YOLOV1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "YOLOV1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO2AaAoyH8fWfWwfttfOnuG",
      "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/mohripan/Object-Detection/blob/main/YOLOV1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7xBd-TyuSfX",
        "outputId": "27576f40-f391-44d4-dc32-5ee7125ad1c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Object-Detection'...\n",
            "remote: Enumerating objects: 38, done.\u001b[K\n",
            "remote: Counting objects: 100% (38/38), done.\u001b[K\n",
            "remote: Compressing objects: 100% (28/28), done.\u001b[K\n",
            "remote: Total 38 (delta 23), reused 17 (delta 8), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (38/38), done.\n",
            "\u001b[K     |████████████████████████████████| 116 kB 7.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 60.9 MB 1.6 MB/s \n",
            "\u001b[?25hLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: qudida in /usr/local/lib/python3.7/dist-packages (0.0.4)\n",
            "Requirement already satisfied: scikit-learn>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from qudida) (1.0.2)\n",
            "Requirement already satisfied: numpy>=0.18.0 in /usr/local/lib/python3.7/dist-packages (from qudida) (1.21.6)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from qudida) (4.1.1)\n",
            "Requirement already satisfied: opencv-python-headless>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from qudida) (4.6.0.66)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->qudida) (1.7.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->qudida) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->qudida) (3.1.0)\n",
            "/content/Object-Detection\n",
            "Downloading pascalvoc-yolo.zip to /content/Object-Detection\n",
            "100% 4.30G/4.31G [00:45<00:00, 114MB/s]\n",
            "100% 4.31G/4.31G [00:45<00:00, 103MB/s]\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/mohripan/Object-Detection.git\n",
        "!pip install --upgrade --force-reinstall --no-deps -q -U albumentations\n",
        "!pip install --upgrade --force-reinstall --no-deps -q -U opencv-python\n",
        "!pip install qudida\n",
        "%cd Object-Detection\n",
        "\n",
        "!pip install -q kaggle\n",
        "!mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle datasets download -d aladdinpersson/pascalvoc-yolo"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import os\n",
        "import random\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from utils import (\n",
        "    intersection_over_union,\n",
        "    non_max_suppression,\n",
        "    mean_average_precision,\n",
        "    cellboxes_to_boxes,\n",
        "    get_bboxes,\n",
        "    plot_image,\n",
        "    save_checkpoint,\n",
        "    load_checkpoint\n",
        ")\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.transforms.functional as FT\n",
        "from tqdm import tqdm"
      ],
      "metadata": {
        "id": "pb56C_iRuUNl"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "architecture_config = [\n",
        "    (7, 64, 2, 3),\n",
        "    \"M\",\n",
        "    (3, 192, 1, 1),\n",
        "    \"M\",\n",
        "    (1, 128, 1, 0),\n",
        "    (3, 256, 1, 1),\n",
        "    (1, 256, 1, 0),\n",
        "    (3, 512, 1, 1),\n",
        "    \"M\",\n",
        "    [(1, 256, 1, 0), (3, 512, 1, 1), 4],\n",
        "    (1, 512, 1, 0),\n",
        "    (3, 1024, 1, 1),\n",
        "    \"M\",\n",
        "    [(1, 512, 1, 0), (3, 1024, 1, 1), 2],\n",
        "    (3, 1024, 1, 1),\n",
        "    (3, 1024, 2, 1),\n",
        "    (3, 1024, 1, 1),\n",
        "    (3, 1024, 1, 1),\n",
        "]"
      ],
      "metadata": {
        "id": "W2jWeTz-uv-H"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CNNBlock(nn.Module):\n",
        "  def __init__(self, in_channels, out_channels, **kwargs):\n",
        "    super(CNNBlock, self).__init__()\n",
        "    self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)\n",
        "    self.batchnorm = nn.BatchNorm2d(out_channels)\n",
        "    self.leakyrelu = nn.LeakyReLU(0.1)\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.leakyrelu(self.batchnorm(self.conv(x)))\n",
        "\n",
        "class Yolov1(nn.Module):\n",
        "  def __init__(self, in_channels=3, **kwargs):\n",
        "    super(Yolov1, self).__init__()\n",
        "    self.architecture = architecture_config\n",
        "    self.in_channels = in_channels\n",
        "    self.darknet = self._create_conv_layers(self.architecture)\n",
        "    self.fcs = self._create_fcs(**kwargs)\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = self.darknet(x)\n",
        "    return self.fcs(torch.flatten(x, start_dim=1))\n",
        "\n",
        "  def _create_conv_layers(self, architecture):\n",
        "    layers = []\n",
        "    in_channels = self.in_channels\n",
        "\n",
        "    for x in architecture:\n",
        "      if type(x) == tuple:\n",
        "        layers += [CNNBlock(in_channels, x[1], kernel_size=x[0], stride=x[2], padding=x[3])]\n",
        "        in_channels = x[1]\n",
        "\n",
        "      elif type(x) == str:\n",
        "        layers += [nn.MaxPool2d(2, 2)]\n",
        "\n",
        "      elif type(x) == list:\n",
        "        conv1 = x[0]\n",
        "        conv2 = x[1]\n",
        "        num_repeats = x[2]\n",
        "\n",
        "        for _ in range(num_repeats):\n",
        "          layers += [CNNBlock(in_channels, conv1[1], kernel_size=conv1[0], stride=conv1[2], padding=conv1[3])]\n",
        "\n",
        "          layers += [CNNBlock(conv1[1], conv2[1], kernel_size=conv2[0], stride=conv2[2], padding=conv2[3])]\n",
        "\n",
        "          in_channels = conv2[1]\n",
        "        \n",
        "    return nn.Sequential(*layers)\n",
        "\n",
        "  def _create_fcs(self, split_size, num_boxes, num_classes):\n",
        "    S, B, C = split_size, num_boxes, num_classes\n",
        "    return nn.Sequential(nn.Flatten(),\n",
        "                         nn.Linear(1024*S*S, 512),\n",
        "                         nn.Dropout(0.0),\n",
        "                         nn.LeakyReLU(0.1),\n",
        "                         nn.Linear(512, S*S*(C+B*5)),)"
      ],
      "metadata": {
        "id": "w2rsiEA3Mj5T"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test(S=7, B=2, C=20):\n",
        "  model = Yolov1(split_size=S, num_boxes=B, num_classes=C)\n",
        "  x = torch.randn((2, 3, 448, 448))\n",
        "  print(model(x).shape)"
      ],
      "metadata": {
        "id": "lXk6quVHPztf"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class YoloLoss(nn.Module):\n",
        "  def __init__(self, S=7, B=2, C=20):\n",
        "    super(YoloLoss, self).__init__()\n",
        "    self.mse = nn.MSELoss(reduction='sum')\n",
        "    self.S = S\n",
        "    self.B = B\n",
        "    self.C = C\n",
        "    self.lambda_noobj = 0.5\n",
        "    self.lambda_coord = 5\n",
        "\n",
        "  def forward(self, predictions, target):\n",
        "    predictions = predictions.reshape(-1, self.S, self.S, self.C + self.B*5)\n",
        "    iou_b1 = intersection_over_union(predictions[..., 21:25], target[..., 21:25])\n",
        "    iou_b2 = intersection_over_union(predictions[..., 26:30], target[..., 26:30])\n",
        "    ious = torch.cat([iou_b1.unsqueeze(0), iou_b2.unsqueeze(0)], dim=0)\n",
        "    iou_maxes, best_box = torch.max(ious, dim=0)\n",
        "    exists_box = target[..., 20].unsqueeze(3)\n",
        "\n",
        "    box_predictions = exists_box * (best_box*predictions[..., 26:30] + (1-best_box)*predictions[..., 21:25])\n",
        "\n",
        "    box_targets = exists_box * target[..., 21:25]\n",
        "    box_predictions[..., 2:4] = torch.sign(\n",
        "        box_predictions[..., 2:4]) * torch.sqrt(torch.abs(box_predictions[..., 2:4] + 1e-6))\n",
        "\n",
        "    box_targets[..., 2:4] = torch.sqrt(box_targets[..., 2:4])\n",
        "\n",
        "    box_loss = self.mse(torch.flatten(box_predictions, end_dim=-2), torch.flatten(box_targets, end_dim=-2))\n",
        "\n",
        "    pred_box = (best_box*predictions[..., 25:26] + (1-best_box) * predictions[..., 20:21])\n",
        "    object_loss = self.mse(torch.flatten(exists_box*pred_box), torch.flatten(exists_box*target[..., 20:21]))\n",
        "\n",
        "    no_object_loss = self.mse(torch.flatten((1-exists_box)*predictions[..., 20:21], start_dim=1),\n",
        "                              torch.flatten((1-exists_box)*target[..., 20:21], start_dim=1))\n",
        "\n",
        "    no_object_loss += self.mse(torch.flatten((1-exists_box)*predictions[..., 25:26], start_dim=1),\n",
        "                               torch.flatten((1-exists_box)*target[..., 20:21], start_dim=1))\n",
        "\n",
        "    class_loss = self.mse(torch.flatten(exists_box*predictions[..., :20], end_dim=-2),\n",
        "                          torch.flatten(exists_box*target[..., :20], end_dim=-2))\n",
        "\n",
        "    loss = (self.lambda_coord * box_loss + object_loss + self.lambda_noobj * no_object_loss + class_loss)\n",
        "\n",
        "    return loss"
      ],
      "metadata": {
        "id": "6QV1yzabQJmD"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.makedirs('dataset', exist_ok=True)"
      ],
      "metadata": {
        "id": "KS55we0nQKt4"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip \"pascalvoc-yolo.zip\" -d \"dataset\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbRfXu4kSo-D",
        "outputId": "a91153e8-2b5b-4661-bdd2-9cf395a13273"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mOutput streaming akan dipotong hingga 5000 baris terakhir.\u001b[0m\n",
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            "  inflating: dataset/test.csv        \n",
            "  inflating: dataset/train.csv       \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class VOCDataset(torch.utils.data.Dataset):\n",
        "    def __init__(\n",
        "        self, csv_file, img_dir, label_dir, S=7, B=2, C=20, transform=None,\n",
        "    ):\n",
        "        self.annotations = pd.read_csv(csv_file)\n",
        "        self.img_dir = img_dir\n",
        "        self.label_dir = label_dir\n",
        "        self.transform = transform\n",
        "        self.S = S\n",
        "        self.B = B\n",
        "        self.C = C\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.annotations)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        label_path = os.path.join(self.label_dir, self.annotations.iloc[index, 1])\n",
        "        boxes = []\n",
        "        with open(label_path) as f:\n",
        "            for label in f.readlines():\n",
        "                class_label, x, y, width, height = [\n",
        "                    float(x) if float(x) != int(float(x)) else int(x)\n",
        "                    for x in label.replace(\"\\n\", \"\").split()\n",
        "                ]\n",
        "\n",
        "                boxes.append([class_label, x, y, width, height])\n",
        "\n",
        "        img_path = os.path.join(self.img_dir, self.annotations.iloc[index, 0])\n",
        "        image = Image.open(img_path)\n",
        "        boxes = torch.tensor(boxes)\n",
        "\n",
        "        if self.transform:\n",
        "            # image = self.transform(image)\n",
        "            image, boxes = self.transform(image, boxes)\n",
        "\n",
        "        # Convert To Cells\n",
        "        label_matrix = torch.zeros((self.S, self.S, self.C + 5 * self.B))\n",
        "        for box in boxes:\n",
        "            class_label, x, y, width, height = box.tolist()\n",
        "            class_label = int(class_label)\n",
        "\n",
        "            # i,j represents the cell row and cell column\n",
        "            i, j = int(self.S * y), int(self.S * x)\n",
        "            x_cell, y_cell = self.S * x - j, self.S * y - i\n",
        "\n",
        "            \"\"\"\n",
        "            Calculating the width and height of cell of bounding box,\n",
        "            relative to the cell is done by the following, with\n",
        "            width as the example:\n",
        "            \n",
        "            width_pixels = (width*self.image_width)\n",
        "            cell_pixels = (self.image_width)\n",
        "            \n",
        "            Then to find the width relative to the cell is simply:\n",
        "            width_pixels/cell_pixels, simplification leads to the\n",
        "            formulas below.\n",
        "            \"\"\"\n",
        "            width_cell, height_cell = (\n",
        "                width * self.S,\n",
        "                height * self.S,\n",
        "            )\n",
        "\n",
        "            # If no object already found for specific cell i,j\n",
        "            # Note: This means we restrict to ONE object\n",
        "            # per cell!\n",
        "            if label_matrix[i, j, 20] == 0:\n",
        "                # Set that there exists an object\n",
        "                label_matrix[i, j, 20] = 1\n",
        "\n",
        "                # Box coordinates\n",
        "                box_coordinates = torch.tensor(\n",
        "                    [x_cell, y_cell, width_cell, height_cell]\n",
        "                )\n",
        "\n",
        "                label_matrix[i, j, 21:25] = box_coordinates\n",
        "\n",
        "                # Set one hot encoding for class_label\n",
        "                label_matrix[i, j, class_label] = 1\n",
        "\n",
        "        return image, label_matrix"
      ],
      "metadata": {
        "id": "-MeuF97jS8xn"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "seed = 123\n",
        "torch.manual_seed(seed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UqSWHJuiXR1v",
        "outputId": "d5ee32e3-0f4e-494a-c786-278b6fd12a24"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f0cfa07a7b0>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LEARNING_RATE = 2e-5\n",
        "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "BATCH_SIZE = 16\n",
        "WEIGHT_DECAY = 0\n",
        "EPOCHS = 100\n",
        "NUM_WORKERS = 2\n",
        "PIN_MEMORY = True\n",
        "LOAD_MODEL = False\n",
        "LOAD_MODEL_FILE = 'overfit.pth.tar'\n",
        "IMG_DIR = \"dataset/images\"\n",
        "LABEL_DIR = \"dataset/labels\""
      ],
      "metadata": {
        "id": "xyaLrtR7bFgK"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Compose:\n",
        "  def __init__(self, transforms):\n",
        "    self.transforms = transforms\n",
        "\n",
        "  def __call__(self, img, bboxes):\n",
        "    for t in self.transforms:\n",
        "      img, bboxes = t(img), bboxes\n",
        "\n",
        "    return img, bboxes"
      ],
      "metadata": {
        "id": "cbl0eq1NbtnX"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transform = Compose([transforms.Resize((448, 448)), transforms.ToTensor()])\n",
        "\n",
        "def train_fn(train_loader, model, optimizer, loss_fn):\n",
        "  loop = tqdm(train_loader, leave=True)\n",
        "  mean_loss = []\n",
        "\n",
        "  for batch_idx, (x, y) in enumerate(loop):\n",
        "    x, y = x.to(DEVICE), y.to(DEVICE)\n",
        "    out = model(x)\n",
        "    loss = loss_fn(out, y)\n",
        "    mean_loss.append(loss.item())\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    loop.set_postfix(loss = loss.item())\n",
        "\n",
        "  print(f'Mean loss was {sum(mean_loss)/len(mean_loss)}')"
      ],
      "metadata": {
        "id": "VCS17IPZcDWl"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Yolov1(split_size=7, num_boxes=2, num_classes=20).to(DEVICE)\n",
        "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY)\n",
        "loss_fn = YoloLoss()\n",
        "\n",
        "if LOAD_MODEL:\n",
        "  load_checkpoint(torch.load(LOAD_MODEL_FILE), model, optimizer)\n",
        "\n",
        "train_dataset = VOCDataset('dataset/100examples.csv', transform=transform, img_dir=IMG_DIR, label_dir=LABEL_DIR)\n",
        "test_dataset = VOCDataset('dataset/test.csv', transform=transform, img_dir=IMG_DIR, label_dir=LABEL_DIR)\n",
        "train_loader = DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE,\n",
        "                          num_workers=NUM_WORKERS, pin_memory=PIN_MEMORY, shuffle=True,\n",
        "                          drop_last=False,)\n",
        "\n",
        "test_loader = DataLoader(dataset=test_dataset, batch_size=BATCH_SIZE,\n",
        "                          num_workers=NUM_WORKERS, pin_memory=PIN_MEMORY, shuffle=False,\n",
        "                          drop_last=True,)\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "  pred_boxes, target_boxes = get_bboxes(train_loader, model, iou_threshold=0.5, threshold=0.4)\n",
        "  mean_avg_prec = mean_average_precision(pred_boxes, target_boxes, iou_threshold=0.5, box_format='midpoint')\n",
        "  print(f'Train mAP: {mean_avg_prec}')\n",
        "\n",
        "  if mean_avg_prec > 0.9:\n",
        "    checkpoint = {\n",
        "        'state_dict': model.state_dict(),\n",
        "        'optimizer': optimizer.state_dict()\n",
        "    }\n",
        "    save_checkpoint(checkpoint, filename=LOAD_MODEL_FILE)\n",
        "  train_fn(train_loader, model, optimizer, loss_fn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PnFeKsctcsmz",
        "outputId": "08a69ec5-eb1e-425e-f074-b03d9a2056e0"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.33it/s, loss=556]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 852.5997401646206\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.30it/s, loss=297]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 523.8743678501675\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.27it/s, loss=146]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 398.4775129045759\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.27it/s, loss=102]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 323.0999548775809\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.25it/s, loss=102]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 274.40772029331754\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 2.0263580154278316e-05\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.23it/s, loss=119]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 242.6114959716797\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.00011261255713179708\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.24it/s, loss=121]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 214.16857038225447\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.000508007884491235\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.26it/s, loss=104]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 183.40917750767298\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.0038753896951675415\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.28it/s, loss=81.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 172.39027513776506\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.08229781687259674\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.27it/s, loss=66.8]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 153.6772918701172\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.1922488808631897\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=105]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 138.6117455618722\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.20315945148468018\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.32it/s, loss=37.2]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 124.3910893031529\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.3024339973926544\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.36it/s, loss=72.7]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 112.51187787737165\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.4563046097755432\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=43.2]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 107.6976432800293\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.5068684220314026\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.33it/s, loss=98.4]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 100.25116075788226\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.5616584420204163\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.35it/s, loss=39.5]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 88.82086508614677\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.6188327074050903\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.33it/s, loss=38.7]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 86.29589080810547\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.6500359177589417\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.33it/s, loss=43.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 83.10909652709961\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.6714727878570557\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=41.9]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 73.16503361293248\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.6740505695343018\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.35it/s, loss=58.4]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 73.58949225289481\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.6663488149642944\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.35it/s, loss=40.3]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 68.14055960518974\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.7392643690109253\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=43.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 70.22146987915039\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.7416008710861206\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=22.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 66.56343759809222\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.7735408544540405\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.32it/s, loss=36.7]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 62.97790036882673\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8208742141723633\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=26.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 56.58053943089077\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8376132845878601\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.33it/s, loss=61]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 59.1559944152832\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8472708463668823\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.29it/s, loss=32.4]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 58.523053305489675\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8603472709655762\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.32it/s, loss=31.3]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 61.1804438999721\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.802524745464325\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.29it/s, loss=47.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 54.789484296526226\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8161088824272156\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.32it/s, loss=25.2]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 59.465653555733816\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8663609623908997\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=18.7]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 54.96195902143206\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.7971734404563904\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=24.8]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 59.06475748334612\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8364549875259399\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=18.9]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 56.8517221723284\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8393022418022156\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.33it/s, loss=24.3]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 55.74847984313965\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.864249587059021\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.32it/s, loss=19.5]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 51.69491359165737\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8617361783981323\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.28it/s, loss=36]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 48.91440636771066\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8490544557571411\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.33it/s, loss=18.7]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 46.01242365155901\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8620489239692688\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=20.2]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 40.79115949358259\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9179242849349976\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.28it/s, loss=14.9]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 47.44698510851179\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8736512064933777\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.36it/s, loss=19.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 44.32935578482492\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.906976580619812\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.10it/s, loss=21.8]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 45.27427346365793\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8148110508918762\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=31.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 57.54457119532994\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8785971403121948\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.30it/s, loss=20.5]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 55.15271513802664\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8952215909957886\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.33it/s, loss=20]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 47.65112440926688\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.877815842628479\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.32it/s, loss=25]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 41.46887697492327\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8453782200813293\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.30it/s, loss=25]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 41.360215868268696\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8632453083992004\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.32it/s, loss=17.3]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 43.997300556727815\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8205443620681763\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.33it/s, loss=43.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 42.500589098249165\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9103277921676636\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=23.2]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 42.397089276994976\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.7964257001876831\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.33it/s, loss=21.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 37.73100226266043\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8971430063247681\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.33it/s, loss=51.2]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 41.70476967947824\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.7797256708145142\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.37it/s, loss=18.6]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 36.99954032897949\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8521256446838379\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.35it/s, loss=22.4]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 41.45920671735491\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.851527988910675\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.32it/s, loss=23.9]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 37.928412028721404\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.821895956993103\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.35it/s, loss=36.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 39.06941141401018\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8495904207229614\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.33it/s, loss=30.8]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 37.74739919389997\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8528609275817871\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=18.6]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 37.61595072065081\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.7870270013809204\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.27it/s, loss=26.6]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 42.97538757324219\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8193166851997375\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=21.3]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 39.16006851196289\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8443026542663574\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.09it/s, loss=23.4]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 42.676156997680664\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.7807797193527222\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.28it/s, loss=26.4]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 45.70592825753348\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8073571920394897\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=19.5]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 40.804167066301616\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8544416427612305\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=28.9]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 39.27867916652134\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8978473544120789\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.32it/s, loss=20]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 34.330804279872346\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8653362989425659\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.35it/s, loss=19.9]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 31.32210077558245\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9036968350410461\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.24it/s, loss=13.8]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 26.89523615155901\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9428518414497375\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=15]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 28.279902594430105\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9004298448562622\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.27it/s, loss=13.2]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 27.410683223179408\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8386114835739136\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.36it/s, loss=16]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 25.8460202898298\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8504179120063782\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=18.9]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 24.197655541556223\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.929098904132843\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.21it/s, loss=58.4]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 28.340334756033762\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8928300738334656\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=18.2]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 26.73473276410784\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.906460165977478\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.30it/s, loss=19.2]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 24.75862775530134\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8985583186149597\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=18.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 25.426317623683385\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.896600067615509\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=34.9]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 26.147653034755162\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8568060994148254\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.10it/s, loss=15.5]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 25.483170918055944\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.887326717376709\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.37it/s, loss=17.7]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 25.139916283743723\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9160499572753906\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.35it/s, loss=7.62]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 23.590275491986954\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9216512441635132\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.30it/s, loss=10.4]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 23.316431318010604\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.868026614189148\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.37it/s, loss=9.75]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 24.097273417881556\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9298323392868042\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.20it/s, loss=12]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 21.979954719543457\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8544055223464966\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=15.3]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 23.003852163042342\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.891767144203186\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.35it/s, loss=10.5]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 22.367677279881068\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8953609466552734\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.29it/s, loss=29]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 24.017008917672293\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8839254379272461\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=17.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 25.4558653150286\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8684943318367004\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=9.88]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 24.676071439470565\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8816110491752625\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.33it/s, loss=11.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 23.636948040553502\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8894698023796082\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=15.8]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 23.3288790839059\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8748183250427246\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.31it/s, loss=6.16]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 20.03856427328927\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9308870434761047\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.23it/s, loss=15.9]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 17.80614675794329\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9340279698371887\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.33it/s, loss=21.9]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 19.055204936436244\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9119133949279785\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.12it/s, loss=6.64]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 16.79593631199428\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9071748852729797\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=13.1]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 19.33187689099993\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8747366666793823\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:02<00:00,  2.34it/s, loss=9.39]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 19.673500061035156\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9175971150398254\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.23it/s, loss=8.28]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 17.17374610900879\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9110527038574219\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.29it/s, loss=9.3]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 17.728978974478586\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9553540945053101\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.25it/s, loss=8.31]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 19.87963158743722\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.9350760579109192\n",
            "=> Saving checkpoint\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.24it/s, loss=16]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 19.107013974870956\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8845785856246948\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.29it/s, loss=19.6]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 20.225855146135604\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train mAP: 0.8991358876228333\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 7/7 [00:03<00:00,  2.30it/s, loss=11.5]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean loss was 21.484423228672572\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "0YNeBRcIeNgi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}