{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-18T17:46:59.397476Z",
     "iopub.status.busy": "2025-10-18T17:46:59.397217Z",
     "iopub.status.idle": "2025-10-18T17:48:19.200063Z",
     "shell.execute_reply": "2025-10-18T17:48:19.199326Z",
     "shell.execute_reply.started": "2025-10-18T17:46:59.397458Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ultralytics in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (8.3.217)\n",
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      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from matplotlib>=3.6.0->supervision) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=3.6.0->supervision) (1.17.0)\n",
      "Requirement already satisfied: charset_normalizer<4,>=2 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from requests>=2.26.0->supervision) (3.4.4)\n",
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      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from requests>=2.26.0->supervision) (2.5.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from requests>=2.26.0->supervision) (2025.10.5)\n",
      "Requirement already satisfied: colorama in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from tqdm>=4.62.3->supervision) (0.4.6)\n"
     ]
    }
   ],
   "source": [
    "!pip install ultralytics Pillow\n",
    "!pip install supervision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-18T17:48:19.201358Z",
     "iopub.status.busy": "2025-10-18T17:48:19.201103Z",
     "iopub.status.idle": "2025-10-18T17:48:26.033248Z",
     "shell.execute_reply": "2025-10-18T17:48:26.032557Z",
     "shell.execute_reply.started": "2025-10-18T17:48:19.201309Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 640x640 1 FACE, 37.9ms\n",
      "Speed: 9.7ms preprocess, 37.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "{'x1': 63.09367752075195, 'y1': 55.93373107910156, 'x2': 236.28103637695312, 'y2': 246.04263305664062, 'confidence': 0.896662712097168, 'class_id': 0}\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import hf_hub_download\n",
    "from ultralytics import YOLO\n",
    "from supervision import Detections, BoxAnnotator\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# download model\n",
    "model_path = hf_hub_download(repo_id=\"arnabdhar/YOLOv8-Face-Detection\", filename=\"model.pt\")\n",
    "\n",
    "# load model\n",
    "model = YOLO(model_path)\n",
    "\n",
    "# load image\n",
    "image_path = \"Data\\\\9\\\\999.png\"\n",
    "image = Image.open(image_path).convert(\"RGB\")\n",
    "image_np = np.array(image)\n",
    "\n",
    "# inference\n",
    "results = model(image_np)\n",
    "detections = Detections.from_ultralytics(results[0])\n",
    "\n",
    "for box, conf, class_id in zip(detections.xyxy, detections.confidence, detections.class_id):\n",
    "    print({\n",
    "        \"x1\": float(box[0]),\n",
    "        \"y1\": float(box[1]),\n",
    "        \"x2\": float(box[2]),\n",
    "        \"y2\": float(box[3]),\n",
    "        \"confidence\": float(conf),\n",
    "        \"class_id\": int(class_id)\n",
    "    })\n",
    "    image = image.crop(box)\n",
    "    image.save(\"test.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# annotate\n",
    "annotator = BoxAnnotator()\n",
    "annotated_frame = annotator.annotate(scene=image_np.copy(), detections=detections)\n",
    "\n",
    "# display\n",
    "plt.imshow(annotated_frame)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-18T17:48:26.035459Z",
     "iopub.status.busy": "2025-10-18T17:48:26.034784Z",
     "iopub.status.idle": "2025-10-18T17:48:26.039925Z",
     "shell.execute_reply": "2025-10-18T17:48:26.039365Z",
     "shell.execute_reply.started": "2025-10-18T17:48:26.035427Z"
    },
    "trusted": true
   },
   "outputs": [],
   "source": [
    "#import math\n",
    "from datasets import Dataset\n",
    "\n",
    "celebrities = {'0':'Sergey Semyonovich Sobyanin', '1':'Khabib Abdulmanapovich Nurmagomedov', '2':'Timur Ildarovich Yunusov', '3':'Sergey Vladimirovich Shnurov', '4':'Vasiliy Mikhaylovich Vakulenko', '5':'Philipp Bedros Kirkorov', '6':'Ivan Andreyevich Urgant', '7':'Sergey Yevgenevich Zhukov', '8':'Aleksandr Vladimirovich Revva', '9':'Yury Aleksandrovich Dud'}\n",
    "def crop_image(img_path, ds:Dataset, target_size=(300, 300)):\n",
    "    img = Image.open(img_path).convert(\"RGB\")\n",
    "    img_np = np.array(img)\n",
    "    results = model(img_np)\n",
    "    detections = Detections.from_ultralytics(results[0])\n",
    "    img_path = img_path.split(\"\\\\\")\n",
    "    img.save(f\"celebrities\\\\{(int(img_path[-1][:-4]) + 1):04}_source.png\")\n",
    "    for box, conf, class_id in zip(detections.xyxy, detections.confidence, detections.class_id):\n",
    "        img = img.crop(box)\n",
    "        img = img.resize(target_size)\n",
    "        \n",
    "        img.save(f\"celebrities\\\\{(int(img_path[-1][:-4]) + 1):04}_crop.png\")\n",
    "        item = {\"file_names\": [f\"{(int(img_path[-1][:-4]) + 1):04}_source.png\", f\"{(int(img_path[-1][:-4]) + 1):04}_crop.png\"], \"label\": celebrities[img_path[-2]]}\n",
    "        ds.add_item(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: huggingface_hub[cli] in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (0.35.3)\n",
      "Requirement already satisfied: filelock in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from huggingface_hub[cli]) (3.20.0)\n",
      "Requirement already satisfied: fsspec>=2023.5.0 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from huggingface_hub[cli]) (2025.9.0)\n",
      "Requirement already satisfied: packaging>=20.9 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from huggingface_hub[cli]) (25.0)\n",
      "Requirement already satisfied: pyyaml>=5.1 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from huggingface_hub[cli]) (6.0.3)\n",
      "Requirement already satisfied: requests in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from huggingface_hub[cli]) (2.32.5)\n",
      "Requirement already satisfied: tqdm>=4.42.1 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from huggingface_hub[cli]) (4.67.1)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from huggingface_hub[cli]) (4.15.0)\n",
      "Collecting InquirerPy==0.3.4 (from huggingface_hub[cli])\n",
      "  Downloading InquirerPy-0.3.4-py3-none-any.whl.metadata (8.1 kB)\n",
      "Collecting pfzy<0.4.0,>=0.3.1 (from InquirerPy==0.3.4->huggingface_hub[cli])\n",
      "  Downloading pfzy-0.3.4-py3-none-any.whl.metadata (4.9 kB)\n",
      "Requirement already satisfied: prompt-toolkit<4.0.0,>=3.0.1 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from InquirerPy==0.3.4->huggingface_hub[cli]) (3.0.52)\n",
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      "Downloading InquirerPy-0.3.4-py3-none-any.whl (67 kB)\n",
      "Downloading pfzy-0.3.4-py3-none-any.whl (8.5 kB)\n",
      "Installing collected packages: pfzy, InquirerPy\n",
      "\n",
      "   -------------------- ------------------- 1/2 [InquirerPy]\n",
      "   -------------------- ------------------- 1/2 [InquirerPy]\n",
      "   ---------------------------------------- 2/2 [InquirerPy]\n",
      "\n",
      "Successfully installed InquirerPy-0.3.4 pfzy-0.3.4\n",
      "Requirement already satisfied: datasets[vision] in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (4.2.0)\n",
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      "Requirement already satisfied: tqdm>=4.66.3 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from datasets[vision]) (4.67.1)\n",
      "Requirement already satisfied: xxhash in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from datasets[vision]) (3.6.0)\n",
      "Requirement already satisfied: multiprocess<0.70.17 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from datasets[vision]) (0.70.16)\n",
      "Requirement already satisfied: fsspec<=2025.9.0,>=2023.1.0 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from fsspec[http]<=2025.9.0,>=2023.1.0->datasets[vision]) (2025.9.0)\n",
      "Requirement already satisfied: huggingface-hub<2.0,>=0.25.0 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from datasets[vision]) (0.35.3)\n",
      "Requirement already satisfied: packaging in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from datasets[vision]) (25.0)\n",
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      "Requirement already satisfied: Pillow>=9.4.0 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from datasets[vision]) (12.0.0)\n",
      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from fsspec[http]<=2025.9.0,>=2023.1.0->datasets[vision]) (3.13.1)\n",
      "Requirement already satisfied: anyio in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from httpx<1.0.0->datasets[vision]) (4.11.0)\n",
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      "Requirement already satisfied: idna in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from httpx<1.0.0->datasets[vision]) (3.11)\n",
      "Requirement already satisfied: h11>=0.16 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from httpcore==1.*->httpx<1.0.0->datasets[vision]) (0.16.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from huggingface-hub<2.0,>=0.25.0->datasets[vision]) (4.15.0)\n",
      "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets[vision]) (2.6.1)\n",
      "Requirement already satisfied: aiosignal>=1.4.0 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets[vision]) (1.4.0)\n",
      "Requirement already satisfied: attrs>=17.3.0 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets[vision]) (25.4.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets[vision]) (1.8.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets[vision]) (6.7.0)\n",
      "Requirement already satisfied: propcache>=0.2.0 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets[vision]) (0.4.1)\n",
      "Requirement already satisfied: yarl<2.0,>=1.17.0 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.9.0,>=2023.1.0->datasets[vision]) (1.22.0)\n",
      "Requirement already satisfied: charset_normalizer<4,>=2 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from requests>=2.32.2->datasets[vision]) (3.4.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from requests>=2.32.2->datasets[vision]) (2.5.0)\n",
      "Requirement already satisfied: colorama in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from tqdm>=4.66.3->datasets[vision]) (0.4.6)\n",
      "Requirement already satisfied: sniffio>=1.1 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from anyio->httpx<1.0.0->datasets[vision]) (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from pandas->datasets[vision]) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from pandas->datasets[vision]) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from pandas->datasets[vision]) (2025.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas->datasets[vision]) (1.17.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install -U \"huggingface_hub[cli]\"\n",
    "!pip install datasets[vision]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hf auth login"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "формат {\"file_names\": [\"0001_source.png\", \"0001_crop.png\"], \"label\": \"0\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['file_names', 'label'],\n",
       "    num_rows: 1\n",
       "})"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "celebrities = Dataset.from_dict({\"file_names\": [], \"label\": []})\n",
    "celebrities.info.dataset_name = \"celebrities\"\n",
    "celebrities.add_item({\"file_names\": [\"0001_source.png\", \"0001_crop.png\"], \"label\": \"0\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "Invalid key: 0 is out of bounds for size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mIndexError\u001b[39m                                Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[49]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mcelebrities\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\arsen\\coding\\.venv\\Lib\\site-packages\\datasets\\arrow_dataset.py:2859\u001b[39m, in \u001b[36mDataset.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   2857\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._format_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m._format_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[33m\"\u001b[39m\u001b[33marrow\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mpandas\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mpolars\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m   2858\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m Column(\u001b[38;5;28mself\u001b[39m, key)\n\u001b[32m-> \u001b[39m\u001b[32m2859\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_getitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\arsen\\coding\\.venv\\Lib\\site-packages\\datasets\\arrow_dataset.py:2840\u001b[39m, in \u001b[36mDataset._getitem\u001b[39m\u001b[34m(self, key, **kwargs)\u001b[39m\n\u001b[32m   2838\u001b[39m format_kwargs = format_kwargs \u001b[38;5;28;01mif\u001b[39;00m format_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[32m   2839\u001b[39m formatter = get_formatter(format_type, features=\u001b[38;5;28mself\u001b[39m._info.features, **format_kwargs)\n\u001b[32m-> \u001b[39m\u001b[32m2840\u001b[39m pa_subtable = \u001b[43mquery_table\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_indices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   2841\u001b[39m formatted_output = format_table(\n\u001b[32m   2842\u001b[39m     pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns\n\u001b[32m   2843\u001b[39m )\n\u001b[32m   2844\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m formatted_output\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\arsen\\coding\\.venv\\Lib\\site-packages\\datasets\\formatting\\formatting.py:612\u001b[39m, in \u001b[36mquery_table\u001b[39m\u001b[34m(table, key, indices)\u001b[39m\n\u001b[32m    610\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    611\u001b[39m     size = indices.num_rows \u001b[38;5;28;01mif\u001b[39;00m indices \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m table.num_rows\n\u001b[32m--> \u001b[39m\u001b[32m612\u001b[39m     \u001b[43m_check_valid_index_key\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    613\u001b[39m \u001b[38;5;66;03m# Query the main table\u001b[39;00m\n\u001b[32m    614\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m indices \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\arsen\\coding\\.venv\\Lib\\site-packages\\datasets\\formatting\\formatting.py:552\u001b[39m, in \u001b[36m_check_valid_index_key\u001b[39m\u001b[34m(key, size)\u001b[39m\n\u001b[32m    550\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mint\u001b[39m):\n\u001b[32m    551\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m (key < \u001b[32m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m key + size < \u001b[32m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (key >= size):\n\u001b[32m--> \u001b[39m\u001b[32m552\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mInvalid key: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m is out of bounds for size \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msize\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m    553\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m    554\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mslice\u001b[39m):\n",
      "\u001b[31mIndexError\u001b[39m: Invalid key: 0 is out of bounds for size 0"
     ]
    }
   ],
   "source": [
    "celebrities[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-18T17:48:26.040867Z",
     "iopub.status.busy": "2025-10-18T17:48:26.040606Z",
     "iopub.status.idle": "2025-10-18T17:48:26.073977Z",
     "shell.execute_reply": "2025-10-18T17:48:26.073262Z",
     "shell.execute_reply.started": "2025-10-18T17:48:26.040830Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 640x640 1 FACE, 40.4ms\n",
      "Speed: 7.4ms preprocess, 40.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    }
   ],
   "source": [
    "with open(\"metadatatest.jsonl\", \"w+\") as f:\n",
    "    crop_image(image_path, celebrities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetInfo(description='', citation='', homepage='', license='', features={'file_names': Value('null'), 'label': Value('null')}, post_processed=None, supervised_keys=None, builder_name=None, dataset_name=None, config_name=None, version=None, splits=None, download_checksums=None, download_size=None, post_processing_size=None, dataset_size=None, size_in_bytes=None)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "celebrities.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data\\0\\0.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 2.7ms preprocess, 38.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\1.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.8ms\n",
      "Speed: 3.1ms preprocess, 35.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\10.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.8ms\n",
      "Speed: 2.9ms preprocess, 35.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\11.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.6ms\n",
      "Speed: 2.7ms preprocess, 35.6ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\12.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 2.7ms preprocess, 38.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\13.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.7ms\n",
      "Speed: 2.6ms preprocess, 34.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\14.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 2.9ms preprocess, 36.6ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\15.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.4ms\n",
      "Speed: 2.6ms preprocess, 35.4ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\16.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 3.2ms preprocess, 37.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\17.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.4ms\n",
      "Speed: 2.6ms preprocess, 34.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\18.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.4ms\n",
      "Speed: 2.5ms preprocess, 35.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\19.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.5ms\n",
      "Speed: 2.5ms preprocess, 35.5ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\2.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.3ms\n",
      "Speed: 3.1ms preprocess, 35.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\20.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.4ms\n",
      "Speed: 2.8ms preprocess, 34.4ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\21.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.7ms\n",
      "Speed: 2.5ms preprocess, 35.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\22.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.9ms\n",
      "Speed: 3.1ms preprocess, 35.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\23.png\n",
      "\n",
      "0: 640x640 1 FACE, 49.3ms\n",
      "Speed: 3.0ms preprocess, 49.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\24.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.4ms\n",
      "Speed: 3.0ms preprocess, 36.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\25.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 2.7ms preprocess, 38.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\26.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.7ms\n",
      "Speed: 2.7ms preprocess, 35.7ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\27.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.0ms\n",
      "Speed: 2.8ms preprocess, 37.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\28.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.9ms\n",
      "Speed: 2.9ms preprocess, 37.9ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\29.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.9ms\n",
      "Speed: 2.5ms preprocess, 34.9ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\3.png\n",
      "\n",
      "0: 640x640 1 FACE, 31.2ms\n",
      "Speed: 2.5ms preprocess, 31.2ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\30.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.4ms\n",
      "Speed: 2.7ms preprocess, 37.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\31.png\n",
      "\n",
      "0: 640x640 1 FACE, 29.9ms\n",
      "Speed: 2.5ms preprocess, 29.9ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\32.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.2ms\n",
      "Speed: 2.9ms preprocess, 35.2ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\33.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.5ms\n",
      "Speed: 3.2ms preprocess, 35.5ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\34.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.5ms\n",
      "Speed: 3.4ms preprocess, 35.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\35.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 2.9ms preprocess, 38.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\36.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 3.2ms preprocess, 36.6ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\37.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.1ms\n",
      "Speed: 2.6ms preprocess, 37.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\38.png\n",
      "\n",
      "0: 640x640 1 FACE, 48.7ms\n",
      "Speed: 2.5ms preprocess, 48.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\39.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.3ms\n",
      "Speed: 2.7ms preprocess, 34.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\4.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.6ms\n",
      "Speed: 2.9ms preprocess, 35.6ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\40.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.5ms\n",
      "Speed: 2.7ms preprocess, 36.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\41.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 2.7ms preprocess, 37.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\42.png\n",
      "\n",
      "0: 640x640 1 FACE, 31.1ms\n",
      "Speed: 2.4ms preprocess, 31.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\43.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.0ms\n",
      "Speed: 2.8ms preprocess, 36.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\44.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.2ms\n",
      "Speed: 3.1ms preprocess, 36.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\45.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.5ms\n",
      "Speed: 3.0ms preprocess, 37.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\46.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.5ms\n",
      "Speed: 2.5ms preprocess, 34.5ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\47.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 3.2ms preprocess, 37.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\48.png\n",
      "\n",
      "0: 640x640 1 FACE, 33.4ms\n",
      "Speed: 2.6ms preprocess, 33.4ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\49.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.7ms\n",
      "Speed: 3.3ms preprocess, 34.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\5.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.9ms\n",
      "Speed: 3.4ms preprocess, 35.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\50.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.0ms\n",
      "Speed: 2.9ms preprocess, 36.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\51.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 2.8ms preprocess, 38.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\52.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 2.6ms preprocess, 36.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\53.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.8ms\n",
      "Speed: 2.7ms preprocess, 40.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\54.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.0ms\n",
      "Speed: 2.9ms preprocess, 38.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\55.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.5ms\n",
      "Speed: 3.0ms preprocess, 36.5ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\56.png\n",
      "\n",
      "0: 640x640 1 FACE, 50.8ms\n",
      "Speed: 3.0ms preprocess, 50.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\57.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.1ms\n",
      "Speed: 2.5ms preprocess, 36.1ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\58.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.1ms\n",
      "Speed: 2.7ms preprocess, 35.1ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\59.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.0ms\n",
      "Speed: 2.7ms preprocess, 34.0ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\6.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.1ms\n",
      "Speed: 2.8ms preprocess, 37.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\60.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.7ms\n",
      "Speed: 2.5ms preprocess, 36.7ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\61.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.0ms\n",
      "Speed: 2.8ms preprocess, 35.0ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\62.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.8ms\n",
      "Speed: 2.8ms preprocess, 35.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\63.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.0ms\n",
      "Speed: 2.7ms preprocess, 35.0ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\64.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.5ms\n",
      "Speed: 2.8ms preprocess, 36.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\65.png\n",
      "\n",
      "0: 640x640 2 FACEs, 36.4ms\n",
      "Speed: 2.7ms preprocess, 36.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\66.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 2.5ms preprocess, 40.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\67.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.8ms\n",
      "Speed: 2.6ms preprocess, 38.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\68.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.0ms\n",
      "Speed: 2.7ms preprocess, 36.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\69.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.7ms\n",
      "Speed: 3.1ms preprocess, 36.7ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\7.png\n",
      "\n",
      "0: 640x640 1 FACE, 33.9ms\n",
      "Speed: 2.6ms preprocess, 33.9ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\70.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.1ms\n",
      "Speed: 2.8ms preprocess, 37.1ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\71.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.5ms\n",
      "Speed: 2.7ms preprocess, 36.5ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\72.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.3ms\n",
      "Speed: 2.7ms preprocess, 35.3ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\73.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 2.9ms preprocess, 38.6ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\74.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 2.7ms preprocess, 36.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\75.png\n",
      "\n",
      "0: 640x640 1 FACE, 49.7ms\n",
      "Speed: 2.7ms preprocess, 49.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\76.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.5ms\n",
      "Speed: 2.6ms preprocess, 35.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\77.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 2.6ms preprocess, 37.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\78.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 2.8ms preprocess, 36.3ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\79.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 2.9ms preprocess, 37.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\8.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.8ms\n",
      "Speed: 2.9ms preprocess, 39.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\80.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 3.0ms preprocess, 39.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\81.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.2ms\n",
      "Speed: 3.7ms preprocess, 36.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\82.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.8ms\n",
      "Speed: 3.0ms preprocess, 35.8ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\83.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 3.1ms preprocess, 40.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\84.png\n",
      "\n",
      "0: 640x640 1 FACE, 45.5ms\n",
      "Speed: 3.1ms preprocess, 45.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\85.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.5ms\n",
      "Speed: 3.0ms preprocess, 42.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\86.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.3ms\n",
      "Speed: 2.9ms preprocess, 37.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\87.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 3.0ms preprocess, 38.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\88.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.0ms\n",
      "Speed: 3.4ms preprocess, 38.0ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\89.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.9ms\n",
      "Speed: 2.7ms preprocess, 34.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\9.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 3.1ms preprocess, 36.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\90.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.6ms\n",
      "Speed: 2.7ms preprocess, 35.6ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\91.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.5ms\n",
      "Speed: 2.9ms preprocess, 35.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\92.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 2.6ms preprocess, 36.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\93.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.0ms\n",
      "Speed: 3.3ms preprocess, 36.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\94.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 3.0ms preprocess, 39.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\95.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 2.9ms preprocess, 38.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\96.png\n",
      "\n",
      "0: 640x640 3 FACEs, 37.2ms\n",
      "Speed: 2.6ms preprocess, 37.2ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\97.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 2.7ms preprocess, 38.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\98.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.0ms\n",
      "Speed: 3.1ms preprocess, 38.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\0\\99.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 2.5ms preprocess, 39.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\100.png\n",
      "\n",
      "0: 640x640 2 FACEs, 37.2ms\n",
      "Speed: 2.8ms preprocess, 37.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\101.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.1ms\n",
      "Speed: 3.2ms preprocess, 37.1ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\102.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.8ms\n",
      "Speed: 3.0ms preprocess, 36.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\103.png\n",
      "\n",
      "0: 640x640 2 FACEs, 38.8ms\n",
      "Speed: 2.9ms preprocess, 38.8ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\104.png\n",
      "\n",
      "0: 640x640 2 FACEs, 42.1ms\n",
      "Speed: 2.7ms preprocess, 42.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\105.png\n",
      "\n",
      "0: 640x640 2 FACEs, 35.5ms\n",
      "Speed: 4.2ms preprocess, 35.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\106.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.5ms\n",
      "Speed: 2.6ms preprocess, 35.5ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\107.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.0ms\n",
      "Speed: 2.6ms preprocess, 37.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\108.png\n",
      "\n",
      "0: 640x640 8 FACEs, 35.2ms\n",
      "Speed: 2.7ms preprocess, 35.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\109.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.3ms\n",
      "Speed: 2.6ms preprocess, 37.3ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\110.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.5ms\n",
      "Speed: 3.2ms preprocess, 37.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\111.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 3.2ms preprocess, 40.5ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\112.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.4ms\n",
      "Speed: 3.0ms preprocess, 40.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\113.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.9ms\n",
      "Speed: 2.6ms preprocess, 36.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\114.png\n",
      "\n",
      "0: 640x640 2 FACEs, 37.7ms\n",
      "Speed: 2.5ms preprocess, 37.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\115.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 2.7ms preprocess, 36.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\116.png\n",
      "\n",
      "0: 640x640 2 FACEs, 38.4ms\n",
      "Speed: 3.0ms preprocess, 38.4ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\117.png\n",
      "\n",
      "0: 640x640 2 FACEs, 39.2ms\n",
      "Speed: 2.8ms preprocess, 39.2ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\118.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.6ms\n",
      "Speed: 2.6ms preprocess, 39.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\119.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.3ms\n",
      "Speed: 2.5ms preprocess, 35.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\120.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.4ms\n",
      "Speed: 2.6ms preprocess, 35.4ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\121.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.8ms\n",
      "Speed: 2.5ms preprocess, 36.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\122.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.4ms\n",
      "Speed: 3.1ms preprocess, 35.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\123.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 2.6ms preprocess, 38.5ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\124.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 2.6ms preprocess, 38.2ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\125.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.1ms\n",
      "Speed: 2.7ms preprocess, 37.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\126.png\n",
      "\n",
      "0: 640x640 1 FACE, 46.9ms\n",
      "Speed: 4.3ms preprocess, 46.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\127.png\n",
      "\n",
      "0: 640x640 1 FACE, 32.8ms\n",
      "Speed: 2.6ms preprocess, 32.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\128.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.7ms\n",
      "Speed: 3.0ms preprocess, 36.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\129.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.0ms\n",
      "Speed: 3.1ms preprocess, 36.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\130.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.7ms\n",
      "Speed: 2.7ms preprocess, 37.7ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\131.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 2.7ms preprocess, 36.3ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\132.png\n",
      "\n",
      "0: 640x640 2 FACEs, 37.1ms\n",
      "Speed: 3.0ms preprocess, 37.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\133.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.5ms\n",
      "Speed: 3.2ms preprocess, 35.5ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\134.png\n",
      "\n",
      "0: 640x640 2 FACEs, 35.5ms\n",
      "Speed: 2.7ms preprocess, 35.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\135.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.9ms\n",
      "Speed: 2.7ms preprocess, 36.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\136.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.1ms\n",
      "Speed: 2.9ms preprocess, 37.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\137.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 2.8ms preprocess, 36.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\138.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.6ms\n",
      "Speed: 2.6ms preprocess, 35.6ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\139.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.4ms\n",
      "Speed: 2.6ms preprocess, 36.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\140.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.0ms\n",
      "Speed: 2.8ms preprocess, 38.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\141.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 2.7ms preprocess, 36.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\142.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.7ms\n",
      "Speed: 3.1ms preprocess, 42.7ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\143.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.7ms\n",
      "Speed: 2.7ms preprocess, 36.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\144.png\n",
      "\n",
      "0: 640x640 1 FACE, 48.5ms\n",
      "Speed: 3.3ms preprocess, 48.5ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\145.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.2ms\n",
      "Speed: 2.6ms preprocess, 36.2ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\146.png\n",
      "\n",
      "0: 640x640 1 FACE, 30.0ms\n",
      "Speed: 2.7ms preprocess, 30.0ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\147.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 2.6ms preprocess, 38.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\148.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 3.1ms preprocess, 38.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\149.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.6ms\n",
      "Speed: 3.3ms preprocess, 35.6ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\150.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 3.6ms preprocess, 38.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\151.png\n",
      "\n",
      "0: 640x640 2 FACEs, 36.6ms\n",
      "Speed: 3.0ms preprocess, 36.6ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\152.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.3ms\n",
      "Speed: 2.6ms preprocess, 37.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\153.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 2.8ms preprocess, 39.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\154.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.8ms\n",
      "Speed: 2.7ms preprocess, 36.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\155.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.0ms\n",
      "Speed: 2.8ms preprocess, 40.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\156.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.0ms\n",
      "Speed: 2.4ms preprocess, 38.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\157.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.3ms\n",
      "Speed: 2.5ms preprocess, 35.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\158.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.0ms\n",
      "Speed: 3.0ms preprocess, 38.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\159.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 3.0ms preprocess, 38.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\160.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.8ms\n",
      "Speed: 3.6ms preprocess, 37.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\161.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.1ms\n",
      "Speed: 3.4ms preprocess, 37.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\162.png\n",
      "\n",
      "0: 640x640 1 FACE, 45.9ms\n",
      "Speed: 2.6ms preprocess, 45.9ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\163.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.2ms\n",
      "Speed: 3.2ms preprocess, 36.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\164.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.2ms\n",
      "Speed: 2.7ms preprocess, 35.2ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\165.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.7ms\n",
      "Speed: 2.9ms preprocess, 36.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\166.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.5ms\n",
      "Speed: 2.6ms preprocess, 39.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\167.png\n",
      "\n",
      "0: 640x640 3 FACEs, 36.9ms\n",
      "Speed: 2.7ms preprocess, 36.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\168.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.5ms\n",
      "Speed: 2.6ms preprocess, 39.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\169.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.4ms\n",
      "Speed: 3.0ms preprocess, 37.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\170.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.7ms\n",
      "Speed: 3.4ms preprocess, 41.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\171.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 3.0ms preprocess, 38.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\172.png\n",
      "\n",
      "0: 640x640 1 FACE, 30.9ms\n",
      "Speed: 2.4ms preprocess, 30.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\173.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.0ms\n",
      "Speed: 2.9ms preprocess, 36.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\174.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.3ms\n",
      "Speed: 2.7ms preprocess, 37.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\175.png\n",
      "\n",
      "0: 640x640 1 FACE, 32.9ms\n",
      "Speed: 2.5ms preprocess, 32.9ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\176.png\n",
      "\n",
      "0: 640x640 1 FACE, 33.6ms\n",
      "Speed: 2.5ms preprocess, 33.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\177.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.0ms\n",
      "Speed: 2.4ms preprocess, 34.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\178.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 2.6ms preprocess, 38.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\179.png\n",
      "\n",
      "0: 640x640 3 FACEs, 35.5ms\n",
      "Speed: 2.9ms preprocess, 35.5ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\180.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.1ms\n",
      "Speed: 3.6ms preprocess, 41.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\181.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.0ms\n",
      "Speed: 2.7ms preprocess, 36.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\182.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.6ms\n",
      "Speed: 2.9ms preprocess, 37.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\183.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.4ms\n",
      "Speed: 2.7ms preprocess, 39.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\184.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.7ms\n",
      "Speed: 2.5ms preprocess, 34.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\185.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 2.6ms preprocess, 36.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\186.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.0ms\n",
      "Speed: 2.4ms preprocess, 37.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\187.png\n",
      "\n",
      "0: 640x640 2 FACEs, 36.2ms\n",
      "Speed: 2.6ms preprocess, 36.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\188.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.9ms\n",
      "Speed: 2.6ms preprocess, 36.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\189.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.8ms\n",
      "Speed: 2.5ms preprocess, 34.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\190.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.8ms\n",
      "Speed: 2.5ms preprocess, 36.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\191.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.7ms\n",
      "Speed: 2.8ms preprocess, 36.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\192.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.0ms\n",
      "Speed: 2.7ms preprocess, 36.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\193.png\n",
      "\n",
      "0: 640x640 1 FACE, 29.0ms\n",
      "Speed: 2.9ms preprocess, 29.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\194.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 3.1ms preprocess, 36.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\195.png\n",
      "\n",
      "0: 640x640 1 FACE, 57.9ms\n",
      "Speed: 3.4ms preprocess, 57.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\196.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 2.9ms preprocess, 37.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\197.png\n",
      "\n",
      "0: 640x640 1 FACE, 28.5ms\n",
      "Speed: 2.5ms preprocess, 28.5ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\198.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.5ms\n",
      "Speed: 2.5ms preprocess, 35.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\1\\199.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.5ms\n",
      "Speed: 2.7ms preprocess, 35.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\200.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 3.1ms preprocess, 37.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\201.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.1ms\n",
      "Speed: 2.5ms preprocess, 36.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\202.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.3ms\n",
      "Speed: 2.6ms preprocess, 35.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\203.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.0ms\n",
      "Speed: 2.7ms preprocess, 36.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\204.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.3ms\n",
      "Speed: 2.6ms preprocess, 38.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\205.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.9ms\n",
      "Speed: 3.0ms preprocess, 37.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\206.png\n",
      "\n",
      "0: 640x640 2 FACEs, 37.9ms\n",
      "Speed: 3.0ms preprocess, 37.9ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\207.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.4ms\n",
      "Speed: 3.1ms preprocess, 37.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\208.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 3.1ms preprocess, 36.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\209.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.9ms\n",
      "Speed: 3.4ms preprocess, 36.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\210.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.8ms\n",
      "Speed: 2.9ms preprocess, 37.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\211.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.0ms\n",
      "Speed: 2.5ms preprocess, 36.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\212.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.7ms\n",
      "Speed: 2.6ms preprocess, 42.7ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\213.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.8ms\n",
      "Speed: 2.9ms preprocess, 34.8ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\214.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 2.6ms preprocess, 38.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\215.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.8ms\n",
      "Speed: 3.2ms preprocess, 38.8ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\216.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.5ms\n",
      "Speed: 2.7ms preprocess, 37.5ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\217.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.7ms\n",
      "Speed: 3.0ms preprocess, 36.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\218.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.9ms\n",
      "Speed: 2.5ms preprocess, 36.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\219.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.0ms\n",
      "Speed: 2.6ms preprocess, 37.0ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\220.png\n",
      "\n",
      "0: 640x640 2 FACEs, 37.4ms\n",
      "Speed: 2.8ms preprocess, 37.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\221.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 2.8ms preprocess, 36.3ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\222.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.4ms\n",
      "Speed: 2.8ms preprocess, 35.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\223.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.7ms\n",
      "Speed: 2.9ms preprocess, 35.7ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\224.png\n",
      "\n",
      "0: 640x640 1 FACE, 33.5ms\n",
      "Speed: 2.7ms preprocess, 33.5ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\225.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.4ms\n",
      "Speed: 3.2ms preprocess, 35.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\226.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.7ms\n",
      "Speed: 2.6ms preprocess, 34.7ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\227.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.8ms\n",
      "Speed: 2.8ms preprocess, 35.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\228.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.5ms\n",
      "Speed: 2.5ms preprocess, 34.5ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\229.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.1ms\n",
      "Speed: 4.2ms preprocess, 44.1ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\230.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.0ms\n",
      "Speed: 2.5ms preprocess, 35.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\231.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 3.1ms preprocess, 36.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\232.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 2.7ms preprocess, 36.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\233.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.9ms\n",
      "Speed: 2.9ms preprocess, 36.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\234.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.9ms\n",
      "Speed: 2.8ms preprocess, 35.9ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\235.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.3ms\n",
      "Speed: 3.1ms preprocess, 37.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\236.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.3ms\n",
      "Speed: 2.7ms preprocess, 35.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\237.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 2.7ms preprocess, 38.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\238.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.8ms\n",
      "Speed: 3.1ms preprocess, 37.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\239.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.6ms\n",
      "Speed: 2.8ms preprocess, 35.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\240.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.5ms\n",
      "Speed: 2.8ms preprocess, 36.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\241.png\n",
      "\n",
      "0: 640x640 2 FACEs, 38.2ms\n",
      "Speed: 3.1ms preprocess, 38.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\242.png\n",
      "\n",
      "0: 640x640 1 FACE, 32.1ms\n",
      "Speed: 2.5ms preprocess, 32.1ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\243.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.7ms\n",
      "Speed: 2.8ms preprocess, 36.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\244.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 3.0ms preprocess, 37.2ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\245.png\n",
      "\n",
      "0: 640x640 1 FACE, 53.6ms\n",
      "Speed: 3.5ms preprocess, 53.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\246.png\n",
      "\n",
      "0: 640x640 1 FACE, 26.6ms\n",
      "Speed: 2.5ms preprocess, 26.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\247.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.4ms\n",
      "Speed: 2.5ms preprocess, 35.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\248.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.8ms\n",
      "Speed: 2.7ms preprocess, 35.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\249.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.5ms\n",
      "Speed: 2.8ms preprocess, 36.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\250.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.7ms\n",
      "Speed: 2.4ms preprocess, 36.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\251.png\n",
      "\n",
      "0: 640x640 1 FACE, 25.8ms\n",
      "Speed: 2.9ms preprocess, 25.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\252.png\n",
      "\n",
      "0: 640x640 (no detections), 29.7ms\n",
      "Speed: 2.7ms preprocess, 29.7ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\253.png\n",
      "\n",
      "0: 640x640 1 FACE, 26.5ms\n",
      "Speed: 2.6ms preprocess, 26.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\254.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.6ms\n",
      "Speed: 2.8ms preprocess, 37.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\255.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.1ms\n",
      "Speed: 3.4ms preprocess, 39.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\256.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.1ms\n",
      "Speed: 2.5ms preprocess, 35.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\257.png\n",
      "\n",
      "0: 640x640 1 FACE, 31.5ms\n",
      "Speed: 3.0ms preprocess, 31.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\258.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.3ms\n",
      "Speed: 2.7ms preprocess, 37.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\259.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.9ms\n",
      "Speed: 2.7ms preprocess, 37.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\260.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.9ms\n",
      "Speed: 2.5ms preprocess, 34.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\261.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.6ms\n",
      "Speed: 3.6ms preprocess, 41.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\262.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.7ms\n",
      "Speed: 2.6ms preprocess, 35.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\263.png\n",
      "\n",
      "0: 640x640 2 FACEs, 36.6ms\n",
      "Speed: 2.4ms preprocess, 36.6ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\264.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 2.4ms preprocess, 38.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\265.png\n",
      "\n",
      "0: 640x640 2 FACEs, 38.0ms\n",
      "Speed: 3.7ms preprocess, 38.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\266.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.5ms\n",
      "Speed: 3.1ms preprocess, 36.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\267.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.7ms\n",
      "Speed: 2.4ms preprocess, 35.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\268.png\n",
      "\n",
      "0: 640x640 2 FACEs, 36.1ms\n",
      "Speed: 2.5ms preprocess, 36.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\269.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.6ms\n",
      "Speed: 2.6ms preprocess, 35.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\270.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 2.9ms preprocess, 38.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\271.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 2.9ms preprocess, 38.7ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\272.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.0ms\n",
      "Speed: 3.4ms preprocess, 36.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\273.png\n",
      "\n",
      "0: 640x640 1 FACE, 33.3ms\n",
      "Speed: 2.8ms preprocess, 33.3ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\274.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.3ms\n",
      "Speed: 2.9ms preprocess, 38.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\275.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 2.6ms preprocess, 38.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\276.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.3ms\n",
      "Speed: 3.7ms preprocess, 43.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\277.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.4ms\n",
      "Speed: 2.5ms preprocess, 36.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\278.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.6ms\n",
      "Speed: 2.8ms preprocess, 41.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\279.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.5ms\n",
      "Speed: 2.6ms preprocess, 39.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\280.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 3.0ms preprocess, 40.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\281.png\n",
      "\n",
      "0: 640x640 1 FACE, 32.7ms\n",
      "Speed: 2.5ms preprocess, 32.7ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\282.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 3.4ms preprocess, 38.5ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\283.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.3ms\n",
      "Speed: 3.0ms preprocess, 37.3ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\284.png\n",
      "\n",
      "0: 640x640 2 FACEs, 34.0ms\n",
      "Speed: 2.9ms preprocess, 34.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\285.png\n",
      "\n",
      "0: 640x640 1 FACE, 32.5ms\n",
      "Speed: 2.6ms preprocess, 32.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\286.png\n",
      "\n",
      "0: 640x640 1 FACE, 32.1ms\n",
      "Speed: 2.6ms preprocess, 32.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\287.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 2.6ms preprocess, 39.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\288.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.4ms\n",
      "Speed: 2.6ms preprocess, 36.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\289.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 2.9ms preprocess, 36.3ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\290.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 3.1ms preprocess, 38.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\291.png\n",
      "\n",
      "0: 640x640 1 FACE, 53.9ms\n",
      "Speed: 3.5ms preprocess, 53.9ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\292.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 2.5ms preprocess, 38.9ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\293.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 2.6ms preprocess, 39.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\294.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.4ms\n",
      "Speed: 2.9ms preprocess, 37.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\295.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.7ms\n",
      "Speed: 2.4ms preprocess, 35.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\296.png\n",
      "\n",
      "0: 640x640 1 FACE, 29.5ms\n",
      "Speed: 3.0ms preprocess, 29.5ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\297.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.4ms\n",
      "Speed: 2.8ms preprocess, 37.4ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\298.png\n",
      "\n",
      "0: 640x640 (no detections), 37.8ms\n",
      "Speed: 2.9ms preprocess, 37.8ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\2\\299.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 2.8ms preprocess, 39.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\300.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.8ms\n",
      "Speed: 2.8ms preprocess, 38.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\301.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 2.6ms preprocess, 39.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\302.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 3.2ms preprocess, 36.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\303.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.9ms\n",
      "Speed: 3.3ms preprocess, 36.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\304.png\n",
      "\n",
      "0: 640x640 2 FACEs, 40.9ms\n",
      "Speed: 2.7ms preprocess, 40.9ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\305.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.5ms\n",
      "Speed: 2.5ms preprocess, 36.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\306.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 2.6ms preprocess, 38.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\307.png\n",
      "\n",
      "0: 640x640 2 FACEs, 41.1ms\n",
      "Speed: 3.0ms preprocess, 41.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\308.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.4ms\n",
      "Speed: 2.5ms preprocess, 35.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\309.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.1ms\n",
      "Speed: 2.9ms preprocess, 41.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\310.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 2.8ms preprocess, 38.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\311.png\n",
      "\n",
      "0: 640x640 1 FACE, 45.6ms\n",
      "Speed: 4.1ms preprocess, 45.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\312.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.4ms\n",
      "Speed: 2.9ms preprocess, 39.4ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\313.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 2.7ms preprocess, 39.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\314.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.1ms\n",
      "Speed: 2.6ms preprocess, 38.1ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\315.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.9ms\n",
      "Speed: 2.6ms preprocess, 40.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\316.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.0ms\n",
      "Speed: 2.9ms preprocess, 40.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\317.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.8ms\n",
      "Speed: 3.4ms preprocess, 38.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\318.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 2.8ms preprocess, 38.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\319.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.1ms\n",
      "Speed: 2.9ms preprocess, 37.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\320.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 2.7ms preprocess, 39.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\321.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 2.7ms preprocess, 38.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\322.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 2.5ms preprocess, 40.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\323.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.5ms\n",
      "Speed: 2.9ms preprocess, 37.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\324.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 3.1ms preprocess, 39.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\325.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 3.1ms preprocess, 39.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\326.png\n",
      "\n",
      "0: 640x640 2 FACEs, 40.9ms\n",
      "Speed: 3.1ms preprocess, 40.9ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\327.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 3.2ms preprocess, 36.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\328.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.2ms\n",
      "Speed: 2.5ms preprocess, 40.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\329.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 3.5ms preprocess, 38.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\330.png\n",
      "\n",
      "0: 640x640 1 FACE, 55.0ms\n",
      "Speed: 4.0ms preprocess, 55.0ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\331.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 2.5ms preprocess, 38.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\332.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.8ms\n",
      "Speed: 2.5ms preprocess, 40.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\333.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 2.9ms preprocess, 39.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\334.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 2.7ms preprocess, 40.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\335.png\n",
      "\n",
      "0: 640x640 2 FACEs, 41.2ms\n",
      "Speed: 3.0ms preprocess, 41.2ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\336.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 2.6ms preprocess, 38.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\337.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 3.3ms preprocess, 40.3ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\338.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 2.6ms preprocess, 38.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\339.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.0ms\n",
      "Speed: 3.5ms preprocess, 41.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\340.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.0ms\n",
      "Speed: 2.7ms preprocess, 34.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\341.png\n",
      "\n",
      "0: 640x640 1 FACE, 29.4ms\n",
      "Speed: 2.5ms preprocess, 29.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\342.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.2ms\n",
      "Speed: 2.6ms preprocess, 40.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\343.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.8ms\n",
      "Speed: 2.5ms preprocess, 38.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\344.png\n",
      "\n",
      "0: 640x640 1 FACE, 59.3ms\n",
      "Speed: 2.6ms preprocess, 59.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\345.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.4ms\n",
      "Speed: 2.8ms preprocess, 35.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\346.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.2ms\n",
      "Speed: 2.6ms preprocess, 36.2ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\347.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 2.8ms preprocess, 39.3ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\348.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.0ms\n",
      "Speed: 2.8ms preprocess, 38.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\349.png\n",
      "\n",
      "0: 640x640 3 FACEs, 40.6ms\n",
      "Speed: 2.7ms preprocess, 40.6ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\350.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 2.9ms preprocess, 38.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\351.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.8ms\n",
      "Speed: 2.8ms preprocess, 35.8ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\352.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 3.3ms preprocess, 39.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\353.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 2.9ms preprocess, 38.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\354.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 2.9ms preprocess, 39.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\355.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 2.7ms preprocess, 37.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\356.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.7ms\n",
      "Speed: 2.6ms preprocess, 35.7ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\357.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 3.2ms preprocess, 40.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\358.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 3.5ms preprocess, 38.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\359.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.9ms\n",
      "Speed: 2.6ms preprocess, 43.9ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\360.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 2.9ms preprocess, 39.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\361.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.6ms\n",
      "Speed: 2.7ms preprocess, 35.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\362.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.7ms\n",
      "Speed: 3.1ms preprocess, 40.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\363.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.5ms\n",
      "Speed: 2.6ms preprocess, 37.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\364.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.8ms\n",
      "Speed: 3.0ms preprocess, 37.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\365.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.1ms\n",
      "Speed: 2.5ms preprocess, 38.1ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\366.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.6ms\n",
      "Speed: 3.3ms preprocess, 41.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\367.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.6ms\n",
      "Speed: 2.8ms preprocess, 39.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\368.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 2.6ms preprocess, 40.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\369.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 3.0ms preprocess, 38.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\370.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 3.2ms preprocess, 38.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\371.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.7ms\n",
      "Speed: 2.5ms preprocess, 37.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\372.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.9ms\n",
      "Speed: 2.6ms preprocess, 37.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\373.png\n",
      "\n",
      "0: 640x640 1 FACE, 31.9ms\n",
      "Speed: 3.0ms preprocess, 31.9ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\374.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.3ms\n",
      "Speed: 3.3ms preprocess, 37.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\375.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.6ms\n",
      "Speed: 2.6ms preprocess, 42.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\376.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.8ms\n",
      "Speed: 3.5ms preprocess, 43.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\377.png\n",
      "\n",
      "0: 640x640 3 FACEs, 39.3ms\n",
      "Speed: 4.1ms preprocess, 39.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\378.png\n",
      "\n",
      "0: 640x640 1 FACE, 31.3ms\n",
      "Speed: 2.6ms preprocess, 31.3ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\379.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.4ms\n",
      "Speed: 2.7ms preprocess, 35.4ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\380.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.9ms\n",
      "Speed: 2.6ms preprocess, 37.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\381.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.8ms\n",
      "Speed: 2.5ms preprocess, 39.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\382.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.3ms\n",
      "Speed: 2.9ms preprocess, 38.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\383.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.0ms\n",
      "Speed: 2.8ms preprocess, 38.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\384.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.2ms\n",
      "Speed: 2.9ms preprocess, 41.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\385.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.8ms\n",
      "Speed: 3.6ms preprocess, 37.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\386.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.0ms\n",
      "Speed: 2.5ms preprocess, 36.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\387.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.7ms\n",
      "Speed: 3.2ms preprocess, 36.7ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\388.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.4ms\n",
      "Speed: 2.7ms preprocess, 37.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\389.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.6ms\n",
      "Speed: 2.7ms preprocess, 35.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\390.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.7ms\n",
      "Speed: 2.8ms preprocess, 37.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\391.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.5ms\n",
      "Speed: 2.7ms preprocess, 37.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\392.png\n",
      "\n",
      "0: 640x640 1 FACE, 52.8ms\n",
      "Speed: 3.1ms preprocess, 52.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\393.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.2ms\n",
      "Speed: 2.6ms preprocess, 36.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\394.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.4ms\n",
      "Speed: 2.6ms preprocess, 40.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\395.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.6ms\n",
      "Speed: 3.5ms preprocess, 40.6ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\396.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 2.6ms preprocess, 36.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\397.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.6ms\n",
      "Speed: 3.1ms preprocess, 37.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\398.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.9ms\n",
      "Speed: 3.0ms preprocess, 37.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\3\\399.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 3.6ms preprocess, 39.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\400.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 2.6ms preprocess, 37.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\401.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.1ms\n",
      "Speed: 2.8ms preprocess, 37.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\402.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.0ms\n",
      "Speed: 2.5ms preprocess, 36.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\403.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.1ms\n",
      "Speed: 2.6ms preprocess, 37.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\404.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 3.3ms preprocess, 39.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\405.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.4ms\n",
      "Speed: 3.5ms preprocess, 43.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\406.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.6ms\n",
      "Speed: 4.6ms preprocess, 35.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\407.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.8ms\n",
      "Speed: 3.0ms preprocess, 38.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\408.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.1ms\n",
      "Speed: 2.9ms preprocess, 39.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\409.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.3ms\n",
      "Speed: 3.0ms preprocess, 37.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\410.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.4ms\n",
      "Speed: 2.8ms preprocess, 36.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\411.png\n",
      "\n",
      "0: 640x640 1 FACE, 32.6ms\n",
      "Speed: 7.0ms preprocess, 32.6ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\412.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.4ms\n",
      "Speed: 3.1ms preprocess, 39.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\413.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.8ms\n",
      "Speed: 3.0ms preprocess, 39.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\414.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 3.1ms preprocess, 38.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\415.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 3.6ms preprocess, 38.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\416.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.4ms\n",
      "Speed: 2.9ms preprocess, 37.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\417.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 2.7ms preprocess, 39.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\418.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.9ms\n",
      "Speed: 2.5ms preprocess, 34.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\419.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 3.0ms preprocess, 40.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\420.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.8ms\n",
      "Speed: 2.8ms preprocess, 43.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\421.png\n",
      "\n",
      "0: 640x640 1 FACE, 53.7ms\n",
      "Speed: 5.1ms preprocess, 53.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\422.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 2.9ms preprocess, 39.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\423.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.9ms\n",
      "Speed: 2.6ms preprocess, 35.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\424.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.5ms\n",
      "Speed: 2.6ms preprocess, 37.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\425.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.1ms\n",
      "Speed: 2.5ms preprocess, 36.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\426.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 2.9ms preprocess, 39.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\427.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 2.8ms preprocess, 36.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\428.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 2.7ms preprocess, 38.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\429.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 3.0ms preprocess, 38.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\430.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 3.5ms preprocess, 38.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\431.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.8ms\n",
      "Speed: 2.7ms preprocess, 37.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\432.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.3ms\n",
      "Speed: 3.3ms preprocess, 34.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\433.png\n",
      "\n",
      "0: 640x640 1 FACE, 31.8ms\n",
      "Speed: 2.7ms preprocess, 31.8ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\434.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 2.6ms preprocess, 39.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\435.png\n",
      "\n",
      "0: 640x640 1 FACE, 48.9ms\n",
      "Speed: 2.9ms preprocess, 48.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\436.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.7ms\n",
      "Speed: 2.7ms preprocess, 34.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\437.png\n",
      "\n",
      "0: 640x640 1 FACE, 33.8ms\n",
      "Speed: 2.6ms preprocess, 33.8ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\438.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 3.0ms preprocess, 38.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\439.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.8ms\n",
      "Speed: 2.9ms preprocess, 40.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\440.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 2.9ms preprocess, 39.2ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\441.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.1ms\n",
      "Speed: 2.9ms preprocess, 41.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\442.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.8ms\n",
      "Speed: 3.4ms preprocess, 42.8ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\443.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.5ms\n",
      "Speed: 2.9ms preprocess, 42.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\444.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.6ms\n",
      "Speed: 2.6ms preprocess, 44.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\445.png\n",
      "\n",
      "0: 640x640 1 FACE, 46.7ms\n",
      "Speed: 3.3ms preprocess, 46.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\446.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.2ms\n",
      "Speed: 3.4ms preprocess, 43.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\447.png\n",
      "\n",
      "0: 640x640 1 FACE, 46.0ms\n",
      "Speed: 3.3ms preprocess, 46.0ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\448.png\n",
      "\n",
      "0: 640x640 1 FACE, 51.1ms\n",
      "Speed: 3.0ms preprocess, 51.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\449.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.8ms\n",
      "Speed: 3.0ms preprocess, 43.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\450.png\n",
      "\n",
      "0: 640x640 1 FACE, 59.6ms\n",
      "Speed: 3.2ms preprocess, 59.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\451.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 3.0ms preprocess, 39.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\452.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 2.8ms preprocess, 40.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\453.png\n",
      "\n",
      "0: 640x640 1 FACE, 62.8ms\n",
      "Speed: 2.6ms preprocess, 62.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\454.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.7ms\n",
      "Speed: 3.9ms preprocess, 42.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\455.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.3ms\n",
      "Speed: 2.6ms preprocess, 43.3ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\456.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.4ms\n",
      "Speed: 3.3ms preprocess, 39.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\457.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 2.5ms preprocess, 39.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\458.png\n",
      "\n",
      "0: 640x640 1 FACE, 46.4ms\n",
      "Speed: 2.8ms preprocess, 46.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\459.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.5ms\n",
      "Speed: 3.2ms preprocess, 41.5ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\460.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 3.0ms preprocess, 40.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\461.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.8ms\n",
      "Speed: 3.0ms preprocess, 40.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\462.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.2ms\n",
      "Speed: 2.6ms preprocess, 40.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\463.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.4ms\n",
      "Speed: 3.5ms preprocess, 35.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\464.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.3ms\n",
      "Speed: 3.4ms preprocess, 35.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\465.png\n",
      "\n",
      "0: 640x640 2 FACEs, 37.8ms\n",
      "Speed: 2.6ms preprocess, 37.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\466.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 4.1ms preprocess, 39.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\467.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.5ms\n",
      "Speed: 2.7ms preprocess, 39.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\468.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.1ms\n",
      "Speed: 2.5ms preprocess, 38.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\469.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 2.6ms preprocess, 38.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\470.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.1ms\n",
      "Speed: 2.9ms preprocess, 36.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\471.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.2ms\n",
      "Speed: 3.2ms preprocess, 40.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\472.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.7ms\n",
      "Speed: 3.0ms preprocess, 37.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\473.png\n",
      "\n",
      "0: 640x640 1 FACE, 32.7ms\n",
      "Speed: 2.6ms preprocess, 32.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\474.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 3.8ms preprocess, 40.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\475.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.6ms\n",
      "Speed: 2.6ms preprocess, 42.6ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\476.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.2ms\n",
      "Speed: 3.8ms preprocess, 44.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\477.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.6ms\n",
      "Speed: 3.3ms preprocess, 39.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\478.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.9ms\n",
      "Speed: 3.2ms preprocess, 37.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\479.png\n",
      "\n",
      "0: 640x640 1 FACE, 34.8ms\n",
      "Speed: 2.5ms preprocess, 34.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\480.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 3.1ms preprocess, 38.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\481.png\n",
      "\n",
      "0: 640x640 1 FACE, 49.1ms\n",
      "Speed: 2.9ms preprocess, 49.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\482.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 3.1ms preprocess, 38.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\483.png\n",
      "\n",
      "0: 640x640 2 FACEs, 33.9ms\n",
      "Speed: 3.0ms preprocess, 33.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\484.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.1ms\n",
      "Speed: 3.1ms preprocess, 38.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\485.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 3.4ms preprocess, 39.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\486.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.4ms\n",
      "Speed: 3.1ms preprocess, 39.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\487.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 2.6ms preprocess, 38.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\488.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 2.7ms preprocess, 38.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\489.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.4ms\n",
      "Speed: 2.9ms preprocess, 35.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\490.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 2.8ms preprocess, 39.3ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\491.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.6ms\n",
      "Speed: 3.1ms preprocess, 40.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\492.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.1ms\n",
      "Speed: 2.7ms preprocess, 39.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\493.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 3.0ms preprocess, 40.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\494.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 3.0ms preprocess, 40.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\495.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.4ms\n",
      "Speed: 2.9ms preprocess, 40.4ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\496.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 2.6ms preprocess, 39.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\497.png\n",
      "\n",
      "0: 640x640 1 FACE, 55.2ms\n",
      "Speed: 3.0ms preprocess, 55.2ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\498.png\n",
      "\n",
      "0: 640x640 1 FACE, 32.5ms\n",
      "Speed: 2.7ms preprocess, 32.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\4\\499.png\n",
      "\n",
      "0: 640x640 1 FACE, 29.4ms\n",
      "Speed: 2.7ms preprocess, 29.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\500.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.7ms\n",
      "Speed: 3.0ms preprocess, 40.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\501.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 2.7ms preprocess, 39.2ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\502.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 2.5ms preprocess, 36.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\503.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.1ms\n",
      "Speed: 3.0ms preprocess, 35.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\504.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 2.9ms preprocess, 38.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\505.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.3ms\n",
      "Speed: 3.3ms preprocess, 41.3ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\506.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.6ms\n",
      "Speed: 2.7ms preprocess, 41.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\507.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 2.8ms preprocess, 40.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\508.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.9ms\n",
      "Speed: 3.1ms preprocess, 37.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\509.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 2.7ms preprocess, 38.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\510.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 2.8ms preprocess, 40.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\511.png\n",
      "\n",
      "0: 640x640 2 FACEs, 49.4ms\n",
      "Speed: 2.9ms preprocess, 49.4ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\512.png\n",
      "\n",
      "0: 640x640 2 FACEs, 38.3ms\n",
      "Speed: 3.0ms preprocess, 38.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\513.png\n",
      "\n",
      "0: 640x640 4 FACEs, 41.4ms\n",
      "Speed: 2.9ms preprocess, 41.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\514.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.7ms\n",
      "Speed: 2.9ms preprocess, 41.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\515.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.0ms\n",
      "Speed: 2.9ms preprocess, 43.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\516.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 2.9ms preprocess, 39.7ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\517.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 3.3ms preprocess, 39.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\518.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 2.9ms preprocess, 40.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\519.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 2.9ms preprocess, 38.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\520.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 3.0ms preprocess, 39.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\521.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.5ms\n",
      "Speed: 3.9ms preprocess, 37.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\522.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 2.7ms preprocess, 36.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\523.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.9ms\n",
      "Speed: 3.1ms preprocess, 41.9ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\524.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.3ms\n",
      "Speed: 2.6ms preprocess, 41.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\525.png\n",
      "\n",
      "0: 640x640 1 FACE, 66.6ms\n",
      "Speed: 3.1ms preprocess, 66.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\526.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 3.6ms preprocess, 40.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\527.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.7ms\n",
      "Speed: 2.8ms preprocess, 37.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\528.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.3ms\n",
      "Speed: 2.8ms preprocess, 38.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\529.png\n",
      "\n",
      "0: 640x640 2 FACEs, 39.0ms\n",
      "Speed: 2.8ms preprocess, 39.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\530.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.5ms\n",
      "Speed: 3.2ms preprocess, 42.5ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\531.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 3.2ms preprocess, 38.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\532.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.6ms\n",
      "Speed: 2.8ms preprocess, 40.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\533.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.8ms\n",
      "Speed: 2.8ms preprocess, 40.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\534.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 3.2ms preprocess, 39.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\535.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.4ms\n",
      "Speed: 3.0ms preprocess, 40.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\536.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.0ms\n",
      "Speed: 2.6ms preprocess, 42.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\537.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 2.7ms preprocess, 37.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\538.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.1ms\n",
      "Speed: 2.8ms preprocess, 39.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\539.png\n",
      "\n",
      "0: 640x640 1 FACE, 45.0ms\n",
      "Speed: 3.0ms preprocess, 45.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\540.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.9ms\n",
      "Speed: 3.3ms preprocess, 40.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\541.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.0ms\n",
      "Speed: 3.0ms preprocess, 40.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\542.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.5ms\n",
      "Speed: 3.0ms preprocess, 36.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\543.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.4ms\n",
      "Speed: 2.8ms preprocess, 39.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\544.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 3.1ms preprocess, 39.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\545.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 3.4ms preprocess, 39.3ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\546.png\n",
      "\n",
      "0: 640x640 2 FACEs, 40.5ms\n",
      "Speed: 3.6ms preprocess, 40.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\547.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.7ms\n",
      "Speed: 3.2ms preprocess, 41.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\548.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.5ms\n",
      "Speed: 3.0ms preprocess, 41.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\549.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.1ms\n",
      "Speed: 3.1ms preprocess, 38.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\550.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.2ms\n",
      "Speed: 2.8ms preprocess, 41.2ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\551.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.4ms\n",
      "Speed: 3.1ms preprocess, 40.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\552.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.3ms\n",
      "Speed: 2.7ms preprocess, 38.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\553.png\n",
      "\n",
      "0: 640x640 1 FACE, 46.5ms\n",
      "Speed: 3.0ms preprocess, 46.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\554.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.7ms\n",
      "Speed: 3.0ms preprocess, 37.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\555.png\n",
      "\n",
      "0: 640x640 4 FACEs, 41.1ms\n",
      "Speed: 3.4ms preprocess, 41.1ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\556.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.5ms\n",
      "Speed: 3.4ms preprocess, 39.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\557.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.8ms\n",
      "Speed: 3.4ms preprocess, 36.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\558.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 3.0ms preprocess, 38.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\559.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.5ms\n",
      "Speed: 3.0ms preprocess, 39.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\560.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.2ms\n",
      "Speed: 3.1ms preprocess, 44.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\561.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.1ms\n",
      "Speed: 2.7ms preprocess, 44.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\562.png\n",
      "\n",
      "0: 640x640 2 FACEs, 38.2ms\n",
      "Speed: 2.9ms preprocess, 38.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\563.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.5ms\n",
      "Speed: 3.0ms preprocess, 43.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\564.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.4ms\n",
      "Speed: 3.0ms preprocess, 39.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\565.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 2.7ms preprocess, 39.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\566.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.4ms\n",
      "Speed: 2.8ms preprocess, 41.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\567.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.0ms\n",
      "Speed: 2.8ms preprocess, 41.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\568.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 3.3ms preprocess, 38.6ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\569.png\n",
      "\n",
      "0: 640x640 1 FACE, 48.1ms\n",
      "Speed: 2.6ms preprocess, 48.1ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\570.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.0ms\n",
      "Speed: 3.4ms preprocess, 40.0ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\571.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.1ms\n",
      "Speed: 2.9ms preprocess, 43.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\572.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.5ms\n",
      "Speed: 3.0ms preprocess, 42.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\573.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 3.1ms preprocess, 39.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\574.png\n",
      "\n",
      "0: 640x640 2 FACEs, 39.7ms\n",
      "Speed: 2.6ms preprocess, 39.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\575.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.8ms\n",
      "Speed: 2.6ms preprocess, 40.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\576.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.9ms\n",
      "Speed: 3.1ms preprocess, 42.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\577.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.9ms\n",
      "Speed: 3.3ms preprocess, 40.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\578.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 3.0ms preprocess, 39.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\579.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 2.8ms preprocess, 38.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\580.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 2.8ms preprocess, 38.7ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\581.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.9ms\n",
      "Speed: 2.8ms preprocess, 40.9ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\582.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 3.6ms preprocess, 39.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\583.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.6ms\n",
      "Speed: 2.6ms preprocess, 39.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\584.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 2.9ms preprocess, 40.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\585.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 3.2ms preprocess, 39.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\586.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 3.3ms preprocess, 39.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\587.png\n",
      "\n",
      "0: 640x640 1 FACE, 45.2ms\n",
      "Speed: 3.9ms preprocess, 45.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\588.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 3.0ms preprocess, 38.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\589.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.8ms\n",
      "Speed: 4.4ms preprocess, 39.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\590.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 3.0ms preprocess, 37.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\591.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 3.0ms preprocess, 39.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\592.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.0ms\n",
      "Speed: 3.0ms preprocess, 40.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\593.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 2.8ms preprocess, 39.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\594.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.6ms\n",
      "Speed: 2.5ms preprocess, 42.6ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\595.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 2.9ms preprocess, 40.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\596.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.8ms\n",
      "Speed: 2.9ms preprocess, 38.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\597.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 2.9ms preprocess, 40.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\598.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 2.9ms preprocess, 40.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\5\\599.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.9ms\n",
      "Speed: 2.6ms preprocess, 40.9ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\600.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.4ms\n",
      "Speed: 3.0ms preprocess, 41.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\601.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.6ms\n",
      "Speed: 4.4ms preprocess, 39.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\602.png\n",
      "\n",
      "0: 640x640 1 FACE, 48.0ms\n",
      "Speed: 3.3ms preprocess, 48.0ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\603.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.8ms\n",
      "Speed: 2.9ms preprocess, 39.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\604.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.3ms\n",
      "Speed: 2.7ms preprocess, 38.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\605.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 2.9ms preprocess, 40.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\606.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 3.3ms preprocess, 39.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\607.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.6ms\n",
      "Speed: 4.8ms preprocess, 37.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\608.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.0ms\n",
      "Speed: 2.9ms preprocess, 40.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\609.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 3.0ms preprocess, 39.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\610.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.1ms\n",
      "Speed: 2.9ms preprocess, 39.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\611.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 3.3ms preprocess, 40.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\612.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 2.9ms preprocess, 40.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\613.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.8ms\n",
      "Speed: 6.0ms preprocess, 37.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\614.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.0ms\n",
      "Speed: 2.9ms preprocess, 38.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\615.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.1ms\n",
      "Speed: 2.8ms preprocess, 39.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\616.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.5ms\n",
      "Speed: 2.5ms preprocess, 42.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\617.png\n",
      "\n",
      "0: 640x640 1 FACE, 46.0ms\n",
      "Speed: 3.5ms preprocess, 46.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\618.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 3.4ms preprocess, 39.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\619.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.7ms\n",
      "Speed: 3.7ms preprocess, 40.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\620.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 2.7ms preprocess, 38.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\621.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.0ms\n",
      "Speed: 2.9ms preprocess, 37.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\622.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 2.7ms preprocess, 40.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\623.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.1ms\n",
      "Speed: 3.0ms preprocess, 41.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\624.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 3.3ms preprocess, 40.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\625.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.1ms\n",
      "Speed: 2.8ms preprocess, 42.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\626.png\n",
      "\n",
      "0: 640x640 2 FACEs, 42.4ms\n",
      "Speed: 3.1ms preprocess, 42.4ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\627.png\n",
      "\n",
      "0: 640x640 2 FACEs, 36.5ms\n",
      "Speed: 3.0ms preprocess, 36.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\628.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 3.1ms preprocess, 39.7ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\629.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.4ms\n",
      "Speed: 3.0ms preprocess, 41.4ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\630.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 2.8ms preprocess, 38.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\631.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 3.1ms preprocess, 39.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\632.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.9ms\n",
      "Speed: 2.9ms preprocess, 37.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\633.png\n",
      "\n",
      "0: 640x640 1 FACE, 51.5ms\n",
      "Speed: 2.8ms preprocess, 51.5ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\634.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 2.5ms preprocess, 39.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\635.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.3ms\n",
      "Speed: 3.6ms preprocess, 38.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\636.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.3ms\n",
      "Speed: 3.0ms preprocess, 42.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\637.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.4ms\n",
      "Speed: 3.6ms preprocess, 41.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\638.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.8ms\n",
      "Speed: 2.6ms preprocess, 39.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\639.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.4ms\n",
      "Speed: 2.8ms preprocess, 37.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\640.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 3.1ms preprocess, 40.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\641.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 4.4ms preprocess, 40.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\642.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 2.6ms preprocess, 39.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\643.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.6ms\n",
      "Speed: 3.1ms preprocess, 37.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\644.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.1ms\n",
      "Speed: 3.2ms preprocess, 38.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\645.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 2.9ms preprocess, 38.9ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\646.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.5ms\n",
      "Speed: 2.7ms preprocess, 41.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\647.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 2.9ms preprocess, 39.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\648.png\n",
      "\n",
      "0: 640x640 1 FACE, 55.8ms\n",
      "Speed: 3.5ms preprocess, 55.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\649.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 2.8ms preprocess, 38.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\650.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.4ms\n",
      "Speed: 2.8ms preprocess, 41.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\651.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.6ms\n",
      "Speed: 2.9ms preprocess, 39.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\652.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 3.0ms preprocess, 40.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\653.png\n",
      "\n",
      "0: 640x640 2 FACEs, 39.1ms\n",
      "Speed: 2.8ms preprocess, 39.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\654.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 2.6ms preprocess, 39.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\655.png\n",
      "\n",
      "0: 640x640 2 FACEs, 40.9ms\n",
      "Speed: 3.0ms preprocess, 40.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\656.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.7ms\n",
      "Speed: 3.4ms preprocess, 36.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\657.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.5ms\n",
      "Speed: 2.8ms preprocess, 42.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\658.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.1ms\n",
      "Speed: 2.9ms preprocess, 37.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\659.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 4.5ms preprocess, 40.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\660.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 3.3ms preprocess, 40.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\661.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.0ms\n",
      "Speed: 3.9ms preprocess, 41.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\662.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.6ms\n",
      "Speed: 3.1ms preprocess, 39.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\663.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.4ms\n",
      "Speed: 2.9ms preprocess, 44.4ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\664.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.4ms\n",
      "Speed: 3.7ms preprocess, 36.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\665.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.0ms\n",
      "Speed: 2.8ms preprocess, 40.0ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\666.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.6ms\n",
      "Speed: 2.9ms preprocess, 41.6ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\667.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.0ms\n",
      "Speed: 2.8ms preprocess, 42.0ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\668.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.4ms\n",
      "Speed: 2.9ms preprocess, 42.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\669.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 3.1ms preprocess, 40.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\670.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 3.3ms preprocess, 40.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\671.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 4.5ms preprocess, 40.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\672.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.8ms\n",
      "Speed: 2.8ms preprocess, 37.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\673.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.5ms\n",
      "Speed: 2.6ms preprocess, 41.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\674.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.9ms\n",
      "Speed: 3.0ms preprocess, 40.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\675.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.2ms\n",
      "Speed: 3.0ms preprocess, 40.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\676.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.8ms\n",
      "Speed: 3.0ms preprocess, 40.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\677.png\n",
      "\n",
      "0: 640x640 1 FACE, 51.0ms\n",
      "Speed: 3.3ms preprocess, 51.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\678.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 2.8ms preprocess, 38.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\679.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.8ms\n",
      "Speed: 2.6ms preprocess, 39.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\680.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.2ms\n",
      "Speed: 2.8ms preprocess, 35.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\681.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 2.9ms preprocess, 40.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\682.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.3ms\n",
      "Speed: 2.6ms preprocess, 37.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\683.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 2.8ms preprocess, 39.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\684.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.9ms\n",
      "Speed: 3.6ms preprocess, 41.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\685.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.4ms\n",
      "Speed: 2.8ms preprocess, 41.4ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\686.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.3ms\n",
      "Speed: 3.8ms preprocess, 41.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\687.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.6ms\n",
      "Speed: 2.9ms preprocess, 39.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\688.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.5ms\n",
      "Speed: 2.9ms preprocess, 39.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\689.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.2ms\n",
      "Speed: 3.5ms preprocess, 36.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\690.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 3.2ms preprocess, 38.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\691.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.4ms\n",
      "Speed: 3.6ms preprocess, 42.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\692.png\n",
      "\n",
      "0: 640x640 2 FACEs, 42.4ms\n",
      "Speed: 3.1ms preprocess, 42.4ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\693.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 2.8ms preprocess, 40.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\694.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.2ms\n",
      "Speed: 2.9ms preprocess, 40.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\695.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.5ms\n",
      "Speed: 4.4ms preprocess, 39.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\696.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.6ms\n",
      "Speed: 2.8ms preprocess, 42.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\697.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 2.9ms preprocess, 40.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\698.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 3.4ms preprocess, 39.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\6\\699.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 2.6ms preprocess, 39.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\700.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 3.1ms preprocess, 38.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\701.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.1ms\n",
      "Speed: 3.5ms preprocess, 38.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\702.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.0ms\n",
      "Speed: 2.5ms preprocess, 38.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\703.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.4ms\n",
      "Speed: 3.1ms preprocess, 40.4ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\704.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.8ms\n",
      "Speed: 3.1ms preprocess, 39.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\705.png\n",
      "\n",
      "0: 640x640 1 FACE, 54.6ms\n",
      "Speed: 3.0ms preprocess, 54.6ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\706.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.6ms\n",
      "Speed: 3.0ms preprocess, 41.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\707.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.4ms\n",
      "Speed: 2.8ms preprocess, 40.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\708.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.1ms\n",
      "Speed: 3.3ms preprocess, 37.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\709.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 3.0ms preprocess, 36.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\710.png\n",
      "\n",
      "0: 640x640 2 FACEs, 37.7ms\n",
      "Speed: 3.2ms preprocess, 37.7ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\711.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 3.1ms preprocess, 37.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\712.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 3.0ms preprocess, 38.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\713.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.0ms\n",
      "Speed: 7.1ms preprocess, 41.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\714.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.4ms\n",
      "Speed: 3.2ms preprocess, 36.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\715.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.2ms\n",
      "Speed: 2.7ms preprocess, 41.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\716.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.1ms\n",
      "Speed: 2.8ms preprocess, 39.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\717.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 3.1ms preprocess, 36.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\718.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 3.2ms preprocess, 40.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\719.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.8ms\n",
      "Speed: 3.1ms preprocess, 42.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\720.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 2.9ms preprocess, 39.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\721.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.6ms\n",
      "Speed: 2.6ms preprocess, 35.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\722.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 3.5ms preprocess, 38.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\723.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.1ms\n",
      "Speed: 2.8ms preprocess, 38.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\724.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.4ms\n",
      "Speed: 5.3ms preprocess, 39.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\725.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.0ms\n",
      "Speed: 3.4ms preprocess, 38.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\726.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.5ms\n",
      "Speed: 3.6ms preprocess, 37.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\727.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.7ms\n",
      "Speed: 2.5ms preprocess, 40.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\728.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 3.0ms preprocess, 38.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\729.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.4ms\n",
      "Speed: 2.6ms preprocess, 37.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\730.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 3.1ms preprocess, 38.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\731.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.5ms\n",
      "Speed: 3.9ms preprocess, 36.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\732.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.9ms\n",
      "Speed: 2.9ms preprocess, 36.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\733.png\n",
      "\n",
      "0: 640x640 1 FACE, 56.5ms\n",
      "Speed: 2.6ms preprocess, 56.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\734.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 3.8ms preprocess, 38.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\735.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.7ms\n",
      "Speed: 2.7ms preprocess, 40.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\736.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 2.7ms preprocess, 40.5ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\737.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.9ms\n",
      "Speed: 3.0ms preprocess, 40.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\738.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.4ms\n",
      "Speed: 2.9ms preprocess, 43.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\739.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 3.0ms preprocess, 39.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\740.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 2.5ms preprocess, 39.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\741.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.4ms\n",
      "Speed: 2.7ms preprocess, 37.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\742.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.2ms\n",
      "Speed: 2.9ms preprocess, 41.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\743.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.1ms\n",
      "Speed: 3.2ms preprocess, 39.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\744.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.2ms\n",
      "Speed: 2.6ms preprocess, 35.2ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\745.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 2.7ms preprocess, 39.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\746.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.2ms\n",
      "Speed: 3.0ms preprocess, 40.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\747.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.4ms\n",
      "Speed: 2.9ms preprocess, 44.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\748.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 3.7ms preprocess, 38.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\749.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.0ms\n",
      "Speed: 3.1ms preprocess, 41.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\750.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.1ms\n",
      "Speed: 2.8ms preprocess, 42.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\751.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.4ms\n",
      "Speed: 2.8ms preprocess, 44.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\752.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 2.9ms preprocess, 39.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\753.png\n",
      "\n",
      "0: 640x640 (no detections), 40.1ms\n",
      "Speed: 3.2ms preprocess, 40.1ms inference, 0.6ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\754.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.8ms\n",
      "Speed: 3.3ms preprocess, 36.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\755.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.9ms\n",
      "Speed: 3.2ms preprocess, 41.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\756.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.0ms\n",
      "Speed: 3.0ms preprocess, 41.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\757.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.8ms\n",
      "Speed: 3.1ms preprocess, 38.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\758.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.4ms\n",
      "Speed: 3.4ms preprocess, 37.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\759.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.8ms\n",
      "Speed: 3.0ms preprocess, 37.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\760.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 3.0ms preprocess, 36.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\761.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.0ms\n",
      "Speed: 2.7ms preprocess, 37.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\762.png\n",
      "\n",
      "0: 640x640 1 FACE, 54.8ms\n",
      "Speed: 3.4ms preprocess, 54.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\763.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.6ms\n",
      "Speed: 3.0ms preprocess, 39.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\764.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.1ms\n",
      "Speed: 2.8ms preprocess, 39.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\765.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.4ms\n",
      "Speed: 2.7ms preprocess, 39.4ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\766.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.7ms\n",
      "Speed: 2.8ms preprocess, 42.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\767.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 3.3ms preprocess, 39.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\768.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 2.9ms preprocess, 38.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\769.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 2.9ms preprocess, 39.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\770.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.8ms\n",
      "Speed: 2.7ms preprocess, 40.8ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\771.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.8ms\n",
      "Speed: 3.2ms preprocess, 43.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\772.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 3.7ms preprocess, 39.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\773.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.8ms\n",
      "Speed: 3.3ms preprocess, 40.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\774.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.6ms\n",
      "Speed: 3.8ms preprocess, 40.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\775.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.8ms\n",
      "Speed: 3.0ms preprocess, 42.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\776.png\n",
      "\n",
      "0: 640x640 1 FACE, 50.7ms\n",
      "Speed: 4.3ms preprocess, 50.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\777.png\n",
      "\n",
      "0: 640x640 1 FACE, 46.3ms\n",
      "Speed: 3.3ms preprocess, 46.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\778.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.5ms\n",
      "Speed: 3.4ms preprocess, 43.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\779.png\n",
      "\n",
      "0: 640x640 1 FACE, 45.4ms\n",
      "Speed: 3.2ms preprocess, 45.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\780.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.1ms\n",
      "Speed: 3.0ms preprocess, 41.1ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\781.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.3ms\n",
      "Speed: 3.2ms preprocess, 41.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\782.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 2.8ms preprocess, 38.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\783.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 3.6ms preprocess, 39.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\784.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.4ms\n",
      "Speed: 3.3ms preprocess, 41.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\785.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 2.8ms preprocess, 39.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\786.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.6ms\n",
      "Speed: 2.7ms preprocess, 39.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\787.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.7ms\n",
      "Speed: 3.8ms preprocess, 43.7ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\788.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.4ms\n",
      "Speed: 3.1ms preprocess, 39.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\789.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.3ms\n",
      "Speed: 2.9ms preprocess, 35.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\790.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.7ms\n",
      "Speed: 3.3ms preprocess, 40.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\791.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 3.0ms preprocess, 39.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\792.png\n",
      "\n",
      "0: 640x640 2 FACEs, 40.3ms\n",
      "Speed: 2.8ms preprocess, 40.3ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\793.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 3.1ms preprocess, 38.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\794.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 2.7ms preprocess, 38.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\795.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 2.8ms preprocess, 38.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\796.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 3.2ms preprocess, 38.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\797.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 3.2ms preprocess, 40.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\798.png\n",
      "\n",
      "0: 640x640 3 FACEs, 40.9ms\n",
      "Speed: 3.0ms preprocess, 40.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\7\\799.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.4ms\n",
      "Speed: 3.7ms preprocess, 40.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\800.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.9ms\n",
      "Speed: 2.9ms preprocess, 36.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\801.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.1ms\n",
      "Speed: 3.0ms preprocess, 44.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\802.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 3.8ms preprocess, 38.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\803.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 3.1ms preprocess, 38.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\804.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.1ms\n",
      "Speed: 3.1ms preprocess, 41.1ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\805.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 2.8ms preprocess, 39.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\806.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.6ms\n",
      "Speed: 3.0ms preprocess, 38.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\807.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 2.9ms preprocess, 38.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\808.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 2.9ms preprocess, 40.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\809.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 3.1ms preprocess, 39.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\810.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.7ms\n",
      "Speed: 3.0ms preprocess, 44.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\811.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.7ms\n",
      "Speed: 3.1ms preprocess, 40.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\812.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.3ms\n",
      "Speed: 2.8ms preprocess, 38.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\813.png\n",
      "\n",
      "0: 640x640 2 FACEs, 41.2ms\n",
      "Speed: 2.9ms preprocess, 41.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\814.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.0ms\n",
      "Speed: 2.6ms preprocess, 40.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\815.png\n",
      "\n",
      "0: 640x640 1 FACE, 50.4ms\n",
      "Speed: 2.9ms preprocess, 50.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\816.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.8ms\n",
      "Speed: 3.0ms preprocess, 37.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\817.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.6ms\n",
      "Speed: 3.0ms preprocess, 41.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\818.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.7ms\n",
      "Speed: 3.0ms preprocess, 41.7ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\819.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.2ms\n",
      "Speed: 3.2ms preprocess, 44.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\820.png\n",
      "\n",
      "0: 640x640 2 FACEs, 36.6ms\n",
      "Speed: 2.8ms preprocess, 36.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\821.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.9ms\n",
      "Speed: 2.9ms preprocess, 41.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\822.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.0ms\n",
      "Speed: 2.7ms preprocess, 41.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\823.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.3ms\n",
      "Speed: 3.1ms preprocess, 38.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\824.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.6ms\n",
      "Speed: 2.8ms preprocess, 41.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\825.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.9ms\n",
      "Speed: 3.3ms preprocess, 42.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\826.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.5ms\n",
      "Speed: 2.9ms preprocess, 37.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\827.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.8ms\n",
      "Speed: 3.5ms preprocess, 39.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\828.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.3ms\n",
      "Speed: 3.0ms preprocess, 38.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\829.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.3ms\n",
      "Speed: 2.9ms preprocess, 41.3ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\830.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.8ms\n",
      "Speed: 2.9ms preprocess, 37.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\831.png\n",
      "\n",
      "0: 640x640 1 FACE, 51.3ms\n",
      "Speed: 5.5ms preprocess, 51.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\832.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.1ms\n",
      "Speed: 2.9ms preprocess, 38.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\833.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.1ms\n",
      "Speed: 3.3ms preprocess, 38.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\834.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 3.0ms preprocess, 38.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\835.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 4.3ms preprocess, 39.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\836.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.4ms\n",
      "Speed: 7.4ms preprocess, 41.4ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\837.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.5ms\n",
      "Speed: 2.7ms preprocess, 41.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\838.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.5ms\n",
      "Speed: 2.7ms preprocess, 39.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\839.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.8ms\n",
      "Speed: 3.7ms preprocess, 40.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\840.png\n",
      "\n",
      "0: 640x640 2 FACEs, 39.7ms\n",
      "Speed: 2.7ms preprocess, 39.7ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\841.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.1ms\n",
      "Speed: 3.0ms preprocess, 38.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\842.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.0ms\n",
      "Speed: 3.0ms preprocess, 41.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\843.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 3.0ms preprocess, 39.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\844.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.3ms\n",
      "Speed: 2.8ms preprocess, 37.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\845.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.6ms\n",
      "Speed: 3.1ms preprocess, 40.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\846.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 3.3ms preprocess, 38.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\847.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.7ms\n",
      "Speed: 3.3ms preprocess, 40.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\848.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.0ms\n",
      "Speed: 3.1ms preprocess, 41.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\849.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.4ms\n",
      "Speed: 3.0ms preprocess, 40.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\850.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 4.1ms preprocess, 40.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\851.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 3.0ms preprocess, 38.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\852.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 2.8ms preprocess, 38.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\853.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 2.9ms preprocess, 40.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\854.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 3.0ms preprocess, 39.7ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\855.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.6ms\n",
      "Speed: 3.2ms preprocess, 39.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\856.png\n",
      "\n",
      "0: 640x640 2 FACEs, 40.0ms\n",
      "Speed: 3.3ms preprocess, 40.0ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\857.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.8ms\n",
      "Speed: 3.0ms preprocess, 39.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\858.png\n",
      "\n",
      "0: 640x640 1 FACE, 47.7ms\n",
      "Speed: 2.7ms preprocess, 47.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\859.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 5.0ms preprocess, 38.4ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\860.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.7ms\n",
      "Speed: 3.7ms preprocess, 37.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\861.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 3.6ms preprocess, 38.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\862.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.1ms\n",
      "Speed: 3.0ms preprocess, 39.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\863.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 2.9ms preprocess, 39.9ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\864.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 2.6ms preprocess, 38.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\865.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 2.7ms preprocess, 37.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\866.png\n",
      "\n",
      "0: 640x640 1 FACE, 56.6ms\n",
      "Speed: 3.1ms preprocess, 56.6ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\867.png\n",
      "\n",
      "0: 640x640 1 FACE, 48.0ms\n",
      "Speed: 3.3ms preprocess, 48.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\868.png\n",
      "\n",
      "0: 640x640 1 FACE, 45.7ms\n",
      "Speed: 3.3ms preprocess, 45.7ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\869.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.2ms\n",
      "Speed: 3.0ms preprocess, 42.2ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\870.png\n",
      "\n",
      "0: 640x640 1 FACE, 61.6ms\n",
      "Speed: 3.6ms preprocess, 61.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\871.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.8ms\n",
      "Speed: 3.2ms preprocess, 42.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\872.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 2.8ms preprocess, 40.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\873.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 3.5ms preprocess, 40.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\874.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.9ms\n",
      "Speed: 3.1ms preprocess, 40.9ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\875.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.3ms\n",
      "Speed: 3.0ms preprocess, 42.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\876.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.5ms\n",
      "Speed: 3.0ms preprocess, 39.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\877.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.6ms\n",
      "Speed: 2.6ms preprocess, 40.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\878.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 2.7ms preprocess, 39.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\879.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.2ms\n",
      "Speed: 3.0ms preprocess, 41.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\880.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.7ms\n",
      "Speed: 3.1ms preprocess, 38.7ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\881.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.1ms\n",
      "Speed: 3.3ms preprocess, 42.1ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\882.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.7ms\n",
      "Speed: 2.7ms preprocess, 44.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\883.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 4.3ms preprocess, 40.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\884.png\n",
      "\n",
      "0: 640x640 2 FACEs, 39.8ms\n",
      "Speed: 2.8ms preprocess, 39.8ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\885.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.1ms\n",
      "Speed: 3.0ms preprocess, 43.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\886.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 2.9ms preprocess, 38.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\887.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.3ms\n",
      "Speed: 2.9ms preprocess, 41.3ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\888.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.4ms\n",
      "Speed: 3.4ms preprocess, 41.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\889.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.0ms\n",
      "Speed: 3.0ms preprocess, 42.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\890.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.7ms\n",
      "Speed: 3.2ms preprocess, 44.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\891.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 2.9ms preprocess, 40.1ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\892.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.0ms\n",
      "Speed: 2.7ms preprocess, 41.0ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\893.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.6ms\n",
      "Speed: 3.0ms preprocess, 39.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\894.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.9ms\n",
      "Speed: 2.6ms preprocess, 36.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\895.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.0ms\n",
      "Speed: 3.5ms preprocess, 43.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\896.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.0ms\n",
      "Speed: 3.9ms preprocess, 41.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\897.png\n",
      "\n",
      "0: 640x640 1 FACE, 44.6ms\n",
      "Speed: 2.9ms preprocess, 44.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\898.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.5ms\n",
      "Speed: 4.5ms preprocess, 42.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\8\\899.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.6ms\n",
      "Speed: 3.2ms preprocess, 41.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\900.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.3ms\n",
      "Speed: 3.1ms preprocess, 41.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\901.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.9ms\n",
      "Speed: 3.3ms preprocess, 39.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\902.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.9ms\n",
      "Speed: 2.9ms preprocess, 42.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\903.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.0ms\n",
      "Speed: 3.4ms preprocess, 40.0ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\904.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 3.6ms preprocess, 39.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\905.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.4ms\n",
      "Speed: 3.0ms preprocess, 43.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\906.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.8ms\n",
      "Speed: 2.8ms preprocess, 40.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\907.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.8ms\n",
      "Speed: 3.0ms preprocess, 39.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\908.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 3.0ms preprocess, 38.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\909.png\n",
      "\n",
      "0: 640x640 1 FACE, 49.0ms\n",
      "Speed: 3.1ms preprocess, 49.0ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\910.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.6ms\n",
      "Speed: 3.6ms preprocess, 40.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\911.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 2.6ms preprocess, 39.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\912.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.6ms\n",
      "Speed: 3.3ms preprocess, 40.6ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\913.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.4ms\n",
      "Speed: 3.4ms preprocess, 40.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\914.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.8ms\n",
      "Speed: 3.4ms preprocess, 41.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\915.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.6ms\n",
      "Speed: 3.1ms preprocess, 41.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\916.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.6ms\n",
      "Speed: 3.0ms preprocess, 39.6ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\917.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.9ms\n",
      "Speed: 2.8ms preprocess, 42.9ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\918.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.0ms\n",
      "Speed: 2.8ms preprocess, 40.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\919.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 2.8ms preprocess, 39.2ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\920.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.9ms\n",
      "Speed: 3.0ms preprocess, 41.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\921.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 3.1ms preprocess, 38.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\922.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.5ms\n",
      "Speed: 3.2ms preprocess, 41.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\923.png\n",
      "\n",
      "0: 640x640 1 FACE, 60.2ms\n",
      "Speed: 3.2ms preprocess, 60.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\924.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.2ms\n",
      "Speed: 2.9ms preprocess, 42.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\925.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 3.2ms preprocess, 39.7ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\926.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.8ms\n",
      "Speed: 2.7ms preprocess, 41.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\927.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.2ms\n",
      "Speed: 3.2ms preprocess, 37.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\928.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.9ms\n",
      "Speed: 2.9ms preprocess, 37.9ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\929.png\n",
      "\n",
      "0: 640x640 1 FACE, 37.8ms\n",
      "Speed: 4.0ms preprocess, 37.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\930.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 3.0ms preprocess, 38.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\931.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 2.8ms preprocess, 38.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\932.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 2.9ms preprocess, 38.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\933.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.0ms\n",
      "Speed: 2.7ms preprocess, 39.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\934.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.1ms\n",
      "Speed: 3.1ms preprocess, 39.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\935.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 2.8ms preprocess, 36.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\936.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.2ms\n",
      "Speed: 3.4ms preprocess, 41.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\937.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 3.1ms preprocess, 40.5ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\938.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.9ms\n",
      "Speed: 2.8ms preprocess, 42.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\939.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.7ms\n",
      "Speed: 3.0ms preprocess, 40.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\940.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.0ms\n",
      "Speed: 2.9ms preprocess, 41.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\941.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.5ms\n",
      "Speed: 2.8ms preprocess, 39.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\942.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.1ms\n",
      "Speed: 3.3ms preprocess, 38.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\943.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.9ms\n",
      "Speed: 3.0ms preprocess, 40.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\944.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.4ms\n",
      "Speed: 3.1ms preprocess, 38.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\945.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.9ms\n",
      "Speed: 3.2ms preprocess, 42.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\946.png\n",
      "\n",
      "0: 640x640 2 FACEs, 38.7ms\n",
      "Speed: 2.9ms preprocess, 38.7ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\947.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.7ms\n",
      "Speed: 4.4ms preprocess, 36.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\948.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.3ms\n",
      "Speed: 2.6ms preprocess, 35.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\949.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.5ms\n",
      "Speed: 2.6ms preprocess, 36.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\950.png\n",
      "\n",
      "0: 640x640 1 FACE, 47.8ms\n",
      "Speed: 6.1ms preprocess, 47.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\951.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 2.9ms preprocess, 40.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\952.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.5ms\n",
      "Speed: 3.2ms preprocess, 38.5ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\953.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.6ms\n",
      "Speed: 2.8ms preprocess, 36.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\954.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.8ms\n",
      "Speed: 3.7ms preprocess, 40.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\955.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.1ms\n",
      "Speed: 3.7ms preprocess, 40.1ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\956.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.3ms\n",
      "Speed: 2.7ms preprocess, 41.3ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\957.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.3ms\n",
      "Speed: 3.0ms preprocess, 39.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\958.png\n",
      "\n",
      "0: 640x640 3 FACEs, 44.0ms\n",
      "Speed: 2.9ms preprocess, 44.0ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\959.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.1ms\n",
      "Speed: 3.3ms preprocess, 39.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\960.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.4ms\n",
      "Speed: 4.5ms preprocess, 36.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\961.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.4ms\n",
      "Speed: 3.5ms preprocess, 39.4ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\962.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.7ms\n",
      "Speed: 3.2ms preprocess, 41.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\963.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.4ms\n",
      "Speed: 3.1ms preprocess, 42.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\964.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.9ms\n",
      "Speed: 2.8ms preprocess, 38.9ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\965.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.2ms\n",
      "Speed: 3.2ms preprocess, 40.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\966.png\n",
      "\n",
      "0: 640x640 1 FACE, 41.7ms\n",
      "Speed: 2.9ms preprocess, 41.7ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\967.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.7ms\n",
      "Speed: 2.9ms preprocess, 39.7ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\968.png\n",
      "\n",
      "0: 640x640 1 FACE, 49.8ms\n",
      "Speed: 6.1ms preprocess, 49.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\969.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 2.9ms preprocess, 39.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\970.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.3ms\n",
      "Speed: 2.8ms preprocess, 40.3ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\971.png\n",
      "\n",
      "0: 640x640 2 FACEs, 39.1ms\n",
      "Speed: 3.2ms preprocess, 39.1ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\972.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.2ms\n",
      "Speed: 4.6ms preprocess, 43.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\973.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 3.8ms preprocess, 38.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\974.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 2.7ms preprocess, 40.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\975.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.3ms\n",
      "Speed: 2.7ms preprocess, 36.3ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\976.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.2ms\n",
      "Speed: 2.7ms preprocess, 42.2ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\977.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.4ms\n",
      "Speed: 4.0ms preprocess, 40.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\978.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.2ms\n",
      "Speed: 3.2ms preprocess, 38.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\979.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.5ms\n",
      "Speed: 3.0ms preprocess, 40.5ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\980.png\n",
      "\n",
      "0: 640x640 1 FACE, 53.8ms\n",
      "Speed: 3.2ms preprocess, 53.8ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\981.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 2.9ms preprocess, 39.2ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\982.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.8ms\n",
      "Speed: 2.6ms preprocess, 42.8ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\983.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.0ms\n",
      "Speed: 2.8ms preprocess, 42.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\984.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.1ms\n",
      "Speed: 3.0ms preprocess, 38.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\985.png\n",
      "\n",
      "0: 640x640 1 FACE, 38.0ms\n",
      "Speed: 2.7ms preprocess, 38.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\986.png\n",
      "\n",
      "0: 640x640 1 FACE, 46.4ms\n",
      "Speed: 3.0ms preprocess, 46.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\987.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.9ms\n",
      "Speed: 3.5ms preprocess, 40.9ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\988.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.6ms\n",
      "Speed: 2.8ms preprocess, 42.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\989.png\n",
      "\n",
      "0: 640x640 1 FACE, 35.7ms\n",
      "Speed: 3.0ms preprocess, 35.7ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\990.png\n",
      "\n",
      "0: 640x640 1 FACE, 40.0ms\n",
      "Speed: 3.1ms preprocess, 40.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\991.png\n",
      "\n",
      "0: 640x640 2 FACEs, 42.5ms\n",
      "Speed: 3.3ms preprocess, 42.5ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\992.png\n",
      "\n",
      "0: 640x640 1 FACE, 53.4ms\n",
      "Speed: 3.0ms preprocess, 53.4ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\993.png\n",
      "\n",
      "0: 640x640 1 FACE, 43.1ms\n",
      "Speed: 3.4ms preprocess, 43.1ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\994.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.9ms\n",
      "Speed: 4.3ms preprocess, 36.9ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\995.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.5ms\n",
      "Speed: 2.7ms preprocess, 36.5ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\996.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.2ms\n",
      "Speed: 2.9ms preprocess, 39.2ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\997.png\n",
      "\n",
      "0: 640x640 1 FACE, 39.4ms\n",
      "Speed: 3.0ms preprocess, 39.4ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\998.png\n",
      "\n",
      "0: 640x640 1 FACE, 36.2ms\n",
      "Speed: 2.8ms preprocess, 36.2ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Data\\9\\999.png\n",
      "\n",
      "0: 640x640 1 FACE, 42.7ms\n",
      "Speed: 2.9ms preprocess, 42.7ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 640)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "with open(\"Dataset\\\\metadata.jsonl\", \"w+\") as f:\n",
    "    for dirname, _, filenames in os.walk('Data'):\n",
    "        for filename in filenames:\n",
    "            print(dirname[-6:] + f'\\\\{filename}')\n",
    "            crop_image(dirname[-6:] + f'\\\\{filename}', celebrities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "перекладываем датасет в zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a347f4103a4b45978546251148bd371f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Resolving data files:   0%|          | 0/2001 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "700eeb6023e241f085bc53922ed611c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0/2001 [00:00<?, ?files/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ArrowInvalid",
     "evalue": "JSON parse error: Missing a name for object member. in row 0",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mArrowInvalid\u001b[39m                              Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[23]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mdatasets\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m load_dataset\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m ds = dataset = \u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mimagefolder\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_dir\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mDataset\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\arsen\\coding\\.venv\\Lib\\site-packages\\datasets\\load.py:1417\u001b[39m, in \u001b[36mload_dataset\u001b[39m\u001b[34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, **config_kwargs)\u001b[39m\n\u001b[32m   1414\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m builder_instance.as_streaming_dataset(split=split)\n\u001b[32m   1416\u001b[39m \u001b[38;5;66;03m# Download and prepare data\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1417\u001b[39m \u001b[43mbuilder_instance\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdownload_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1418\u001b[39m \u001b[43m    \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1419\u001b[39m \u001b[43m    \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1420\u001b[39m \u001b[43m    \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1421\u001b[39m \u001b[43m    \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1422\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1423\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1425\u001b[39m \u001b[38;5;66;03m# Build dataset for splits\u001b[39;00m\n\u001b[32m   1426\u001b[39m keep_in_memory = (\n\u001b[32m   1427\u001b[39m     keep_in_memory \u001b[38;5;28;01mif\u001b[39;00m keep_in_memory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m is_small_dataset(builder_instance.info.dataset_size)\n\u001b[32m   1428\u001b[39m )\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\arsen\\coding\\.venv\\Lib\\site-packages\\datasets\\builder.py:894\u001b[39m, in \u001b[36mDatasetBuilder.download_and_prepare\u001b[39m\u001b[34m(self, output_dir, download_config, download_mode, verification_mode, dl_manager, base_path, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[39m\n\u001b[32m    892\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    893\u001b[39m     prepare_split_kwargs[\u001b[33m\"\u001b[39m\u001b[33mnum_proc\u001b[39m\u001b[33m\"\u001b[39m] = num_proc\n\u001b[32m--> \u001b[39m\u001b[32m894\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    895\u001b[39m \u001b[43m    \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    896\u001b[39m \u001b[43m    \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m=\u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    897\u001b[39m \u001b[43m    \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    898\u001b[39m \u001b[43m    \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mdownload_and_prepare_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    899\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    900\u001b[39m \u001b[38;5;66;03m# Sync info\u001b[39;00m\n\u001b[32m    901\u001b[39m \u001b[38;5;28mself\u001b[39m.info.dataset_size = \u001b[38;5;28msum\u001b[39m(split.num_bytes \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.info.splits.values())\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\arsen\\coding\\.venv\\Lib\\site-packages\\datasets\\builder.py:1609\u001b[39m, in \u001b[36mGeneratorBasedBuilder._download_and_prepare\u001b[39m\u001b[34m(self, dl_manager, verification_mode, **prepare_splits_kwargs)\u001b[39m\n\u001b[32m   1608\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_download_and_prepare\u001b[39m(\u001b[38;5;28mself\u001b[39m, dl_manager, verification_mode, **prepare_splits_kwargs):\n\u001b[32m-> \u001b[39m\u001b[32m1609\u001b[39m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1610\u001b[39m \u001b[43m        \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1611\u001b[39m \u001b[43m        \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1612\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcheck_duplicate_keys\u001b[49m\u001b[43m=\u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[43mVerificationMode\u001b[49m\u001b[43m.\u001b[49m\u001b[43mBASIC_CHECKS\u001b[49m\n\u001b[32m   1613\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[43mVerificationMode\u001b[49m\u001b[43m.\u001b[49m\u001b[43mALL_CHECKS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1614\u001b[39m \u001b[43m        \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mprepare_splits_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1615\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\arsen\\coding\\.venv\\Lib\\site-packages\\datasets\\builder.py:948\u001b[39m, in \u001b[36mDatasetBuilder._download_and_prepare\u001b[39m\u001b[34m(self, dl_manager, verification_mode, **prepare_split_kwargs)\u001b[39m\n\u001b[32m    946\u001b[39m split_dict = SplitDict(dataset_name=\u001b[38;5;28mself\u001b[39m.dataset_name)\n\u001b[32m    947\u001b[39m split_generators_kwargs = \u001b[38;5;28mself\u001b[39m._make_split_generators_kwargs(prepare_split_kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m948\u001b[39m split_generators = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_split_generators\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43msplit_generators_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    950\u001b[39m \u001b[38;5;66;03m# Checksums verification\u001b[39;00m\n\u001b[32m    951\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m verification_mode == VerificationMode.ALL_CHECKS \u001b[38;5;129;01mand\u001b[39;00m dl_manager.record_checksums:\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\arsen\\coding\\.venv\\Lib\\site-packages\\datasets\\packaged_modules\\folder_based_builder\\folder_based_builder.py:191\u001b[39m, in \u001b[36mFolderBasedBuilder._split_generators\u001b[39m\u001b[34m(self, dl_manager)\u001b[39m\n\u001b[32m    189\u001b[39m pa_metadata_table = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    190\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m _, downloaded_metadata_file \u001b[38;5;129;01min\u001b[39;00m split_metadata_files:\n\u001b[32m--> \u001b[39m\u001b[32m191\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpa_metadata_table\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_read_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdownloaded_metadata_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata_ext\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmetadata_ext\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m    192\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mbreak\u001b[39;49;00m  \u001b[38;5;66;03m# just fetch the first rows\u001b[39;00m\n\u001b[32m    193\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m pa_metadata_table \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\arsen\\coding\\.venv\\Lib\\site-packages\\datasets\\packaged_modules\\folder_based_builder\\folder_based_builder.py:319\u001b[39m, in \u001b[36mFolderBasedBuilder._read_metadata\u001b[39m\u001b[34m(self, metadata_file, metadata_ext)\u001b[39m\n\u001b[32m    317\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[32m    318\u001b[39m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m319\u001b[39m         pa_table = \u001b[43mpaj\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_json\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    320\u001b[39m \u001b[43m            \u001b[49m\u001b[43mio\u001b[49m\u001b[43m.\u001b[49m\u001b[43mBytesIO\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mread_options\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpaj\u001b[49m\u001b[43m.\u001b[49m\u001b[43mReadOptions\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblock_size\u001b[49m\u001b[43m=\u001b[49m\u001b[43mblock_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    321\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    322\u001b[39m         \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m    323\u001b[39m     \u001b[38;5;28;01mexcept\u001b[39;00m (pa.ArrowInvalid, pa.ArrowNotImplementedError) \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\arsen\\coding\\.venv\\Lib\\site-packages\\pyarrow\\_json.pyx:342\u001b[39m, in \u001b[36mpyarrow._json.read_json\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\arsen\\coding\\.venv\\Lib\\site-packages\\pyarrow\\error.pxi:155\u001b[39m, in \u001b[36mpyarrow.lib.pyarrow_internal_check_status\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\arsen\\coding\\.venv\\Lib\\site-packages\\pyarrow\\error.pxi:92\u001b[39m, in \u001b[36mpyarrow.lib.check_status\u001b[39m\u001b[34m()\u001b[39m\n",
      "\u001b[31mArrowInvalid\u001b[39m: JSON parse error: Missing a name for object member. in row 0"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting umap-learn\n",
      "  Downloading umap_learn-0.5.9.post2-py3-none-any.whl.metadata (25 kB)\n",
      "Requirement already satisfied: numpy>=1.23 in c:\\users\\arsen\\coding\\.venv\\lib\\site-packages (from umap-learn) (2.2.6)\n",
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      "Collecting numba>=0.51.2 (from umap-learn)\n",
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      "Collecting pynndescent>=0.5 (from umap-learn)\n",
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      "Collecting joblib>=0.11 (from pynndescent>=0.5->umap-learn)\n",
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      "Collecting threadpoolctl>=3.1.0 (from scikit-learn>=1.6->umap-learn)\n",
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      "   ---------------------------------- ----- 6/7 [umap-learn]\n",
      "   ---------------------------------------- 7/7 [umap-learn]\n",
      "\n",
      "Successfully installed joblib-1.5.2 llvmlite-0.45.1 numba-0.62.1 pynndescent-0.5.13 scikit-learn-1.7.2 threadpoolctl-3.6.0 umap-learn-0.5.9.post2\n"
     ]
    }
   ],
   "source": [
    "!pip install umap-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "machine_shape": "hm"
  },
  "kaggle": {
   "accelerator": "nvidiaTeslaT4",
   "dataSources": [
    {
     "datasetId": 7637755,
     "sourceId": 12129049,
     "sourceType": "datasetVersion"
    },
    {
     "datasetId": 8520366,
     "sourceId": 13424270,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 31041,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}