STR_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pqmtyZOqguLP"
   },
   "source": [
    "## 0. Download dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2608,
     "status": "ok",
     "timestamp": 1701169137819,
     "user": {
      "displayName": "Bui Minh Duc",
      "userId": "11823337494791188170"
     },
     "user_tz": -420
    },
    "id": "1oS4YsQ4guLQ",
    "outputId": "30cf8661-b988-4443-8745-6448047288ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From (uriginal): https://drive.google.com/uc?id=1x9e2FNDlKc_lBkJvHvWSKKfCSSqNsQfM\n",
      "From (redirected): https://drive.google.com/uc?id=1x9e2FNDlKc_lBkJvHvWSKKfCSSqNsQfM&confirm=t&uuid=82952035-c935-41ac-b6bc-64db67e0e5ff\n",
      "To: /home/aivn12s1/thangdd/project_scene_text_detection/icdar2003.zip\n",
      "100%|██████████████████████████████████████| 45.7M/45.7M [00:02<00:00, 19.6MB/s]\n"
     ]
    }
   ],
   "source": [
    "# Download ICDAR 2003 dataset\n",
    "# https://drive.google.com/file/d/1x9e2FNDlKc_lBkJvHvWSKKfCSSqNsQfM/view\n",
    "!gdown 1x9e2FNDlKc_lBkJvHvWSKKfCSSqNsQfM\n",
    "!unzip -q icdar2003.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K5zz-Q8lguLR"
   },
   "source": [
    "## 1. Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8069,
     "status": "ok",
     "timestamp": 1701171478530,
     "user": {
      "displayName": "Bui Minh Duc",
      "userId": "11823337494791188170"
     },
     "user_tz": -420
    },
    "id": "e9bTi_OnhFR7",
    "outputId": "3e1df45c-4e64-4658-fdbe-bb4d7db352fb",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.0.222 🚀 Python-3.11.5 torch-2.1.1 CUDA:0 (NVIDIA GeForce RTX 3060, 12036MiB)\n",
      "Setup complete ✅ (20 CPUs, 31.1 GB RAM, 408.5/915.3 GB disk)\n"
     ]
    }
   ],
   "source": [
    "#%pip install ultralytics\n",
    "import ultralytics\n",
    "ultralytics.checks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "qX-Z8lpSguLR"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import yaml\n",
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XTxnNaQPguLR"
   },
   "source": [
    "## 2. Extract Data from XML and Store in YOLOv8 format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "MJvZVIjFguLR"
   },
   "outputs": [],
   "source": [
    "def extract_data_from_xml(root_dir):\n",
    "    \"\"\"\n",
    "    Trích xuất dữ liệu từ file words.xml trong bộ IC03\n",
    "    \n",
    "    Hàm này dùng để trích các thông tin từ file .xml bao gồm: \n",
    "    image paths, image sizes, image labels và bboxes \n",
    "    \n",
    "    Parameters:\n",
    "        root_dir (str): Đường dẫn đến thư mục root của dataset\n",
    "    \n",
    "    Returns:\n",
    "        tuple: Chứa 4 lists lần lượt là: image paths, image sizes, image labels, và bboxes.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Tạo path đến file words.xml \n",
    "    xml_path = os.path.join(root_dir, 'words.xml')\n",
    "    # Parse file xml\n",
    "    tree = ET.parse(xml_path)\n",
    "    # Đọc thẻ root của file\n",
    "    root = tree.getroot()\n",
    "\n",
    "    # Khai báo các list rỗng để lưu dữ liệu\n",
    "    img_paths = []\n",
    "    img_sizes = []\n",
    "    img_labels = []\n",
    "    bboxes = []\n",
    "\n",
    "    # Duyệt qua từng thẻ ảnh <image>\n",
    "    for img in root:\n",
    "        # Khai báo các list rỗng chứa bboxes và labels của ảnh đang xét\n",
    "        bbs_of_img = []\n",
    "        labels_of_img = []\n",
    "\n",
    "        # Duyệt qua từng thẻ boundingbox \n",
    "        for bbs in img.findall('taggedRectangles'):\n",
    "            for bb in bbs:\n",
    "                # Bỏ qua trường hợp label không phải kí tự alphabet hoặc number\n",
    "                if not bb[0].text.isalnum():\n",
    "                    continue\n",
    "                    \n",
    "                # Bỏ qua trường hợp label là chữ 'é' hoặc ñ'\n",
    "                if 'é' in bb[0].text.lower() or 'ñ' in bb[0].text.lower():\n",
    "                    continue\n",
    "\n",
    "                # Đưa thông tin tọa độ bbox vào list bbs_of_img\n",
    "                # Format bbox: (xmin, ymin, bbox_width, bbox_height)\n",
    "                bbs_of_img.append(\n",
    "                    [\n",
    "                        float(bb.attrib['x']), \n",
    "                        float(bb.attrib['y']), \n",
    "                        float(bb.attrib['width']), \n",
    "                        float(bb.attrib['height'])\n",
    "                    ]\n",
    "                )\n",
    "                # Đưa label vào list labels_of_img (đã chuyển chữ viết thường)\n",
    "                labels_of_img.append(bb[0].text.lower())\n",
    "                \n",
    "        # Đưa thông tin path ảnh đang xét vào list img_paths\n",
    "        img_path = os.path.join(root_dir, img[0].text)\n",
    "        img_paths.append(img_path)\n",
    "        # Đưa thông tin độ phân giải ảnh vào list img_sizes\n",
    "        img_sizes.append((int(img[1].attrib['x']), int(img[1].attrib['y'])))\n",
    "        # Đưa list bbox vào list bboxes\n",
    "        bboxes.append(bbs_of_img)\n",
    "        # Đưa list labels vào list img_labels\n",
    "        img_labels.append(labels_of_img)\n",
    "\n",
    "    return img_paths, img_sizes, img_labels, bboxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Thực hiện lời gọi hàm extract_data_from_xml()\n",
    "dataset_dir = 'datasets/SceneTrialTrain'\n",
    "img_paths, img_sizes, img_labels, bboxes = extract_data_from_xml(dataset_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Total images: {len(img_paths)}\")\n",
    "print(f\"Total bounding boxes: {sum([len(bbs) for bbs in bboxes])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Convert to YOLOv8 format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "DaKo0dYBguLR"
   },
   "outputs": [],
   "source": [
    "def convert_to_yolov8_format(image_paths, image_sizes, bounding_boxes):\n",
    "    \"\"\"\n",
    "    Thực hiện normalize bounding box\n",
    "    \n",
    "    Parameters:\n",
    "        image_paths (list): Danh sách chứa các path ảnh.\n",
    "        image_sizes (list): Danh sách chứa độ phân giải ảnh.\n",
    "        bounding_boxes (list): Danh sách chứa danh sách bounding box.\n",
    "    \n",
    "    Returns:\n",
    "        yolov8_data (list): Danh sách gồm (image_path, image_size, bboxes)\n",
    "    \"\"\"\n",
    "    # Khai báo list rỗng để chứa kết quả\n",
    "    yolov8_data = []\n",
    "\n",
    "    # Duyệt qua từng bộ path, resolution và bboxes ảnh.\n",
    "    for image_path, image_size, bboxes in zip(image_paths, image_sizes, bounding_boxes):\n",
    "        image_width, image_height = image_size\n",
    "\n",
    "        # Khai báo list rỗng để chứa label (format mới)\n",
    "        yolov8_labels = []\n",
    "\n",
    "        # Duyệt qua từng bbox\n",
    "        for bbox in bboxes:\n",
    "            x, y, w, h = bbox\n",
    "\n",
    "            # Thực hiện normalize bbox\n",
    "            # Format bbox hiện tại: (x_min, y_min, width, height)\n",
    "            # Format bbox của yolo: (x_center, y_center, width, height)\n",
    "            center_x = (x + w / 2) / image_width\n",
    "            center_y = (y + h / 2) / image_height\n",
    "            normalized_width = w / image_width\n",
    "            normalized_height = h / image_height\n",
    "\n",
    "            # Mã class, mặc định = 0 vì chỉ có 1 class 'text'\n",
    "            class_id = 0\n",
    "\n",
    "            # Đổi format label\n",
    "            # Format: \"class_id x_center y_center width height\"\n",
    "            yolov8_label = f\"{class_id} {center_x} {center_y} {normalized_width} {normalized_height}\"\n",
    "            yolov8_labels.append(yolov8_label)\n",
    "\n",
    "        yolov8_data.append((image_path, yolov8_labels))\n",
    "\n",
    "    return yolov8_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Định nghĩa class\n",
    "class_labels = ['text']\n",
    "\n",
    "# Thực hiện lời gọi hàm\n",
    "yolov8_data = convert_to_yolov8_format(\n",
    "    image_paths, \n",
    "    image_sizes, \n",
    "    bounding_boxes\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       " ('ryoungt_03.09.2002/goose.jpg', []),\n",
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       " ('ryoungt_03.09.2002/PICT0004.JPG',\n",
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       " ('ryoungt_03.09.2002/PICT0011.JPG',\n",
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       " ('ryoungt_03.09.2002/PICT0015.JPG',\n",
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       " ('ryoungt_03.09.2002/PICT0019.JPG',\n",
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       "   '0 0.5489583333333333 0.97578125 0.18125 0.0359375']),\n",
       " ('ryoungt_03.09.2002/Pict0020.jpg',\n",
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       " ('ryoungt_03.09.2002/PICT0029.JPG',\n",
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       " ('ryoungt_03.09.2002/PICT0031.JPG',\n",
       "  ['0 0.7421875 0.7416666666666667 0.0625 0.041666666666666664',\n",
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       " ('ryoungt_03.09.2002/PICT0042.JPG',\n",
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       " ('ryoungt_03.09.2002/PICT0043.JPG',\n",
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       " ('ryoungt_03.09.2002/PICT0048.JPG',\n",
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       " ('ryoungt_05.08.2002/052.Kungstradgarden.jpg',\n",
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       " ('ryoungt_05.08.2002/abPICT0005.JPG',\n",
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       " ('ryoungt_05.08.2002/aPICT0001.JPG',\n",
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       "   '0 0.772265625 0.665625 0.03203125 0.05625',\n",
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       " ('ryoungt_05.08.2002/aPICT0002.JPG',\n",
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       "   '0 0.5015625 0.6135416666666667 0.3125 0.11041666666666666']),\n",
       " ('ryoungt_05.08.2002/aPICT0004.JPG',\n",
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       " ('ryoungt_05.08.2002/aPICT0006.JPG',\n",
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       " ('ryoungt_05.08.2002/aPICT0007.JPG',\n",
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       " ('ryoungt_05.08.2002/aPICT0010.JPG',\n",
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       " ('ryoungt_05.08.2002/aPICT0011.JPG',\n",
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       " ('ryoungt_05.08.2002/aPICT0013.JPG',\n",
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       "   '0 0.6111675126903553 0.4249706916764361 0.1218274111675127 0.06916764361078546']),\n",
       " ('ryoungt_05.08.2002/aPICT0014.JPG',\n",
       "  ['0 0.50625 0.478125 0.4953125 0.21666666666666667']),\n",
       " ('ryoungt_05.08.2002/aPICT0017.JPG',\n",
       "  ['0 0.098046875 0.14791666666666667 0.11640625 0.07291666666666667',\n",
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       "   '0 0.246875 0.28489583333333335 0.1984375 0.06979166666666667']),\n",
       " ('ryoungt_05.08.2002/aPICT0018.JPG',\n",
       "  ['0 0.7895833333333333 0.0390625 0.058333333333333334 0.03125',\n",
       "   '0 0.5041666666666667 0.180859375 0.47708333333333336 0.06328125',\n",
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       "   '0 0.5041666666666667 0.253125 0.3333333333333333 0.0515625']),\n",
       " ('ryoungt_05.08.2002/aPICT0020.JPG',\n",
       "  ['0 0.8162692847124825 0.8125854993160054 0.13183730715287517 0.09028727770177838',\n",
       "   '0 0.5007012622720898 0.9432284541723667 0.8583450210378681 0.10259917920656635']),\n",
       " ('ryoungt_05.08.2002/aPICT0021.JPG',\n",
       "  ['0 0.589585666293393 0.6356492969396195 0.8208286674132139 0.08354011579818031',\n",
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       "   '0 0.5907054871220605 0.7969396195202647 0.46472564389697646 0.06699751861042183']),\n",
       " ('ryoungt_05.08.2002/aPICT0023.JPG',\n",
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       " ('ryoungt_05.08.2002/aPICT0024.JPG',\n",
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       " ('ryoungt_05.08.2002/aPICT0025.JPG',\n",
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       "   '0 0.75 0.341796875 0.022916666666666665 0.01796875',\n",
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       "   '0 0.6760416666666667 0.36875 0.041666666666666664 0.01875']),\n",
       " ('ryoungt_05.08.2002/aPICT0026.JPG',\n",
       "  ['0 0.778125 0.7260416666666667 0.0875 0.13333333333333333',\n",
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       "   '0 0.49296875 0.5447916666666667 0.203125 0.15208333333333332']),\n",
       " ('ryoungt_05.08.2002/aPICT0027.JPG',\n",
       "  ['0 0.369921875 0.6776041666666667 0.45078125 0.13020833333333334',\n",
       "   '0 0.280859375 0.8713541666666667 0.16796875 0.13854166666666667',\n",
       "   '0 0.61484375 0.8703125 0.44375 0.13020833333333334',\n",
       "   '0 0.75703125 0.675 0.25625 0.125',\n",
       "   '0 0.520703125 0.47760416666666666 0.43515625 0.134375'])]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yolov8_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Train, val, test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 0\n",
    "val_size = 0.2\n",
    "test_size = 0.125\n",
    "is_shuffle = True\n",
    "\n",
    "train_data, test_data = train_test_split(\n",
    "    yolov8_data, \n",
    "    test_size=val_size, \n",
    "    random_state=seed,\n",
    "    shuffle=is_shuffle\n",
    ")\n",
    "test_data, val_data = train_test_split(\n",
    "    test_data, \n",
    "    test_size=test_size, \n",
    "    random_state=seed,\n",
    "    shuffle=is_shuffle\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Save data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "EXMNkicBguLR"
   },
   "outputs": [],
   "source": [
    "def save_data(data, src_img_dir, save_dir):\n",
    "    \"\"\"\n",
    "    Xây dựng thư mục chứa dữ liệu theo format YOLO\n",
    "    \n",
    "    Parameters:\n",
    "        data (list): Danh sách chứa thông tin label ảnh.\n",
    "        src_img_dir (str): Path đến thư mục dữ liệu gốc.\n",
    "        save_dir (str): Path đến thư mục dữ liệu mới.\n",
    "    \"\"\"\n",
    "    # Tạo thư mục dữ liệu mới nếu chưa có\n",
    "    os.makedirs(save_dir, exist_ok=True)\n",
    "\n",
    "    # Tạo thư mục 'images' và 'labels'\n",
    "    os.makedirs(os.path.join(save_dir, 'images'), exist_ok=True)\n",
    "    os.makedirs(os.path.join(save_dir, 'labels'), exist_ok=True)\n",
    "\n",
    "    # Duyệt qua từng bộ path, bbox, label ảnh\n",
    "    for image_path, yolov8_labels in data:\n",
    "        # Copy ảnh từ thư mục gốc sang thư mục 'images'\n",
    "        shutil.copy(\n",
    "            os.path.join(src_img_dir, image_path), \n",
    "            os.path.join(save_dir, 'images')\n",
    "        )\n",
    "\n",
    "        # Ghi nội dung label vào file image_name.txt ở thư mục 'labels'\n",
    "        image_name = os.path.basename(image_path)\n",
    "        image_name = os.path.splitext(image_name)[0]\n",
    "\n",
    "        with open(os.path.join(save_dir, 'labels', f\"{image_name}.txt\"), 'w') as f:\n",
    "            for label in yolov8_labels:\n",
    "                f.write(f\"{label}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Thực hiện lời gọi hàm cho 3 set train, val, test\n",
    "save_yolo_data_dir = 'datasets/yolo_data'\n",
    "os.makedirs(save_yolo_data_dir, exist_ok=True)\n",
    "save_train_dir = os.path.join(\n",
    "    save_yolo_data_dir,\n",
    "    'train'\n",
    ")\n",
    "save_val_dir = os.path.join(\n",
    "    save_yolo_data_dir,\n",
    "    'val'\n",
    ")\n",
    "save_test_dir = os.path.join(\n",
    "    save_yolo_data_dir,\n",
    "    'test'\n",
    ")\n",
    "\n",
    "save_data(\n",
    "    train_data, \n",
    "    dataset_dir, \n",
    "    save_train_dir\n",
    ")\n",
    "save_data(\n",
    "    test_data, \n",
    "    dataset_dir, \n",
    "    save_val_dir\n",
    ")\n",
    "save_data(\n",
    "    val_data, \n",
    "    dataset_dir, \n",
    "    save_test_dir\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Create yaml file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "RCdtnCkZguLS"
   },
   "outputs": [],
   "source": [
    "data_yaml = {\n",
    "    'path': 'yolo_data',\n",
    "    'train': 'train/images',\n",
    "    'test': 'test/images',\n",
    "    'val': 'val/images',\n",
    "    'nc': 1,\n",
    "    'names': class_labels\n",
    "}\n",
    "\n",
    "yolo_yaml_path = os.path.join(\n",
    "    save_yolo_data_dir,\n",
    "    'data.yml'\n",
    ")\n",
    "with open(yolo_yaml_path, 'w') as f:\n",
    "    yaml.dump(data_yaml, f, default_flow_style=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZeDYzXQOguLS"
   },
   "source": [
    "## 7. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZGQkMfzYguLS",
    "outputId": "582b8c53-b266-44d0-a230-9eeb184a585c",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                   from  n    params  module                                       arguments                     \n",
      "  0                  -1  1       928  ultralytics.nn.modules.conv.Conv             [3, 32, 3, 2]                 \n",
      "  1                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
      "  2                  -1  1     29056  ultralytics.nn.modules.block.C2f             [64, 64, 1, True]             \n",
      "  3                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
      "  4                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
      "  5                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
      "  6                  -1  2    788480  ultralytics.nn.modules.block.C2f             [256, 256, 2, True]           \n",
      "  7                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              \n",
      "  8                  -1  1   1838080  ultralytics.nn.modules.block.C2f             [512, 512, 1, True]           \n",
      "  9                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 5]                 \n",
      " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 12                  -1  1    591360  ultralytics.nn.modules.block.C2f             [768, 256, 1]                 \n",
      " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 15                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n",
      " 16                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
      " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 18                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n",
      " 19                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              \n",
      " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 21                  -1  1   1969152  ultralytics.nn.modules.block.C2f             [768, 512, 1]                 \n",
      " 22        [15, 18, 21]  1   2147008  ultralytics.nn.modules.head.Detect           [80, [128, 256, 512]]         \n",
      "YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs\n",
      "\n",
      "Transferred 355/355 items from pretrained weights\n",
      "Ultralytics YOLOv8.0.222 🚀 Python-3.11.5 torch-2.1.0 CUDA:0 (NVIDIA GeForce RTX 3060, 12036MiB)\n",
      "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8s.yaml, data=datasets/yolo_data/data.yml, epochs=200, patience=50, batch=16, imgsz=1024, save=True, save_period=-1, cache=False, device=None, workers=8, project=models, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, tracker=botsort.yaml, save_dir=models/yolov8/detect/train\n",
      "Overriding model.yaml nc=80 with nc=1\n",
      "\n",
      "                   from  n    params  module                                       arguments                     \n",
      "  0                  -1  1       928  ultralytics.nn.modules.conv.Conv             [3, 32, 3, 2]                 \n",
      "  1                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
      "  2                  -1  1     29056  ultralytics.nn.modules.block.C2f             [64, 64, 1, True]             \n",
      "  3                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
      "  4                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
      "  5                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
      "  6                  -1  2    788480  ultralytics.nn.modules.block.C2f             [256, 256, 2, True]           \n",
      "  7                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              \n",
      "  8                  -1  1   1838080  ultralytics.nn.modules.block.C2f             [512, 512, 1, True]           \n",
      "  9                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 5]                 \n",
      " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 12                  -1  1    591360  ultralytics.nn.modules.block.C2f             [768, 256, 1]                 \n",
      " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 15                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n",
      " 16                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
      " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 18                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n",
      " 19                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              \n",
      " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 21                  -1  1   1969152  ultralytics.nn.modules.block.C2f             [768, 512, 1]                 \n",
      " 22        [15, 18, 21]  1   2116435  ultralytics.nn.modules.head.Detect           [1, [128, 256, 512]]          \n",
      "YOLOv8s summary: 225 layers, 11135987 parameters, 11135971 gradients, 28.6 GFLOPs\n",
      "\n",
      "Transferred 349/355 items from pretrained weights\n",
      "Freezing layer 'model.22.dfl.conv.weight'\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/aivn12s1/thangdd/project_scene_text_detection/datasets/yolo_data/train/labels.cache... 200 images, 7 backgrounds, 0 corrupt: 100%|██████████| 200/200 [\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /home/aivn12s1/thangdd/project_scene_text_detection/datasets/yolo_data/val/labels.cache... 43 images, 2 backgrounds, 0 corrupt: 100%|██████████| 43/43 [00:00<?\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting labels to models/yolov8/detect/train/labels.jpg... \n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n",
      "Image sizes 1024 train, 1024 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mmodels/yolov8/detect/train\u001b[0m\n",
      "Starting training for 200 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      1/200      10.7G      2.038       5.46      1.827         61       1024: 100%|██████████| 13/13 [00:05<00:00,  2.42it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  4.28it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.312      0.455      0.307       0.15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      2/200        10G      1.102      1.815      1.182         69       1024: 100%|██████████| 13/13 [00:05<00:00,  2.46it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  4.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.718      0.714      0.775      0.508\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      3/200      10.2G      1.049      1.449      1.116         42       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  4.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.727      0.667      0.692      0.433\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      4/200        10G      1.142      1.336      1.165        109       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200        0.7       0.72      0.703      0.406\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      5/200      10.3G      1.141      1.237      1.196         49       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.14it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.497       0.61      0.522      0.287\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      6/200      10.1G      1.193      1.148      1.174         47       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.491      0.695       0.56      0.313\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      7/200      10.3G      1.188      1.078      1.215         67       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.289       0.47      0.254       0.11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      8/200      10.1G      1.362      1.351       1.29         30       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.456      0.519      0.418      0.159\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      9/200      10.2G      1.284      1.222      1.253         51       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.584      0.635      0.605      0.301\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     10/200      10.2G      1.228      1.223      1.245         84       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.352      0.585      0.417      0.236\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     11/200      10.2G      1.163      1.121      1.158         52       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200       0.54       0.66      0.595      0.318\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     12/200      10.1G      1.231      1.171      1.231         73       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.714       0.65      0.688      0.438\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     13/200        10G       1.15      1.087      1.208         39       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.724      0.645      0.717       0.43\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     14/200      10.3G      1.113      1.098      1.188         60       1024: 100%|██████████| 13/13 [00:05<00:00,  2.46it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.13it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.759      0.693      0.755      0.439\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     15/200      9.97G       1.13      1.035      1.169         43       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.688       0.71      0.698       0.43\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     16/200      10.1G      1.145      1.081      1.192         56       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.664       0.72      0.706      0.453\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     17/200      9.83G      1.157      1.009      1.177         83       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.797       0.75      0.795      0.552\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     18/200      10.1G      1.069     0.9139      1.139         87       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.13it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.676      0.745      0.726      0.464\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     19/200      10.2G      1.065     0.9153      1.136         86       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.667      0.782      0.714      0.485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     20/200      9.98G      1.109     0.9531      1.135         58       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.782      0.805      0.822      0.542\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     21/200      9.99G      1.046     0.8734      1.123         62       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.823      0.838      0.871      0.574\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     22/200      9.84G      1.071     0.9147       1.14         65       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.792       0.85      0.866      0.576\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     23/200      10.1G      1.041     0.8788      1.106         67       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.19it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.697      0.865      0.824      0.551\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     24/200      10.1G      1.046     0.8848      1.122         74       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200       0.82       0.84      0.865      0.595\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     25/200        10G       1.01     0.8666      1.122         49       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.749       0.85       0.79      0.553\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     26/200      10.1G      1.013     0.8465      1.123         27       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.814      0.852      0.841      0.573\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     27/200      10.4G     0.9925     0.8562      1.119         34       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.769      0.783      0.777      0.547\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "     28/200        10G      1.013     0.8631      1.118         40       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200       0.81       0.83      0.818      0.566\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     29/200      9.81G      0.975     0.8163      1.094         47       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.728      0.805      0.775      0.541\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     30/200      10.3G      1.013     0.8064      1.082         54       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.796       0.86      0.839      0.568\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     31/200      10.2G     0.9685     0.7779      1.087         53       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.785      0.785      0.833      0.597\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     32/200      10.4G      1.038     0.7939      1.126         81       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200       0.78       0.84      0.836      0.589\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "     33/200      10.4G     0.9601     0.7974      1.071         46       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200       0.76       0.88      0.859      0.608\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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      "     34/200      10.2G      0.967     0.7689      1.074         72       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.829      0.865      0.862       0.61\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
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      "     35/200        10G      0.949     0.7335      1.064         50       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.823      0.895      0.909       0.65\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     36/200      9.98G     0.9496     0.7952      1.085         64       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.875       0.83      0.888      0.628\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "     37/200      10.4G     0.9438     0.7929      1.063         81       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.835       0.86      0.867      0.628\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "     38/200      10.1G     0.9581     0.7137      1.068         52       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.849      0.855      0.862      0.626\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
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      "     39/200      10.1G     0.9228     0.7305      1.074         90       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.875       0.86      0.896      0.669\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
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    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
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      "     40/200      10.1G     0.9219     0.7315      1.063         46       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.839      0.859      0.879      0.627\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     41/200        10G     0.9036     0.7204      1.064         68       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.826      0.815      0.839      0.602\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
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    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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      "     42/200      9.85G     0.9022     0.7042      1.051         61       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.859      0.855      0.888      0.632\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "     43/200      10.1G     0.8519     0.6671       1.03         48       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.824       0.88      0.869      0.631\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "     44/200      10.3G     0.9102     0.6897      1.038         32       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.827      0.895      0.855      0.596\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "     45/200        10G     0.8758     0.7133      1.039         53       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200       0.82       0.89       0.87      0.626\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
     "text": [
      "     46/200      10.2G     0.8723     0.7161      1.042         48       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.828      0.875      0.885      0.644\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     47/200      10.3G     0.8416     0.6579       1.03         49       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.807       0.92      0.885      0.642\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     48/200      10.1G     0.9288     0.7022      1.042         52       1024: 100%|██████████| 13/13 [00:05<00:00,  2.42it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.839      0.855      0.894      0.624\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     49/200      9.98G     0.8537     0.6592      1.016         96       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200       0.87      0.834      0.878      0.639\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     50/200      10.3G     0.8478     0.6677      1.026         63       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.802      0.914      0.875      0.622\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "     51/200      9.98G     0.8723     0.6863      1.024         62       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.868      0.888      0.908      0.647\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "     52/200      10.1G     0.8705     0.6908      1.014         55       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.818      0.879       0.87      0.622\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "     53/200      9.82G     0.9138     0.7017      1.025         36       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.836       0.87      0.859      0.618\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "     54/200      10.2G     0.8671     0.6574      1.011         61       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.888      0.872       0.88      0.654\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
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      "     55/200      10.1G      0.835     0.6377      1.002         52       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.869      0.845       0.89      0.665\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
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      "     56/200      10.2G     0.8264     0.6257      1.014         53       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.865       0.89        0.9      0.675\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "     57/200      10.1G     0.8076     0.6095      1.006         76       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.887       0.86      0.907      0.657\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "\n"
     ]
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    {
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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      "     58/200        10G     0.8212     0.5927          1         57       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.802      0.932      0.912      0.669\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     59/200      10.2G     0.8103      0.614     0.9915         70       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.822       0.87      0.894      0.659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     60/200      10.1G     0.8017      0.612      1.006         53       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.844      0.841      0.884      0.657\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     61/200      9.98G      0.789     0.5887     0.9979         60       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.887      0.885      0.909      0.678\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     62/200      10.2G     0.8084     0.6415      1.016         52       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.895      0.835      0.897      0.667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     63/200      10.4G     0.8165     0.6088      1.016         70       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200       0.84       0.87      0.881      0.637\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     64/200      10.2G     0.8054     0.6119     0.9946         51       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.878       0.83      0.881      0.668\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     65/200        10G     0.8301      0.625      1.014         63       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.859      0.852      0.895      0.675\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     66/200      10.2G     0.8126     0.6188      1.005         59       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.812      0.865      0.864      0.638\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     67/200      10.1G     0.8164     0.5988     0.9856         52       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.885      0.865      0.911      0.678\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     68/200      9.97G     0.7694     0.5858     0.9736         49       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.862      0.878      0.921      0.676\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     69/200        10G     0.8035     0.6251      1.004         44       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.858      0.907      0.916      0.673\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     70/200      10.3G     0.8078     0.5968      1.004         55       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.847      0.888      0.919      0.691\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     71/200      10.3G      0.823     0.5893     0.9955         99       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.878      0.835      0.888      0.655\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     72/200      10.2G     0.8193     0.6034     0.9973        122       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200       0.89      0.853       0.91      0.659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     73/200      10.1G     0.8006     0.5908     0.9878         68       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.796      0.877      0.881      0.656\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     74/200      10.4G     0.8092     0.5838      1.019         46       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.826      0.865      0.889      0.662\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     75/200        10G     0.7739     0.5568      0.966         68       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.882      0.859      0.917      0.694\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     76/200      10.1G     0.7825     0.5723     0.9967         42       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.894      0.905      0.944      0.712\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     77/200      9.96G     0.7975     0.5703     0.9804         37       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.889      0.865       0.91      0.695\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     78/200      10.1G     0.7741     0.5605     0.9831         38       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.887      0.864      0.904      0.702\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     79/200      9.59G     0.7444     0.5098     0.9602         56       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.865      0.885      0.912      0.706\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     80/200      9.86G     0.7842     0.5564     0.9709         51       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.871       0.88      0.901      0.692\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
     "text": [
      "     81/200      9.82G     0.7647     0.5495      0.967         67       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.859      0.887        0.9      0.681\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     82/200      9.88G     0.7882     0.5631     0.9924         56       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.877      0.889      0.908      0.696\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     83/200        10G     0.7721     0.5282      0.987         90       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.881      0.885      0.912      0.689\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     84/200      10.1G      0.764     0.5327     0.9747         73       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.894      0.888      0.908      0.683\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     85/200      9.83G      0.771     0.5345     0.9872         53       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.876       0.92       0.93      0.711\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     86/200      9.98G     0.7189     0.5085     0.9514         54       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.896        0.9      0.934      0.708\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     87/200      10.2G     0.7284     0.5083     0.9463        102       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.929      0.895      0.929      0.706\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     88/200      10.2G     0.7177     0.4907     0.9504         72       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.895       0.89      0.918      0.698\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     89/200      9.89G     0.7475     0.5162     0.9614         87       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.874        0.9      0.925      0.707\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     90/200      10.2G     0.6991     0.4987     0.9448         72       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.882      0.899      0.919      0.701\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     91/200      10.1G     0.7358     0.5391     0.9641         87       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.899      0.925      0.927      0.698\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     92/200        10G     0.7164      0.522      0.976         39       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.857      0.925      0.928      0.714\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     93/200        10G     0.7425     0.4998     0.9618         87       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.926       0.88      0.926      0.691\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     94/200      10.2G     0.7087     0.5076     0.9611         37       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.897       0.89      0.931      0.704\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     95/200      10.2G     0.6805     0.4887      0.958         44       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.932      0.829      0.912       0.69\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     96/200        10G     0.7039     0.4993     0.9643         55       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.871      0.912      0.928      0.692\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     97/200      10.3G     0.7125     0.4941     0.9457         79       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.919      0.854      0.931      0.696\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     98/200      9.97G     0.7084      0.507     0.9585         74       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.844      0.945      0.917      0.696\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     99/200      10.2G     0.7159     0.5072     0.9606         53       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.821      0.916      0.918      0.688\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    100/200        10G     0.7116     0.4986       0.95         47       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.891        0.9       0.93      0.709\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
     "text": [
      "    101/200        10G      0.734     0.5084     0.9808         30       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.878        0.9      0.926      0.707\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
     "text": [
      "    102/200      10.3G      0.716     0.4876     0.9655         56       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.913       0.89       0.93      0.707\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
     "text": [
      "    103/200      10.2G     0.7098     0.5014     0.9525         50       1024: 100%|██████████| 13/13 [00:05<00:00,  2.46it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.902      0.876      0.917      0.712\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
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      "    104/200      10.1G     0.7147     0.4858     0.9514         71       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.884       0.91      0.926      0.706\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "    105/200      9.84G     0.7188     0.4832     0.9425         84       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.897       0.92      0.927      0.705\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
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      "    106/200      10.4G     0.6848     0.4811     0.9554         36       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.848      0.922      0.922      0.713\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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      "    107/200      9.99G     0.6566     0.4635     0.9465         59       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.862      0.909      0.909        0.7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    108/200        10G     0.6847     0.4972     0.9479         43       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.878       0.92      0.926      0.708\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    109/200      10.2G     0.6905     0.4742      0.956         49       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.868      0.935      0.942      0.725\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
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      "    110/200      10.3G     0.6664     0.4566     0.9486         62       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.879      0.908      0.931      0.715\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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      "    111/200        10G     0.7074     0.4937     0.9597         97       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.896        0.9      0.927      0.713\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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      "    112/200      9.97G     0.6984     0.4846     0.9559         49       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.886        0.9      0.923      0.722\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    113/200        10G     0.6403      0.478     0.9298         67       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.901      0.869      0.916      0.702\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    114/200      9.86G     0.6588     0.4649     0.9263         55       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.912       0.88      0.923      0.699\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
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      "    115/200      10.2G     0.6572     0.4559      0.933         85       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.884      0.912      0.927      0.719\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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      "    116/200        10G     0.6644     0.4656     0.9399         65       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.915       0.84      0.921      0.709\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
     "text": [
      "    117/200      10.2G       0.68     0.4667     0.9364         76       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.908      0.845      0.912      0.699\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
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      "    118/200      9.99G     0.6721     0.4681     0.9495         62       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.875       0.87      0.915      0.708\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    119/200      10.1G     0.6594     0.4641     0.9364         67       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.884       0.87      0.917      0.701\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    120/200      10.2G     0.6604     0.4596     0.9259         62       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.895       0.92      0.927      0.709\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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      "    121/200        10G     0.6366     0.4405     0.9374         36       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.891       0.91      0.922      0.715\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    122/200      10.1G     0.6566     0.4488     0.9361         59       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.887      0.905      0.914      0.703\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
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      "    123/200      9.95G     0.6516     0.4376     0.9312         38       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200        0.9      0.901      0.916        0.7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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      "    124/200        10G     0.6815     0.4552     0.9473         53       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.896       0.91       0.93      0.717\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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      "    125/200        10G     0.6128     0.4181     0.9213         66       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.898        0.9      0.931      0.715\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
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      "    126/200      10.3G      0.615     0.4164     0.9153         56       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.912      0.882      0.926      0.702\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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      "    127/200      10.2G      0.622     0.4406     0.9324         30       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.926       0.86      0.918      0.702\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    128/200      10.1G     0.6326     0.4331      0.938         44       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.921       0.88       0.93      0.717\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    129/200      9.84G     0.6664     0.4528      0.931        123       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.913      0.905      0.935      0.717\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "    130/200      10.3G     0.6315     0.4333     0.9233         46       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.933      0.907      0.931      0.728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    131/200      10.2G     0.6414     0.4343     0.9206         54       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.915      0.917      0.927      0.739\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    132/200      9.97G     0.6251     0.4333     0.9326         49       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.897      0.905      0.929      0.724\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    133/200      9.94G     0.5953     0.4066     0.9136         80       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.918      0.894      0.935      0.738\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    134/200      10.2G     0.6122     0.4058     0.9187         62       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.908       0.91      0.934      0.731\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    135/200      10.4G     0.5798     0.3987     0.9212         43       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.912      0.886      0.926      0.723\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    136/200      10.2G      0.593     0.4051     0.9105         47       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.911        0.9      0.928      0.719\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    137/200      9.98G     0.5916     0.4067     0.9246         27       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.863      0.913      0.921      0.714\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    138/200      10.2G     0.5775     0.4035     0.9029         76       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.877      0.905      0.933      0.727\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    139/200      10.1G     0.5962     0.4026     0.9152        102       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.881      0.925      0.938      0.728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    140/200      10.3G     0.5905     0.4191     0.9135        111       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.883      0.915      0.919      0.722\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    141/200      10.4G     0.5746     0.4011     0.9131         88       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.885      0.884      0.908       0.71\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    142/200      10.2G      0.594     0.4143     0.9139         52       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.894       0.89      0.915      0.719\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    143/200      10.1G     0.5757     0.3979     0.9128         58       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.934       0.89      0.936      0.733\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    144/200      10.1G     0.5922     0.4124     0.9303         50       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200       0.91      0.895      0.931      0.732\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    145/200      10.2G     0.6002     0.4039     0.9229         75       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.878        0.9      0.919      0.711\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    146/200      10.2G     0.5726     0.4024     0.9108         51       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.927      0.885      0.918       0.72\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    147/200      10.1G     0.6099     0.4194     0.9132         49       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.915        0.9      0.931      0.739\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "    148/200        10G       0.59     0.4062     0.9197         33       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.908      0.905      0.931      0.729\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "    149/200      9.84G     0.5782     0.4003     0.9112         44       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200       0.89      0.885      0.923      0.719\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    150/200      10.3G     0.5568     0.3895     0.9053         67       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.909      0.897      0.923       0.73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
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      "    151/200      10.4G     0.5541      0.383     0.9187         49       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.916       0.89      0.923      0.734\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
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      "    152/200        10G      0.579     0.4091     0.9091         46       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.899        0.9      0.925      0.739\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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      "    153/200      10.3G     0.5628     0.3805     0.9099         74       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.881      0.915      0.931       0.73\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
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      "    154/200      10.2G     0.5477     0.3714      0.902         69       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.897      0.917      0.939      0.737\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    155/200      10.4G     0.5655     0.3992     0.8925         69       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.909       0.92      0.937      0.739\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    156/200      10.1G     0.5665     0.3822     0.9066         65       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.892      0.913      0.933      0.732\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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      "    157/200      10.3G     0.5568      0.401     0.9136         21       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.904      0.897      0.932      0.734\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    158/200      10.3G     0.5739     0.3915     0.9148         47       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200       0.91      0.875      0.931      0.731\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    159/200      10.1G     0.5659     0.3943     0.9151         73       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.892      0.895      0.935      0.731\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    160/200      9.99G     0.5719     0.3858     0.9052         55       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.916        0.9      0.943      0.747\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    161/200      10.1G     0.5626      0.382     0.8986         57       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.902       0.91      0.946      0.747\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "    162/200      10.1G      0.554     0.3691     0.8998         45       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.888        0.9       0.94       0.73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "    163/200      10.2G     0.5889     0.3924     0.9074         47       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200        0.9       0.87      0.931      0.738\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "    164/200        10G     0.5537     0.3749     0.8906         63       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.912      0.879      0.928      0.725\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "    165/200      10.2G     0.5337     0.3707     0.8891         63       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.919      0.908      0.929      0.739\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "    166/200      10.3G     0.5362     0.3628     0.9043         45       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.932        0.9      0.931      0.739\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "    167/200      10.1G     0.5232     0.3626     0.8841         78       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.951      0.895      0.934      0.736\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    168/200      10.1G     0.5174     0.3492     0.8839         86       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.932      0.888      0.931       0.74\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
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     "output_type": "stream",
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      "    169/200      10.1G     0.5085     0.3627     0.8955         51       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.936      0.884      0.932      0.739\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
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     "output_type": "stream",
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      "    170/200      10.2G     0.5075     0.3524     0.8828         70       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.927      0.885      0.934      0.741\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
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      "    171/200      10.2G     0.5273     0.3618     0.9017         65       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.941      0.885       0.93      0.735\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    172/200      10.4G     0.5052     0.3335     0.8917         40       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.922       0.88      0.927      0.732\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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    },
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     "output_type": "stream",
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      "    173/200      9.94G     0.4984     0.3512     0.8829         73       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.887      0.915      0.928      0.735\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
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    {
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     "output_type": "stream",
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      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
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     "output_type": "stream",
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      "    174/200      10.3G     0.5101     0.3493     0.8829         56       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.913      0.895      0.932      0.737\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "    175/200      10.4G     0.5346     0.3613     0.8934         87       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.922      0.892      0.938      0.743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "    176/200      10.4G     0.5363     0.3677     0.9039         47       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.926      0.879      0.938      0.736\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    177/200      10.3G     0.5256     0.3621     0.8931         89       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.897      0.919      0.939      0.747\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    178/200      10.2G     0.5179     0.3521     0.8848         43       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.933      0.895      0.941      0.751\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    179/200      10.2G     0.4996     0.3403     0.8676         46       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.911       0.92      0.943      0.742\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    180/200      10.2G     0.5052     0.3602     0.8915         55       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.934        0.9      0.942      0.738\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "    181/200      10.2G      0.504     0.3426     0.8987         55       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.928      0.898      0.937      0.742\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    182/200      9.99G     0.5268     0.3573     0.9009         32       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.928       0.91      0.933      0.736\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    183/200      9.99G      0.518     0.3512     0.8996         60       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.923      0.903      0.932      0.736\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    184/200        10G     0.4973     0.3408     0.8755         63       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.903      0.929       0.93      0.739\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    185/200        10G     0.4943      0.343      0.888         33       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.912      0.925      0.928      0.742\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    186/200      9.81G      0.513     0.3581     0.8966         42       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.906       0.93      0.929      0.734\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    187/200      10.1G     0.5048     0.3472     0.8896         48       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.898      0.935      0.927      0.734\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    188/200      10.2G     0.4713     0.3255     0.8742         55       1024: 100%|██████████| 13/13 [00:05<00:00,  2.43it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.904       0.92      0.927      0.741\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    189/200        10G     0.4767     0.3317     0.8894         36       1024: 100%|██████████| 13/13 [00:05<00:00,  2.45it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.914        0.9      0.929      0.736\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    190/200      9.81G     0.4745     0.3295     0.8592         44       1024: 100%|██████████| 13/13 [00:05<00:00,  2.44it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.939      0.885      0.928      0.738\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Closing dataloader mosaic\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    191/200      10.2G     0.4645     0.3405     0.8518         52       1024: 100%|██████████| 13/13 [00:05<00:00,  2.26it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.22it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.937      0.892      0.922      0.729\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    192/200      9.93G     0.4992     0.3399     0.8599         20       1024: 100%|██████████| 13/13 [00:05<00:00,  2.46it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.935      0.895      0.921      0.727\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    193/200        10G     0.4577     0.3297     0.8538         16       1024: 100%|██████████| 13/13 [00:05<00:00,  2.46it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.935      0.895      0.918      0.725\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    194/200      10.2G     0.4712     0.3348     0.8476         19       1024: 100%|██████████| 13/13 [00:05<00:00,  2.46it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.923        0.9       0.92      0.723\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    195/200      10.1G     0.4707     0.3364     0.8535         70       1024: 100%|██████████| 13/13 [00:05<00:00,  2.47it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.924        0.9       0.92      0.724\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    196/200      10.1G     0.4505      0.319       0.85         19       1024: 100%|██████████| 13/13 [00:05<00:00,  2.46it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.918      0.899      0.921      0.723\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "    197/200      10.2G     0.4456     0.3072     0.8446         35       1024: 100%|██████████| 13/13 [00:05<00:00,  2.46it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.912        0.9      0.921      0.729\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    198/200      10.2G     0.4405     0.3164     0.8565         21       1024: 100%|██████████| 13/13 [00:05<00:00,  2.46it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.916        0.9      0.922      0.733\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    199/200      9.93G     0.4485      0.322      0.841         41       1024: 100%|██████████| 13/13 [00:05<00:00,  2.46it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.908       0.91      0.923      0.736\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    200/200      10.1G     0.4256     0.3037     0.8328         56       1024: 100%|██████████| 13/13 [00:05<00:00,  2.46it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.25it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.924      0.895      0.924      0.736\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "200 epochs completed in 0.338 hours.\n",
      "Optimizer stripped from models/yolov8/detect/train/weights/last.pt, 22.6MB\n",
      "Optimizer stripped from models/yolov8/detect/train/weights/best.pt, 22.6MB\n",
      "\n",
      "Validating models/yolov8/detect/train/weights/best.pt...\n",
      "Ultralytics YOLOv8.0.222 🚀 Python-3.11.5 torch-2.1.0 CUDA:0 (NVIDIA GeForce RTX 3060, 12036MiB)\n",
      "YOLOv8s summary (fused): 168 layers, 11125971 parameters, 0 gradients, 28.4 GFLOPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 2/2 [00:00<00:00,  5.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.933      0.895      0.941      0.751\n",
      "Speed: 0.2ms preprocess, 7.3ms inference, 0.0ms loss, 0.2ms postprocess per image\n",
      "Results saved to \u001b[1mmodels/yolov8/detect/train\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from ultralytics import YOLO\n",
    "\n",
    "# Load a model\n",
    "model = YOLO('yolov8s.yaml').load('yolov8s.pt') \n",
    "\n",
    "# Train the model\n",
    "epochs = 200\n",
    "imgsz = 1024\n",
    "results = model.train(\n",
    "    data=yolo_yaml_path, \n",
    "    epochs=epochs, \n",
    "    imgsz=imgsz,\n",
    "    project='models',\n",
    "    name='yolov8/detect/train'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OfYzvs0cguLT"
   },
   "source": [
    "## 8. Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5wAMpiJPguLT",
    "outputId": "ff38f18d-3014-4683-8822-2a9bb85270a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.0.222 🚀 Python-3.11.5 torch-2.1.0 CUDA:0 (NVIDIA GeForce RTX 3060, 12036MiB)\n",
      "YOLOv8s summary (fused): 168 layers, 11125971 parameters, 0 gradients, 28.4 GFLOPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /home/aivn12s1/thangdd/project_scene_text_detection/datasets/yolo_data/val/labels.cache... 43 images, 2 backgrounds, 0 corrupt: 100%|██████████| 43/43 [00:00<?\u001b[0m\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 3/3 [00:00<00:00,  3.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all         43        200      0.933      0.895      0.941      0.748\n",
      "Speed: 0.3ms preprocess, 12.3ms inference, 0.0ms loss, 1.1ms postprocess per image\n",
      "Results saved to \u001b[1mmodels/yolov8/detect/val\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from ultralytics import YOLO\n",
    "\n",
    "model_path = 'models/yolov8/detect/train/weights/best.pt'\n",
    "model = YOLO(model_path)\n",
    "\n",
    "metrics = model.val(\n",
    "    project='models',\n",
    "    name='yolov8/detect/val'\n",
    ")  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def visualize_bbox(\n",
    "    img_path, predictions,\n",
    "    conf_thres=0.8,\n",
    "    font=cv2.FONT_HERSHEY_SIMPLEX\n",
    "):\n",
    "    img = cv2.imread(img_path)\n",
    "    h, w = img.shape[:2]\n",
    "    \n",
    "    for prediction in predictions:\n",
    "        conf_score = prediction['confidence']\n",
    "        \n",
    "        if conf_score < conf_thres:\n",
    "            continue\n",
    "            \n",
    "        bbox = prediction['box']\n",
    "        xmin = int(bbox['x1'])\n",
    "        ymin = int(bbox['y1'])\n",
    "        xmax = int(bbox['x2'])\n",
    "        ymax = int(bbox['y2'])\n",
    "        \n",
    "        cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 3)\n",
    "\n",
    "\n",
    "        text = f\"{conf_score:.2f}\"\n",
    "        (text_width, text_height), _ = cv2.getTextSize(text, font, 1, 2)\n",
    "\n",
    "        cv2.rectangle(img, (xmin, ymin - text_height - 5), (xmin + text_width, ymin), (0, 255, 0), -1)\n",
    "        cv2.putText(img, text, (xmin, ymin - 5), font, 1, (0, 0, 0), 2)\n",
    "\n",
    "\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMG_1247.JPG  IMG_1269.JPG  IMG_1289.JPG  IMG_1300.JPG\tIMG_1308.JPG\r\n",
      "IMG_1252.JPG  IMG_1281.JPG  IMG_1290.JPG  IMG_1301.JPG\tIMG_1311.JPG\r\n",
      "IMG_1253.JPG  IMG_1282.JPG  IMG_1291.JPG  IMG_1302.JPG\tIMG_1312.JPG\r\n",
      "IMG_1255.JPG  IMG_1283.JPG  IMG_1292.JPG  IMG_1303.JPG\tImg_1313.jpg\r\n",
      "IMG_1259.JPG  IMG_1284.JPG  IMG_1293.JPG  IMG_1304.JPG\tIMG_1315.JPG\r\n",
      "IMG_1261.JPG  IMG_1285.JPG  IMG_1294.JPG  Img_1305.jpg\tIMG_1316.JPG\r\n",
      "IMG_1263.JPG  IMG_1286.JPG  IMG_1298.JPG  IMG_1306.JPG\tIMG_1317.JPG\r\n",
      "IMG_1265.JPG  IMG_1288.JPG  IMG_1299.JPG  IMG_1307.JPG\r\n"
     ]
    }
   ],
   "source": [
    "!ls datasets/SceneTrialTrain/apanar_06.08.2002"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'text', 'class': 0, 'confidence': 0.9537504315376282, 'box': {'x1': 171.4465789794922, 'y1': 392.06658935546875, 'x2': 447.82232666015625, 'y2': 584.3841552734375}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ultralytics import YOLO\n",
    "\n",
    "model_path = 'models/yolov8/detect/train/weights/best.pt'\n",
    "img_path = 'datasets/SceneTrialTrain/apanar_06.08.2002/IMG_1261.JPG'\n",
    "conf_thres=0.75\n",
    "model = YOLO(model_path)\n",
    "\n",
    "# Run inference\n",
    "results = model(img_path, verbose=False)\n",
    "predictions = json.loads(results[0].tojson())\n",
    "print(predictions[0])\n",
    "visualized_img = visualize_bbox(img_path, predictions, conf_thres)\n",
    "\n",
    "%matplotlib inline\n",
    "plt.imshow(visualized_img)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}