H_Tatarstan2022_Faster_RCNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "H_Tatarstan2022_Faster_RCNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1UKWGrSTZuSq9AhhhBo6UbwmxfgpR1O_x",
      "authorship_tag": "ABX9TyOpp/WB202VwCHIV+12cwG7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TayJen/H_Tatarsan2022/blob/main/H_Tatarstan2022_Faster_RCNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Импорт библиотек + необходимые установки"
      ],
      "metadata": {
        "id": "20Qf0Uj-HFAK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NvLO4WB-G_Wx",
        "outputId": "d6d249aa-610d-4d59-c414-d5df90d11dce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/H_Tatarstan2022\n",
            "/content/drive/MyDrive/H_Tatarstan2022\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/H_Tatarstan2022/\n",
        "!pwd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --quiet map-boxes\n",
        "!pip install --quiet pycocotools\n",
        "!pip install --quiet map-boxes"
      ],
      "metadata": {
        "id": "6YPEe8NdHCuj"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !git clone https://github.com/pytorch/vision.git\n",
        "\n",
        "# !cp vision/references/detection/utils.py ./\n",
        "# !cp vision/references/detection/transforms.py ./\n",
        "# !cp vision/references/detection/coco_eval.py ./\n",
        "# !cp vision/references/detection/engine.py ./\n",
        "# !cp vision/references/detection/coco_utils.py ./"
      ],
      "metadata": {
        "id": "ZeVSIIFav3qC"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "\n",
        "np.random.seed(59)\n",
        "\n",
        "# for ignoring warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "qIO8pJE4HCwj"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We will be reading images using OpenCV\n",
        "import cv2"
      ],
      "metadata": {
        "id": "Z2mFtufoHCyj"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torchvision\n",
        "from torchvision import transforms as torchtransforms\n",
        "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n",
        "\n",
        "from engine import train_one_epoch, evaluate\n",
        "import utils\n",
        "import transforms as T\n",
        "\n",
        "# for image augmentations\n",
        "import albumentations as A\n",
        "from albumentations.pytorch.transforms import ToTensorV2\n",
        "from albumentations.augmentations.transforms import HorizontalFlip, AdvancedBlur\n",
        "from albumentations.augmentations.transforms import GaussianBlur, ToGray \n",
        "from albumentations.augmentations.crops.transforms import RandomSizedCrop "
      ],
      "metadata": {
        "id": "z7VMAfSRHC0e"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset"
      ],
      "metadata": {
        "id": "VuFNUTyJNMvb"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Подготовка данных"
      ],
      "metadata": {
        "id": "pRwQ_7qnM-_j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Для модели FasterRCNN нам необходимо создать кастомный датасет"
      ],
      "metadata": {
        "id": "FRyDWuuaNCft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DATA_PATH = './data/train'\n",
        "\n",
        "class ImagesDataset(torch.utils.data.Dataset):\n",
        "\n",
        "    def __init__(self, files_dir, width, height, transforms=None):\n",
        "        self.transforms = transforms\n",
        "        self.files_dir = files_dir\n",
        "\n",
        "        self.images_dir = os.path.join(self.files_dir, 'images')\n",
        "        self.labels_dir = os.path.join(self.files_dir, 'yolo_labels')\n",
        "\n",
        "        self.height = height\n",
        "        self.width = width\n",
        "        \n",
        "        self.imgs = [image\n",
        "                     for image in sorted(os.listdir(self.images_dir))\n",
        "                     if image[-4:] == '.jpg']\n",
        "        \n",
        "        # classes: 0 index is reserved for background\n",
        "        self.classes = ['', 'human', 'head', 'face', 'car', 'carplate']\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_name = self.imgs[idx]\n",
        "        image_path = os.path.join(self.images_dir, img_name)\n",
        "\n",
        "        # reading the images and converting them to correct size   \n",
        "        img = cv2.imread(image_path)\n",
        "        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32)\n",
        "        img_res = cv2.resize(img_rgb, (self.width, self.height), cv2.INTER_AREA)\n",
        "        # diving by 255\n",
        "        img_res = img_res / 255.0\n",
        "        \n",
        "        # annotation file\n",
        "        annot_filename = img_name[:-4] + '.txt'\n",
        "        annot_file_path = os.path.join(self.labels_dir, annot_filename)\n",
        "        \n",
        "        boxes = []\n",
        "        labels = []\n",
        "        \n",
        "        # box coordinates for xml files are extracted and corrected for image size given\n",
        "        with open(annot_file_path, 'r') as annot_f:\n",
        "            for ex in annot_f.readlines():\n",
        "                ex = ex.split(' ')\n",
        "\n",
        "                # Так как 0 нужен для отсутствия\n",
        "                label = int(ex[0]) + 1\n",
        "\n",
        "                # Имеющиеся данные\n",
        "                x_center = float(ex[1])\n",
        "                y_center = float(ex[2])\n",
        "                width_obj = float(ex[3])\n",
        "                height_obj = float(ex[4])\n",
        "\n",
        "                # Необходимые для модели данные\n",
        "                xmin = (x_center - width_obj / 2) * self.width\n",
        "                xmax = (x_center + width_obj / 2) * self.width\n",
        "                ymin = (y_center - height_obj / 2) * self.height\n",
        "                ymax = (y_center + height_obj / 2) * self.height\n",
        "\n",
        "                labels.append(label)\n",
        "                boxes.append([xmin, ymin, xmax, ymax])\n",
        "        \n",
        "        # convert boxes and labels into a torch.Tensor\n",
        "        boxes = torch.as_tensor(boxes, dtype=torch.float)\n",
        "        labels = torch.as_tensor(labels, dtype=torch.int64)\n",
        "        \n",
        "        # getting the areas of the boxes\n",
        "        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])\n",
        "        # suppose all instances are not crowd\n",
        "        iscrowd = torch.zeros((boxes.shape[0],), dtype=torch.int64)\n",
        "\n",
        "        target = {}\n",
        "        target[\"boxes\"] = boxes\n",
        "        target[\"labels\"] = labels\n",
        "        target[\"area\"] = area\n",
        "        target[\"iscrowd\"] = iscrowd\n",
        "\n",
        "        # image_id\n",
        "        image_id = torch.tensor([idx])\n",
        "        target[\"image_id\"] = image_id\n",
        "\n",
        "        if self.transforms:\n",
        "            sample = self.transforms(image=img_res,\n",
        "                                     bboxes=target['boxes'],\n",
        "                                     labels=labels)\n",
        "            img_res = sample['image']\n",
        "            target['boxes'] = torch.Tensor(sample['bboxes'])\n",
        "\n",
        "        return img_res, target\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.imgs)\n",
        "\n",
        "\n",
        "# check dataset\n",
        "dataset = ImagesDataset(DATA_PATH, 416, 416)\n",
        "print('Length of dataset = ', len(dataset), '\\n')\n",
        "\n",
        "# getting the image and target for a test index.\n",
        "img, target = dataset[599]\n",
        "print(img.shape, '\\n', target)"
      ],
      "metadata": {
        "id": "pDb1zG16HC2O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "118c5ed9-f555-44d6-dd73-90b0d5eaa098"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of dataset =  2002 \n",
            "\n",
            "(416, 416, 3) \n",
            " {'boxes': tensor([[346.5367, 262.5807, 362.0500, 305.5520],\n",
            "        [245.2450, 153.7659, 252.1848, 174.6699],\n",
            "        [171.6650, 156.3467, 178.5138, 179.7197],\n",
            "        [186.9920, 204.2329, 203.3958, 254.2839],\n",
            "        [157.6662, 143.0578, 163.7610, 166.9855],\n",
            "        [166.3783, 202.2607, 177.2377, 239.3078],\n",
            "        [134.5760, 141.9022, 141.0868, 166.6080],\n",
            "        [234.9100, 149.6444, 242.2073, 172.7671],\n",
            "        [185.8783, 230.2444, 199.4070, 273.9861],\n",
            "        [211.3800, 253.4904, 226.0050, 299.6356],\n",
            "        [287.5492, 277.3988, 302.4038, 325.4969],\n",
            "        [170.1440, 216.4510, 181.6728, 258.2243],\n",
            "        [126.0220, 141.3244, 133.0788, 165.6566],\n",
            "        [352.5167, 277.9496, 373.1217, 316.5837],\n",
            "        [190.0080, 229.7129, 194.3608, 236.3804],\n",
            "        [356.1372, 262.5846, 360.1282, 271.8329],\n",
            "        [129.0575, 141.7790, 130.7497, 145.0068],\n",
            "        [238.0430, 149.6252, 240.7643, 153.7428],\n",
            "        [190.8552, 209.5022, 194.3608, 215.0951],\n",
            "        [248.5253, 153.7428, 252.0527, 158.2225],\n",
            "        [136.4328, 141.7790, 138.6103, 146.2933],\n",
            "        [296.0317, 277.4219, 302.3215, 289.4628],\n",
            "        [169.5698, 203.6975, 173.1990, 209.9336],\n",
            "        [174.4080, 155.5416, 176.8260, 159.4089],\n",
            "        [215.6462, 254.0065, 220.3630, 263.0391],\n",
            "        [172.9563, 217.8877, 177.9137, 225.6299],\n",
            "        [159.7743, 143.0693, 162.7968, 148.0151],\n",
            "        [367.5057, 277.4219, 371.6158, 287.7410],\n",
            "        [356.6940, 265.6006, 358.3385, 270.3961],\n",
            "        [296.2223, 283.0302, 299.1343, 287.9028],\n",
            "        [327.1082, 156.6856, 359.5150, 184.8234],\n",
            "        [373.3643, 191.4447, 398.9093, 215.1490],\n",
            "        [278.5077, 166.8776, 318.5087, 193.4631]]), 'labels': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
            "        2, 2, 2, 2, 3, 3, 4, 4, 4]), 'area': tensor([ 666.6275,  145.0703,  160.0784,  821.0272,  145.8354,  402.3073,\n",
            "         160.8552,  168.7338,  591.7662,  674.8734,  714.4814,  481.5982,\n",
            "         171.7076,  796.0559,   29.0228,   36.9099,    5.4621,   11.2054,\n",
            "          19.6068,   15.8015,    9.8301,   75.7351,   22.6320,    9.3510,\n",
            "          42.6054,   38.3808,   14.9485,   42.4132,    7.8863,   14.1891,\n",
            "         911.8573,  605.5262, 1063.4459]), 'iscrowd': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0]), 'image_id': tensor([599])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Проверим скрипт, визуализировав"
      ],
      "metadata": {
        "id": "KGqEo8l0tGXG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patches\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "def plot_img_bbox(img, target):\n",
        "    # plot the image and bboxes\n",
        "    # Bounding boxes are defined as follows: x-min y-min width height\n",
        "    fig, a = plt.subplots(1, 1)\n",
        "    fig.set_size_inches(5, 5)\n",
        "    a.imshow(img)\n",
        "    for box in (target['boxes']):\n",
        "        x, y, width, height  = box[0], box[1], box[2]-box[0], box[3]-box[1]\n",
        "        rect = patches.Rectangle((x, y),\n",
        "                                 width, height,\n",
        "                                 linewidth=2,\n",
        "                                 edgecolor='r',\n",
        "                                 facecolor='none')\n",
        "\n",
        "        # Draw the bounding box on top of the image\n",
        "        a.add_patch(rect)\n",
        "    plt.show()\n",
        "    \n",
        "# plotting the image with bboxes.\n",
        "img, target = dataset[599]\n",
        "plot_img_bbox(img, target)"
      ],
      "metadata": {
        "id": "LGPvzkV3M9_n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "71fabc9a-e884-4f4b-ead7-be554ee65ff5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Send train=True fro training transforms and False for val/test transforms\n",
        "# ToTensorV2 converts image to pytorch tensor without div by 255\n",
        "def get_transform(train):\n",
        "    if train:\n",
        "        return A.Compose([\n",
        "                            # RandomSizedCrop([200, 200], width=416, height=416, p=0.5),\n",
        "                            A.OneOf([\n",
        "                                A.HueSaturationValue(hue_shift_limit=0.2, sat_shift_limit= 0.2, \n",
        "                                                    val_shift_limit=0.2, p=0.5),\n",
        "                                A.RandomBrightnessContrast(brightness_limit=0.2, \n",
        "                                                        contrast_limit=0.2, p=0.5),\n",
        "                            ], p=0.5),\n",
        "                            A.OneOf([\n",
        "                                AdvancedBlur(p=0.5),\n",
        "                                GaussianBlur(p=0.5),\n",
        "                            ], p=0.5),\n",
        "                            ToGray(p=0.1), \n",
        "                            HorizontalFlip(p=0.5),\n",
        "                            ToTensorV2(p=1.0)\n",
        "                        ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})\n",
        "    else:\n",
        "        return A.Compose([\n",
        "                            ToTensorV2(p=1.0)\n",
        "                        ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})"
      ],
      "metadata": {
        "id": "rUav8zCVM-By"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# use our dataset and defined transformations\n",
        "dataset = ImagesDataset(DATA_PATH, 416, 416, transforms=get_transform(train=True))\n",
        "dataset_test = ImagesDataset(DATA_PATH, 416, 416, transforms=get_transform(train=False))\n",
        "\n",
        "# split the dataset in train and test set\n",
        "torch.manual_seed(59)\n",
        "indices = torch.randperm(len(dataset)).tolist()\n",
        "\n",
        "# train test split\n",
        "test_split = 0.2\n",
        "tsize = int(len(dataset) * test_split)\n",
        "dataset = torch.utils.data.Subset(dataset, indices[:-tsize])\n",
        "dataset_test = torch.utils.data.Subset(dataset_test, indices[-tsize:])"
      ],
      "metadata": {
        "id": "8sWUm87PtKzj"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# for img, target in dataset:\n",
        "#     if target['boxes'].shape == torch.Size([0]):\n",
        "#         print(dataset.dataset.imgs[target['image_id']])\n",
        "#         print(target)\n",
        "#         break"
      ],
      "metadata": {
        "id": "qnsrSGL0MMSN"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define training and validation data loaders\n",
        "data_loader = torch.utils.data.DataLoader(\n",
        "    dataset,\n",
        "    batch_size=16, shuffle=True,\n",
        "    collate_fn=utils.collate_fn)\n",
        "\n",
        "data_loader_test = torch.utils.data.DataLoader(\n",
        "    dataset_test,\n",
        "    batch_size=16, shuffle=False,\n",
        "    collate_fn=utils.collate_fn)"
      ],
      "metadata": {
        "id": "3PhSiEgytK1l"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Модель"
      ],
      "metadata": {
        "id": "UocRfoGAuTSD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5kUZ3X_m6Rc",
        "outputId": "4f778741-3cea-4552-f022-dea8065e116f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sat Aug 13 12:55:52 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   63C    P0    27W /  70W |    820MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.models.detection import fasterrcnn_resnet50_fpn\n",
        "\n",
        "def get_object_detection_model(num_classes):\n",
        "    # load a model pre-trained pre-trained on COCO\n",
        "    model = fasterrcnn_resnet50_fpn(pretrained=True,\n",
        "                                    trainable_backbone_layers=2)\n",
        "    \n",
        "    # get number of input features for the classifier\n",
        "    in_features = model.roi_heads.box_predictor.cls_score.in_features\n",
        "    \n",
        "    # replace the pre-trained head with a new one\n",
        "    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) \n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "HTWcsMQptK3e"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# to train on gpu if selected.\n",
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
        "device"
      ],
      "metadata": {
        "id": "anM0pzxXulxS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7f19409-babb-4907-8a5c-b392af6e437b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = 6\n",
        "\n",
        "# get the model using our helper function\n",
        "model = get_object_detection_model(num_classes)\n",
        "\n",
        "# move model to the right device\n",
        "model.to(device)\n",
        "\n",
        "# construct an optimizer\n",
        "params = [p for p in model.parameters() if p.requires_grad]\n",
        "optimizer = torch.optim.SGD(params, lr=0.005,\n",
        "                            momentum=0.9, weight_decay=0.0005)\n",
        "\n",
        "# and a learning rate scheduler which decreases the learning rate by\n",
        "# 10x every 3 epochs\n",
        "lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,\n",
        "                                               step_size=3,\n",
        "                                               gamma=0.1)"
      ],
      "metadata": {
        "id": "8nzDJWWbM-Dx"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from engine import train_one_epoch, evaluate\n",
        "\n",
        "# training for 10 epochs\n",
        "num_epochs = 10\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    # training for one epoch\n",
        "    train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)\n",
        "    # update the learning rate\n",
        "    lr_scheduler.step()\n",
        "    # evaluate on the test dataset\n",
        "    evaluate(model, data_loader_test, device=device)"
      ],
      "metadata": {
        "id": "98ceFZMKM-Fu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d468fc8-d02a-44a5-e765-75f2e861aa5f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: [0]  [  0/101]  eta: 0:08:30  lr: 0.000055  loss: 5.2200 (5.2200)  loss_classifier: 1.8475 (1.8475)  loss_box_reg: 0.7403 (0.7403)  loss_objectness: 2.1383 (2.1383)  loss_rpn_box_reg: 0.4940 (0.4940)  time: 5.0545  data: 1.1981  max mem: 9700\n",
            "Epoch: [0]  [ 10/101]  eta: 0:13:01  lr: 0.000554  loss: 3.4628 (3.4453)  loss_classifier: 1.5779 (1.4306)  loss_box_reg: 0.6555 (0.6414)  loss_objectness: 0.7852 (0.9474)  loss_rpn_box_reg: 0.4101 (0.4259)  time: 8.5905  data: 6.1420  max mem: 9953\n",
            "Epoch: [0]  [ 20/101]  eta: 0:11:37  lr: 0.001054  loss: 2.1908 (2.6880)  loss_classifier: 0.6778 (1.0167)  loss_box_reg: 0.5527 (0.5834)  loss_objectness: 0.3908 (0.6872)  loss_rpn_box_reg: 0.3918 (0.4008)  time: 8.7951  data: 6.4763  max mem: 9953\n",
            "Epoch: [0]  [ 30/101]  eta: 0:10:12  lr: 0.001553  loss: 1.6974 (2.3193)  loss_classifier: 0.4712 (0.8170)  loss_box_reg: 0.5029 (0.5676)  loss_objectness: 0.2978 (0.5467)  loss_rpn_box_reg: 0.3549 (0.3880)  time: 8.6505  data: 6.3054  max mem: 9954\n",
            "Epoch: [0]  [ 40/101]  eta: 0:08:55  lr: 0.002053  loss: 1.3715 (2.0736)  loss_classifier: 0.3510 (0.6986)  loss_box_reg: 0.4975 (0.5460)  loss_objectness: 0.1962 (0.4553)  loss_rpn_box_reg: 0.3502 (0.3736)  time: 8.9633  data: 6.6315  max mem: 9954\n",
            "Epoch: [0]  [ 50/101]  eta: 0:07:32  lr: 0.002552  loss: 1.1902 (1.8843)  loss_classifier: 0.3062 (0.6200)  loss_box_reg: 0.4210 (0.5128)  loss_objectness: 0.1513 (0.3956)  loss_rpn_box_reg: 0.2930 (0.3558)  time: 9.2306  data: 6.9294  max mem: 9954\n",
            "Epoch: [0]  [ 60/101]  eta: 0:06:04  lr: 0.003052  loss: 1.0351 (1.7451)  loss_classifier: 0.2668 (0.5607)  loss_box_reg: 0.3499 (0.4859)  loss_objectness: 0.1401 (0.3533)  loss_rpn_box_reg: 0.2876 (0.3452)  time: 9.1227  data: 6.8091  max mem: 9954\n",
            "Epoch: [0]  [ 70/101]  eta: 0:04:35  lr: 0.003551  loss: 1.0045 (1.6373)  loss_classifier: 0.2446 (0.5137)  loss_box_reg: 0.3354 (0.4618)  loss_objectness: 0.1273 (0.3222)  loss_rpn_box_reg: 0.2961 (0.3395)  time: 8.9577  data: 6.6151  max mem: 9954\n",
            "Epoch: [0]  [ 80/101]  eta: 0:03:05  lr: 0.004051  loss: 0.9459 (1.5489)  loss_classifier: 0.2283 (0.4792)  loss_box_reg: 0.3081 (0.4434)  loss_objectness: 0.1208 (0.2977)  loss_rpn_box_reg: 0.2629 (0.3286)  time: 8.7204  data: 6.3598  max mem: 9954\n",
            "Epoch: [0]  [ 90/101]  eta: 0:01:37  lr: 0.004550  loss: 0.9105 (1.4760)  loss_classifier: 0.2240 (0.4497)  loss_box_reg: 0.3012 (0.4266)  loss_objectness: 0.1165 (0.2793)  loss_rpn_box_reg: 0.2429 (0.3205)  time: 8.6210  data: 6.2558  max mem: 9954\n",
            "Epoch: [0]  [100/101]  eta: 0:00:08  lr: 0.005000  loss: 0.8786 (1.4164)  loss_classifier: 0.2063 (0.4256)  loss_box_reg: 0.2992 (0.4137)  loss_objectness: 0.1160 (0.2625)  loss_rpn_box_reg: 0.2572 (0.3146)  time: 8.3325  data: 6.0680  max mem: 9954\n",
            "Epoch: [0] Total time: 0:14:43 (8.7500 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [ 0/25]  eta: 0:00:54  model_time: 1.1617 (1.1617)  evaluator_time: 0.1439 (0.1439)  time: 2.1619  data: 0.8466  max mem: 9954\n",
            "Test:  [24/25]  eta: 0:00:02  model_time: 1.1424 (1.1415)  evaluator_time: 0.2363 (0.2440)  time: 2.2232  data: 0.8157  max mem: 9954\n",
            "Test: Total time: 0:00:55 (2.2087 s / it)\n",
            "Averaged stats: model_time: 1.1424 (1.1415)  evaluator_time: 0.2363 (0.2440)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.41s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.189\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.361\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.164\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.130\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.403\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.401\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.113\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.240\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.283\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.206\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.547\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428\n",
            "Epoch: [1]  [  0/101]  eta: 0:05:32  lr: 0.005000  loss: 0.7386 (0.7386)  loss_classifier: 0.1857 (0.1857)  loss_box_reg: 0.2556 (0.2556)  loss_objectness: 0.0781 (0.0781)  loss_rpn_box_reg: 0.2193 (0.2193)  time: 3.2960  data: 0.9589  max mem: 9954\n",
            "Epoch: [1]  [ 10/101]  eta: 0:05:10  lr: 0.005000  loss: 0.8426 (0.8435)  loss_classifier: 0.1927 (0.1997)  loss_box_reg: 0.2817 (0.2895)  loss_objectness: 0.0947 (0.0989)  loss_rpn_box_reg: 0.2632 (0.2555)  time: 3.4125  data: 1.0789  max mem: 9954\n",
            "Epoch: [1]  [ 20/101]  eta: 0:04:32  lr: 0.005000  loss: 0.8138 (0.8364)  loss_classifier: 0.1882 (0.1966)  loss_box_reg: 0.2784 (0.2806)  loss_objectness: 0.0959 (0.1001)  loss_rpn_box_reg: 0.2602 (0.2591)  time: 3.3662  data: 1.0426  max mem: 9954\n",
            "Epoch: [1]  [ 30/101]  eta: 0:03:59  lr: 0.005000  loss: 0.8100 (0.8371)  loss_classifier: 0.1916 (0.1971)  loss_box_reg: 0.2784 (0.2854)  loss_objectness: 0.0976 (0.0984)  loss_rpn_box_reg: 0.2516 (0.2563)  time: 3.3432  data: 1.0186  max mem: 9954\n",
            "Epoch: [1]  [ 40/101]  eta: 0:03:25  lr: 0.005000  loss: 0.8100 (0.8360)  loss_classifier: 0.1936 (0.1966)  loss_box_reg: 0.3007 (0.2865)  loss_objectness: 0.0935 (0.0982)  loss_rpn_box_reg: 0.2346 (0.2547)  time: 3.3700  data: 1.0417  max mem: 9954\n",
            "Epoch: [1]  [ 50/101]  eta: 0:02:51  lr: 0.005000  loss: 0.7821 (0.8164)  loss_classifier: 0.1765 (0.1897)  loss_box_reg: 0.2503 (0.2765)  loss_objectness: 0.0884 (0.0968)  loss_rpn_box_reg: 0.2395 (0.2533)  time: 3.3306  data: 1.0084  max mem: 9954\n",
            "Epoch: [1]  [ 60/101]  eta: 0:02:17  lr: 0.005000  loss: 0.7732 (0.8180)  loss_classifier: 0.1719 (0.1900)  loss_box_reg: 0.2497 (0.2788)  loss_objectness: 0.0871 (0.0962)  loss_rpn_box_reg: 0.2424 (0.2530)  time: 3.3636  data: 1.0347  max mem: 9954\n",
            "Epoch: [1]  [ 70/101]  eta: 0:01:44  lr: 0.005000  loss: 0.8180 (0.8145)  loss_classifier: 0.1798 (0.1883)  loss_box_reg: 0.2776 (0.2777)  loss_objectness: 0.0919 (0.0972)  loss_rpn_box_reg: 0.2527 (0.2514)  time: 3.4047  data: 1.0711  max mem: 9954\n",
            "Epoch: [1]  [ 80/101]  eta: 0:01:10  lr: 0.005000  loss: 0.7599 (0.8052)  loss_classifier: 0.1678 (0.1857)  loss_box_reg: 0.2633 (0.2757)  loss_objectness: 0.0917 (0.0964)  loss_rpn_box_reg: 0.2300 (0.2473)  time: 3.3705  data: 1.0405  max mem: 9954\n",
            "Epoch: [1]  [ 90/101]  eta: 0:00:37  lr: 0.005000  loss: 0.7544 (0.7982)  loss_classifier: 0.1611 (0.1827)  loss_box_reg: 0.2550 (0.2737)  loss_objectness: 0.0770 (0.0954)  loss_rpn_box_reg: 0.2174 (0.2465)  time: 3.3715  data: 1.0430  max mem: 9954\n",
            "Epoch: [1]  [100/101]  eta: 0:00:03  lr: 0.005000  loss: 0.7387 (0.7885)  loss_classifier: 0.1611 (0.1810)  loss_box_reg: 0.2492 (0.2724)  loss_objectness: 0.0745 (0.0932)  loss_rpn_box_reg: 0.2162 (0.2420)  time: 3.2078  data: 0.9792  max mem: 9954\n",
            "Epoch: [1] Total time: 0:05:36 (3.3354 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [ 0/25]  eta: 0:00:52  model_time: 1.1359 (1.1359)  evaluator_time: 0.1079 (0.1079)  time: 2.1157  data: 0.8624  max mem: 9954\n",
            "Test:  [24/25]  eta: 0:00:02  model_time: 1.1002 (1.1011)  evaluator_time: 0.1800 (0.1808)  time: 2.1169  data: 0.8181  max mem: 9954\n",
            "Test: Total time: 0:00:52 (2.1119 s / it)\n",
            "Averaged stats: model_time: 1.1002 (1.1011)  evaluator_time: 0.1800 (0.1808)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.32s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.271\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.460\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.291\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.156\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.555\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.420\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.173\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.300\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.343\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.228\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.649\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.439\n",
            "Epoch: [2]  [  0/101]  eta: 0:05:49  lr: 0.005000  loss: 0.8351 (0.8351)  loss_classifier: 0.2034 (0.2034)  loss_box_reg: 0.3285 (0.3285)  loss_objectness: 0.0780 (0.0780)  loss_rpn_box_reg: 0.2252 (0.2252)  time: 3.4581  data: 1.2179  max mem: 9954\n",
            "Epoch: [2]  [ 10/101]  eta: 0:04:59  lr: 0.005000  loss: 0.7072 (0.7007)  loss_classifier: 0.1584 (0.1626)  loss_box_reg: 0.2625 (0.2593)  loss_objectness: 0.0680 (0.0728)  loss_rpn_box_reg: 0.1998 (0.2061)  time: 3.2861  data: 1.0431  max mem: 9954\n",
            "Epoch: [2]  [ 20/101]  eta: 0:04:26  lr: 0.005000  loss: 0.7072 (0.7168)  loss_classifier: 0.1582 (0.1642)  loss_box_reg: 0.2593 (0.2623)  loss_objectness: 0.0666 (0.0753)  loss_rpn_box_reg: 0.2088 (0.2151)  time: 3.2789  data: 1.0337  max mem: 9954\n",
            "Epoch: [2]  [ 30/101]  eta: 0:03:54  lr: 0.005000  loss: 0.7530 (0.7264)  loss_classifier: 0.1614 (0.1661)  loss_box_reg: 0.2574 (0.2587)  loss_objectness: 0.0759 (0.0803)  loss_rpn_box_reg: 0.2325 (0.2213)  time: 3.3071  data: 1.0578  max mem: 9954\n",
            "Epoch: [2]  [ 40/101]  eta: 0:03:22  lr: 0.005000  loss: 0.7183 (0.7163)  loss_classifier: 0.1612 (0.1636)  loss_box_reg: 0.2511 (0.2557)  loss_objectness: 0.0795 (0.0789)  loss_rpn_box_reg: 0.2144 (0.2182)  time: 3.3406  data: 1.0846  max mem: 9954\n",
            "Epoch: [2]  [ 50/101]  eta: 0:02:49  lr: 0.005000  loss: 0.7195 (0.7166)  loss_classifier: 0.1607 (0.1622)  loss_box_reg: 0.2560 (0.2545)  loss_objectness: 0.0792 (0.0794)  loss_rpn_box_reg: 0.2170 (0.2205)  time: 3.3393  data: 1.0801  max mem: 9954\n",
            "Epoch: [2]  [ 60/101]  eta: 0:02:16  lr: 0.005000  loss: 0.7265 (0.7174)  loss_classifier: 0.1563 (0.1605)  loss_box_reg: 0.2465 (0.2523)  loss_objectness: 0.0748 (0.0802)  loss_rpn_box_reg: 0.2348 (0.2244)  time: 3.3307  data: 1.0712  max mem: 9954\n",
            "Epoch: [2]  [ 70/101]  eta: 0:01:42  lr: 0.005000  loss: 0.6930 (0.7108)  loss_classifier: 0.1507 (0.1588)  loss_box_reg: 0.2465 (0.2515)  loss_objectness: 0.0740 (0.0797)  loss_rpn_box_reg: 0.2185 (0.2208)  time: 3.3382  data: 1.0775  max mem: 9954\n",
            "Epoch: [2]  [ 80/101]  eta: 0:01:09  lr: 0.005000  loss: 0.6722 (0.7090)  loss_classifier: 0.1511 (0.1586)  loss_box_reg: 0.2477 (0.2511)  loss_objectness: 0.0739 (0.0802)  loss_rpn_box_reg: 0.1961 (0.2190)  time: 3.3114  data: 1.0528  max mem: 9954\n",
            "Epoch: [2]  [ 90/101]  eta: 0:00:36  lr: 0.005000  loss: 0.6722 (0.7033)  loss_classifier: 0.1518 (0.1574)  loss_box_reg: 0.2464 (0.2501)  loss_objectness: 0.0728 (0.0791)  loss_rpn_box_reg: 0.1847 (0.2166)  time: 3.2713  data: 1.0132  max mem: 9954\n",
            "Epoch: [2]  [100/101]  eta: 0:00:03  lr: 0.005000  loss: 0.6720 (0.7010)  loss_classifier: 0.1527 (0.1572)  loss_box_reg: 0.2464 (0.2497)  loss_objectness: 0.0651 (0.0780)  loss_rpn_box_reg: 0.1847 (0.2162)  time: 3.1883  data: 1.0245  max mem: 9954\n",
            "Epoch: [2] Total time: 0:05:32 (3.2918 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [ 0/25]  eta: 0:00:51  model_time: 1.1204 (1.1204)  evaluator_time: 0.0883 (0.0883)  time: 2.0462  data: 0.8280  max mem: 9954\n",
            "Test:  [24/25]  eta: 0:00:02  model_time: 1.0907 (1.0908)  evaluator_time: 0.1578 (0.1610)  time: 2.0539  data: 0.7860  max mem: 9954\n",
            "Test: Total time: 0:00:51 (2.0503 s / it)\n",
            "Averaged stats: model_time: 1.0907 (1.0908)  evaluator_time: 0.1578 (0.1610)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.28s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.313\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.505\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.349\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.177\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.621\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.581\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.197\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.325\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.367\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.244\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.686\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.599\n",
            "Epoch: [3]  [  0/101]  eta: 0:05:23  lr: 0.000500  loss: 0.6245 (0.6245)  loss_classifier: 0.1333 (0.1333)  loss_box_reg: 0.1830 (0.1830)  loss_objectness: 0.1013 (0.1013)  loss_rpn_box_reg: 0.2069 (0.2069)  time: 3.2004  data: 0.9855  max mem: 9954\n",
            "Epoch: [3]  [ 10/101]  eta: 0:04:57  lr: 0.000500  loss: 0.6899 (0.6683)  loss_classifier: 0.1548 (0.1506)  loss_box_reg: 0.2384 (0.2343)  loss_objectness: 0.0612 (0.0686)  loss_rpn_box_reg: 0.2165 (0.2149)  time: 3.2723  data: 1.0471  max mem: 9954\n",
            "Epoch: [3]  [ 20/101]  eta: 0:04:29  lr: 0.000500  loss: 0.6899 (0.6877)  loss_classifier: 0.1577 (0.1579)  loss_box_reg: 0.2407 (0.2424)  loss_objectness: 0.0676 (0.0724)  loss_rpn_box_reg: 0.2147 (0.2150)  time: 3.3318  data: 1.1006  max mem: 9954\n",
            "Epoch: [3]  [ 30/101]  eta: 0:03:53  lr: 0.000500  loss: 0.6645 (0.6629)  loss_classifier: 0.1476 (0.1530)  loss_box_reg: 0.2244 (0.2343)  loss_objectness: 0.0644 (0.0684)  loss_rpn_box_reg: 0.1801 (0.2073)  time: 3.3021  data: 1.0662  max mem: 9954\n",
            "Epoch: [3]  [ 40/101]  eta: 0:03:20  lr: 0.000500  loss: 0.6223 (0.6527)  loss_classifier: 0.1408 (0.1519)  loss_box_reg: 0.2243 (0.2355)  loss_objectness: 0.0553 (0.0647)  loss_rpn_box_reg: 0.1801 (0.2006)  time: 3.2527  data: 1.0125  max mem: 9954\n",
            "Epoch: [3]  [ 50/101]  eta: 0:02:47  lr: 0.000500  loss: 0.6223 (0.6462)  loss_classifier: 0.1408 (0.1500)  loss_box_reg: 0.2289 (0.2353)  loss_objectness: 0.0546 (0.0656)  loss_rpn_box_reg: 0.1738 (0.1953)  time: 3.2824  data: 1.0355  max mem: 9954\n",
            "Epoch: [3]  [ 60/101]  eta: 0:02:14  lr: 0.000500  loss: 0.6399 (0.6520)  loss_classifier: 0.1440 (0.1497)  loss_box_reg: 0.2354 (0.2357)  loss_objectness: 0.0601 (0.0668)  loss_rpn_box_reg: 0.1952 (0.1998)  time: 3.2728  data: 1.0228  max mem: 9954\n",
            "Epoch: [3]  [ 70/101]  eta: 0:01:41  lr: 0.000500  loss: 0.6521 (0.6485)  loss_classifier: 0.1471 (0.1487)  loss_box_reg: 0.2374 (0.2362)  loss_objectness: 0.0594 (0.0653)  loss_rpn_box_reg: 0.1952 (0.1983)  time: 3.2576  data: 1.0074  max mem: 9954\n",
            "Epoch: [3]  [ 80/101]  eta: 0:01:09  lr: 0.000500  loss: 0.6223 (0.6463)  loss_classifier: 0.1420 (0.1482)  loss_box_reg: 0.2342 (0.2362)  loss_objectness: 0.0537 (0.0639)  loss_rpn_box_reg: 0.1920 (0.1980)  time: 3.2985  data: 1.0429  max mem: 9954\n",
            "Epoch: [3]  [ 90/101]  eta: 0:00:36  lr: 0.000500  loss: 0.6376 (0.6488)  loss_classifier: 0.1430 (0.1481)  loss_box_reg: 0.2327 (0.2368)  loss_objectness: 0.0562 (0.0646)  loss_rpn_box_reg: 0.2011 (0.1993)  time: 3.3045  data: 1.0456  max mem: 9954\n",
            "Epoch: [3]  [100/101]  eta: 0:00:03  lr: 0.000500  loss: 0.6151 (0.6442)  loss_classifier: 0.1428 (0.1473)  loss_box_reg: 0.2357 (0.2359)  loss_objectness: 0.0585 (0.0642)  loss_rpn_box_reg: 0.1794 (0.1969)  time: 3.1194  data: 0.9594  max mem: 9954\n",
            "Epoch: [3] Total time: 0:05:28 (3.2546 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [ 0/25]  eta: 0:00:51  model_time: 1.1270 (1.1270)  evaluator_time: 0.0899 (0.0899)  time: 2.0548  data: 0.8279  max mem: 9954\n",
            "Test:  [24/25]  eta: 0:00:02  model_time: 1.0872 (1.0887)  evaluator_time: 0.1656 (0.1698)  time: 2.0943  data: 0.8187  max mem: 9954\n",
            "Test: Total time: 0:00:52 (2.0829 s / it)\n",
            "Averaged stats: model_time: 1.0872 (1.0887)  evaluator_time: 0.1656 (0.1698)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.30s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.328\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.509\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.361\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.186\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.644\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.593\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.205\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.335\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.378\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.253\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.701\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.609\n",
            "Epoch: [4]  [  0/101]  eta: 0:05:52  lr: 0.000500  loss: 0.7748 (0.7748)  loss_classifier: 0.2002 (0.2002)  loss_box_reg: 0.2723 (0.2723)  loss_objectness: 0.0764 (0.0764)  loss_rpn_box_reg: 0.2259 (0.2259)  time: 3.4875  data: 1.2687  max mem: 9954\n",
            "Epoch: [4]  [ 10/101]  eta: 0:04:53  lr: 0.000500  loss: 0.6159 (0.6285)  loss_classifier: 0.1421 (0.1450)  loss_box_reg: 0.2409 (0.2348)  loss_objectness: 0.0579 (0.0597)  loss_rpn_box_reg: 0.1865 (0.1891)  time: 3.2290  data: 1.0128  max mem: 9954\n",
            "Epoch: [4]  [ 20/101]  eta: 0:04:22  lr: 0.000500  loss: 0.6257 (0.6397)  loss_classifier: 0.1421 (0.1458)  loss_box_reg: 0.2409 (0.2395)  loss_objectness: 0.0517 (0.0575)  loss_rpn_box_reg: 0.1889 (0.1969)  time: 3.2309  data: 1.0066  max mem: 9954\n",
            "Epoch: [4]  [ 30/101]  eta: 0:03:51  lr: 0.000500  loss: 0.6472 (0.6370)  loss_classifier: 0.1390 (0.1436)  loss_box_reg: 0.2395 (0.2382)  loss_objectness: 0.0538 (0.0577)  loss_rpn_box_reg: 0.2039 (0.1975)  time: 3.2851  data: 1.0468  max mem: 9954\n",
            "Epoch: [4]  [ 40/101]  eta: 0:03:19  lr: 0.000500  loss: 0.6214 (0.6333)  loss_classifier: 0.1376 (0.1440)  loss_box_reg: 0.2340 (0.2386)  loss_objectness: 0.0538 (0.0570)  loss_rpn_box_reg: 0.1937 (0.1937)  time: 3.2944  data: 1.0525  max mem: 9954\n",
            "Epoch: [4]  [ 50/101]  eta: 0:02:45  lr: 0.000500  loss: 0.6056 (0.6291)  loss_classifier: 0.1383 (0.1432)  loss_box_reg: 0.2311 (0.2358)  loss_objectness: 0.0537 (0.0571)  loss_rpn_box_reg: 0.1832 (0.1930)  time: 3.2262  data: 0.9835  max mem: 9954\n",
            "Epoch: [4]  [ 60/101]  eta: 0:02:12  lr: 0.000500  loss: 0.5964 (0.6255)  loss_classifier: 0.1283 (0.1413)  loss_box_reg: 0.2196 (0.2330)  loss_objectness: 0.0566 (0.0575)  loss_rpn_box_reg: 0.1904 (0.1937)  time: 3.1893  data: 0.9409  max mem: 9954\n",
            "Epoch: [4]  [ 70/101]  eta: 0:01:40  lr: 0.000500  loss: 0.5727 (0.6199)  loss_classifier: 0.1277 (0.1404)  loss_box_reg: 0.2130 (0.2316)  loss_objectness: 0.0545 (0.0570)  loss_rpn_box_reg: 0.1904 (0.1908)  time: 3.2352  data: 0.9820  max mem: 9954\n",
            "Epoch: [4]  [ 80/101]  eta: 0:01:08  lr: 0.000500  loss: 0.6210 (0.6261)  loss_classifier: 0.1434 (0.1422)  loss_box_reg: 0.2374 (0.2332)  loss_objectness: 0.0590 (0.0584)  loss_rpn_box_reg: 0.1893 (0.1923)  time: 3.2830  data: 1.0281  max mem: 9954\n",
            "Epoch: [4]  [ 90/101]  eta: 0:00:35  lr: 0.000500  loss: 0.6610 (0.6328)  loss_classifier: 0.1521 (0.1435)  loss_box_reg: 0.2523 (0.2355)  loss_objectness: 0.0626 (0.0598)  loss_rpn_box_reg: 0.1919 (0.1940)  time: 3.3166  data: 1.0595  max mem: 9954\n",
            "Epoch: [4]  [100/101]  eta: 0:00:03  lr: 0.000500  loss: 0.6475 (0.6331)  loss_classifier: 0.1418 (0.1434)  loss_box_reg: 0.2369 (0.2351)  loss_objectness: 0.0596 (0.0595)  loss_rpn_box_reg: 0.1811 (0.1952)  time: 3.1617  data: 0.9987  max mem: 9954\n",
            "Epoch: [4] Total time: 0:05:26 (3.2345 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [ 0/25]  eta: 0:00:51  model_time: 1.1150 (1.1150)  evaluator_time: 0.0829 (0.0829)  time: 2.0403  data: 0.8328  max mem: 9954\n",
            "Test:  [24/25]  eta: 0:00:02  model_time: 1.0887 (1.0891)  evaluator_time: 0.1598 (0.1626)  time: 2.0552  data: 0.7875  max mem: 9954\n",
            "Test: Total time: 0:00:51 (2.0509 s / it)\n",
            "Averaged stats: model_time: 1.0887 (1.0891)  evaluator_time: 0.1598 (0.1626)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.27s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.332\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.514\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.380\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.187\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.649\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.539\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.208\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.339\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.382\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.254\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.704\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.556\n",
            "Epoch: [5]  [  0/101]  eta: 0:05:16  lr: 0.000500  loss: 0.5235 (0.5235)  loss_classifier: 0.1409 (0.1409)  loss_box_reg: 0.1972 (0.1972)  loss_objectness: 0.0532 (0.0532)  loss_rpn_box_reg: 0.1321 (0.1321)  time: 3.1376  data: 0.9216  max mem: 9954\n",
            "Epoch: [5]  [ 10/101]  eta: 0:04:59  lr: 0.000500  loss: 0.6751 (0.6520)  loss_classifier: 0.1562 (0.1520)  loss_box_reg: 0.2398 (0.2383)  loss_objectness: 0.0634 (0.0648)  loss_rpn_box_reg: 0.2048 (0.1969)  time: 3.2871  data: 1.0637  max mem: 9954\n",
            "Epoch: [5]  [ 20/101]  eta: 0:04:27  lr: 0.000500  loss: 0.6607 (0.6412)  loss_classifier: 0.1458 (0.1480)  loss_box_reg: 0.2341 (0.2390)  loss_objectness: 0.0615 (0.0623)  loss_rpn_box_reg: 0.1960 (0.1919)  time: 3.3082  data: 1.0768  max mem: 9954\n",
            "Epoch: [5]  [ 30/101]  eta: 0:03:53  lr: 0.000500  loss: 0.6217 (0.6310)  loss_classifier: 0.1389 (0.1456)  loss_box_reg: 0.2290 (0.2350)  loss_objectness: 0.0533 (0.0595)  loss_rpn_box_reg: 0.1916 (0.1910)  time: 3.2841  data: 1.0456  max mem: 9954\n",
            "Epoch: [5]  [ 40/101]  eta: 0:03:19  lr: 0.000500  loss: 0.6324 (0.6306)  loss_classifier: 0.1449 (0.1450)  loss_box_reg: 0.2353 (0.2358)  loss_objectness: 0.0527 (0.0584)  loss_rpn_box_reg: 0.1902 (0.1913)  time: 3.2525  data: 1.0122  max mem: 9954\n",
            "Epoch: [5]  [ 50/101]  eta: 0:02:47  lr: 0.000500  loss: 0.6542 (0.6315)  loss_classifier: 0.1504 (0.1455)  loss_box_reg: 0.2441 (0.2359)  loss_objectness: 0.0573 (0.0588)  loss_rpn_box_reg: 0.1902 (0.1913)  time: 3.2734  data: 1.0282  max mem: 9954\n",
            "Epoch: [5]  [ 60/101]  eta: 0:02:14  lr: 0.000500  loss: 0.6362 (0.6362)  loss_classifier: 0.1455 (0.1462)  loss_box_reg: 0.2353 (0.2380)  loss_objectness: 0.0585 (0.0601)  loss_rpn_box_reg: 0.1908 (0.1919)  time: 3.2946  data: 1.0441  max mem: 9954\n",
            "Epoch: [5]  [ 70/101]  eta: 0:01:41  lr: 0.000500  loss: 0.6437 (0.6351)  loss_classifier: 0.1410 (0.1454)  loss_box_reg: 0.2353 (0.2375)  loss_objectness: 0.0567 (0.0592)  loss_rpn_box_reg: 0.1979 (0.1930)  time: 3.2726  data: 1.0227  max mem: 9954\n",
            "Epoch: [5]  [ 80/101]  eta: 0:01:08  lr: 0.000500  loss: 0.6223 (0.6291)  loss_classifier: 0.1320 (0.1435)  loss_box_reg: 0.2106 (0.2349)  loss_objectness: 0.0507 (0.0586)  loss_rpn_box_reg: 0.1949 (0.1921)  time: 3.2662  data: 1.0159  max mem: 9954\n",
            "Epoch: [5]  [ 90/101]  eta: 0:00:36  lr: 0.000500  loss: 0.6076 (0.6266)  loss_classifier: 0.1311 (0.1423)  loss_box_reg: 0.2143 (0.2339)  loss_objectness: 0.0516 (0.0585)  loss_rpn_box_reg: 0.1883 (0.1919)  time: 3.2656  data: 1.0108  max mem: 9954\n",
            "Epoch: [5]  [100/101]  eta: 0:00:03  lr: 0.000500  loss: 0.6076 (0.6275)  loss_classifier: 0.1355 (0.1434)  loss_box_reg: 0.2252 (0.2333)  loss_objectness: 0.0587 (0.0591)  loss_rpn_box_reg: 0.1881 (0.1918)  time: 3.1193  data: 0.9595  max mem: 9954\n",
            "Epoch: [5] Total time: 0:05:27 (3.2472 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [ 0/25]  eta: 0:00:51  model_time: 1.1215 (1.1215)  evaluator_time: 0.0882 (0.0882)  time: 2.0510  data: 0.8320  max mem: 9954\n",
            "Test:  [24/25]  eta: 0:00:02  model_time: 1.0891 (1.0908)  evaluator_time: 0.1803 (0.1716)  time: 2.0779  data: 0.7969  max mem: 9954\n",
            "Test: Total time: 0:00:51 (2.0663 s / it)\n",
            "Averaged stats: model_time: 1.0891 (1.0908)  evaluator_time: 0.1803 (0.1716)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.52s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.336\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.514\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.376\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.654\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.541\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.210\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.343\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.386\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.259\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.706\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.558\n",
            "Epoch: [6]  [  0/101]  eta: 0:05:47  lr: 0.000050  loss: 0.5682 (0.5682)  loss_classifier: 0.1215 (0.1215)  loss_box_reg: 0.2286 (0.2286)  loss_objectness: 0.0555 (0.0555)  loss_rpn_box_reg: 0.1625 (0.1625)  time: 3.4360  data: 1.1717  max mem: 9954\n",
            "Epoch: [6]  [ 10/101]  eta: 0:04:57  lr: 0.000050  loss: 0.6400 (0.6396)  loss_classifier: 0.1447 (0.1438)  loss_box_reg: 0.2286 (0.2390)  loss_objectness: 0.0570 (0.0595)  loss_rpn_box_reg: 0.2064 (0.1974)  time: 3.2661  data: 1.0401  max mem: 9954\n",
            "Epoch: [6]  [ 20/101]  eta: 0:04:23  lr: 0.000050  loss: 0.6400 (0.6385)  loss_classifier: 0.1447 (0.1448)  loss_box_reg: 0.2273 (0.2414)  loss_objectness: 0.0570 (0.0575)  loss_rpn_box_reg: 0.2024 (0.1949)  time: 3.2479  data: 1.0205  max mem: 9954\n",
            "Epoch: [6]  [ 30/101]  eta: 0:03:50  lr: 0.000050  loss: 0.6196 (0.6312)  loss_classifier: 0.1412 (0.1432)  loss_box_reg: 0.2372 (0.2378)  loss_objectness: 0.0566 (0.0567)  loss_rpn_box_reg: 0.1932 (0.1933)  time: 3.2373  data: 1.0012  max mem: 9954\n",
            "Epoch: [6]  [ 40/101]  eta: 0:03:19  lr: 0.000050  loss: 0.6196 (0.6313)  loss_classifier: 0.1412 (0.1435)  loss_box_reg: 0.2339 (0.2356)  loss_objectness: 0.0566 (0.0577)  loss_rpn_box_reg: 0.1932 (0.1945)  time: 3.2826  data: 1.0404  max mem: 9954\n",
            "Epoch: [6]  [ 50/101]  eta: 0:02:46  lr: 0.000050  loss: 0.6011 (0.6233)  loss_classifier: 0.1350 (0.1418)  loss_box_reg: 0.2311 (0.2336)  loss_objectness: 0.0542 (0.0564)  loss_rpn_box_reg: 0.2015 (0.1914)  time: 3.2719  data: 1.0250  max mem: 9954\n",
            "Epoch: [6]  [ 60/101]  eta: 0:02:13  lr: 0.000050  loss: 0.5976 (0.6193)  loss_classifier: 0.1324 (0.1410)  loss_box_reg: 0.2156 (0.2319)  loss_objectness: 0.0542 (0.0569)  loss_rpn_box_reg: 0.1675 (0.1894)  time: 3.2437  data: 0.9963  max mem: 9954\n",
            "Epoch: [6]  [ 70/101]  eta: 0:01:41  lr: 0.000050  loss: 0.6043 (0.6155)  loss_classifier: 0.1329 (0.1397)  loss_box_reg: 0.2156 (0.2296)  loss_objectness: 0.0570 (0.0574)  loss_rpn_box_reg: 0.1717 (0.1888)  time: 3.2929  data: 1.0414  max mem: 9954\n",
            "Epoch: [6]  [ 80/101]  eta: 0:01:08  lr: 0.000050  loss: 0.6302 (0.6168)  loss_classifier: 0.1373 (0.1397)  loss_box_reg: 0.2274 (0.2301)  loss_objectness: 0.0599 (0.0580)  loss_rpn_box_reg: 0.1848 (0.1889)  time: 3.2984  data: 1.0446  max mem: 9954\n",
            "Epoch: [6]  [ 90/101]  eta: 0:00:35  lr: 0.000050  loss: 0.6244 (0.6176)  loss_classifier: 0.1424 (0.1404)  loss_box_reg: 0.2357 (0.2304)  loss_objectness: 0.0619 (0.0583)  loss_rpn_box_reg: 0.1933 (0.1885)  time: 3.2746  data: 1.0209  max mem: 9954\n",
            "Epoch: [6]  [100/101]  eta: 0:00:03  lr: 0.000050  loss: 0.6119 (0.6211)  loss_classifier: 0.1438 (0.1410)  loss_box_reg: 0.2380 (0.2326)  loss_objectness: 0.0609 (0.0585)  loss_rpn_box_reg: 0.1818 (0.1890)  time: 3.1420  data: 0.9803  max mem: 9954\n",
            "Epoch: [6] Total time: 0:05:27 (3.2450 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [ 0/25]  eta: 0:00:51  model_time: 1.1280 (1.1280)  evaluator_time: 0.0919 (0.0919)  time: 2.0547  data: 0.8253  max mem: 9954\n",
            "Test:  [24/25]  eta: 0:00:02  model_time: 1.0887 (1.0901)  evaluator_time: 0.1594 (0.1670)  time: 2.0408  data: 0.7805  max mem: 9954\n",
            "Test: Total time: 0:00:51 (2.0765 s / it)\n",
            "Averaged stats: model_time: 1.0887 (1.0901)  evaluator_time: 0.1594 (0.1670)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.28s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.335\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.514\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.376\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.654\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.543\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.209\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.343\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.386\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.258\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.710\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.560\n",
            "Epoch: [7]  [  0/101]  eta: 0:05:42  lr: 0.000050  loss: 0.6418 (0.6418)  loss_classifier: 0.1377 (0.1377)  loss_box_reg: 0.2124 (0.2124)  loss_objectness: 0.0471 (0.0471)  loss_rpn_box_reg: 0.2447 (0.2447)  time: 3.3956  data: 1.1875  max mem: 9954\n",
            "Epoch: [7]  [ 10/101]  eta: 0:04:52  lr: 0.000050  loss: 0.6361 (0.6226)  loss_classifier: 0.1418 (0.1434)  loss_box_reg: 0.2272 (0.2291)  loss_objectness: 0.0592 (0.0603)  loss_rpn_box_reg: 0.1975 (0.1898)  time: 3.2179  data: 0.9978  max mem: 9954\n",
            "Epoch: [7]  [ 20/101]  eta: 0:04:22  lr: 0.000050  loss: 0.6161 (0.6211)  loss_classifier: 0.1418 (0.1426)  loss_box_reg: 0.2272 (0.2280)  loss_objectness: 0.0539 (0.0574)  loss_rpn_box_reg: 0.1859 (0.1931)  time: 3.2349  data: 1.0064  max mem: 9954\n",
            "Epoch: [7]  [ 30/101]  eta: 0:03:50  lr: 0.000050  loss: 0.6175 (0.6241)  loss_classifier: 0.1411 (0.1429)  loss_box_reg: 0.2255 (0.2275)  loss_objectness: 0.0537 (0.0583)  loss_rpn_box_reg: 0.1883 (0.1954)  time: 3.2723  data: 1.0283  max mem: 9954\n",
            "Epoch: [7]  [ 40/101]  eta: 0:03:18  lr: 0.000050  loss: 0.6011 (0.6181)  loss_classifier: 0.1401 (0.1415)  loss_box_reg: 0.2224 (0.2278)  loss_objectness: 0.0519 (0.0577)  loss_rpn_box_reg: 0.1845 (0.1911)  time: 3.2680  data: 0.9917  max mem: 9954\n",
            "Epoch: [7]  [ 50/101]  eta: 0:02:47  lr: 0.000050  loss: 0.6010 (0.6210)  loss_classifier: 0.1401 (0.1417)  loss_box_reg: 0.2341 (0.2297)  loss_objectness: 0.0483 (0.0563)  loss_rpn_box_reg: 0.1784 (0.1933)  time: 3.3160  data: 0.9954  max mem: 9954\n",
            "Epoch: [7]  [ 60/101]  eta: 0:02:15  lr: 0.000050  loss: 0.6371 (0.6219)  loss_classifier: 0.1448 (0.1422)  loss_box_reg: 0.2363 (0.2308)  loss_objectness: 0.0565 (0.0576)  loss_rpn_box_reg: 0.1885 (0.1914)  time: 3.3764  data: 1.0440  max mem: 9954\n",
            "Epoch: [7]  [ 70/101]  eta: 0:01:42  lr: 0.000050  loss: 0.6426 (0.6209)  loss_classifier: 0.1413 (0.1416)  loss_box_reg: 0.2288 (0.2308)  loss_objectness: 0.0569 (0.0576)  loss_rpn_box_reg: 0.1828 (0.1909)  time: 3.3894  data: 1.0619  max mem: 9954\n",
            "Epoch: [7]  [ 80/101]  eta: 0:01:09  lr: 0.000050  loss: 0.5890 (0.6187)  loss_classifier: 0.1350 (0.1411)  loss_box_reg: 0.2252 (0.2300)  loss_objectness: 0.0555 (0.0573)  loss_rpn_box_reg: 0.1714 (0.1902)  time: 3.3687  data: 1.0378  max mem: 9954\n",
            "Epoch: [7]  [ 90/101]  eta: 0:00:36  lr: 0.000050  loss: 0.6301 (0.6221)  loss_classifier: 0.1424 (0.1418)  loss_box_reg: 0.2382 (0.2322)  loss_objectness: 0.0550 (0.0576)  loss_rpn_box_reg: 0.1779 (0.1904)  time: 3.4159  data: 1.0821  max mem: 9954\n",
            "Epoch: [7]  [100/101]  eta: 0:00:03  lr: 0.000050  loss: 0.6438 (0.6197)  loss_classifier: 0.1424 (0.1414)  loss_box_reg: 0.2379 (0.2321)  loss_objectness: 0.0591 (0.0575)  loss_rpn_box_reg: 0.1785 (0.1886)  time: 3.2363  data: 1.0048  max mem: 9954\n",
            "Epoch: [7] Total time: 0:05:33 (3.2980 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [ 0/25]  eta: 0:00:51  model_time: 1.1454 (1.1454)  evaluator_time: 0.0810 (0.0810)  time: 2.0688  data: 0.8332  max mem: 9954\n",
            "Test:  [24/25]  eta: 0:00:02  model_time: 1.1466 (1.1425)  evaluator_time: 0.1646 (0.1630)  time: 2.1032  data: 0.7816  max mem: 9954\n",
            "Test: Total time: 0:00:52 (2.0972 s / it)\n",
            "Averaged stats: model_time: 1.1466 (1.1425)  evaluator_time: 0.1646 (0.1630)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.28s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.335\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.515\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.375\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.655\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.542\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.209\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.342\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.385\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.257\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.710\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.559\n",
            "Epoch: [8]  [  0/101]  eta: 0:05:51  lr: 0.000050  loss: 0.6227 (0.6227)  loss_classifier: 0.1403 (0.1403)  loss_box_reg: 0.2345 (0.2345)  loss_objectness: 0.0594 (0.0594)  loss_rpn_box_reg: 0.1885 (0.1885)  time: 3.4835  data: 1.1460  max mem: 9954\n",
            "Epoch: [8]  [ 10/101]  eta: 0:05:12  lr: 0.000050  loss: 0.5703 (0.5858)  loss_classifier: 0.1305 (0.1312)  loss_box_reg: 0.2275 (0.2218)  loss_objectness: 0.0538 (0.0525)  loss_rpn_box_reg: 0.1750 (0.1804)  time: 3.4382  data: 1.1032  max mem: 9954\n",
            "Epoch: [8]  [ 20/101]  eta: 0:04:33  lr: 0.000050  loss: 0.5703 (0.5949)  loss_classifier: 0.1305 (0.1317)  loss_box_reg: 0.2140 (0.2194)  loss_objectness: 0.0538 (0.0556)  loss_rpn_box_reg: 0.1792 (0.1882)  time: 3.3729  data: 1.0465  max mem: 9954\n",
            "Epoch: [8]  [ 30/101]  eta: 0:03:59  lr: 0.000050  loss: 0.5948 (0.6060)  loss_classifier: 0.1370 (0.1371)  loss_box_reg: 0.2287 (0.2250)  loss_objectness: 0.0540 (0.0558)  loss_rpn_box_reg: 0.1897 (0.1881)  time: 3.3370  data: 1.0121  max mem: 9954\n",
            "Epoch: [8]  [ 40/101]  eta: 0:03:25  lr: 0.000050  loss: 0.6042 (0.6122)  loss_classifier: 0.1378 (0.1369)  loss_box_reg: 0.2304 (0.2268)  loss_objectness: 0.0515 (0.0560)  loss_rpn_box_reg: 0.1864 (0.1924)  time: 3.3692  data: 1.0410  max mem: 9954\n",
            "Epoch: [8]  [ 50/101]  eta: 0:02:51  lr: 0.000050  loss: 0.6394 (0.6209)  loss_classifier: 0.1446 (0.1399)  loss_box_reg: 0.2433 (0.2311)  loss_objectness: 0.0549 (0.0570)  loss_rpn_box_reg: 0.1869 (0.1930)  time: 3.3398  data: 1.0124  max mem: 9954\n",
            "Epoch: [8]  [ 60/101]  eta: 0:02:17  lr: 0.000050  loss: 0.6353 (0.6238)  loss_classifier: 0.1476 (0.1408)  loss_box_reg: 0.2393 (0.2329)  loss_objectness: 0.0574 (0.0573)  loss_rpn_box_reg: 0.1894 (0.1928)  time: 3.3462  data: 1.0171  max mem: 9954\n",
            "Epoch: [8]  [ 70/101]  eta: 0:01:44  lr: 0.000050  loss: 0.6231 (0.6256)  loss_classifier: 0.1417 (0.1416)  loss_box_reg: 0.2381 (0.2342)  loss_objectness: 0.0550 (0.0582)  loss_rpn_box_reg: 0.1855 (0.1915)  time: 3.4047  data: 1.0758  max mem: 9954\n",
            "Epoch: [8]  [ 80/101]  eta: 0:01:10  lr: 0.000050  loss: 0.5884 (0.6203)  loss_classifier: 0.1344 (0.1403)  loss_box_reg: 0.2140 (0.2316)  loss_objectness: 0.0526 (0.0569)  loss_rpn_box_reg: 0.1861 (0.1916)  time: 3.3681  data: 1.0408  max mem: 9954\n",
            "Epoch: [8]  [ 90/101]  eta: 0:00:36  lr: 0.000050  loss: 0.5515 (0.6123)  loss_classifier: 0.1291 (0.1391)  loss_box_reg: 0.2102 (0.2296)  loss_objectness: 0.0491 (0.0565)  loss_rpn_box_reg: 0.1584 (0.1870)  time: 3.3109  data: 0.9844  max mem: 9954\n",
            "Epoch: [8]  [100/101]  eta: 0:00:03  lr: 0.000050  loss: 0.5660 (0.6143)  loss_classifier: 0.1360 (0.1396)  loss_box_reg: 0.2219 (0.2301)  loss_objectness: 0.0508 (0.0564)  loss_rpn_box_reg: 0.1613 (0.1882)  time: 3.1963  data: 0.9683  max mem: 9954\n",
            "Epoch: [8] Total time: 0:05:36 (3.3322 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [ 0/25]  eta: 0:00:52  model_time: 1.1595 (1.1595)  evaluator_time: 0.1031 (0.1031)  time: 2.0961  data: 0.8242  max mem: 9954\n",
            "Test:  [24/25]  eta: 0:00:02  model_time: 1.1470 (1.1447)  evaluator_time: 0.1706 (0.1732)  time: 2.1373  data: 0.8044  max mem: 9954\n",
            "Test: Total time: 0:00:53 (2.1259 s / it)\n",
            "Averaged stats: model_time: 1.1470 (1.1447)  evaluator_time: 0.1706 (0.1732)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.28s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.332\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.515\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.370\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.649\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.541\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.207\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.341\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.384\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.257\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.705\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.557\n",
            "Epoch: [9]  [  0/101]  eta: 0:05:24  lr: 0.000005  loss: 0.5187 (0.5187)  loss_classifier: 0.1221 (0.1221)  loss_box_reg: 0.2193 (0.2193)  loss_objectness: 0.0364 (0.0364)  loss_rpn_box_reg: 0.1409 (0.1409)  time: 3.2139  data: 0.8806  max mem: 9954\n",
            "Epoch: [9]  [ 10/101]  eta: 0:05:08  lr: 0.000005  loss: 0.6255 (0.6333)  loss_classifier: 0.1426 (0.1462)  loss_box_reg: 0.2330 (0.2363)  loss_objectness: 0.0544 (0.0566)  loss_rpn_box_reg: 0.1955 (0.1942)  time: 3.3951  data: 1.0576  max mem: 9954\n",
            "Epoch: [9]  [ 20/101]  eta: 0:04:30  lr: 0.000005  loss: 0.6129 (0.6183)  loss_classifier: 0.1420 (0.1431)  loss_box_reg: 0.2317 (0.2323)  loss_objectness: 0.0583 (0.0565)  loss_rpn_box_reg: 0.1888 (0.1864)  time: 3.3449  data: 1.0175  max mem: 9954\n",
            "Epoch: [9]  [ 30/101]  eta: 0:03:57  lr: 0.000005  loss: 0.5822 (0.6090)  loss_classifier: 0.1348 (0.1403)  loss_box_reg: 0.2197 (0.2294)  loss_objectness: 0.0551 (0.0551)  loss_rpn_box_reg: 0.1729 (0.1842)  time: 3.3229  data: 0.9969  max mem: 9954\n",
            "Epoch: [9]  [ 40/101]  eta: 0:03:23  lr: 0.000005  loss: 0.5873 (0.6081)  loss_classifier: 0.1339 (0.1397)  loss_box_reg: 0.2202 (0.2298)  loss_objectness: 0.0544 (0.0554)  loss_rpn_box_reg: 0.1752 (0.1833)  time: 3.3424  data: 1.0132  max mem: 9954\n",
            "Epoch: [9]  [ 50/101]  eta: 0:02:50  lr: 0.000005  loss: 0.6477 (0.6144)  loss_classifier: 0.1372 (0.1399)  loss_box_reg: 0.2241 (0.2299)  loss_objectness: 0.0589 (0.0562)  loss_rpn_box_reg: 0.1987 (0.1885)  time: 3.3427  data: 1.0169  max mem: 9954\n",
            "Epoch: [9]  [ 60/101]  eta: 0:02:17  lr: 0.000005  loss: 0.6527 (0.6163)  loss_classifier: 0.1455 (0.1405)  loss_box_reg: 0.2396 (0.2309)  loss_objectness: 0.0585 (0.0562)  loss_rpn_box_reg: 0.1999 (0.1888)  time: 3.3811  data: 1.0513  max mem: 9954\n",
            "Epoch: [9]  [ 70/101]  eta: 0:01:44  lr: 0.000005  loss: 0.6304 (0.6191)  loss_classifier: 0.1445 (0.1411)  loss_box_reg: 0.2339 (0.2318)  loss_objectness: 0.0554 (0.0567)  loss_rpn_box_reg: 0.1861 (0.1894)  time: 3.3818  data: 1.0519  max mem: 9954\n",
            "Epoch: [9]  [ 80/101]  eta: 0:01:10  lr: 0.000005  loss: 0.6258 (0.6184)  loss_classifier: 0.1360 (0.1409)  loss_box_reg: 0.2260 (0.2316)  loss_objectness: 0.0566 (0.0566)  loss_rpn_box_reg: 0.1833 (0.1894)  time: 3.3705  data: 1.0420  max mem: 9954\n",
            "Epoch: [9]  [ 90/101]  eta: 0:00:36  lr: 0.000005  loss: 0.5836 (0.6167)  loss_classifier: 0.1353 (0.1406)  loss_box_reg: 0.2283 (0.2313)  loss_objectness: 0.0564 (0.0565)  loss_rpn_box_reg: 0.1729 (0.1882)  time: 3.3701  data: 1.0420  max mem: 9954\n",
            "Epoch: [9]  [100/101]  eta: 0:00:03  lr: 0.000005  loss: 0.5779 (0.6125)  loss_classifier: 0.1384 (0.1397)  loss_box_reg: 0.2256 (0.2298)  loss_objectness: 0.0442 (0.0553)  loss_rpn_box_reg: 0.1645 (0.1876)  time: 3.2077  data: 0.9789  max mem: 9954\n",
            "Epoch: [9] Total time: 0:05:36 (3.3283 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [ 0/25]  eta: 0:00:52  model_time: 1.1499 (1.1499)  evaluator_time: 0.0813 (0.0813)  time: 2.0940  data: 0.8515  max mem: 9954\n",
            "Test:  [24/25]  eta: 0:00:02  model_time: 1.1417 (1.1416)  evaluator_time: 0.1673 (0.1629)  time: 2.1158  data: 0.7969  max mem: 9954\n",
            "Test: Total time: 0:00:52 (2.1115 s / it)\n",
            "Averaged stats: model_time: 1.1417 (1.1416)  evaluator_time: 0.1673 (0.1629)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.29s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.332\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.515\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.370\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.649\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.541\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.207\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.340\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.383\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.257\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.704\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.558\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Предсказания и проверка модели"
      ],
      "metadata": {
        "id": "o0DO47FQLd_h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# the function takes the original prediction and the iou threshold.\n",
        "def apply_nms(orig_prediction, iou_thresh=0.5):\n",
        "    # torchvision returns the indices of the bboxes to keep\n",
        "    keep = torchvision.ops.nms(orig_prediction['boxes'], orig_prediction['scores'], iou_thresh)\n",
        "    \n",
        "    final_prediction = orig_prediction\n",
        "    final_prediction['boxes'] = final_prediction['boxes'][keep]\n",
        "    final_prediction['scores'] = final_prediction['scores'][keep]\n",
        "    final_prediction['labels'] = final_prediction['labels'][keep]\n",
        "    \n",
        "    return final_prediction\n",
        "\n",
        "# function to convert a torchtensor back to PIL image\n",
        "def torch_to_pil(img):\n",
        "    return torchtransforms.ToPILImage()(img.cpu()).convert('RGB')"
      ],
      "metadata": {
        "id": "oNwDwlPOM-H2"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# pick one image from the test set\n",
        "img, target = dataset_test[59]\n",
        "# put the model in evaluation mode\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    prediction = model([img.to(device)])[0]\n",
        "    \n",
        "print('predicted #boxes: ', len(prediction['labels']))\n",
        "print('real #boxes: ', len(target['labels']))"
      ],
      "metadata": {
        "id": "ck3kUpPmM-Jv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea4fb990-b971-42e8-ad64-9cf42b7b7274"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted #boxes:  48\n",
            "real #boxes:  12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('EXPECTED OUTPUT')\n",
        "plot_img_bbox(torch_to_pil(img), target)"
      ],
      "metadata": {
        "id": "f1cD_MFiM-NU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "63e9da5f-224c-4a2a-ddd3-c2c3711df9df"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EXPECTED OUTPUT\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = {key: prediction[key].cpu() for key in prediction}"
      ],
      "metadata": {
        "id": "Jlz3SkgGEzS6"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('MODEL OUTPUT')\n",
        "plot_img_bbox(torch_to_pil(img), prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "y60UkqGZDtsK",
        "outputId": "c3d0946d-cb67-4ec7-d4cc-6677be540772"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MODEL OUTPUT\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nms_prediction = apply_nms(prediction, iou_thresh=0.25)\n",
        "print('nms prediciton #boxes: ', len(nms_prediction['labels']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HyZ8LunhDtuM",
        "outputId": "734f3303-8159-4490-ef1a-511a77f0a448"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nms prediciton #boxes:  25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('NMS APPLIED MODEL OUTPUT')\n",
        "plot_img_bbox(torch_to_pil(img), nms_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "eZzbEGXKMgfz",
        "outputId": "62e79ca2-1932-4c5d-9500-0394eef2ae25"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NMS APPLIED MODEL OUTPUT\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(nms_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adL2kGGqMD6z",
        "outputId": "393da137-0807-4475-8cb7-ecbb760ece94"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'boxes': tensor([[ 90.0700, 167.1532, 145.3924, 221.1909],\n",
            "        [127.9891, 159.0345, 174.0689, 204.5785],\n",
            "        [280.5901, 195.9399, 400.8178, 299.5273],\n",
            "        [262.7601, 128.5408, 302.3799, 166.1961],\n",
            "        [164.4512, 148.1523, 212.2083, 193.9001],\n",
            "        [209.8859, 132.2898, 252.0246, 168.9898],\n",
            "        [194.2881, 142.1611, 224.7573, 180.3956],\n",
            "        [306.1440, 171.2392, 396.1373, 234.4672],\n",
            "        [228.8584, 122.1938, 265.5200, 150.9546],\n",
            "        [336.8778, 107.8062, 360.2737, 132.3162],\n",
            "        [ 31.4551, 158.0123,  72.8910, 199.7820],\n",
            "        [333.9022, 145.8245, 397.5766, 187.3792],\n",
            "        [  7.9050, 158.2916,  40.5632, 189.8909],\n",
            "        [362.3618, 134.6314, 403.0228, 158.6347],\n",
            "        [ 58.1371, 158.6990,  94.8817, 212.2739],\n",
            "        [126.6258, 119.7102, 175.2911, 166.6487],\n",
            "        [ 75.2664, 146.0756, 126.9808, 190.2462],\n",
            "        [259.6164, 114.7644, 282.0331, 140.0649],\n",
            "        [380.2394, 107.2968, 406.5509, 133.2015],\n",
            "        [271.5345, 110.5509, 298.4719, 135.9423],\n",
            "        [392.0696, 128.2778, 405.1267, 148.1802],\n",
            "        [327.0370,  99.0407, 330.3205, 111.6740],\n",
            "        [290.5245, 118.4105, 308.2535, 144.2240],\n",
            "        [356.0238,  98.6325, 387.1624, 125.5998],\n",
            "        [239.5051,  96.1874, 244.4392, 118.1929]]), 'labels': tensor([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 4, 4,\n",
            "        1]), 'scores': tensor([0.9967, 0.9966, 0.9961, 0.9957, 0.9956, 0.9859, 0.9852, 0.9654, 0.9393,\n",
            "        0.9277, 0.8939, 0.8716, 0.7911, 0.7570, 0.6430, 0.4783, 0.3565, 0.3204,\n",
            "        0.2289, 0.2057, 0.1762, 0.1196, 0.0782, 0.0624, 0.0534])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# У сабсета мы можем обратится к главному датасету и получить имя картинки,\n",
        "# Для корректного сохранения результатов в pd.DataFrame\n",
        "dataset_test.dataset.imgs[target['image_id']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "gw8sroMlFq__",
        "outputId": "c0c66022-afe2-4c24-cd78-98fc3cb67538"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'25-11-2021_10-31-38_PM.jpg'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_predictions(dataset_subset):\n",
        "    solution = []\n",
        "    scale = dataset_subset.dataset.width\n",
        "\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for img, target in tqdm(dataset_subset):\n",
        "            prediction = model([img.to(device)])[0]\n",
        "            nms_prediction = apply_nms(prediction, iou_thresh=0.15)\n",
        "            prediction = {key: nms_prediction[key].cpu() for key in nms_prediction}\n",
        "\n",
        "            for pred_num in range(len(prediction['labels'])):\n",
        "                # Для предсказания\n",
        "                xmin, ymin, xmax, ymax = prediction['boxes'][pred_num] / scale\n",
        "                # Обратно к режиму без background\n",
        "                cls = prediction['labels'][pred_num] - 1\n",
        "                conf = prediction['scores'][pred_num]\n",
        "                img_name = dataset_subset.dataset.imgs[target['image_id']]\n",
        "\n",
        "                solution.append([img_name, cls.item(), conf.item(),\n",
        "                                 xmin.item(), xmax.item(), ymin.item(), ymax.item()])\n",
        "    return solution\n",
        "\n",
        "\n",
        "def get_labels(dataset_subset):\n",
        "    labels = []\n",
        "    scale = dataset_subset.dataset.width\n",
        "    for _, target in tqdm(dataset_subset):\n",
        "        for lbl_num in range(len(target['labels'])):\n",
        "            # Для предсказания\n",
        "            xmin, ymin, xmax, ymax = target['boxes'][lbl_num] / scale\n",
        "            # Обратно к режиму без background\n",
        "            cls = target['labels'][lbl_num] - 1\n",
        "            img_name = dataset_subset.dataset.imgs[target['image_id']]\n",
        "\n",
        "            labels.append([img_name, cls.item(),\n",
        "                                xmin.item(), xmax.item(), ymin.item(), ymax.item()])\n",
        "    return labels"
      ],
      "metadata": {
        "id": "fYlMwCEnL4c2"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "solutions = get_predictions(dataset_test)\n",
        "solutions[-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TJGEufatNOqk",
        "outputId": "849fd614-7b69-43b3-f3c6-f123122906cd"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 400/400 [00:54<00:00,  7.40it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['26-11-2021_07-28-13_AM.jpg',\n",
              " 0,\n",
              " 0.06160254031419754,\n",
              " 0.3217499852180481,\n",
              " 0.3307025134563446,\n",
              " 0.2656785547733307,\n",
              " 0.29744407534599304]"
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          "metadata": {},
          "execution_count": 40
        }
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    {
      "cell_type": "code",
      "source": [
        "labels = get_labels(dataset_test)\n",
        "labels[-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Z9OG5E0S2o7",
        "outputId": "8bc58226-ed3a-4391-e1fb-2ff166bf5cca"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 400/400 [00:21<00:00, 18.99it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['26-11-2021_07-28-13_AM.jpg',\n",
              " 3,\n",
              " 0.7379062175750732,\n",
              " 0.9624114632606506,\n",
              " 0.39988887310028076,\n",
              " 0.6145555377006531]"
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          "metadata": {},
          "execution_count": 41
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "solutions_df = pd.DataFrame(solutions, columns=['ImageID', 'LabelName', 'Conf', 'XMin', 'XMax', 'YMin', 'YMax'])\n",
        "solutions_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ID0bFnDdTuPn",
        "outputId": "a5ae0309-43db-4ac9-eca4-96cb748e5a23"
      },
      "execution_count": 42,
      "outputs": [
        {
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              "                      ImageID  LabelName      Conf      XMin      XMax  \\\n",
              "0  25-11-2021_07-04-25_AM.jpg          3  0.996546  0.117024  0.290878   \n",
              "1  25-11-2021_07-04-25_AM.jpg          3  0.995712  0.652700  0.850748   \n",
              "2  25-11-2021_07-04-25_AM.jpg          3  0.995307  0.475670  0.625484   \n",
              "3  25-11-2021_07-04-25_AM.jpg          0  0.993600  0.426839  0.472502   \n",
              "4  25-11-2021_07-04-25_AM.jpg          1  0.792092  0.435842  0.450628   \n",
              "\n",
              "       YMin      YMax  \n",
              "0  0.207864  0.644586  \n",
              "1  0.076008  0.517113  \n",
              "2  0.122164  0.526187  \n",
              "3  0.122508  0.295859  \n",
              "4  0.113758  0.143845  "
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    {
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      "source": [
        "test_labels_df = pd.DataFrame(labels, columns=['ImageID', 'LabelName', 'XMin', 'XMax', 'YMin', 'YMax'])\n",
        "test_labels_df.head()"
      ],
      "metadata": {
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      "execution_count": 43,
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              "                      ImageID  LabelName      XMin      XMax      YMin  \\\n",
              "0  25-11-2021_07-04-25_AM.jpg          0  0.426559  0.482853  0.118217   \n",
              "1  25-11-2021_07-04-25_AM.jpg          1  0.432016  0.450283  0.117000   \n",
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              "\n",
              "       YMax  \n",
              "0  0.294033  \n",
              "1  0.147145  \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c6f29992-f29b-4565-a150-27aacd30a75a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c6f29992-f29b-4565-a150-27aacd30a75a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from map_boxes import mean_average_precision_for_boxes as map_boxes\n",
        "\n",
        "mean_ap, average_precisions = map_boxes(test_labels_df,\n",
        "                                        solutions_df,\n",
        "                                        iou_threshold=0.25,\n",
        "                                        verbose=False)\n",
        "mean_ap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y3nBIdBBDtv_",
        "outputId": "a9aeedc7-0c8a-4344-d980-f9991ebb78e4"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5366306673766281"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Предсказание для сайта"
      ],
      "metadata": {
        "id": "1wCggfS-li12"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ImagesDatasetTest(torch.utils.data.Dataset):\n",
        "\n",
        "    def __init__(self, test_dir, width, height, transforms=None):\n",
        "        self.transforms = transforms\n",
        "        self.test_dir = test_dir\n",
        "\n",
        "        self.height = height\n",
        "        self.width = width\n",
        "        \n",
        "        self.imgs = [image\n",
        "                     for image in sorted(os.listdir(self.test_dir))\n",
        "                     if image[-4:] == '.jpg']\n",
        "        \n",
        "        # classes: 0 index is reserved for background\n",
        "        self.classes = ['', 'human', 'head', 'face', 'car', 'carplate']\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_name = self.imgs[idx]\n",
        "        image_path = os.path.join(self.test_dir, img_name)\n",
        "\n",
        "        # reading the images and converting them to correct size   \n",
        "        img = cv2.imread(image_path)\n",
        "        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32)\n",
        "        img_res = cv2.resize(img_rgb, (self.width, self.height), cv2.INTER_AREA)\n",
        "        # diving by 255\n",
        "        img_res = img_res / 255.0\n",
        "\n",
        "        if self.transforms:\n",
        "            sample = self.transforms(image=img_res)\n",
        "            img_res = sample['image']\n",
        "\n",
        "        return img_res, img_name\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.imgs)"
      ],
      "metadata": {
        "id": "uE11_PSvUi24"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TEST_PATH = './data/test/images'\n",
        "\n",
        "dataset_test_site = ImagesDatasetTest(TEST_PATH, 416, 416, transforms=ToTensorV2(p=1.0))\n",
        "print('Length of dataset = ', len(dataset_test_site), '\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZIvdRv-XU8Ib",
        "outputId": "812eaa6f-6e06-4b7c-90a9-31a60e5a3f6f"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of dataset =  855 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test_site[0][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "PnK1lcY3XL7E",
        "outputId": "c9903479-c6a8-458e-c13c-33ce32ae6ef4"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'0_23-11-2021_02-18-50_PM.jpg'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_predictions_site(dataset):\n",
        "    solution = []\n",
        "    scale = dataset.width\n",
        "\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for img, img_name in tqdm(dataset):\n",
        "            prediction = model([img.to(device)])[0]\n",
        "            nms_prediction = apply_nms(prediction, iou_thresh=0.25)\n",
        "            prediction = {key: nms_prediction[key].cpu() for key in nms_prediction}\n",
        "\n",
        "            for pred_num in range(len(prediction['labels'])):\n",
        "                # Для предсказания\n",
        "                xmin, ymin, xmax, ymax = prediction['boxes'][pred_num] / scale\n",
        "                # Обратно к режиму без background\n",
        "                cls = prediction['labels'][pred_num] - 1\n",
        "                conf = prediction['scores'][pred_num]\n",
        "\n",
        "                solution.append([img_name, cls.item(), conf.item(),\n",
        "                                 xmin.item(), xmax.item(), ymin.item(), ymax.item()])\n",
        "    return solution"
      ],
      "metadata": {
        "id": "Sd8sMveflkkn"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "solutions_site = get_predictions_site(dataset_test_site)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VbWN72OxllHY",
        "outputId": "49ecfd0a-3711-4790-c41e-97dcad1e48bd"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 855/855 [02:03<00:00,  6.92it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "solutions_site_df = pd.DataFrame(solutions_site, columns=['ImageID', 'LabelName', 'Conf', 'XMin', 'XMax', 'YMin', 'YMax'])\n",
        "solutions_site_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4kmKj9tFllJT",
        "outputId": "78fbfaef-31cc-4042-fbf0-fc91e353a06a"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        ImageID  LabelName      Conf      XMin      XMax  \\\n",
              "0  0_23-11-2021_02-18-50_PM.jpg          4  0.972457  0.090112  0.248816   \n",
              "1  0_23-11-2021_02-18-50_PM.jpg          3  0.255015  0.000000  1.000000   \n",
              "2  0_23-11-2021_02-18-50_PM.jpg          3  0.072024  0.001379  0.174807   \n",
              "3  0_23-11-2021_02-18-56_PM.jpg          4  0.974620  0.533293  0.793660   \n",
              "4  0_23-11-2021_02-18-56_PM.jpg          0  0.504034  0.223643  0.256229   \n",
              "\n",
              "       YMin      YMax  \n",
              "0  0.673685  0.797254  \n",
              "1  0.000000  0.952199  \n",
              "2  0.251970  0.379287  \n",
              "3  0.742102  0.849608  \n",
              "4  0.527978  0.679826  "
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              "      <td>0_23-11-2021_02-18-50_PM.jpg</td>\n",
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              "          const element = document.querySelector('#df-efaa08f7-de63-4c4a-8b93-d8d511289820');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
          "execution_count": 50
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "solutions_site_df.sort_values(['ImageID', 'LabelName', 'Conf'],\n",
        "                              ascending=[True, True, False],\n",
        "                              inplace=True)\n",
        "solutions_site_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tTkDbglOZnxj",
        "outputId": "f93514a2-9f89-4222-f1b0-31da2b7b4711"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        ImageID  LabelName      Conf      XMin      XMax  \\\n",
              "1  0_23-11-2021_02-18-50_PM.jpg          3  0.255015  0.000000  1.000000   \n",
              "2  0_23-11-2021_02-18-50_PM.jpg          3  0.072024  0.001379  0.174807   \n",
              "0  0_23-11-2021_02-18-50_PM.jpg          4  0.972457  0.090112  0.248816   \n",
              "4  0_23-11-2021_02-18-56_PM.jpg          0  0.504034  0.223643  0.256229   \n",
              "7  0_23-11-2021_02-18-56_PM.jpg          0  0.236587  0.889664  0.948755   \n",
              "\n",
              "       YMin      YMax  \n",
              "1  0.000000  0.952199  \n",
              "2  0.251970  0.379287  \n",
              "0  0.673685  0.797254  \n",
              "4  0.527978  0.679826  \n",
              "7  0.414593  0.564620  "
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              "      <th>YMax</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0_23-11-2021_02-18-50_PM.jpg</td>\n",
              "      <td>3</td>\n",
              "      <td>0.255015</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.952199</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0_23-11-2021_02-18-50_PM.jpg</td>\n",
              "      <td>3</td>\n",
              "      <td>0.072024</td>\n",
              "      <td>0.001379</td>\n",
              "      <td>0.174807</td>\n",
              "      <td>0.251970</td>\n",
              "      <td>0.379287</td>\n",
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              "      <th>0</th>\n",
              "      <td>0_23-11-2021_02-18-50_PM.jpg</td>\n",
              "      <td>4</td>\n",
              "      <td>0.972457</td>\n",
              "      <td>0.090112</td>\n",
              "      <td>0.248816</td>\n",
              "      <td>0.673685</td>\n",
              "      <td>0.797254</td>\n",
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              "      <th>4</th>\n",
              "      <td>0_23-11-2021_02-18-56_PM.jpg</td>\n",
              "      <td>0</td>\n",
              "      <td>0.504034</td>\n",
              "      <td>0.223643</td>\n",
              "      <td>0.256229</td>\n",
              "      <td>0.527978</td>\n",
              "      <td>0.679826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0_23-11-2021_02-18-56_PM.jpg</td>\n",
              "      <td>0</td>\n",
              "      <td>0.236587</td>\n",
              "      <td>0.889664</td>\n",
              "      <td>0.948755</td>\n",
              "      <td>0.414593</td>\n",
              "      <td>0.564620</td>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-593731fe-f691-4801-b50d-98026c7cc9ee');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "solutions_site_df = solutions_site_df[solutions_site_df.Conf >= 0.25].reset_index(drop=True)\n",
        "solutions_site_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "coRmL9k5av_f",
        "outputId": "8d9dc7ff-a8f2-4664-a228-96c7e3b5002e"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        ImageID  LabelName      Conf      XMin      XMax  \\\n",
              "0  0_23-11-2021_02-18-50_PM.jpg          3  0.255015  0.000000  1.000000   \n",
              "1  0_23-11-2021_02-18-50_PM.jpg          4  0.972457  0.090112  0.248816   \n",
              "2  0_23-11-2021_02-18-56_PM.jpg          0  0.504034  0.223643  0.256229   \n",
              "3  0_23-11-2021_02-18-56_PM.jpg          1  0.466110  0.894743  0.917030   \n",
              "4  0_23-11-2021_02-18-56_PM.jpg          4  0.974620  0.533293  0.793660   \n",
              "\n",
              "       YMin      YMax  \n",
              "0  0.000000  0.952199  \n",
              "1  0.673685  0.797254  \n",
              "2  0.527978  0.679826  \n",
              "3  0.416114  0.457088  \n",
              "4  0.742102  0.849608  "
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-821f52cc-facc-412a-ac39-8a19dfda0bdd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "solutions_site_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "5XJ7pU1IllLQ",
        "outputId": "2c6fbf3e-93ed-4041-9b0a-89a3eae2f378"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          LabelName          Conf          XMin          XMax          YMin  \\\n",
              "count  11913.000000  11913.000000  11913.000000  11913.000000  11913.000000   \n",
              "mean       2.216150      0.803083      0.439769      0.518410      0.290398   \n",
              "std        1.293303      0.246840      0.266205      0.269849      0.186313   \n",
              "min        0.000000      0.250002      0.000000      0.007756      0.000000   \n",
              "25%        1.000000      0.624472      0.203479      0.280977      0.151428   \n",
              "50%        3.000000      0.952210      0.420315      0.515345      0.253618   \n",
              "75%        3.000000      0.992773      0.661577      0.743337      0.398654   \n",
              "max        4.000000      0.999319      0.985489      1.000000      0.957576   \n",
              "\n",
              "               YMax  \n",
              "count  11913.000000  \n",
              "mean       0.392768  \n",
              "std        0.224256  \n",
              "min        0.021886  \n",
              "25%        0.218891  \n",
              "50%        0.342616  \n",
              "75%        0.517987  \n",
              "max        1.000000  "
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              "      <th></th>\n",
              "      <th>LabelName</th>\n",
              "      <th>Conf</th>\n",
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              "      <th>YMax</th>\n",
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              "      <th>count</th>\n",
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              "      <th>mean</th>\n",
              "      <td>2.216150</td>\n",
              "      <td>0.803083</td>\n",
              "      <td>0.439769</td>\n",
              "      <td>0.518410</td>\n",
              "      <td>0.290398</td>\n",
              "      <td>0.392768</td>\n",
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              "      <td>0.269849</td>\n",
              "      <td>0.186313</td>\n",
              "      <td>0.224256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250002</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007756</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.021886</td>\n",
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              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.624472</td>\n",
              "      <td>0.203479</td>\n",
              "      <td>0.280977</td>\n",
              "      <td>0.151428</td>\n",
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              "      <td>0.952210</td>\n",
              "      <td>0.420315</td>\n",
              "      <td>0.515345</td>\n",
              "      <td>0.253618</td>\n",
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              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.992773</td>\n",
              "      <td>0.661577</td>\n",
              "      <td>0.743337</td>\n",
              "      <td>0.398654</td>\n",
              "      <td>0.517987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.999319</td>\n",
              "      <td>0.985489</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.957576</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62bce66e-2de1-4a93-a3cd-46f07f888027')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-62bce66e-2de1-4a93-a3cd-46f07f888027 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-62bce66e-2de1-4a93-a3cd-46f07f888027');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "solutions_site_df.to_csv(\"solutions/faster_rcnn_lr_scheduler_more_aug.csv\",\n",
        "                         sep=';', index=False)"
      ],
      "metadata": {
        "id": "Fjf2y6FGYGKe"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Проверка предсказания"
      ],
      "metadata": {
        "id": "VOjmhXw6YS2T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"solutions/faster_rcnn_lr_scheduler_more_aug.csv\"\n",
        "\n",
        "df = pd.read_csv(path, sep=';')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vU07QhlnYR2i",
        "outputId": "41f85898-38df-412c-bab5-be00300c4f06"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "\n",
              "       YMin      YMax  \n",
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              "2  0.527978  0.679826  \n",
              "3  0.416114  0.457088  \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-09adffa2-0a37-47f3-9425-0b6d4add8f01 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-09adffa2-0a37-47f3-9425-0b6d4add8f01');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    }
  ]
}