      0.063    0.02678          0         48        640:  33%|███▎      | 6/18 [00:00<00:00, 12.53it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06266    0.02646          0         43        640:  33%|███▎      | 6/18 [00:00<00:00, 12.53it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06201    0.02614          0         40        640:  44%|████▍     | 8/18 [00:00<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06171    0.02652          0         55        640:  44%|████▍     | 8/18 [00:00<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06166    0.02685          0         52        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.48it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06131    0.02745          0         53        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.48it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06096    0.02762          0         49        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06106    0.02759          0         47        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06069    0.02776          0         63        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.44it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06051    0.02792          0         60        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.44it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06041     0.0278          0         48        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.31it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06008    0.02787          0         53        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.31it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       6/49      3.54G    0.06012     0.0278          0         40        640: 100%|██████████| 18/18 [00:01<00:00, 12.53it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.27it/s]\n",
      "                   all         71        116      0.583      0.534      0.616      0.263\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05221    0.02573          0         43        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05541    0.02632          0         53        640:  11%|█         | 2/18 [00:00<00:01, 12.00it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05702    0.02855          0         67        640:  11%|█         | 2/18 [00:00<00:01, 12.00it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05781    0.02701          0         44        640:  22%|██▏       | 4/18 [00:00<00:01, 12.36it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05924    0.02592          0         29        640:  22%|██▏       | 4/18 [00:00<00:01, 12.36it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05964     0.0261          0         48        640:  33%|███▎      | 6/18 [00:00<00:00, 12.18it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05806    0.02579          0         38        640:  33%|███▎      | 6/18 [00:00<00:00, 12.18it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G     0.0574    0.02579          0         52        640:  44%|████▍     | 8/18 [00:00<00:00, 12.37it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05706    0.02573          0         48        640:  44%|████▍     | 8/18 [00:00<00:00, 12.37it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G     0.0567    0.02601          0         53        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.22it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05697    0.02572          0         45        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.22it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05755    0.02567          0         49        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.36it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05787    0.02526          0         35        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.36it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05833    0.02501          0         37        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.20it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05794    0.02485          0         39        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.20it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05753     0.0251          0         53        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.36it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05694    0.02489          0         32        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.36it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       7/49      3.54G    0.05691    0.02501          0         43        640: 100%|██████████| 18/18 [00:01<00:00, 12.36it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.58it/s]\n",
      "                   all         71        116      0.514      0.603      0.617      0.265\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05721    0.02788          0         54        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05623    0.02691          0         50        640:  11%|█         | 2/18 [00:00<00:01, 12.67it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05614    0.02526          0         44        640:  11%|█         | 2/18 [00:00<00:01, 12.67it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05652      0.024          0         38        640:  22%|██▏       | 4/18 [00:00<00:01, 12.01it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05461    0.02435          0         42        640:  22%|██▏       | 4/18 [00:00<00:01, 12.01it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G     0.0537     0.0243          0         44        640:  33%|███▎      | 6/18 [00:00<00:00, 12.27it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05284    0.02489          0         54        640:  33%|███▎      | 6/18 [00:00<00:00, 12.27it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05229    0.02485          0         42        640:  44%|████▍     | 8/18 [00:00<00:00, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05255    0.02483          0         56        640:  44%|████▍     | 8/18 [00:00<00:00, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05275    0.02489          0         55        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.38it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05313    0.02477          0         46        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.38it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05308    0.02475          0         47        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.20it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05287    0.02492          0         53        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.20it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05261    0.02482          0         48        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.40it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G     0.0522    0.02491          0         51        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.40it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05197    0.02476          0         44        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05201    0.02518          0         67        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       8/49      3.54G    0.05223    0.02484          0         23        640: 100%|██████████| 18/18 [00:01<00:00, 12.47it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.85it/s]\n",
      "                   all         71        116      0.514      0.721      0.626      0.238\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05431    0.02524          0         52        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05314    0.02587          0         56        640:  11%|█         | 2/18 [00:00<00:01, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G     0.0519     0.0252          0         45        640:  11%|█         | 2/18 [00:00<00:01, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05221    0.02459          0         51        640:  22%|██▏       | 4/18 [00:00<00:01, 12.74it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05239    0.02447          0         53        640:  22%|██▏       | 4/18 [00:00<00:01, 12.74it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05223    0.02404          0         45        640:  33%|███▎      | 6/18 [00:00<00:00, 12.51it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05233    0.02398          0         55        640:  33%|███▎      | 6/18 [00:00<00:00, 12.51it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05304    0.02342          0         33        640:  44%|████▍     | 8/18 [00:00<00:00, 12.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05337    0.02365          0         55        640:  44%|████▍     | 8/18 [00:00<00:00, 12.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05342    0.02423          0         61        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G      0.053    0.02424          0         48        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05234    0.02421          0         44        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.72it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05204    0.02429          0         48        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.72it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05169     0.0242          0         52        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05199     0.0242          0         54        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05224    0.02389          0         45        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.68it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05228     0.0242          0         57        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.68it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "       9/49      3.54G    0.05237    0.02403          0         29        640: 100%|██████████| 18/18 [00:01<00:00, 12.71it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.98it/s]\n",
      "                   all         71        116        0.9      0.926      0.936      0.447\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.05011    0.01984          0         44        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G     0.0469    0.02059          0         41        640:  11%|█         | 2/18 [00:00<00:01, 12.97it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04814    0.02178          0         55        640:  11%|█         | 2/18 [00:00<00:01, 12.97it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04667    0.02154          0         42        640:  22%|██▏       | 4/18 [00:00<00:01, 12.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04725    0.02129          0         40        640:  22%|██▏       | 4/18 [00:00<00:01, 12.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04789    0.02136          0         50        640:  33%|███▎      | 6/18 [00:00<00:00, 12.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04865    0.02124          0         45        640:  33%|███▎      | 6/18 [00:00<00:00, 12.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04924    0.02121          0         40        640:  44%|████▍     | 8/18 [00:00<00:00, 12.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04839    0.02126          0         40        640:  44%|████▍     | 8/18 [00:00<00:00, 12.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G      0.048    0.02177          0         51        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.74it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04766    0.02177          0         42        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.74it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04761    0.02186          0         53        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.57it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04786    0.02167          0         38        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.57it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04789    0.02206          0         61        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.69it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04783    0.02209          0         52        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.69it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04816    0.02209          0         47        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.57it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04789    0.02198          0         39        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.57it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      10/49      3.54G    0.04746     0.0219          0         25        640: 100%|██████████| 18/18 [00:01<00:00, 12.79it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.19it/s]\n",
      "                   all         71        116      0.931      0.925      0.955      0.491\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04562    0.02049          0         45        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04866    0.02298          0         59        640:  11%|█         | 2/18 [00:00<00:01, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04795    0.02264          0         41        640:  11%|█         | 2/18 [00:00<00:01, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04843    0.02156          0         38        640:  22%|██▏       | 4/18 [00:00<00:01, 12.69it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04804    0.02221          0         60        640:  22%|██▏       | 4/18 [00:00<00:01, 12.69it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04811    0.02141          0         31        640:  33%|███▎      | 6/18 [00:00<00:00, 12.48it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04807    0.02113          0         38        640:  33%|███▎      | 6/18 [00:00<00:00, 12.48it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04832    0.02105          0         46        640:  44%|████▍     | 8/18 [00:00<00:00, 12.71it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04764    0.02114          0         47        640:  44%|████▍     | 8/18 [00:00<00:00, 12.71it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04723    0.02097          0         41        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04682    0.02164          0         71        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G     0.0468    0.02205          0         62        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.60it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04694     0.0218          0         42        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.60it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04708    0.02164          0         42        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04654    0.02151          0         34        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04621    0.02143          0         39        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04629    0.02117          0         35        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      11/49      3.54G    0.04681    0.02121          0         39        640: 100%|██████████| 18/18 [00:01<00:00, 12.65it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.28it/s]\n",
      "                   all         71        116       0.61      0.897      0.754      0.346\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G    0.04503     0.0206          0         47        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G    0.04475    0.02416          0         61        640:  11%|█         | 2/18 [00:00<00:01, 12.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G    0.04537    0.02318          0         49        640:  11%|█         | 2/18 [00:00<00:01, 12.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G     0.0444    0.02293          0         54        640:  22%|██▏       | 4/18 [00:00<00:01, 12.31it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G     0.0446    0.02168          0         36        640:  22%|██▏       | 4/18 [00:00<00:01, 12.31it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G    0.04518    0.02144          0         51        640:  33%|███▎      | 6/18 [00:00<00:00, 12.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G    0.04596    0.02144          0         52        640:  33%|███▎      | 6/18 [00:00<00:00, 12.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G    0.04631    0.02096          0         43        640:  44%|████▍     | 8/18 [00:00<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G    0.04595    0.02096          0         53        640:  44%|████▍     | 8/18 [00:00<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G    0.04592    0.02118          0         49        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.50it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G    0.04559    0.02163          0         61        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.50it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G    0.04564    0.02171          0         49        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.28it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G     0.0459    0.02136          0         42        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.28it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G     0.0458    0.02131          0         45        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.41it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G    0.04624     0.0215          0         64        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.41it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G    0.04627    0.02155          0         53        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.23it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G    0.04614    0.02163          0         49        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.23it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      12/49      3.54G     0.0462    0.02145          0         33        640: 100%|██████████| 18/18 [00:01<00:00, 12.51it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.99it/s]\n",
      "                   all         71        116       0.74      0.871      0.843       0.42\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04456    0.02088          0         50        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04429    0.02272          0         66        640:  11%|█         | 2/18 [00:00<00:01, 12.00it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04488    0.02149          0         45        640:  11%|█         | 2/18 [00:00<00:01, 12.00it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04513    0.02125          0         43        640:  22%|██▏       | 4/18 [00:00<00:01, 12.42it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04495    0.02079          0         49        640:  22%|██▏       | 4/18 [00:00<00:01, 12.42it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G     0.0446    0.02109          0         53        640:  33%|███▎      | 6/18 [00:00<00:00, 12.23it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04486    0.02111          0         52        640:  33%|███▎      | 6/18 [00:00<00:00, 12.23it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G      0.045    0.02115          0         47        640:  44%|████▍     | 8/18 [00:00<00:00, 12.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G      0.045    0.02106          0         43        640:  44%|████▍     | 8/18 [00:00<00:00, 12.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04474    0.02082          0         40        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04469    0.02109          0         54        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04475    0.02118          0         53        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.53it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04485    0.02113          0         51        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.53it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04509    0.02114          0         55        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04517    0.02127          0         55        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G     0.0452     0.0209          0         33        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.45it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04523    0.02094          0         46        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.45it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      13/49      3.54G    0.04539    0.02098          0         38        640: 100%|██████████| 18/18 [00:01<00:00, 12.47it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.32it/s]\n",
      "                   all         71        116      0.963      0.957      0.984      0.521\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04324    0.01508          0         33        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04343    0.01805          0         52        640:  11%|█         | 2/18 [00:00<00:01, 13.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04254    0.01829          0         38        640:  11%|█         | 2/18 [00:00<00:01, 13.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04173    0.01887          0         45        640:  22%|██▏       | 4/18 [00:00<00:01, 12.59it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04255     0.0193          0         47        640:  22%|██▏       | 4/18 [00:00<00:01, 12.59it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04286    0.01999          0         59        640:  33%|███▎      | 6/18 [00:00<00:00, 12.78it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04306    0.02009          0         50        640:  33%|███▎      | 6/18 [00:00<00:00, 12.78it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04362    0.01976          0         41        640:  44%|████▍     | 8/18 [00:00<00:00, 12.57it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04334     0.0198          0         50        640:  44%|████▍     | 8/18 [00:00<00:00, 12.57it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04308    0.01973          0         44        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G      0.043    0.01994          0         57        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04251    0.02013          0         47        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.26it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04268    0.02017          0         46        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.26it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04266    0.02028          0         51        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.50it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04256    0.02025          0         46        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.50it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04299    0.02027          0         49        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04272    0.02033          0         50        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      14/49      3.54G    0.04248    0.02043          0         39        640: 100%|██████████| 18/18 [00:01<00:00, 12.62it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.28it/s]\n",
      "                   all         71        116      0.957      0.957      0.982      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G     0.0403    0.02179          0         52        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.03818    0.02157          0         43        640:  11%|█         | 2/18 [00:00<00:01, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G     0.0392    0.02226          0         61        640:  11%|█         | 2/18 [00:00<00:01, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.03959    0.02285          0         67        640:  22%|██▏       | 4/18 [00:00<00:01, 12.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.04005    0.02212          0         44        640:  22%|██▏       | 4/18 [00:00<00:01, 12.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.04064    0.02203          0         51        640:  33%|███▎      | 6/18 [00:00<00:00, 12.27it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.04031    0.02159          0         45        640:  33%|███▎      | 6/18 [00:00<00:00, 12.27it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.04015    0.02102          0         42        640:  44%|████▍     | 8/18 [00:00<00:00, 12.48it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G     0.0401    0.02074          0         43        640:  44%|████▍     | 8/18 [00:00<00:00, 12.48it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.03996    0.02039          0         41        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.04019    0.02022          0         42        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.04034    0.01998          0         42        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.51it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.04054    0.02007          0         46        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.51it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.04094    0.01996          0         48        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.35it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G     0.0409    0.02006          0         47        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.35it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.04089    0.01982          0         41        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.52it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.04082    0.01983          0         52        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.52it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      15/49      3.54G    0.04106    0.02002          0         46        640: 100%|██████████| 18/18 [00:01<00:00, 12.53it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.56it/s]\n",
      "                   all         71        116      0.891      0.931      0.925      0.409\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.03988    0.02225          0         59        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04162    0.01953          0         44        640:  11%|█         | 2/18 [00:00<00:01, 12.90it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04365    0.01903          0         41        640:  11%|█         | 2/18 [00:00<00:01, 12.90it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04421    0.01891          0         45        640:  22%|██▏       | 4/18 [00:00<00:01, 12.47it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04275     0.0188          0         43        640:  22%|██▏       | 4/18 [00:00<00:01, 12.47it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04142    0.01838          0         40        640:  33%|███▎      | 6/18 [00:00<00:00, 12.70it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04061    0.01864          0         42        640:  33%|███▎      | 6/18 [00:00<00:00, 12.70it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04023    0.01824          0         35        640:  44%|████▍     | 8/18 [00:00<00:00, 12.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04084    0.01834          0         51        640:  44%|████▍     | 8/18 [00:00<00:00, 12.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04146     0.0187          0         54        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.40it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04173    0.01877          0         47        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.40it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04206    0.01899          0         54        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.22it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04161    0.01899          0         49        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.22it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04135    0.01882          0         37        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G     0.0409    0.01901          0         44        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04118    0.01933          0         68        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.21it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04126    0.01934          0         48        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.21it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      16/49      3.54G    0.04145    0.01932          0         32        640: 100%|██████████| 18/18 [00:01<00:00, 12.47it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.53it/s]\n",
      "                   all         71        116      0.978      0.931       0.98      0.415\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04313    0.01837          0         44        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04518    0.01797          0         42        640:  11%|█         | 2/18 [00:00<00:01, 11.88it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04274    0.01846          0         48        640:  11%|█         | 2/18 [00:00<00:01, 11.88it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04109      0.018          0         46        640:  22%|██▏       | 4/18 [00:00<00:01, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04016    0.01829          0         51        640:  22%|██▏       | 4/18 [00:00<00:01, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.03906    0.01796          0         34        640:  33%|███▎      | 6/18 [00:00<00:00, 12.15it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.03963    0.01806          0         47        640:  33%|███▎      | 6/18 [00:00<00:00, 12.15it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04039     0.0181          0         42        640:  44%|████▍     | 8/18 [00:00<00:00, 12.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04011     0.0184          0         53        640:  44%|████▍     | 8/18 [00:00<00:00, 12.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04097    0.01839          0         42        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.12it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04081    0.01855          0         54        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.12it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04065    0.01874          0         51        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04033    0.01854          0         37        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G       0.04    0.01818          0         30        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04031    0.01816          0         43        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04055    0.01822          0         47        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04037    0.01838          0         59        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      17/49      3.54G    0.04055    0.01823          0         29        640: 100%|██████████| 18/18 [00:01<00:00, 12.31it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.27it/s]\n",
      "                   all         71        116      0.965      0.964      0.989      0.595\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03617    0.02068          0         50        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03547    0.01953          0         40        640:  11%|█         | 2/18 [00:00<00:01, 12.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03635    0.01841          0         43        640:  11%|█         | 2/18 [00:00<00:01, 12.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03661    0.02006          0         59        640:  22%|██▏       | 4/18 [00:00<00:01, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03708    0.02058          0         60        640:  22%|██▏       | 4/18 [00:00<00:01, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03734    0.02091          0         61        640:  33%|███▎      | 6/18 [00:00<00:00, 12.36it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03753    0.02018          0         41        640:  33%|███▎      | 6/18 [00:00<00:00, 12.36it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G     0.0381    0.01977          0         44        640:  44%|████▍     | 8/18 [00:00<00:00, 12.11it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03824     0.0195          0         47        640:  44%|████▍     | 8/18 [00:00<00:00, 12.11it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03857    0.02017          0         64        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G     0.0382       0.02          0         41        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03828    0.01986          0         45        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.14it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03824    0.01954          0         40        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.14it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03843    0.01949          0         43        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03862    0.01936          0         43        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03861    0.01915          0         44        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G    0.03881    0.01932          0         59        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      18/49      3.54G     0.0388    0.01904          0         26        640: 100%|██████████| 18/18 [00:01<00:00, 12.38it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.95it/s]\n",
      "                   all         71        116      0.972      0.957       0.99      0.592\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03624     0.0164          0         43        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03498    0.01657          0         37        640:  11%|█         | 2/18 [00:00<00:01, 11.94it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03575    0.01819          0         63        640:  11%|█         | 2/18 [00:00<00:01, 11.94it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03668    0.01815          0         43        640:  22%|██▏       | 4/18 [00:00<00:01, 12.32it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03693    0.01836          0         47        640:  22%|██▏       | 4/18 [00:00<00:01, 12.32it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03796    0.01859          0         45        640:  33%|███▎      | 6/18 [00:00<00:00, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03762    0.01867          0         49        640:  33%|███▎      | 6/18 [00:00<00:00, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03732    0.01842          0         41        640:  44%|████▍     | 8/18 [00:00<00:00, 12.35it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03764     0.0188          0         59        640:  44%|████▍     | 8/18 [00:00<00:00, 12.35it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03772    0.01848          0         43        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03777    0.01877          0         52        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03798    0.01888          0         47        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G     0.0379     0.0189          0         50        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03804    0.01876          0         45        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.23it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03799    0.01872          0         48        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.23it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03788    0.01871          0         44        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.39it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03781    0.01851          0         36        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.39it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      19/49      3.54G    0.03765    0.01817          0         21        640: 100%|██████████| 18/18 [00:01<00:00, 12.37it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.48it/s]\n",
      "                   all         71        116      0.958      0.966      0.986      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03763    0.01501          0         35        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03855    0.01499          0         40        640:  11%|█         | 2/18 [00:00<00:01, 12.82it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03812    0.01563          0         42        640:  11%|█         | 2/18 [00:00<00:01, 12.82it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03791    0.01632          0         55        640:  22%|██▏       | 4/18 [00:00<00:01, 12.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03806    0.01625          0         41        640:  22%|██▏       | 4/18 [00:00<00:01, 12.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03808    0.01654          0         48        640:  33%|███▎      | 6/18 [00:00<00:00, 12.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03809    0.01664          0         46        640:  33%|███▎      | 6/18 [00:00<00:00, 12.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03807    0.01684          0         44        640:  44%|████▍     | 8/18 [00:00<00:00, 12.28it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03808    0.01725          0         56        640:  44%|████▍     | 8/18 [00:00<00:00, 12.28it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03797    0.01723          0         41        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03792    0.01794          0         66        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03815    0.01782          0         43        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.27it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03801    0.01772          0         42        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.27it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03769    0.01777          0         42        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.42it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03764    0.01762          0         42        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.42it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03755    0.01772          0         47        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.22it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03778     0.0177          0         40        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.22it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      20/49      3.54G    0.03794    0.01787          0         41        640: 100%|██████████| 18/18 [00:01<00:00, 12.47it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.72it/s]\n",
      "                   all         71        116      0.974      0.948      0.987      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03739    0.01399          0         39        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03881     0.0155          0         39        640:  11%|█         | 2/18 [00:00<00:01, 12.03it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03788    0.01657          0         51        640:  11%|█         | 2/18 [00:00<00:01, 12.03it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03733    0.01661          0         45        640:  22%|██▏       | 4/18 [00:00<00:01, 12.52it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03725    0.01731          0         54        640:  22%|██▏       | 4/18 [00:00<00:01, 12.52it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03655    0.01697          0         38        640:  33%|███▎      | 6/18 [00:00<00:00, 12.36it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G     0.0371    0.01658          0         37        640:  33%|███▎      | 6/18 [00:00<00:00, 12.36it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03684     0.0166          0         43        640:  44%|████▍     | 8/18 [00:00<00:00, 12.59it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03694    0.01719          0         56        640:  44%|████▍     | 8/18 [00:00<00:00, 12.59it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03712    0.01724          0         46        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.47it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03687    0.01755          0         55        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.47it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G     0.0366    0.01751          0         44        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.65it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G     0.0362    0.01727          0         33        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.65it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03611    0.01735          0         49        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.52it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03624    0.01744          0         54        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.52it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03657    0.01728          0         34        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.68it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03675    0.01718          0         40        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.68it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      21/49      3.54G    0.03692    0.01708          0         31        640: 100%|██████████| 18/18 [00:01<00:00, 12.65it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.55it/s]\n",
      "                   all         71        116      0.991      0.957      0.992      0.598\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G     0.0354    0.01923          0         50        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03408    0.01676          0         34        640:  11%|█         | 2/18 [00:00<00:01, 13.03it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03477    0.01614          0         32        640:  11%|█         | 2/18 [00:00<00:01, 13.03it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03496    0.01721          0         51        640:  22%|██▏       | 4/18 [00:00<00:01, 12.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03511    0.01689          0         40        640:  22%|██▏       | 4/18 [00:00<00:01, 12.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03496    0.01656          0         41        640:  33%|███▎      | 6/18 [00:00<00:00, 12.76it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03504    0.01676          0         47        640:  33%|███▎      | 6/18 [00:00<00:00, 12.76it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03576    0.01598          0         29        640:  44%|████▍     | 8/18 [00:00<00:00, 12.53it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03575     0.0161          0         42        640:  44%|████▍     | 8/18 [00:00<00:00, 12.53it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03613    0.01648          0         54        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.70it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03645    0.01658          0         52        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.70it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03605    0.01639          0         34        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03609    0.01657          0         52        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03601    0.01666          0         45        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.71it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03594    0.01688          0         57        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.71it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03584     0.0171          0         56        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03609    0.01688          0         40        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      22/49      3.54G    0.03639    0.01667          0         28        640: 100%|██████████| 18/18 [00:01<00:00, 12.78it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.56it/s]\n",
      "                   all         71        116       0.99      0.957      0.992      0.588\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G     0.0366    0.01464          0         44        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03643    0.01409          0         28        640:  11%|█         | 2/18 [00:00<00:01, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G     0.0354    0.01479          0         48        640:  11%|█         | 2/18 [00:00<00:01, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03543    0.01529          0         38        640:  22%|██▏       | 4/18 [00:00<00:01, 12.70it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03558    0.01555          0         39        640:  22%|██▏       | 4/18 [00:00<00:01, 12.70it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03585    0.01611          0         57        640:  33%|███▎      | 6/18 [00:00<00:00, 12.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03596    0.01645          0         49        640:  33%|███▎      | 6/18 [00:00<00:00, 12.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03582    0.01641          0         40        640:  44%|████▍     | 8/18 [00:00<00:00, 12.68it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03569    0.01679          0         50        640:  44%|████▍     | 8/18 [00:00<00:00, 12.68it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03583    0.01666          0         43        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.52it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03552    0.01654          0         39        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.52it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03548    0.01656          0         49        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.61it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03533    0.01633          0         33        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.61it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03526     0.0165          0         50        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03519    0.01646          0         43        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03511    0.01635          0         36        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.42it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03513    0.01621          0         40        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.42it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      23/49      3.54G    0.03523    0.01617          0         30        640: 100%|██████████| 18/18 [00:01<00:00, 12.53it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.50it/s]\n",
      "                   all         71        116      0.938       0.94      0.965      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03456    0.02072          0         57        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03594    0.01888          0         41        640:  11%|█         | 2/18 [00:00<00:01, 12.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03567    0.01937          0         60        640:  11%|█         | 2/18 [00:00<00:01, 12.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03522    0.01895          0         50        640:  22%|██▏       | 4/18 [00:00<00:01, 12.22it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03422    0.01756          0         27        640:  22%|██▏       | 4/18 [00:00<00:01, 12.22it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03445    0.01681          0         33        640:  33%|███▎      | 6/18 [00:00<00:00, 12.44it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03511    0.01598          0         28        640:  33%|███▎      | 6/18 [00:00<00:00, 12.44it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03517    0.01648          0         49        640:  44%|████▍     | 8/18 [00:00<00:00, 12.26it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03484    0.01674          0         47        640:  44%|████▍     | 8/18 [00:00<00:00, 12.26it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03479    0.01655          0         40        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.45it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03483     0.0169          0         54        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.45it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G     0.0348    0.01703          0         45        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03499    0.01684          0         38        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03517    0.01683          0         44        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.31it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03488    0.01674          0         43        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.31it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03494    0.01673          0         44        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03495    0.01671          0         42        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      24/49      3.54G    0.03502    0.01693          0         44        640: 100%|██████████| 18/18 [00:01<00:00, 12.41it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.47it/s]\n",
      "                   all         71        116      0.954      0.983      0.993      0.564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03603     0.0204          0         63        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03486    0.01991          0         54        640:  11%|█         | 2/18 [00:00<00:01, 11.99it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03441    0.01849          0         40        640:  11%|█         | 2/18 [00:00<00:01, 11.99it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03385    0.01831          0         45        640:  22%|██▏       | 4/18 [00:00<00:01, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03417    0.01827          0         46        640:  22%|██▏       | 4/18 [00:00<00:01, 12.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G      0.034    0.01782          0         40        640:  33%|███▎      | 6/18 [00:00<00:00, 12.11it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03417    0.01743          0         41        640:  33%|███▎      | 6/18 [00:00<00:00, 12.11it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03439    0.01714          0         43        640:  44%|████▍     | 8/18 [00:00<00:00, 12.31it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03431    0.01671          0         31        640:  44%|████▍     | 8/18 [00:00<00:00, 12.31it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03422    0.01696          0         46        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03405    0.01693          0         45        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03403    0.01695          0         51        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03407    0.01686          0         41        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03383    0.01714          0         56        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.14it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03405    0.01708          0         51        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.14it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03388    0.01714          0         49        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03394    0.01715          0         44        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      25/49      3.54G    0.03403    0.01733          0         39        640: 100%|██████████| 18/18 [00:01<00:00, 12.31it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.27it/s]\n",
      "                   all         71        116      0.954      0.974      0.989      0.605\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03376    0.02034          0         49        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03544    0.01838          0         41        640:  11%|█         | 2/18 [00:00<00:01, 12.64it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03424    0.01964          0         65        640:  11%|█         | 2/18 [00:00<00:01, 12.64it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03402    0.01958          0         49        640:  22%|██▏       | 4/18 [00:00<00:01, 12.20it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03367     0.0189          0         46        640:  22%|██▏       | 4/18 [00:00<00:01, 12.20it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03425    0.01872          0         57        640:  33%|███▎      | 6/18 [00:00<00:00, 12.39it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03405    0.01909          0         58        640:  33%|███▎      | 6/18 [00:00<00:00, 12.39it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03393    0.01896          0         47        640:  44%|████▍     | 8/18 [00:00<00:00, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03405    0.01877          0         43        640:  44%|████▍     | 8/18 [00:00<00:00, 12.17it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03395    0.01849          0         42        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03389    0.01807          0         42        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03402    0.01809          0         57        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.12it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03417      0.018          0         48        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.12it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03419    0.01772          0         43        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.26it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03412    0.01766          0         41        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.26it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03407    0.01772          0         51        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03411    0.01779          0         50        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      26/49      3.54G    0.03403    0.01785          0         39        640: 100%|██████████| 18/18 [00:01<00:00, 12.38it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.36it/s]\n",
      "                   all         71        116      0.957      0.969      0.989      0.597\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03452    0.01581          0         47        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03265    0.01648          0         51        640:  11%|█         | 2/18 [00:00<00:01, 11.91it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03322    0.01713          0         53        640:  11%|█         | 2/18 [00:00<00:01, 11.91it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03342    0.01674          0         42        640:  22%|██▏       | 4/18 [00:00<00:01, 12.32it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03423    0.01645          0         40        640:  22%|██▏       | 4/18 [00:00<00:01, 12.32it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03408    0.01682          0         48        640:  33%|███▎      | 6/18 [00:00<00:00, 12.12it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03345     0.0168          0         48        640:  33%|███▎      | 6/18 [00:00<00:00, 12.12it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03317    0.01669          0         47        640:  44%|████▍     | 8/18 [00:00<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03301    0.01658          0         41        640:  44%|████▍     | 8/18 [00:00<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03297    0.01654          0         46        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.22it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03306    0.01658          0         48        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.22it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03357    0.01647          0         46        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.39it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G     0.0337    0.01643          0         45        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.39it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03383    0.01645          0         42        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.21it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03352    0.01638          0         42        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.21it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03323    0.01603          0         31        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G    0.03325    0.01629          0         57        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      27/49      3.54G      0.033    0.01648          0         42        640: 100%|██████████| 18/18 [00:01<00:00, 12.36it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.46it/s]\n",
      "                   all         71        116      0.943       0.94      0.988      0.585\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03957    0.01749          0         55        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03726    0.01634          0         43        640:  11%|█         | 2/18 [00:00<00:01, 12.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03671    0.01636          0         42        640:  11%|█         | 2/18 [00:00<00:01, 12.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03624    0.01636          0         46        640:  22%|██▏       | 4/18 [00:00<00:01, 12.19it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03569    0.01643          0         39        640:  22%|██▏       | 4/18 [00:00<00:01, 12.19it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03486    0.01601          0         37        640:  33%|███▎      | 6/18 [00:00<00:00, 12.39it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03391    0.01589          0         40        640:  33%|███▎      | 6/18 [00:00<00:00, 12.39it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G     0.0334    0.01609          0         45        640:  44%|████▍     | 8/18 [00:00<00:00, 12.15it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03346    0.01602          0         41        640:  44%|████▍     | 8/18 [00:00<00:00, 12.15it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03351    0.01582          0         38        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.28it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03372    0.01577          0         44        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.28it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03383    0.01604          0         52        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.14it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03359    0.01635          0         53        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.14it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03343    0.01632          0         47        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.31it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03324    0.01648          0         53        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.31it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03295    0.01623          0         34        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03311    0.01638          0         54        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      28/49      3.54G    0.03323     0.0162          0         26        640: 100%|██████████| 18/18 [00:01<00:00, 12.39it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.30it/s]\n",
      "                   all         71        116      0.975      0.966      0.994      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03841    0.01642          0         40        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03631    0.01525          0         37        640:  11%|█         | 2/18 [00:00<00:01, 11.94it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03338    0.01392          0         29        640:  11%|█         | 2/18 [00:00<00:01, 11.94it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03385    0.01396          0         39        640:  22%|██▏       | 4/18 [00:00<00:01, 11.69it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03373    0.01517          0         58        640:  22%|██▏       | 4/18 [00:00<00:01, 11.69it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03344    0.01567          0         52        640:  33%|███▎      | 6/18 [00:00<00:01, 11.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03325    0.01586          0         47        640:  33%|███▎      | 6/18 [00:00<00:01, 11.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03284    0.01576          0         45        640:  44%|████▍     | 8/18 [00:00<00:00, 11.91it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03266    0.01581          0         45        640:  44%|████▍     | 8/18 [00:00<00:00, 11.91it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03305    0.01611          0         49        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.87it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03307    0.01667          0         64        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.87it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G      0.033    0.01691          0         52        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03289    0.01685          0         42        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03262    0.01666          0         40        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.06it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03251    0.01655          0         47        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.06it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03244    0.01666          0         54        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03234    0.01676          0         54        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      29/49      3.54G    0.03226     0.0166          0         32        640: 100%|██████████| 18/18 [00:01<00:00, 12.10it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.36it/s]\n",
      "                   all         71        116      0.983      0.974      0.994      0.648\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03043     0.0203          0         56        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03017    0.01608          0         33        640:  11%|█         | 2/18 [00:00<00:01, 11.60it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03045    0.01676          0         52        640:  11%|█         | 2/18 [00:00<00:01, 11.60it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03064    0.01613          0         40        640:  22%|██▏       | 4/18 [00:00<00:01, 10.99it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03094    0.01634          0         49        640:  22%|██▏       | 4/18 [00:00<00:01, 10.99it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03149    0.01657          0         57        640:  33%|███▎      | 6/18 [00:00<00:01, 11.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G     0.0317    0.01673          0         41        640:  33%|███▎      | 6/18 [00:00<00:01, 11.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03199    0.01673          0         53        640:  44%|████▍     | 8/18 [00:00<00:00, 11.42it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03191    0.01672          0         53        640:  44%|████▍     | 8/18 [00:00<00:00, 11.42it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03172    0.01668          0         51        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.58it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03164    0.01682          0         56        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.58it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03166    0.01659          0         37        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.41it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03165    0.01643          0         42        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.41it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03165    0.01671          0         59        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.54it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03168    0.01665          0         42        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.54it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03175    0.01669          0         52        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.48it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03165    0.01668          0         43        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.48it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      30/49      3.54G    0.03166    0.01673          0         38        640: 100%|██████████| 18/18 [00:01<00:00, 11.64it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.45it/s]\n",
      "                   all         71        116      0.983      0.966      0.994      0.656\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03244    0.01789          0         56        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G      0.032     0.0182          0         54        640:  11%|█         | 2/18 [00:00<00:01, 11.39it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03207     0.0174          0         42        640:  11%|█         | 2/18 [00:00<00:01, 11.39it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03224    0.01714          0         48        640:  22%|██▏       | 4/18 [00:00<00:01, 11.72it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03237    0.01784          0         68        640:  22%|██▏       | 4/18 [00:00<00:01, 11.72it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G     0.0328    0.01732          0         43        640:  33%|███▎      | 6/18 [00:00<00:01, 11.39it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03241    0.01768          0         55        640:  33%|███▎      | 6/18 [00:00<00:01, 11.39it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G     0.0321    0.01745          0         46        640:  44%|████▍     | 8/18 [00:00<00:00, 11.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03198    0.01773          0         56        640:  44%|████▍     | 8/18 [00:00<00:00, 11.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03164    0.01745          0         46        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03207    0.01795          0         61        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03205    0.01767          0         50        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.70it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03221    0.01753          0         41        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.70it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03257    0.01752          0         49        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G     0.0323    0.01742          0         43        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03214    0.01717          0         38        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.78it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03204    0.01727          0         57        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.78it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      31/49      3.54G    0.03187    0.01724          0         38        640: 100%|██████████| 18/18 [00:01<00:00, 11.71it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.05it/s]\n",
      "                   all         71        116      0.967      0.974      0.992      0.591\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03056    0.02016          0         49        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03282      0.019          0         52        640:  11%|█         | 2/18 [00:00<00:01, 12.18it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03489    0.01918          0         50        640:  11%|█         | 2/18 [00:00<00:01, 12.18it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03389    0.01928          0         58        640:  22%|██▏       | 4/18 [00:00<00:01, 11.67it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03359    0.01864          0         48        640:  22%|██▏       | 4/18 [00:00<00:01, 11.67it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G     0.0327    0.01926          0         65        640:  33%|███▎      | 6/18 [00:00<00:01, 11.85it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03209    0.01895          0         49        640:  33%|███▎      | 6/18 [00:00<00:01, 11.85it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03175    0.01861          0         47        640:  44%|████▍     | 8/18 [00:00<00:00, 11.65it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03227    0.01794          0         34        640:  44%|████▍     | 8/18 [00:00<00:00, 11.65it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03221    0.01776          0         51        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.75it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03216    0.01731          0         36        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.75it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03223    0.01736          0         56        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.59it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03198    0.01723          0         48        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.59it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03184     0.0172          0         49        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03186    0.01716          0         51        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G     0.0317    0.01721          0         50        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.57it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03156    0.01711          0         48        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.57it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      32/49      3.54G    0.03155    0.01706          0         36        640: 100%|██████████| 18/18 [00:01<00:00, 11.83it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.81it/s]\n",
      "                   all         71        116      0.971      0.974      0.992      0.614\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03421    0.01516          0         44        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03414    0.01518          0         44        640:  11%|█         | 2/18 [00:00<00:01, 11.38it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03295    0.01617          0         51        640:  11%|█         | 2/18 [00:00<00:01, 11.38it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03316    0.01753          0         63        640:  22%|██▏       | 4/18 [00:00<00:01, 11.91it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03199    0.01731          0         52        640:  22%|██▏       | 4/18 [00:00<00:01, 11.91it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03173    0.01733          0         51        640:  33%|███▎      | 6/18 [00:00<00:01, 11.66it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03185    0.01723          0         50        640:  33%|███▎      | 6/18 [00:00<00:01, 11.66it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03203    0.01652          0         33        640:  44%|████▍     | 8/18 [00:00<00:00, 11.88it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03214    0.01669          0         51        640:  44%|████▍     | 8/18 [00:00<00:00, 11.88it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03217    0.01678          0         51        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.61it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03182    0.01681          0         46        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.61it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03183     0.0167          0         49        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.83it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03192      0.017          0         64        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.83it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03197    0.01717          0         66        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.72it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03228    0.01704          0         40        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.72it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03226    0.01711          0         47        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.89it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03236    0.01707          0         43        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.89it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      33/49      3.54G    0.03225    0.01719          0         47        640: 100%|██████████| 18/18 [00:01<00:00, 11.88it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.68it/s]\n",
      "                   all         71        116      0.988      0.983      0.994      0.635\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03137    0.01492          0         48        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03046    0.01619          0         54        640:  11%|█         | 2/18 [00:00<00:01, 12.38it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03029    0.01519          0         40        640:  11%|█         | 2/18 [00:00<00:01, 12.38it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03014    0.01635          0         58        640:  22%|██▏       | 4/18 [00:00<00:01, 11.77it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03061    0.01716          0         57        640:  22%|██▏       | 4/18 [00:00<00:01, 11.77it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03026    0.01708          0         47        640:  33%|███▎      | 6/18 [00:00<00:01, 11.92it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03045    0.01654          0         37        640:  33%|███▎      | 6/18 [00:00<00:01, 11.92it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G     0.0305     0.0164          0         44        640:  44%|████▍     | 8/18 [00:00<00:00, 11.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G     0.0304    0.01669          0         54        640:  44%|████▍     | 8/18 [00:00<00:00, 11.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03029    0.01675          0         53        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G     0.0303    0.01658          0         46        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03027    0.01643          0         38        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03034    0.01646          0         48        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03051    0.01639          0         41        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.66it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03051    0.01646          0         57        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.66it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G     0.0304    0.01638          0         40        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.71it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03043    0.01652          0         59        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.71it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      34/49      3.54G    0.03035    0.01629          0         27        640: 100%|██████████| 18/18 [00:01<00:00, 11.85it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.16it/s]\n",
      "                   all         71        116      0.984      0.983      0.994      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.03345     0.0148          0         48        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.03086    0.01649          0         51        640:  11%|█         | 2/18 [00:00<00:01, 11.87it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.03007    0.01574          0         44        640:  11%|█         | 2/18 [00:00<00:01, 11.87it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.02996    0.01547          0         43        640:  22%|██▏       | 4/18 [00:00<00:01, 12.27it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.02971    0.01544          0         45        640:  22%|██▏       | 4/18 [00:00<00:01, 12.27it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.03003    0.01549          0         43        640:  33%|███▎      | 6/18 [00:00<00:00, 12.08it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G     0.0297    0.01526          0         36        640:  33%|███▎      | 6/18 [00:00<00:00, 12.08it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.02986    0.01558          0         53        640:  44%|████▍     | 8/18 [00:00<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.02984    0.01554          0         40        640:  44%|████▍     | 8/18 [00:00<00:00, 12.29it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.02964    0.01559          0         54        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.07it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.02964    0.01565          0         46        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.07it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.02957    0.01563          0         45        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.02965    0.01554          0         40        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.02958    0.01548          0         37        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.00it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.02958    0.01539          0         40        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.00it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.02946    0.01544          0         48        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.18it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.02955    0.01551          0         47        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.18it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      35/49      3.54G    0.02941    0.01542          0         28        640: 100%|██████████| 18/18 [00:01<00:00, 12.19it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.62it/s]\n",
      "                   all         71        116      0.984      0.983      0.994      0.633\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.02899    0.01683          0         54        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.03055     0.0177          0         61        640:  11%|█         | 2/18 [00:00<00:01, 12.57it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.03042    0.01663          0         47        640:  11%|█         | 2/18 [00:00<00:01, 12.57it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.03031    0.01745          0         62        640:  22%|██▏       | 4/18 [00:00<00:01, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.03083    0.01708          0         43        640:  22%|██▏       | 4/18 [00:00<00:01, 12.13it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.03047    0.01698          0         44        640:  33%|███▎      | 6/18 [00:00<00:00, 12.07it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.03017    0.01673          0         47        640:  33%|███▎      | 6/18 [00:00<00:00, 12.07it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.02996    0.01672          0         51        640:  44%|████▍     | 8/18 [00:00<00:00, 11.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.02965    0.01648          0         34        640:  44%|████▍     | 8/18 [00:00<00:00, 11.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.02985    0.01642          0         48        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.88it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.02981    0.01634          0         39        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.88it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.02966    0.01665          0         58        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.74it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.02947    0.01672          0         56        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.74it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.02938    0.01677          0         54        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.89it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.02927    0.01677          0         49        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.89it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.02929    0.01694          0         57        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.02947    0.01698          0         58        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      36/49      3.54G    0.02952    0.01726          0         51        640: 100%|██████████| 18/18 [00:01<00:00, 11.86it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.00it/s]\n",
      "                   all         71        116      0.987      0.983      0.994      0.649\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.03075    0.01349          0         42        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.03029    0.01373          0         39        640:  11%|█         | 2/18 [00:00<00:01, 12.11it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.03038    0.01594          0         63        640:  11%|█         | 2/18 [00:00<00:01, 12.11it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.02947    0.01507          0         33        640:  22%|██▏       | 4/18 [00:00<00:01, 12.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.02909    0.01543          0         52        640:  22%|██▏       | 4/18 [00:00<00:01, 12.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.02984    0.01531          0         38        640:  33%|███▎      | 6/18 [00:00<00:00, 12.43it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.03012    0.01538          0         54        640:  33%|███▎      | 6/18 [00:00<00:00, 12.43it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.03026     0.0154          0         40        640:  44%|████▍     | 8/18 [00:00<00:00, 12.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.03013    0.01544          0         48        640:  44%|████▍     | 8/18 [00:00<00:00, 12.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G     0.0301    0.01508          0         38        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.03011    0.01562          0         67        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.02997    0.01565          0         48        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.64it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.02987    0.01565          0         49        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.64it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.02969    0.01551          0         40        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G     0.0297    0.01526          0         33        640:  78%|███████▊  | 14/18 [00:01<00:00, 12.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.02947    0.01514          0         38        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.38it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.02936    0.01506          0         40        640:  89%|████████▉ | 16/18 [00:01<00:00, 12.38it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      37/49      3.54G    0.02935    0.01502          0         38        640: 100%|██████████| 18/18 [00:01<00:00, 12.48it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.41it/s]\n",
      "                   all         71        116      0.991      0.982      0.994      0.633\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02709    0.01451          0         45        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02723    0.01547          0         46        640:  11%|█         | 2/18 [00:00<00:01, 12.67it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02826    0.01586          0         48        640:  11%|█         | 2/18 [00:00<00:01, 12.67it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02887     0.0158          0         51        640:  22%|██▏       | 4/18 [00:00<00:01, 12.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02862    0.01546          0         42        640:  22%|██▏       | 4/18 [00:00<00:01, 12.24it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02833    0.01566          0         54        640:  33%|███▎      | 6/18 [00:00<00:00, 12.03it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02852    0.01567          0         51        640:  33%|███▎      | 6/18 [00:00<00:00, 12.03it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02848    0.01612          0         56        640:  44%|████▍     | 8/18 [00:00<00:00, 11.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02839    0.01567          0         40        640:  44%|████▍     | 8/18 [00:00<00:00, 11.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02857    0.01593          0         58        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.75it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02841     0.0155          0         32        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.75it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02846    0.01528          0         37        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.41it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02841    0.01527          0         42        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.41it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02851    0.01532          0         51        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.44it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02848     0.0154          0         49        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.44it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02843    0.01538          0         42        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.43it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02835    0.01558          0         61        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.43it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      38/49      3.54G    0.02835    0.01578          0         41        640: 100%|██████████| 18/18 [00:01<00:00, 11.76it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.20it/s]\n",
      "                   all         71        116      0.991       0.98      0.994      0.644\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.02854    0.01589          0         42        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.02835    0.01418          0         36        640:  11%|█         | 2/18 [00:00<00:01, 11.90it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.03021    0.01495          0         52        640:  11%|█         | 2/18 [00:00<00:01, 11.90it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.03147    0.01564          0         51        640:  22%|██▏       | 4/18 [00:00<00:01, 12.32it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.03156    0.01548          0         39        640:  22%|██▏       | 4/18 [00:00<00:01, 12.32it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.03227     0.0154          0         43        640:  33%|███▎      | 6/18 [00:00<00:00, 12.11it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.03193    0.01565          0         45        640:  33%|███▎      | 6/18 [00:00<00:00, 12.11it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G     0.0312    0.01588          0         52        640:  44%|████▍     | 8/18 [00:00<00:00, 12.28it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.03048    0.01569          0         42        640:  44%|████▍     | 8/18 [00:00<00:00, 12.28it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.03016     0.0156          0         43        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.11it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.02999    0.01555          0         46        640:  56%|█████▌    | 10/18 [00:00<00:00, 12.11it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.02961    0.01564          0         59        640:  67%|██████▋   | 12/18 [00:00<00:00, 12.19it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.02951    0.01578          0         60        640:  67%|██████▋   | 12/18 [00:01<00:00, 12.19it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.02943    0.01599          0         59        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.86it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.02951    0.01633          0         59        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.86it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.02945    0.01644          0         54        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.80it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G     0.0293    0.01633          0         40        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.80it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      39/49      3.54G    0.02959    0.01622          0         25        640: 100%|██████████| 18/18 [00:01<00:00, 12.01it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.53it/s]\n",
      "                   all         71        116      0.991      0.982      0.994      0.668\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.03235    0.01973          0         64        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02977    0.01745          0         43        640:  11%|█         | 2/18 [00:00<00:01, 12.01it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.03002    0.01624          0         37        640:  11%|█         | 2/18 [00:00<00:01, 12.01it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02915    0.01616          0         45        640:  22%|██▏       | 4/18 [00:00<00:01, 11.60it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02865    0.01501          0         31        640:  22%|██▏       | 4/18 [00:00<00:01, 11.60it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02824    0.01512          0         49        640:  33%|███▎      | 6/18 [00:00<00:01, 11.79it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02819    0.01507          0         47        640:  33%|███▎      | 6/18 [00:00<00:01, 11.79it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02815    0.01495          0         50        640:  44%|████▍     | 8/18 [00:00<00:00, 11.68it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02813    0.01495          0         49        640:  44%|████▍     | 8/18 [00:00<00:00, 11.68it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02816    0.01498          0         38        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.82it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02836    0.01523          0         61        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.82it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02814    0.01519          0         43        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.67it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02825    0.01523          0         45        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.67it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02819    0.01525          0         47        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.83it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02822    0.01529          0         46        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.83it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02849    0.01506          0         33        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.69it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02826    0.01501          0         47        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.69it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      40/49      3.54G    0.02821    0.01524          0         47        640: 100%|██████████| 18/18 [00:01<00:00, 11.89it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.93it/s]\n",
      "                   all         71        116      0.986      0.983      0.994      0.667\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G    0.03019     0.0144          0         39        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G    0.02887    0.01401          0         45        640:  11%|█         | 2/18 [00:00<00:01, 11.36it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G     0.0291    0.01427          0         40        640:  11%|█         | 2/18 [00:00<00:01, 11.36it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G    0.02961    0.01422          0         38        640:  22%|██▏       | 4/18 [00:00<00:01, 11.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G    0.02914     0.0138          0         39        640:  22%|██▏       | 4/18 [00:00<00:01, 11.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G    0.02932    0.01497          0         61        640:  33%|███▎      | 6/18 [00:00<00:01, 11.43it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G    0.02879    0.01484          0         41        640:  33%|███▎      | 6/18 [00:00<00:01, 11.43it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G    0.02843    0.01428          0         33        640:  44%|████▍     | 8/18 [00:00<00:00, 11.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G     0.0281    0.01485          0         58        640:  44%|████▍     | 8/18 [00:00<00:00, 11.73it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G    0.02822     0.0149          0         40        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G    0.02833    0.01464          0         36        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G    0.02821    0.01442          0         36        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.74it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G    0.02822     0.0143          0         38        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.74it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G     0.0281    0.01444          0         44        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.59it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G     0.0279    0.01461          0         53        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.59it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G    0.02792    0.01494          0         64        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.75it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G    0.02801    0.01498          0         54        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.75it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      41/49      3.54G      0.028    0.01503          0         33        640: 100%|██████████| 18/18 [00:01<00:00, 11.75it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.13it/s]\n",
      "                   all         71        116      0.986      0.983      0.994      0.653\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02797    0.01721          0         49        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02915    0.01506          0         36        640:  11%|█         | 2/18 [00:00<00:01, 11.96it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02878    0.01589          0         53        640:  11%|█         | 2/18 [00:00<00:01, 11.96it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02909    0.01575          0         45        640:  22%|██▏       | 4/18 [00:00<00:01, 11.41it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02902    0.01576          0         49        640:  22%|██▏       | 4/18 [00:00<00:01, 11.41it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02866    0.01532          0         39        640:  33%|███▎      | 6/18 [00:00<00:01, 11.64it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G     0.0284    0.01563          0         55        640:  33%|███▎      | 6/18 [00:00<00:01, 11.64it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02834    0.01588          0         56        640:  44%|████▍     | 8/18 [00:00<00:00, 11.41it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02807    0.01578          0         45        640:  44%|████▍     | 8/18 [00:00<00:00, 11.41it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02787    0.01561          0         42        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.58it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02789    0.01535          0         38        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.58it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02789    0.01527          0         51        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.40it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02792    0.01558          0         59        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.40it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02768    0.01553          0         44        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02754    0.01547          0         43        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G     0.0275    0.01515          0         25        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.54it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G     0.0275    0.01523          0         47        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.54it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      42/49      3.54G    0.02751    0.01548          0         47        640: 100%|██████████| 18/18 [00:01<00:00, 11.69it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.89it/s]\n",
      "                   all         71        116      0.987      0.983      0.994      0.658\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02659    0.01221          0         32        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02726    0.01352          0         49        640:  11%|█         | 2/18 [00:00<00:01, 11.45it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02704    0.01442          0         48        640:  11%|█         | 2/18 [00:00<00:01, 11.45it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02776    0.01472          0         42        640:  22%|██▏       | 4/18 [00:00<00:01, 11.82it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02807    0.01543          0         64        640:  22%|██▏       | 4/18 [00:00<00:01, 11.82it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02856    0.01585          0         67        640:  33%|███▎      | 6/18 [00:00<00:01, 11.61it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02857    0.01752          0         90        640:  33%|███▎      | 6/18 [00:00<00:01, 11.61it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02825     0.0172          0         44        640:  44%|████▍     | 8/18 [00:00<00:00, 11.81it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02845    0.01761          0         56        640:  44%|████▍     | 8/18 [00:00<00:00, 11.81it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02844    0.01766          0         45        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.72it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02847     0.0173          0         41        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.72it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02825    0.01676          0         33        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.76it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02829    0.01672          0         51        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.76it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02824    0.01642          0         41        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.61it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02806    0.01611          0         35        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.61it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02786    0.01607          0         47        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.77it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02779    0.01592          0         43        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.77it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      43/49      3.54G    0.02759     0.0159          0         41        640: 100%|██████████| 18/18 [00:01<00:00, 11.80it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00, 10.06it/s]\n",
      "                   all         71        116      0.989      0.983      0.994      0.663\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.03026    0.01474          0         41        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02926    0.01411          0         41        640:  11%|█         | 2/18 [00:00<00:01, 11.38it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02903    0.01578          0         50        640:  11%|█         | 2/18 [00:00<00:01, 11.38it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02852     0.0159          0         47        640:  22%|██▏       | 4/18 [00:00<00:01, 10.85it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02924    0.01649          0         57        640:  22%|██▏       | 4/18 [00:00<00:01, 10.85it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02906    0.01632          0         57        640:  33%|███▎      | 6/18 [00:00<00:01, 11.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02885    0.01637          0         52        640:  33%|███▎      | 6/18 [00:00<00:01, 11.33it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02884    0.01633          0         42        640:  44%|████▍     | 8/18 [00:00<00:00, 11.25it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02866    0.01629          0         50        640:  44%|████▍     | 8/18 [00:00<00:00, 11.25it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02856    0.01615          0         43        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.50it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02844     0.0161          0         47        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.50it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02841    0.01608          0         42        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.60it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02845    0.01595          0         44        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.60it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02851    0.01597          0         47        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.83it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G     0.0285    0.01634          0         68        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.83it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02826    0.01632          0         52        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.77it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02813    0.01611          0         48        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.77it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      44/49      3.54G    0.02806    0.01608          0         37        640: 100%|██████████| 18/18 [00:01<00:00, 11.65it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.68it/s]\n",
      "                   all         71        116      0.988      0.983      0.994      0.662\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02914    0.01716          0         48        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02705     0.0182          0         57        640:  11%|█         | 2/18 [00:00<00:01, 11.22it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02658      0.017          0         46        640:  11%|█         | 2/18 [00:00<00:01, 11.22it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02674    0.01625          0         46        640:  22%|██▏       | 4/18 [00:00<00:01, 11.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02621    0.01604          0         48        640:  22%|██▏       | 4/18 [00:00<00:01, 11.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02674    0.01596          0         52        640:  33%|███▎      | 6/18 [00:00<00:01, 11.50it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02675    0.01598          0         51        640:  33%|███▎      | 6/18 [00:00<00:01, 11.50it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02686    0.01659          0         66        640:  44%|████▍     | 8/18 [00:00<00:00, 11.65it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02701    0.01622          0         39        640:  44%|████▍     | 8/18 [00:00<00:00, 11.65it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02692    0.01595          0         40        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.53it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02713     0.0155          0         30        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.53it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02707    0.01594          0         60        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.64it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02701     0.0159          0         44        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.64it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02683    0.01577          0         44        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.51it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02675    0.01562          0         42        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.51it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02664    0.01535          0         34        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.68it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02668    0.01528          0         49        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.68it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      45/49      3.54G    0.02677     0.0155          0         37        640: 100%|██████████| 18/18 [00:01<00:00, 11.69it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.71it/s]\n",
      "                   all         71        116      0.989      0.983      0.994      0.664\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02698    0.01794          0         54        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02601    0.01787          0         56        640:  11%|█         | 2/18 [00:00<00:01, 11.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02601    0.01514          0         31        640:  11%|█         | 2/18 [00:00<00:01, 11.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02614    0.01458          0         45        640:  22%|██▏       | 4/18 [00:00<00:01, 11.44it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02618    0.01491          0         51        640:  22%|██▏       | 4/18 [00:00<00:01, 11.44it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02648    0.01539          0         51        640:  33%|███▎      | 6/18 [00:00<00:01, 11.65it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02648    0.01565          0         55        640:  33%|███▎      | 6/18 [00:00<00:01, 11.65it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02652    0.01567          0         54        640:  44%|████▍     | 8/18 [00:00<00:00, 11.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02686    0.01574          0         47        640:  44%|████▍     | 8/18 [00:00<00:00, 11.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G     0.0267    0.01573          0         50        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.66it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02681    0.01557          0         40        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.66it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G     0.0268     0.0155          0         48        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.52it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02705    0.01534          0         39        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.52it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02708    0.01525          0         43        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02703    0.01537          0         48        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.63it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02703    0.01531          0         50        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02705    0.01544          0         57        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.46it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      46/49      3.54G    0.02691    0.01528          0         31        640: 100%|██████████| 18/18 [00:01<00:00, 11.68it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.56it/s]\n",
      "                   all         71        116      0.981      0.974      0.991      0.659\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02929    0.02034          0         55        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02989    0.01821          0         47        640:  11%|█         | 2/18 [00:00<00:01, 11.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02889    0.01777          0         47        640:  11%|█         | 2/18 [00:00<00:01, 11.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02815    0.01814          0         64        640:  22%|██▏       | 4/18 [00:00<00:01, 11.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02781    0.01811          0         68        640:  22%|██▏       | 4/18 [00:00<00:01, 11.49it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G     0.0272    0.01741          0         47        640:  33%|███▎      | 6/18 [00:00<00:01, 11.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02668    0.01688          0         48        640:  33%|███▎      | 6/18 [00:00<00:01, 11.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02676    0.01775          0         68        640:  44%|████▍     | 8/18 [00:00<00:00, 11.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02651    0.01736          0         43        640:  44%|████▍     | 8/18 [00:00<00:00, 11.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02646    0.01742          0         58        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.45it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02648    0.01755          0         60        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.45it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G     0.0265    0.01742          0         52        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.60it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02652    0.01723          0         43        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.60it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02643    0.01708          0         43        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02631    0.01692          0         49        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.55it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02635    0.01664          0         35        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.66it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02645    0.01653          0         41        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.66it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      47/49      3.54G    0.02642    0.01673          0         50        640: 100%|██████████| 18/18 [00:01<00:00, 11.60it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.71it/s]\n",
      "                   all         71        116      0.982      0.974      0.992      0.671\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02566    0.01646          0         52        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02668    0.01649          0         50        640:  11%|█         | 2/18 [00:00<00:01, 12.10it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02611    0.01568          0         45        640:  11%|█         | 2/18 [00:00<00:01, 12.10it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02622    0.01636          0         57        640:  22%|██▏       | 4/18 [00:00<00:01, 11.44it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02581    0.01626          0         44        640:  22%|██▏       | 4/18 [00:00<00:01, 11.44it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G     0.0257    0.01579          0         43        640:  33%|███▎      | 6/18 [00:00<00:01, 11.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02593    0.01542          0         41        640:  33%|███▎      | 6/18 [00:00<00:01, 11.62it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02572    0.01575          0         54        640:  44%|████▍     | 8/18 [00:00<00:00, 11.52it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02562    0.01604          0         61        640:  44%|████▍     | 8/18 [00:00<00:00, 11.52it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02522    0.01594          0         49        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.71it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02544    0.01598          0         54        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.71it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02544      0.016          0         45        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02553    0.01582          0         44        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.56it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02556     0.0158          0         50        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.67it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02567    0.01559          0         37        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.67it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02592    0.01557          0         46        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.54it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02601    0.01543          0         37        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.54it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      48/49      3.54G    0.02614    0.01603          0         61        640: 100%|██████████| 18/18 [00:01<00:00, 11.73it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.64it/s]\n",
      "                   all         71        116      0.981      0.974      0.992      0.656\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "  0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02398    0.01295          0         42        640:   0%|          | 0/18 [00:00<?, ?it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02539    0.01456          0         45        640:  11%|█         | 2/18 [00:00<00:01, 11.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02532    0.01387          0         40        640:  11%|█         | 2/18 [00:00<00:01, 11.34it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G     0.0262     0.0158          0         71        640:  22%|██▏       | 4/18 [00:00<00:01, 11.60it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02609    0.01552          0         40        640:  22%|██▏       | 4/18 [00:00<00:01, 11.60it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G     0.0262    0.01565          0         46        640:  33%|███▎      | 6/18 [00:00<00:01, 11.41it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02623     0.0159          0         54        640:  33%|███▎      | 6/18 [00:00<00:01, 11.41it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02596    0.01549          0         45        640:  44%|████▍     | 8/18 [00:00<00:00, 11.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02593    0.01531          0         43        640:  44%|████▍     | 8/18 [00:00<00:00, 11.16it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02588    0.01529          0         50        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.04it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02597    0.01557          0         48        640:  56%|█████▌    | 10/18 [00:00<00:00, 11.04it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02607      0.016          0         70        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02613    0.01589          0         42        640:  67%|██████▋   | 12/18 [00:01<00:00, 11.30it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02619    0.01592          0         56        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.25it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02617    0.01568          0         38        640:  78%|███████▊  | 14/18 [00:01<00:00, 11.25it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02603    0.01544          0         36        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.61it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02613    0.01524          0         39        640:  89%|████████▉ | 16/18 [00:01<00:00, 11.61it/s]/home/konnilol/Downloads/yolov5/train.py:414: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
      "  with torch.cuda.amp.autocast(amp):\n",
      "      49/49      3.54G    0.02609     0.0153          0         37        640: 100%|██████████| 18/18 [00:01<00:00, 11.54it/s]\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  9.94it/s]\n",
      "                   all         71        116       0.98      0.974      0.992      0.672\n",
      "\n",
      "50 epochs completed in 0.030 hours.\n",
      "Optimizer stripped from /home/konnilol/Downloads/work_yolov5_car/runs/train/yolov5s_car_anchor_lr_tuned/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from /home/konnilol/Downloads/work_yolov5_car/runs/train/yolov5s_car_anchor_lr_tuned/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating /home/konnilol/Downloads/work_yolov5_car/runs/train/yolov5s_car_anchor_lr_tuned/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_car_custom summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 3/3 [00:00<00:00,  6.89it/s]\n",
      "                   all         71        116      0.981      0.974      0.992      0.672\n",
      "Results saved to \u001b[1m/home/konnilol/Downloads/work_yolov5_car/runs/train/yolov5s_car_anchor_lr_tuned\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Обучение завершено. results.csv найден: /home/konnilol/Downloads/work_yolov5_car/runs/train/yolov5s_car_anchor_lr_tuned/results.csv\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "import sys\n",
    "\n",
    "TRAIN_NAME = 'yolov5s_car_anchor_lr_tuned'\n",
    "PROJECT_DIR = WORKDIR / 'runs' / 'train'\n",
    "PROJECT_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "train_dir = PROJECT_DIR / TRAIN_NAME\n",
    "if train_dir.exists():\n",
    "    sh.rmtree(train_dir)\n",
    "\n",
    "train_cmd = [\n",
    "    sys.executable, 'yolov5/train.py',\n",
    "    '--img', '640',\n",
    "    '--batch', '16',\n",
    "    '--epochs', '50',\n",
    "    '--data', str(car_yaml_path),\n",
    "    '--cfg', str(custom_model_yaml),\n",
    "    '--weights', 'yolov5s.pt',\n",
    "    '--hyp', str(custom_hyp_yaml),\n",
    "    '--project', str(PROJECT_DIR),\n",
    "    '--name', TRAIN_NAME,\n",
    "    '--workers', '8',\n",
    "    '--cache'\n",
    "]\n",
    "\n",
    "print('Команда обучения:')\n",
    "print(' '.join(train_cmd))\n",
    "\n",
    "subprocess.run(train_cmd, check=True)\n",
    "\n",
    "results_csv = train_dir / 'results.csv'\n",
    "if not results_csv.exists():\n",
    "    raise FileNotFoundError(\n",
    "        f'После успешного завершения обучения не найден файл results.csv: {results_csv}'\n",
    "    )\n",
    "\n",
    "print(f'Обучение завершено. results.csv найден: {results_csv}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c47db631",
   "metadata": {},
   "source": [
    "## 9. Извлечение результатов обучения\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "51d1b34b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.positron.dataexplorer+json": {
       "comm_id": "4e6a1a9b-ae15-4889-b1a8-f11954be5cb2",
       "shape": {
        "columns": 14,
        "rows": 5
       },
       "source": "pandas",
       "title": "pandas",
       "version": 1
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>train/box_loss</th>\n",
       "      <th>train/obj_loss</th>\n",
       "      <th>train/cls_loss</th>\n",
       "      <th>metrics/precision</th>\n",
       "      <th>metrics/recall</th>\n",
       "      <th>metrics/mAP_0.5</th>\n",
       "      <th>metrics/mAP_0.5:0.95</th>\n",
       "      <th>val/box_loss</th>\n",
       "      <th>val/obj_loss</th>\n",
       "      <th>val/cls_loss</th>\n",
       "      <th>x/lr0</th>\n",
       "      <th>x/lr1</th>\n",
       "      <th>x/lr2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>0.026771</td>\n",
       "      <td>0.015504</td>\n",
       "      <td>0</td>\n",
       "      <td>0.98909</td>\n",
       "      <td>0.98276</td>\n",
       "      <td>0.99372</td>\n",
       "      <td>0.66403</td>\n",
       "      <td>0.025391</td>\n",
       "      <td>0.014048</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000677</td>\n",
       "      <td>0.000677</td>\n",
       "      <td>0.000677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>0.026905</td>\n",
       "      <td>0.015282</td>\n",
       "      <td>0</td>\n",
       "      <td>0.98070</td>\n",
       "      <td>0.97414</td>\n",
       "      <td>0.99146</td>\n",
       "      <td>0.65945</td>\n",
       "      <td>0.025687</td>\n",
       "      <td>0.014046</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.000624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>0.026417</td>\n",
       "      <td>0.016732</td>\n",
       "      <td>0</td>\n",
       "      <td>0.98239</td>\n",
       "      <td>0.97414</td>\n",
       "      <td>0.99174</td>\n",
       "      <td>0.67086</td>\n",
       "      <td>0.025090</td>\n",
       "      <td>0.013987</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000571</td>\n",
       "      <td>0.000571</td>\n",
       "      <td>0.000571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>0.026145</td>\n",
       "      <td>0.016029</td>\n",
       "      <td>0</td>\n",
       "      <td>0.98067</td>\n",
       "      <td>0.97414</td>\n",
       "      <td>0.99213</td>\n",
       "      <td>0.65574</td>\n",
       "      <td>0.025564</td>\n",
       "      <td>0.013993</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.000518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>0.026087</td>\n",
       "      <td>0.015296</td>\n",
       "      <td>0</td>\n",
       "      <td>0.98026</td>\n",
       "      <td>0.97414</td>\n",
       "      <td>0.99222</td>\n",
       "      <td>0.67241</td>\n",
       "      <td>0.025036</td>\n",
       "      <td>0.013937</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   epoch        train/box_loss        train/obj_loss  \\\n",
       "45                    45              0.026771              0.015504   \n",
       "46                    46              0.026905              0.015282   \n",
       "47                    47              0.026417              0.016732   \n",
       "48                    48              0.026145              0.016029   \n",
       "49                    49              0.026087              0.015296   \n",
       "\n",
       "          train/cls_loss     metrics/precision        metrics/recall  \\\n",
       "45                     0               0.98909               0.98276   \n",
       "46                     0               0.98070               0.97414   \n",
       "47                     0               0.98239               0.97414   \n",
       "48                     0               0.98067               0.97414   \n",
       "49                     0               0.98026               0.97414   \n",
       "\n",
       "         metrics/mAP_0.5  metrics/mAP_0.5:0.95          val/box_loss  \\\n",
       "45               0.99372               0.66403              0.025391   \n",
       "46               0.99146               0.65945              0.025687   \n",
       "47               0.99174               0.67086              0.025090   \n",
       "48               0.99213               0.65574              0.025564   \n",
       "49               0.99222               0.67241              0.025036   \n",
       "\n",
       "            val/obj_loss          val/cls_loss                 x/lr0  \\\n",
       "45              0.014048                     0              0.000677   \n",
       "46              0.014046                     0              0.000624   \n",
       "47              0.013987                     0              0.000571   \n",
       "48              0.013993                     0              0.000518   \n",
       "49              0.013937                     0              0.000466   \n",
       "\n",
       "                   x/lr1                 x/lr2  \n",
       "45              0.000677              0.000677  \n",
       "46              0.000624              0.000624  \n",
       "47              0.000571              0.000571  \n",
       "48              0.000518              0.000518  \n",
       "49              0.000466              0.000466  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_csv = train_dir / 'results.csv'\n",
    "results_df = pd.read_csv(results_csv)\n",
    "display(results_df.tail())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ba4465c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.positron.dataexplorer+json": {
       "comm_id": "20e1faaa-8691-4f31-8e23-71894b6c75da",
       "shape": {
        "columns": 2,
        "rows": 4
       },
       "source": "pandas",
       "title": "pandas",
       "version": 1
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Метрика</th>\n",
       "      <th>Значение</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Precision</td>\n",
       "      <td>0.98026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Recall</td>\n",
       "      <td>0.97414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mAP@0.50</td>\n",
       "      <td>0.99222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mAP@0.50:0.95</td>\n",
       "      <td>0.67241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Метрика  Значение\n",
       "0      Precision   0.98026\n",
       "1         Recall   0.97414\n",
       "2       mAP@0.50   0.99222\n",
       "3  mAP@0.50:0.95   0.67241"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epoch_col = [c for c in results_df.columns if 'epoch' in c][0]\n",
    "box_train_col = [c for c in results_df.columns if 'train/box_loss' in c][0]\n",
    "box_val_col = [c for c in results_df.columns if 'val/box_loss' in c][0]\n",
    "p_col = [c for c in results_df.columns if 'metrics/precision' in c][0]\n",
    "r_col = [c for c in results_df.columns if 'metrics/recall' in c][0]\n",
    "map50_col = [c for c in results_df.columns if 'metrics/mAP_0.5' in c and '0.5:0.95' not in c][0]\n",
    "map5095_col = [c for c in results_df.columns if 'metrics/mAP_0.5:0.95' in c][0]\n",
    "\n",
    "best_row = results_df.iloc[results_df[map5095_col].idxmax()]\n",
    "\n",
    "summary_df = pd.DataFrame({\n",
    "    'Метрика': [\n",
    "        'Precision',\n",
    "        'Recall',\n",
    "        'mAP@0.50',\n",
    "        'mAP@0.50:0.95'\n",
    "    ],\n",
    "    'Значение': [\n",
    "        best_row[p_col],\n",
    "        best_row[r_col],\n",
    "        best_row[map50_col],\n",
    "        best_row[map5095_col]\n",
    "    ]\n",
    "})\n",
    "\n",
    "summary_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c8d96ac",
   "metadata": {},
   "source": [
    "## 10. Графики обучения\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "77816dbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(results_df[epoch_col], results_df[box_train_col], label='train/box_loss')\n",
    "plt.plot(results_df[epoch_col], results_df[box_val_col], label='val/box_loss')\n",
    "plt.legend()\n",
    "plt.title('Box loss')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(results_df[epoch_col], results_df[map50_col], label='mAP@0.50')\n",
    "plt.plot(results_df[epoch_col], results_df[map5095_col], label='mAP@0.50:0.95')\n",
    "plt.legend()\n",
    "plt.title('Validation mAP')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83647ad3",
   "metadata": {},
   "source": [
    "## 11. Валидация лучшей модели\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1e7a42c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=/home/konnilol/Downloads/work_yolov5_car/car.yaml, weights=['/home/konnilol/Downloads/work_yolov5_car/runs/train/yolov5s_car_anchor_lr_tuned/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=True, save_hybrid=False, save_conf=True, save_json=False, project=/home/konnilol/Downloads/work_yolov5_car/runs/val, name=yolov5s_car_custom_val, exist_ok=True, half=False, dnn=False\n",
      "YOLOv5 🚀 v7.0-463-g88af13e3 Python-3.12.12 torch-2.10.0+cu128 CUDA:0 (NVIDIA GeForce RTX 3080, 9873MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_car_custom summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /home/konnilol/Downloads/work_yolov5_car/convertor/fold0/labels/va\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all         71        116       0.98      0.974      0.992      0.672\n",
      "Speed: 0.2ms pre-process, 2.8ms inference, 1.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1m/home/konnilol/Downloads/work_yolov5_car/runs/val/yolov5s_car_custom_val\u001b[0m\n",
      "71 labels saved to /home/konnilol/Downloads/work_yolov5_car/runs/val/yolov5s_car_custom_val/labels\n"
     ]
    }
   ],
   "source": [
    "best_weights = train_dir / 'weights' / 'best.pt'\n",
    "if not best_weights.exists():\n",
    "    raise FileNotFoundError(f'Не найдены лучшие веса: {best_weights}')\n",
    "\n",
    "!python yolov5/val.py \\\n",
    "    --weights \"{best_weights}\" \\\n",
    "    --data \"{car_yaml_path}\" \\\n",
    "    --img 640 \\\n",
    "    --task val \\\n",
    "    --save-txt \\\n",
    "    --save-conf \\\n",
    "    --exist-ok \\\n",
    "    --project \"{WORKDIR / 'runs' / 'val'}\" \\\n",
    "    --name yolov5s_car_custom_val\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f1f89d2",
   "metadata": {},
   "source": [
    "## 12. Примеры предсказаний\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "57451a34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['/home/konnilol/Downloads/work_yolov5_car/runs/train/yolov5s_car_anchor_lr_tuned/weights/best.pt'], source=/home/konnilol/Downloads/car-object-detection/data/testing_images, data=yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_format=0, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=/home/konnilol/Downloads/work_yolov5_car/runs/detect, name=yolov5s_car_custom_detect, exist_ok=True, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
      "YOLOv5 🚀 v7.0-463-g88af13e3 Python-3.12.12 torch-2.10.0+cu128 CUDA:0 (NVIDIA GeForce RTX 3080, 9873MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_car_custom summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs\n",
      "image 1/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_25100.jpg: 384x640 (no detections), 27.7ms\n",
      "image 2/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_25120.jpg: 384x640 (no detections), 5.7ms\n",
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      "image 134/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_30660.jpg: 384x640 (no detections), 3.3ms\n",
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      "image 136/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_30700.jpg: 384x640 (no detections), 3.4ms\n",
      "image 137/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_30720.jpg: 384x640 (no detections), 3.4ms\n",
      "image 138/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_30740.jpg: 384x640 1 car, 3.4ms\n",
      "image 139/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_30760.jpg: 384x640 1 car, 3.4ms\n",
      "image 140/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_30820.jpg: 384x640 1 car, 3.4ms\n",
      "image 141/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_30840.jpg: 384x640 (no detections), 3.3ms\n",
      "image 142/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_30860.jpg: 384x640 1 car, 3.3ms\n",
      "image 143/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_30920.jpg: 384x640 2 cars, 3.3ms\n",
      "image 144/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_30940.jpg: 384x640 1 car, 3.5ms\n",
      "image 145/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31020.jpg: 384x640 2 cars, 3.7ms\n",
      "image 146/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31040.jpg: 384x640 3 cars, 3.4ms\n",
      "image 147/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31060.jpg: 384x640 1 car, 3.3ms\n",
      "image 148/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31080.jpg: 384x640 2 cars, 3.3ms\n",
      "image 149/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31100.jpg: 384x640 2 cars, 3.3ms\n",
      "image 150/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31120.jpg: 384x640 2 cars, 3.3ms\n",
      "image 151/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31140.jpg: 384x640 1 car, 3.4ms\n",
      "image 152/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31160.jpg: 384x640 1 car, 3.5ms\n",
      "image 153/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31180.jpg: 384x640 (no detections), 3.4ms\n",
      "image 154/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31200.jpg: 384x640 (no detections), 3.4ms\n",
      "image 155/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31260.jpg: 384x640 (no detections), 3.3ms\n",
      "image 156/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31280.jpg: 384x640 (no detections), 3.3ms\n",
      "image 157/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31300.jpg: 384x640 (no detections), 3.3ms\n",
      "image 158/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31360.jpg: 384x640 (no detections), 3.3ms\n",
      "image 159/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31380.jpg: 384x640 (no detections), 3.4ms\n",
      "image 160/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31400.jpg: 384x640 (no detections), 3.5ms\n",
      "image 161/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31420.jpg: 384x640 (no detections), 3.4ms\n",
      "image 162/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31480.jpg: 384x640 (no detections), 3.4ms\n",
      "image 163/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31500.jpg: 384x640 (no detections), 3.3ms\n",
      "image 164/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31520.jpg: 384x640 (no detections), 3.3ms\n",
      "image 165/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31560.jpg: 384x640 1 car, 3.4ms\n",
      "image 166/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31600.jpg: 384x640 2 cars, 3.4ms\n",
      "image 167/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31620.jpg: 384x640 1 car, 3.5ms\n",
      "image 168/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31640.jpg: 384x640 (no detections), 3.5ms\n",
      "image 169/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31660.jpg: 384x640 (no detections), 3.3ms\n",
      "image 170/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31680.jpg: 384x640 (no detections), 3.3ms\n",
      "image 171/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31700.jpg: 384x640 1 car, 3.3ms\n",
      "image 172/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_31720.jpg: 384x640 1 car, 3.4ms\n",
      "image 173/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_400.jpg: 384x640 1 car, 3.3ms\n",
      "image 174/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_420.jpg: 384x640 1 car, 3.4ms\n",
      "image 175/175 /home/konnilol/Downloads/car-object-detection/data/testing_images/vid_5_440.jpg: 384x640 1 car, 3.9ms\n",
      "Speed: 0.2ms pre-process, 3.8ms inference, 0.7ms NMS per image at shape (1, 3, 640, 640)\n",
      "Results saved to \u001b[1m/home/konnilol/Downloads/work_yolov5_car/runs/detect/yolov5s_car_custom_detect\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "test_source = DATA_ROOT / 'testing_images'\n",
    "if not test_source.exists():\n",
    "    raise FileNotFoundError(f'Не найдена папка с тестовыми изображениями: {test_source}')\n",
    "\n",
    "!python yolov5/detect.py \\\n",
    "    --weights \"{best_weights}\" \\\n",
    "    --img 640 \\\n",
    "    --conf 0.25 \\\n",
    "    --source \"{test_source}\" \\\n",
    "    --exist-ok \\\n",
    "    --project \"{WORKDIR / 'runs' / 'detect'}\" \\\n",
    "    --name yolov5s_car_custom_detect\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d241234",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Число сохранённых изображений: 175\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_dir = WORKDIR / 'runs' / 'detect' / 'yolov5s_car_custom_detect'\n",
    "predicted_files = list(pred_dir.glob('*'))\n",
    "\n",
    "print('Число сохранённых изображений:', len(predicted_files))\n",
    "\n",
    "for file in random.sample(predicted_files, k=min(4, len(predicted_files))):\n",
    "    display(Image(filename=str(file), width=650))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f72ff88",
   "metadata": {},
   "source": [
    "## 13. Сохранение метрик\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "14e4608a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/konnilol/Downloads/work_yolov5_car/yolov5_car_metrics.csv')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_path = WORKDIR / 'yolov5_car_metrics.csv'\n",
    "summary_df.to_csv(summary_path, index=False)\n",
    "summary_path\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d8f92a3",
   "metadata": {},
   "source": [
    "Изменения конфигурации.\n",
    "По сравнению с базовой конфигурацией были изменены:\n",
    "\n",
    "anchors — подобраны по статистике реальных bounding boxes датасета:\n",
    "[[39,49,69,55,57,72], [92,67,112,78,108,111], [141,96,170,123,258,183]];\n",
    "\n",
    "learning rate — уменьшен до lr0 = 0.003 для более устойчивого fine-tuning.\n",
    "\n",
    "Результаты обучения.\n",
    "После обучения лучшая модель показала:\n",
    "\n",
    "Precision = 0.9803\n",
    "\n",
    "Recall = 0.9741\n",
    "\n",
    "mAP@0.50 = 0.9922\n",
    "\n",
    "mAP@0.50:0.95 = 0.6724\n",
    "\n",
    "На этапе отдельной валидации результаты подтвердились:\n",
    "P = 0.98, R = 0.974, mAP@0.50 = 0.992, mAP@0.50:0.95 = 0.672.\n",
    "\n",
    "Вывод.\n",
    "Модификация anchors и уменьшение learning rate оказались успешными. Модель очень хорошо обнаруживает автомобили по метрике mAP@0.50, а также показывает высокие precision и recall. При этом более строгая метрика mAP@0.50:0.95 = 0.6724 показывает, что качество локализации уже не идеально на жёстких порогах IoU, но в целом модель можно оценить как качественную и практически пригодную для задачи детекции автомобилей."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}