main_with_visual_featuremap.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z8S2xZBws1Oa",
        "outputId": "1364a536-6125-48b6-9275-aa8778c74a8f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Successfully built efficientnet-pytorch pretrainedmodels\n",
            "Installing collected packages: munch, efficientnet-pytorch, timm, pretrainedmodels, segmentation_models_pytorch\n",
            "Successfully installed efficientnet-pytorch-0.7.1 munch-4.0.0 pretrainedmodels-0.7.4 segmentation_models_pytorch-0.3.3 timm-0.9.2\n"
          ]
        }
      ],
      "source": [
        "!pip install monai\n",
        "!pip install torchmetrics\n",
        "!pip install segmentation_models_pytorch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "c-5JXV1HvZ39"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import pickle\n",
        "import segmentation_models_pytorch as smp\n",
        "from monai.data import DataLoader, Dataset\n",
        "from monai.utils import set_determinism\n",
        "import torch\n",
        "from torchmetrics import JaccardIndex\n",
        "from tqdm import tqdm\n",
        "from transformers import SegformerConfig, SegformerForSemanticSegmentation\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import warnings\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "warnings.filterwarnings('ignore')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXjdEg6ctFED",
        "outputId": "f4d5fc64-1328-4f30-9811-44a6bffdd8bb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# mount google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/My\\ Drive/Stroke_Segmentation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQAuCgZn9tFB",
        "outputId": "30c37e17-70f5-4db1-a8ce-877dffe734f9"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/Stroke_Segmentation\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "2gZBTvFBt0Bx"
      },
      "outputs": [],
      "source": [
        "root_dir = \"/content/drive/MyDrive/Stroke_Segmentation\"\n",
        "# root_dir = os.path.join(root_dir, \"ISLES-2022\")\n",
        "# os.path.exists(root_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "8lTNCNSWvwnY"
      },
      "outputs": [],
      "source": [
        "# import custom model\n",
        "from custom_model import CustomModel"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "root_dir"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "lB-hUPkv_2Wc",
        "outputId": "fe351504-1ace-4a52-c3f2-871cd057141a"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/MyDrive/Stroke_Segmentation'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4dm-dcBvg3b"
      },
      "source": [
        "### Load preprocessed data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "jP5jyHOet32s"
      },
      "outputs": [],
      "source": [
        "# make sure those files exist before running this cell\n",
        "with open(os.path.join(root_dir, 'kfold_splits.pkl'), 'rb') as f:\n",
        "    folds = pickle.load(f)\n",
        "with open(os.path.join(root_dir, 'test_set.pkl'), 'rb') as f:\n",
        "    test_set = pickle.load(f)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WZ8KE32hs1Oa"
      },
      "outputs": [],
      "source": [
        "# gpu = \"cuda\"\n",
        "# loss = \"Jaccard\"\n",
        "# model_type = \"resnet\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kiq260_Ks1Oa",
        "outputId": "224692ea-8051-4066-92ad-587bf8b64259"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([1, 64, 32, 32])\n",
            "torch.Size([1, 128, 16, 16])\n",
            "torch.Size([1, 256, 8, 8])\n",
            "torch.Size([1, 512, 4, 4])\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "torch.Size([1, 256, 64, 64])"
            ]
          },
          "execution_count": 152,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "# x = torch.randn(1, 3, 128, 128)\n",
        "# model = CustomModel(\n",
        "#         channels = [64, 128, 256, 512],\n",
        "#         scale = [4, 2, 2, 2],\n",
        "#         num_blocks = [2, 2, 2, 2],\n",
        "#         num_heads = [2, 4, 8, 16],\n",
        "#         mlp_hidden = [256, 512, 1024, 2048],\n",
        "#         dropout = 0.0,                     # change to dropout rate=0.2\n",
        "#         decoder_hidden = 256,\n",
        "#         attention=True, # whether to use attention gate\n",
        "#     )\n",
        "# l =model.encoder(x)\n",
        "# for i in range(len(l)):\n",
        "#     print(l[i].shape)\n",
        "# l.reverse()\n",
        "# model.decoder(l).shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "px_2FfUes1Ob"
      },
      "outputs": [],
      "source": [
        "# if gpu == \"cuda\":\n",
        "#     device = torch.device(\"cuda:0\")\n",
        "\n",
        "# if loss == \"Dice\":\n",
        "#     loss_function = smp.losses.DiceLoss(mode=\"binary\", from_logits=False)\n",
        "# elif loss == \"Jaccard\":\n",
        "#     loss_function = smp.losses.JaccardLoss(mode=\"binary\", from_logits=False)\n",
        "\n",
        "# metric = JaccardIndex(task=\"binary\").to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vqjaWAbvm3Y"
      },
      "source": [
        "### Define training function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "oK90Sg0Gs1Ob"
      },
      "outputs": [],
      "source": [
        "def get_model(model_type, device, attention_gate_flag=False):\n",
        "    if model_type == \"resnet\":\n",
        "        # Create the UNet model with the specified backbone\n",
        "        model = smp.Unet(\n",
        "            encoder_name=\"resnet34\",\n",
        "            in_channels=3,\n",
        "            classes=1,\n",
        "            activation=\"sigmoid\",\n",
        "            encoder_weights=None\n",
        "        ).to(device)\n",
        "    elif model_type == \"unet\":\n",
        "        model = torch.hub.load('mateuszbuda/brain-segmentation-pytorch', 'unet',\n",
        "        in_channels=3, out_channels=1, init_features=32, pretrained=False).to(device)\n",
        "    elif model_type == \"segformer\":\n",
        "        config = SegformerConfig(num_labels=1)\n",
        "        model = SegformerForSemanticSegmentation.from_pretrained(\"nvidia/segformer-b0-finetuned-ade-512-512\", num_labels=1, ignore_mismatched_sizes=True).to(device)\n",
        "    elif model_type == \"custom\":\n",
        "        model = CustomModel(\n",
        "            channels = [64, 128, 256, 512],\n",
        "            scale = [4, 2, 2, 2],\n",
        "            num_blocks = [2, 2, 2, 2],\n",
        "            num_heads = [2, 4, 8, 16],\n",
        "            mlp_hidden = [256, 512, 1024, 2048],\n",
        "            dropout = 0.2,                     # change to dropout rate=0.2\n",
        "            decoder_hidden = 256,\n",
        "            attention=attention_gate_flag, # whether to use attention gate\n",
        "        ).to(device)\n",
        "\n",
        "    return model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "yYt7dUZJs1Ob"
      },
      "outputs": [],
      "source": [
        "def count_parameters(model):\n",
        "    total_params = sum(p.numel() for p in model.parameters())\n",
        "    # trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "    print(f\"Total number of parameters: {total_params}\")\n",
        "    # print(f\"Number of trainable parameters: {trainable_params}\")\n",
        "    return total_params\n",
        "# count_parameters(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "DVFG_PZhs1Oc"
      },
      "outputs": [],
      "source": [
        "def add_model_par(df_model_par, model_type, parameters_num):\n",
        "    \"\"\"\n",
        "    df_model_par: dataframe to save model parameters\n",
        "    model_type: resnet, unet, segformer, custom\n",
        "    parameters_num: number of parameters\n",
        "    \"\"\"\n",
        "    next_index = len(df_model_par)\n",
        "    df_model_par.loc[next_index] = [model_type, parameters_num]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "BlJ7Xny8s1Oc"
      },
      "outputs": [],
      "source": [
        "def train_model(model_type, df_model_par, out_dir, loss_function, metric, device, lr=1e-4, attention_gate_flag=False):\n",
        "    \"\"\"\n",
        "    model_type: resnet, unet, segformer, custom\n",
        "    df_model_par: dataframe to save model parameters size\n",
        "    out_dir: root directory of the dataset\n",
        "    loss_function: loss function to evaluate the model, Dice or Jaccard\n",
        "    metric: metric to evaluate the model, JaccardIndex(IoU) or  Dice\n",
        "    device: device to train the model\n",
        "    lr: learning rate\n",
        "    attention_gate_flag: whether to use attention gate in custom model\n",
        "    \"\"\"\n",
        "\n",
        "\n",
        "    model = get_model(model_type, device, attention_gate_flag=attention_gate_flag)\n",
        "    add_model_par(df_model_par, model_type, count_parameters(model))\n",
        "\n",
        "\n",
        "\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
        "    # lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=100)\n",
        "\n",
        "    # use amp to accelerate training\n",
        "    scaler = torch.cuda.amp.GradScaler()\n",
        "    # enable cuDNN benchmark\n",
        "    torch.backends.cudnn.benchmark = True\n",
        "\n",
        "    val_interval = 2\n",
        "    best_metric = -1\n",
        "    best_metric_epoch = -1\n",
        "    epoch_loss_values = []\n",
        "    metric_values = []\n",
        "\n",
        "    num_CV = 5\n",
        "    max_epochs = 100\n",
        "    epoch_per_CV = max_epochs // num_CV\n",
        "\n",
        "    for i in range(num_CV):\n",
        "\n",
        "        print(\"-\" * 10)\n",
        "        print(f\"Fold {i}\")\n",
        "        train_ds = folds[i][\"train\"]\n",
        "        train_loader = DataLoader(train_ds, batch_size=32, shuffle=True, num_workers=4)\n",
        "        val_ds = folds[i][\"val\"]\n",
        "        val_loader = DataLoader(val_ds, batch_size=8, num_workers=4)\n",
        "\n",
        "        if i < num_CV - 1:\n",
        "            start_epoch = i * epoch_per_CV\n",
        "            end_epoch = (i + 1) * epoch_per_CV\n",
        "        else:\n",
        "            start_epoch = i * epoch_per_CV\n",
        "            end_epoch = max_epochs\n",
        "\n",
        "        for epoch in range(start_epoch, end_epoch):\n",
        "            print(\"-\" * 10)\n",
        "            print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
        "\n",
        "            # Training\n",
        "            model.train()\n",
        "            epoch_loss = 0\n",
        "            step = 0\n",
        "\n",
        "            for batch_data in tqdm(train_loader, desc=\"Training\"):\n",
        "                step += 1\n",
        "                inputs, labels = (\n",
        "                    batch_data[\"image\"].to(device),\n",
        "                    batch_data[\"label\"].to(device),\n",
        "                )\n",
        "\n",
        "                optimizer.zero_grad()\n",
        "\n",
        "                with torch.cuda.amp.autocast():\n",
        "                    inputs.to(torch.float32)\n",
        "                    outputs = model(inputs)\n",
        "                    if model_type == \"segformer\":\n",
        "                        outputs = torch.nn.functional.sigmoid(torch.nn.functional.interpolate(outputs.logits,128,mode=\"bilinear\",align_corners=False))\n",
        "                    loss = loss_function(outputs, labels)\n",
        "\n",
        "                if torch.isnan(loss) or torch.isnan(torch.flatten(outputs)).any().item():\n",
        "                    print(outputs)\n",
        "                    print(torch.isnan(torch.flatten(outputs)).any())\n",
        "                    print(labels)\n",
        "                    print(loss)\n",
        "\n",
        "                scaler.scale(loss).backward()\n",
        "                scaler.step(optimizer)\n",
        "                scaler.update()\n",
        "\n",
        "                epoch_loss += loss.item()\n",
        "\n",
        "            epoch_loss /= step\n",
        "            epoch_loss_values.append(epoch_loss)\n",
        "            print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
        "\n",
        "            # Validation\n",
        "            if (epoch + 1) % val_interval == 0:\n",
        "                model.eval()\n",
        "                val_iou = 0.0\n",
        "                num_batches = len(val_loader)\n",
        "\n",
        "                with torch.no_grad():\n",
        "                    for val_data in tqdm(val_loader, desc=\"Validation\"):\n",
        "                        val_inputs, val_labels = (\n",
        "                            val_data[\"image\"].to(device),\n",
        "                            val_data[\"label\"].to(device),\n",
        "                        )\n",
        "\n",
        "                        val_outputs = model(val_inputs)\n",
        "                        if model_type == \"segformer\":\n",
        "                            val_outputs = torch.nn.functional.sigmoid(torch.nn.functional.interpolate(val_outputs.logits,128,mode=\"bilinear\",align_corners=False))\n",
        "\n",
        "                        # Calculate IoU\n",
        "                        iou = metric(val_outputs, val_labels)\n",
        "                        val_iou += iou.item()\n",
        "\n",
        "                # Calculate average IoU across all batches\n",
        "                average_val_iou = val_iou / num_batches\n",
        "                metric_values.append(average_val_iou)\n",
        "\n",
        "                if average_val_iou > best_metric:\n",
        "                    best_metric = average_val_iou\n",
        "                    best_metric_epoch = epoch + 1\n",
        "                    torch.save(model.state_dict(), os.path.join(out_dir, \"best_metric_model.pth\"))\n",
        "                    print(\"saved new best metric model\")\n",
        "\n",
        "                print(\n",
        "                    f\"current epoch: {epoch + 1} current mean IoU: {average_val_iou:.4f}\"\n",
        "                    f\"\\nbest mean IoU: {best_metric:.4f} \"\n",
        "                    f\"at epoch: {best_metric_epoch}\"\n",
        "                )\n",
        "    return epoch_loss_values, metric_values\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "RBeRthDRJXum"
      },
      "outputs": [],
      "source": [
        "def plot_train_curve(epoch_loss_values, metric_values, model_type, out_dir):\n",
        "\n",
        "    epoch_loss_array = np.array(epoch_loss_values)\n",
        "    metric_array = np.array(metric_values)\n",
        "    np.savetxt(os.path.join(out_dir, 'epoch_loss_array.csv'), epoch_loss_array, delimiter=',')\n",
        "    np.savetxt(os.path.join(out_dir, 'metric_array.csv'), metric_array, delimiter=',')\n",
        "\n",
        "\n",
        "    plt.figure(\"train\", (12, 6))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.title(f\"{model_type} Epoch Average Loss\")\n",
        "    x = [i + 1 for i in range(len(epoch_loss_values))]\n",
        "    y = epoch_loss_values\n",
        "    plt.xlabel(\"epoch\")\n",
        "    plt.plot(x, y)\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.title(f\"{model_type} Val Mean IoU \")\n",
        "    x = [2 * (i + 1) for i in range(len(metric_values))]\n",
        "    y = metric_values\n",
        "    plt.xlabel(\"epoch\")\n",
        "    plt.plot(x, y)\n",
        "    plt.savefig(os.path.join(out_dir, \"train_curve.png\"))\n",
        "    plt.show()\n",
        "\n",
        "# plot_train_curve(epoch_loss_values, metric_values, out_dir)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ctpaNbmZZ9QC"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "-ngL3QqpZs0q"
      },
      "outputs": [],
      "source": [
        "gpu = \"cuda\"\n",
        "loss = \"Jaccard\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "FPOmQE31ZiRI"
      },
      "outputs": [],
      "source": [
        "if gpu == \"cuda\":\n",
        "    device = torch.device(\"cuda:0\")\n",
        "\n",
        "if loss == \"Dice\":\n",
        "    loss_function = smp.losses.DiceLoss(mode=\"binary\", from_logits=False)\n",
        "elif loss == \"Jaccard\":\n",
        "    loss_function = smp.losses.JaccardLoss(mode=\"binary\", from_logits=False)\n",
        "\n",
        "metric = JaccardIndex(task=\"binary\").to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lYROBjTjZnK1",
        "outputId": "4ef521ad-cec0-4730-d4cf-d1f29e0f3a57"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(JaccardLoss(), BinaryJaccardIndex())"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "loss_function, metric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i9ABxVKXaEZw",
        "outputId": "782acfb7-2f29-48ad-9559-9e9faa4ddfc3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Stroke_Segmentation/\n",
            "cuda:0\n"
          ]
        }
      ],
      "source": [
        "# dataframe to save model parameters\n",
        "df_model_par = pd.DataFrame(columns=['model_type', 'parameters_num'])\n",
        "print(root_dir)\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "3YegENAAXCQb"
      },
      "outputs": [],
      "source": [
        "model_list = [\"resnet\", \"unet\", \"segformer\", \"custom\"]\n",
        "# model_list = [\"unet\"]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "y-ZAQFZ4gdGY"
      },
      "outputs": [],
      "source": [
        "set_determinism(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "b97d38968aac4574823ab943106d9407",
            "ab27ced82bcb48edae824400356add62",
            "cf1d54d51b8442b9a459e9b537305a21",
            "dfac6e2782064181866c4f1f0b16212c",
            "28802e692b3549b5978ef08861b99b45",
            "2b328b01831541608f24a937436bb084",
            "ab2d2bcb9aad438983c27315ffb87758",
            "f0a12823c6cf4c80bf06a24d6d9a2a44",
            "bb7788f68f794e979acd49c47b2e619e",
            "0ecd1683669e496b8de08aa4ea4dcbda",
            "97ba6ca8baec43af9313494edbec8bd5",
            "172e95d3d2504680aa69d79ec948b268",
            "b8d4c23cefa544a0ae3860a07d815607",
            "b30f9e665a6c45ebb3adeb8d17a67981",
            "7b0fad0ae8754d6b82cebdfd63ee201d",
            "ddf01ff968ec4f609d9a15fea2bdbf44",
            "7490e9fa2c1440fda89a4e4e83abf82e",
            "48cb8a5f562f417284fe63eedc71f8eb",
            "65001f01fe4e4c43ba0cc66a9b5732b7",
            "b16ffa293ef541219cb556eddc977c3c",
            "1bd81a644b72441ba7b1ccc4473f1746",
            "4027d6e698e4452d86987bfcc7e4b155"
          ]
        },
        "id": "KptwqDYgcpG3",
        "outputId": "08606822-0286-4bd1-f0c0-359d0796daec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of parameters: 24436369\n",
            "----------\n",
            "Fold 0\n",
            "----------\n",
            "epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 12.09it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 average loss: 0.9796\n",
            "----------\n",
            "epoch 2/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.95it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 average loss: 0.9664\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 31.51it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 2 current mean IoU: 0.1852\n",
            "best mean IoU: 0.1852 at epoch: 2\n",
            "----------\n",
            "epoch 3/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.28it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 average loss: 0.9506\n",
            "----------\n",
            "epoch 4/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.38it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 average loss: 0.9218\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.98it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 4 current mean IoU: 0.3851\n",
            "best mean IoU: 0.3851 at epoch: 4\n",
            "----------\n",
            "epoch 5/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.34it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 average loss: 0.8272\n",
            "----------\n",
            "epoch 6/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.48it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 6 average loss: 0.6315\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 36.49it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 6 current mean IoU: 0.6194\n",
            "best mean IoU: 0.6194 at epoch: 6\n",
            "----------\n",
            "epoch 7/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.61it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 7 average loss: 0.4753\n",
            "----------\n",
            "epoch 8/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.41it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 8 average loss: 0.3876\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.32it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 8 current mean IoU: 0.6699\n",
            "best mean IoU: 0.6699 at epoch: 8\n",
            "----------\n",
            "epoch 9/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.60it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 9 average loss: 0.3354\n",
            "----------\n",
            "epoch 10/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.33it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 10 average loss: 0.3058\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.57it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 10 current mean IoU: 0.7134\n",
            "best mean IoU: 0.7134 at epoch: 10\n",
            "----------\n",
            "epoch 11/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.74it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 11 average loss: 0.2799\n",
            "----------\n",
            "epoch 12/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.56it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 12 average loss: 0.2636\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 37.42it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 12 current mean IoU: 0.7151\n",
            "best mean IoU: 0.7151 at epoch: 12\n",
            "----------\n",
            "epoch 13/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.00it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 13 average loss: 0.2479\n",
            "----------\n",
            "epoch 14/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.53it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 14 average loss: 0.2404\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.35it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 14 current mean IoU: 0.7280\n",
            "best mean IoU: 0.7280 at epoch: 14\n",
            "----------\n",
            "epoch 15/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.19it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 15 average loss: 0.2296\n",
            "----------\n",
            "epoch 16/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.78it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 16 average loss: 0.2291\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.13it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 16 current mean IoU: 0.7388\n",
            "best mean IoU: 0.7388 at epoch: 16\n",
            "----------\n",
            "epoch 17/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 17 average loss: 0.2149\n",
            "----------\n",
            "epoch 18/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.36it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 18 average loss: 0.2062\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 37.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 18 current mean IoU: 0.7512\n",
            "best mean IoU: 0.7512 at epoch: 18\n",
            "----------\n",
            "epoch 19/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.47it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 19 average loss: 0.2054\n",
            "----------\n",
            "epoch 20/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.11it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 20 average loss: 0.2048\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 39.51it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 20 current mean IoU: 0.7598\n",
            "best mean IoU: 0.7598 at epoch: 20\n",
            "----------\n",
            "Fold 1\n",
            "----------\n",
            "epoch 21/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:06<00:00, 16.08it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 21 average loss: 0.2050\n",
            "----------\n",
            "epoch 22/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.23it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 22 average loss: 0.2098\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 34.71it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 22 current mean IoU: 0.7776\n",
            "best mean IoU: 0.7776 at epoch: 22\n",
            "----------\n",
            "epoch 23/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.98it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 23 average loss: 0.2031\n",
            "----------\n",
            "epoch 24/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.78it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 24 average loss: 0.1927\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.88it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 24 current mean IoU: 0.7775\n",
            "best mean IoU: 0.7776 at epoch: 22\n",
            "----------\n",
            "epoch 25/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.22it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 25 average loss: 0.1921\n",
            "----------\n",
            "epoch 26/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.82it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 26 average loss: 0.1959\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.32it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 26 current mean IoU: 0.7594\n",
            "best mean IoU: 0.7776 at epoch: 22\n",
            "----------\n",
            "epoch 27/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.62it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 27 average loss: 0.1806\n",
            "----------\n",
            "epoch 28/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 28 average loss: 0.1768\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 37.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 28 current mean IoU: 0.7817\n",
            "best mean IoU: 0.7817 at epoch: 28\n",
            "----------\n",
            "epoch 29/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.67it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 29 average loss: 0.1736\n",
            "----------\n",
            "epoch 30/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.61it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 30 average loss: 0.1771\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.07it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 30 current mean IoU: 0.7915\n",
            "best mean IoU: 0.7915 at epoch: 30\n",
            "----------\n",
            "epoch 31/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.87it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 31 average loss: 0.1697\n",
            "----------\n",
            "epoch 32/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.68it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 32 average loss: 0.1651\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.09it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 32 current mean IoU: 0.7921\n",
            "best mean IoU: 0.7921 at epoch: 32\n",
            "----------\n",
            "epoch 33/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.77it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 33 average loss: 0.1650\n",
            "----------\n",
            "epoch 34/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 34 average loss: 0.1747\n"
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            "current epoch: 34 current mean IoU: 0.7853\n",
            "best mean IoU: 0.7921 at epoch: 32\n",
            "----------\n",
            "epoch 35/100\n"
          ]
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            "epoch 36/100\n"
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            "epoch 36 average loss: 0.1599\n"
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            "current epoch: 36 current mean IoU: 0.7852\n",
            "best mean IoU: 0.7921 at epoch: 32\n",
            "----------\n",
            "epoch 37/100\n"
          ]
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            "epoch 37 average loss: 0.1594\n",
            "----------\n",
            "epoch 38/100\n"
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            "epoch 38 average loss: 0.1601\n"
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            "current epoch: 38 current mean IoU: 0.7890\n",
            "best mean IoU: 0.7921 at epoch: 32\n",
            "----------\n",
            "epoch 39/100\n"
          ]
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            "epoch 39 average loss: 0.1535\n",
            "----------\n",
            "epoch 40/100\n"
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            "epoch 40 average loss: 0.1523\n"
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          "text": [
            "current epoch: 40 current mean IoU: 0.7914\n",
            "best mean IoU: 0.7921 at epoch: 32\n",
            "----------\n",
            "Fold 2\n",
            "----------\n",
            "epoch 41/100\n"
          ]
        },
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            "epoch 41 average loss: 0.1634\n",
            "----------\n",
            "epoch 42/100\n"
          ]
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            "epoch 42 average loss: 0.1565\n"
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          ]
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        {
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          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 42 current mean IoU: 0.8441\n",
            "best mean IoU: 0.8441 at epoch: 42\n",
            "----------\n",
            "epoch 43/100\n"
          ]
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            "epoch 43 average loss: 0.1558\n",
            "----------\n",
            "epoch 44/100\n"
          ]
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            "epoch 44 average loss: 0.1551\n"
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          "text": [
            "current epoch: 44 current mean IoU: 0.8407\n",
            "best mean IoU: 0.8441 at epoch: 42\n",
            "----------\n",
            "epoch 45/100\n"
          ]
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            "epoch 45 average loss: 0.1549\n",
            "----------\n",
            "epoch 46/100\n"
          ]
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            "epoch 46 average loss: 0.1515\n"
          ]
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          ]
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          "text": [
            "current epoch: 46 current mean IoU: 0.8367\n",
            "best mean IoU: 0.8441 at epoch: 42\n",
            "----------\n",
            "epoch 47/100\n"
          ]
        },
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            "epoch 47 average loss: 0.1474\n",
            "----------\n",
            "epoch 48/100\n"
          ]
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            "epoch 48 average loss: 0.1474\n"
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          "text": [
            "current epoch: 48 current mean IoU: 0.8301\n",
            "best mean IoU: 0.8441 at epoch: 42\n",
            "----------\n",
            "epoch 49/100\n"
          ]
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            "epoch 49 average loss: 0.1447\n",
            "----------\n",
            "epoch 50/100\n"
          ]
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          "text": [
            "epoch 50 average loss: 0.1456\n"
          ]
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          "text": [
            "current epoch: 50 current mean IoU: 0.8400\n",
            "best mean IoU: 0.8441 at epoch: 42\n",
            "----------\n",
            "epoch 51/100\n"
          ]
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            "epoch 51 average loss: 0.1409\n",
            "----------\n",
            "epoch 52/100\n"
          ]
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            "epoch 52 average loss: 0.1402\n"
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          "text": [
            "current epoch: 52 current mean IoU: 0.8357\n",
            "best mean IoU: 0.8441 at epoch: 42\n",
            "----------\n",
            "epoch 53/100\n"
          ]
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            "epoch 53 average loss: 0.1402\n",
            "----------\n",
            "epoch 54/100\n"
          ]
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            "epoch 54 average loss: 0.1441\n"
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          "text": [
            "current epoch: 54 current mean IoU: 0.8361\n",
            "best mean IoU: 0.8441 at epoch: 42\n",
            "----------\n",
            "epoch 55/100\n"
          ]
        },
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            "epoch 55 average loss: 0.1396\n",
            "----------\n",
            "epoch 56/100\n"
          ]
        },
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            "epoch 56 average loss: 0.1388\n"
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          "text": [
            "current epoch: 56 current mean IoU: 0.8166\n",
            "best mean IoU: 0.8441 at epoch: 42\n",
            "----------\n",
            "epoch 57/100\n"
          ]
        },
        {
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            "epoch 57 average loss: 0.1375\n",
            "----------\n",
            "epoch 58/100\n"
          ]
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            "epoch 58 average loss: 0.1334\n"
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          ]
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          "name": "stdout",
          "text": [
            "current epoch: 58 current mean IoU: 0.8338\n",
            "best mean IoU: 0.8441 at epoch: 42\n",
            "----------\n",
            "epoch 59/100\n"
          ]
        },
        {
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            "epoch 59 average loss: 0.1343\n",
            "----------\n",
            "epoch 60/100\n"
          ]
        },
        {
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        {
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          "text": [
            "epoch 60 average loss: 0.1306\n"
          ]
        },
        {
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          ]
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        {
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          "name": "stdout",
          "text": [
            "current epoch: 60 current mean IoU: 0.8341\n",
            "best mean IoU: 0.8441 at epoch: 42\n",
            "----------\n",
            "Fold 3\n",
            "----------\n",
            "epoch 61/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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          "text": [
            "epoch 61 average loss: 0.1442\n",
            "----------\n",
            "epoch 62/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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          "text": [
            "epoch 62 average loss: 0.1373\n"
          ]
        },
        {
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 62 current mean IoU: 0.8717\n",
            "best mean IoU: 0.8717 at epoch: 62\n",
            "----------\n",
            "epoch 63/100\n"
          ]
        },
        {
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          ]
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        {
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            "epoch 63 average loss: 0.1364\n",
            "----------\n",
            "epoch 64/100\n"
          ]
        },
        {
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          ]
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        {
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          "text": [
            "epoch 64 average loss: 0.1354\n"
          ]
        },
        {
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 64 current mean IoU: 0.8744\n",
            "best mean IoU: 0.8744 at epoch: 64\n",
            "----------\n",
            "epoch 65/100\n"
          ]
        },
        {
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          ]
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        {
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            "epoch 65 average loss: 0.1332\n",
            "----------\n",
            "epoch 66/100\n"
          ]
        },
        {
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          ]
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            "epoch 66 average loss: 0.1334\n"
          ]
        },
        {
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          ]
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        {
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          "name": "stdout",
          "text": [
            "current epoch: 66 current mean IoU: 0.8616\n",
            "best mean IoU: 0.8744 at epoch: 64\n",
            "----------\n",
            "epoch 67/100\n"
          ]
        },
        {
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          ]
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        {
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            "epoch 67 average loss: 0.1297\n",
            "----------\n",
            "epoch 68/100\n"
          ]
        },
        {
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          ]
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            "epoch 68 average loss: 0.1256\n"
          ]
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          ]
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        {
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          "name": "stdout",
          "text": [
            "current epoch: 68 current mean IoU: 0.8722\n",
            "best mean IoU: 0.8744 at epoch: 64\n",
            "----------\n",
            "epoch 69/100\n"
          ]
        },
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            "epoch 69 average loss: 0.1243\n",
            "----------\n",
            "epoch 70/100\n"
          ]
        },
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            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.97it/s]\n"
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        {
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          "text": [
            "epoch 70 average loss: 0.1252\n"
          ]
        },
        {
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            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.20it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 70 current mean IoU: 0.8676\n",
            "best mean IoU: 0.8744 at epoch: 64\n",
            "----------\n",
            "epoch 71/100\n"
          ]
        },
        {
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        {
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          "text": [
            "epoch 71 average loss: 0.1238\n",
            "----------\n",
            "epoch 72/100\n"
          ]
        },
        {
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        {
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          "text": [
            "epoch 72 average loss: 0.1303\n"
          ]
        },
        {
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            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.14it/s]\n"
          ]
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          "text": [
            "current epoch: 72 current mean IoU: 0.8636\n",
            "best mean IoU: 0.8744 at epoch: 64\n",
            "----------\n",
            "epoch 73/100\n"
          ]
        },
        {
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            "epoch 73 average loss: 0.1222\n",
            "----------\n",
            "epoch 74/100\n"
          ]
        },
        {
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          ]
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        {
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          "text": [
            "epoch 74 average loss: 0.1235\n"
          ]
        },
        {
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            "Validation: 100%|██████████| 97/97 [00:02<00:00, 37.77it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 74 current mean IoU: 0.8586\n",
            "best mean IoU: 0.8744 at epoch: 64\n",
            "----------\n",
            "epoch 75/100\n"
          ]
        },
        {
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            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.21it/s]\n"
          ]
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        {
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          "text": [
            "epoch 75 average loss: 0.1214\n",
            "----------\n",
            "epoch 76/100\n"
          ]
        },
        {
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          ]
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        {
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          "text": [
            "epoch 76 average loss: 0.1219\n"
          ]
        },
        {
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          "name": "stderr",
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            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.62it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 76 current mean IoU: 0.8614\n",
            "best mean IoU: 0.8744 at epoch: 64\n",
            "----------\n",
            "epoch 77/100\n"
          ]
        },
        {
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            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.56it/s]\n"
          ]
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        {
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          "text": [
            "epoch 77 average loss: 0.1225\n",
            "----------\n",
            "epoch 78/100\n"
          ]
        },
        {
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            "Training: 100%|██████████| 97/97 [00:06<00:00, 16.14it/s]\n"
          ]
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        {
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          "text": [
            "epoch 78 average loss: 0.1192\n"
          ]
        },
        {
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            "Validation: 100%|██████████| 97/97 [00:02<00:00, 37.51it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 78 current mean IoU: 0.8638\n",
            "best mean IoU: 0.8744 at epoch: 64\n",
            "----------\n",
            "epoch 79/100\n"
          ]
        },
        {
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          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.80it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 79 average loss: 0.1231\n",
            "----------\n",
            "epoch 80/100\n"
          ]
        },
        {
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          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.62it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 80 average loss: 0.1174\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Validation: 100%|██████████| 97/97 [00:02<00:00, 37.36it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 80 current mean IoU: 0.8606\n",
            "best mean IoU: 0.8744 at epoch: 64\n",
            "----------\n",
            "Fold 4\n",
            "----------\n",
            "epoch 81/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 81 average loss: 0.1230\n",
            "----------\n",
            "epoch 82/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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          "text": [
            "epoch 82 average loss: 0.1243\n"
          ]
        },
        {
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          "name": "stderr",
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            "Validation: 100%|██████████| 97/97 [00:02<00:00, 37.21it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 82 current mean IoU: 0.8888\n",
            "best mean IoU: 0.8888 at epoch: 82\n",
            "----------\n",
            "epoch 83/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
        },
        {
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          "text": [
            "epoch 83 average loss: 0.1276\n",
            "----------\n",
            "epoch 84/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:06<00:00, 16.02it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 84 average loss: 0.1204\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 37.76it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 84 current mean IoU: 0.8882\n",
            "best mean IoU: 0.8888 at epoch: 82\n",
            "----------\n",
            "epoch 85/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 85 average loss: 0.1187\n",
            "----------\n",
            "epoch 86/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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          "name": "stdout",
          "text": [
            "epoch 86 average loss: 0.1204\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 37.12it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 86 current mean IoU: 0.8873\n",
            "best mean IoU: 0.8888 at epoch: 82\n",
            "----------\n",
            "epoch 87/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.33it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 87 average loss: 0.1179\n",
            "----------\n",
            "epoch 88/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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          "text": [
            "epoch 88 average loss: 0.1160\n"
          ]
        },
        {
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 88 current mean IoU: 0.8874\n",
            "best mean IoU: 0.8888 at epoch: 82\n",
            "----------\n",
            "epoch 89/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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          "text": [
            "epoch 89 average loss: 0.1170\n",
            "----------\n",
            "epoch 90/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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          "text": [
            "epoch 90 average loss: 0.1166\n"
          ]
        },
        {
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 90 current mean IoU: 0.8827\n",
            "best mean IoU: 0.8888 at epoch: 82\n",
            "----------\n",
            "epoch 91/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
        },
        {
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          "text": [
            "epoch 91 average loss: 0.1140\n",
            "----------\n",
            "epoch 92/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
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          ]
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        {
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          "name": "stdout",
          "text": [
            "epoch 92 average loss: 0.1147\n"
          ]
        },
        {
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          "name": "stderr",
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            "Validation: 100%|██████████| 97/97 [00:02<00:00, 36.85it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 92 current mean IoU: 0.8787\n",
            "best mean IoU: 0.8888 at epoch: 82\n",
            "----------\n",
            "epoch 93/100\n"
          ]
        },
        {
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          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.72it/s]\n"
          ]
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        {
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          "name": "stdout",
          "text": [
            "epoch 93 average loss: 0.1124\n",
            "----------\n",
            "epoch 94/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
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          ]
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        {
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          "name": "stdout",
          "text": [
            "epoch 94 average loss: 0.1119\n"
          ]
        },
        {
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          "name": "stderr",
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            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.70it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 94 current mean IoU: 0.8805\n",
            "best mean IoU: 0.8888 at epoch: 82\n",
            "----------\n",
            "epoch 95/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.27it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 95 average loss: 0.1122\n",
            "----------\n",
            "epoch 96/100\n"
          ]
        },
        {
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          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.38it/s]\n"
          ]
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        {
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          "name": "stdout",
          "text": [
            "epoch 96 average loss: 0.1074\n"
          ]
        },
        {
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            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.56it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 96 current mean IoU: 0.8752\n",
            "best mean IoU: 0.8888 at epoch: 82\n",
            "----------\n",
            "epoch 97/100\n"
          ]
        },
        {
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            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.11it/s]\n"
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        {
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          "text": [
            "epoch 97 average loss: 0.1092\n",
            "----------\n",
            "epoch 98/100\n"
          ]
        },
        {
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          ]
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        {
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          "name": "stdout",
          "text": [
            "epoch 98 average loss: 0.1084\n"
          ]
        },
        {
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            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.31it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 98 current mean IoU: 0.8782\n",
            "best mean IoU: 0.8888 at epoch: 82\n",
            "----------\n",
            "epoch 99/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.94it/s]\n"
          ]
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        {
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          "name": "stdout",
          "text": [
            "epoch 99 average loss: 0.1095\n",
            "----------\n",
            "epoch 100/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 16.38it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 100 average loss: 0.1090\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 38.01it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 100 current mean IoU: 0.8756\n",
            "best mean IoU: 0.8888 at epoch: 82\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://github.com/mateuszbuda/brain-segmentation-pytorch/zipball/master\" to /root/.cache/torch/hub/master.zip\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of parameters: 7763041\n",
            "----------\n",
            "Fold 0\n",
            "----------\n",
            "epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:05<00:00, 17.77it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 average loss: 0.9735\n",
            "----------\n",
            "epoch 2/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.53it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 average loss: 0.9618\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:02<00:00, 45.82it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 2 current mean IoU: 0.1507\n",
            "best mean IoU: 0.1507 at epoch: 2\n",
            "----------\n",
            "epoch 3/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.50it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 average loss: 0.9551\n",
            "----------\n",
            "epoch 4/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.11it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 average loss: 0.9474\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 54.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 4 current mean IoU: 0.1661\n",
            "best mean IoU: 0.1661 at epoch: 4\n",
            "----------\n",
            "epoch 5/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.73it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 average loss: 0.9380\n",
            "----------\n",
            "epoch 6/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 24.05it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 6 average loss: 0.9252\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 54.29it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 6 current mean IoU: 0.1810\n",
            "best mean IoU: 0.1810 at epoch: 6\n",
            "----------\n",
            "epoch 7/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.62it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 7 average loss: 0.9098\n",
            "----------\n",
            "epoch 8/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.87it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 8 average loss: 0.8904\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 58.12it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 8 current mean IoU: 0.3809\n",
            "best mean IoU: 0.3809 at epoch: 8\n",
            "----------\n",
            "epoch 9/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.01it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 9 average loss: 0.8601\n",
            "----------\n",
            "epoch 10/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.31it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 10 average loss: 0.8165\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 58.61it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 10 current mean IoU: 0.6609\n",
            "best mean IoU: 0.6609 at epoch: 10\n",
            "----------\n",
            "epoch 11/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.17it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 11 average loss: 0.7526\n",
            "----------\n",
            "epoch 12/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.58it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 12 average loss: 0.6826\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 55.67it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 12 current mean IoU: 0.6568\n",
            "best mean IoU: 0.6609 at epoch: 10\n",
            "----------\n",
            "epoch 13/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.78it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 13 average loss: 0.6147\n",
            "----------\n",
            "epoch 14/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 14 average loss: 0.5424\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 56.64it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 14 current mean IoU: 0.7167\n",
            "best mean IoU: 0.7167 at epoch: 14\n",
            "----------\n",
            "epoch 15/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 15 average loss: 0.4805\n",
            "----------\n",
            "epoch 16/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.52it/s]\n"
          ]
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        {
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          "text": [
            "epoch 16 average loss: 0.4267\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 56.94it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 16 current mean IoU: 0.7301\n",
            "best mean IoU: 0.7301 at epoch: 16\n",
            "----------\n",
            "epoch 17/100\n"
          ]
        },
        {
          "output_type": "stream",
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            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.44it/s]\n"
          ]
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        {
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          "text": [
            "epoch 17 average loss: 0.3788\n",
            "----------\n",
            "epoch 18/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.27it/s]\n"
          ]
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        {
          "output_type": "stream",
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          "text": [
            "epoch 18 average loss: 0.3466\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 57.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 18 current mean IoU: 0.7536\n",
            "best mean IoU: 0.7536 at epoch: 18\n",
            "----------\n",
            "epoch 19/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:04<00:00, 24.04it/s]\n"
          ]
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        {
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          "text": [
            "epoch 19 average loss: 0.3068\n",
            "----------\n",
            "epoch 20/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.72it/s]\n"
          ]
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        {
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          "text": [
            "epoch 20 average loss: 0.2862\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 55.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 20 current mean IoU: 0.7598\n",
            "best mean IoU: 0.7598 at epoch: 20\n",
            "----------\n",
            "Fold 1\n",
            "----------\n",
            "epoch 21/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:04<00:00, 20.47it/s]\n"
          ]
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        {
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          "text": [
            "epoch 21 average loss: 0.2768\n",
            "----------\n",
            "epoch 22/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.58it/s]\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "epoch 22 average loss: 0.2598\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 50.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 22 current mean IoU: 0.7649\n",
            "best mean IoU: 0.7649 at epoch: 22\n",
            "----------\n",
            "epoch 23/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.52it/s]\n"
          ]
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        {
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          "text": [
            "epoch 23 average loss: 0.2445\n",
            "----------\n",
            "epoch 24/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.95it/s]\n"
          ]
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        {
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          "text": [
            "epoch 24 average loss: 0.2348\n"
          ]
        },
        {
          "output_type": "stream",
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            "Validation: 100%|██████████| 97/97 [00:01<00:00, 54.60it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 24 current mean IoU: 0.7937\n",
            "best mean IoU: 0.7937 at epoch: 24\n",
            "----------\n",
            "epoch 25/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.13it/s]\n"
          ]
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        {
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          "text": [
            "epoch 25 average loss: 0.2186\n",
            "----------\n",
            "epoch 26/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
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        {
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          "text": [
            "epoch 26 average loss: 0.2124\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 57.03it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 26 current mean IoU: 0.8026\n",
            "best mean IoU: 0.8026 at epoch: 26\n",
            "----------\n",
            "epoch 27/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.92it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 27 average loss: 0.2025\n",
            "----------\n",
            "epoch 28/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.32it/s]\n"
          ]
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        {
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          "name": "stdout",
          "text": [
            "epoch 28 average loss: 0.1999\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 56.25it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 28 current mean IoU: 0.8003\n",
            "best mean IoU: 0.8026 at epoch: 26\n",
            "----------\n",
            "epoch 29/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.69it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 29 average loss: 0.1905\n",
            "----------\n",
            "epoch 30/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.40it/s]\n"
          ]
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        {
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          "name": "stdout",
          "text": [
            "epoch 30 average loss: 0.1806\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 54.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 30 current mean IoU: 0.8029\n",
            "best mean IoU: 0.8029 at epoch: 30\n",
            "----------\n",
            "epoch 31/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.28it/s]\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "epoch 31 average loss: 0.1750\n",
            "----------\n",
            "epoch 32/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.21it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 32 average loss: 0.1735\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 55.61it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 32 current mean IoU: 0.7859\n",
            "best mean IoU: 0.8029 at epoch: 30\n",
            "----------\n",
            "epoch 33/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 22.93it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 33 average loss: 0.1713\n",
            "----------\n",
            "epoch 34/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.74it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 34 average loss: 0.1647\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 57.53it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 34 current mean IoU: 0.8035\n",
            "best mean IoU: 0.8035 at epoch: 34\n",
            "----------\n",
            "epoch 35/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.22it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 35 average loss: 0.1616\n",
            "----------\n",
            "epoch 36/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.31it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 36 average loss: 0.1544\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 56.56it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 36 current mean IoU: 0.8105\n",
            "best mean IoU: 0.8105 at epoch: 36\n",
            "----------\n",
            "epoch 37/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.41it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 37 average loss: 0.1581\n",
            "----------\n",
            "epoch 38/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 22.96it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 38 average loss: 0.1512\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 54.14it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 38 current mean IoU: 0.8093\n",
            "best mean IoU: 0.8105 at epoch: 36\n",
            "----------\n",
            "epoch 39/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.47it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 39 average loss: 0.1508\n",
            "----------\n",
            "epoch 40/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 40 average loss: 0.1458\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 56.64it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 40 current mean IoU: 0.8048\n",
            "best mean IoU: 0.8105 at epoch: 36\n",
            "----------\n",
            "Fold 2\n",
            "----------\n",
            "epoch 41/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.77it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 41 average loss: 0.1581\n",
            "----------\n",
            "epoch 42/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.86it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 42 average loss: 0.1600\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 57.87it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 42 current mean IoU: 0.8577\n",
            "best mean IoU: 0.8577 at epoch: 42\n",
            "----------\n",
            "epoch 43/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 43 average loss: 0.1540\n",
            "----------\n",
            "epoch 44/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 44 average loss: 0.1475\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 56.14it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 44 current mean IoU: 0.8587\n",
            "best mean IoU: 0.8587 at epoch: 44\n",
            "----------\n",
            "epoch 45/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.14it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 45 average loss: 0.1435\n",
            "----------\n",
            "epoch 46/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.56it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 46 average loss: 0.1415\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 55.98it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 46 current mean IoU: 0.8568\n",
            "best mean IoU: 0.8587 at epoch: 44\n",
            "----------\n",
            "epoch 47/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.34it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 47 average loss: 0.1426\n",
            "----------\n",
            "epoch 48/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 24.06it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 48 average loss: 0.1410\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 56.97it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 48 current mean IoU: 0.8560\n",
            "best mean IoU: 0.8587 at epoch: 44\n",
            "----------\n",
            "epoch 49/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 49 average loss: 0.1347\n",
            "----------\n",
            "epoch 50/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.24it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 50 average loss: 0.1353\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 56.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 50 current mean IoU: 0.8594\n",
            "best mean IoU: 0.8594 at epoch: 50\n",
            "----------\n",
            "epoch 51/100\n"
          ]
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            "epoch 51 average loss: 0.1307\n",
            "----------\n",
            "epoch 52/100\n"
          ]
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            "epoch 52 average loss: 0.1297\n"
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            "current epoch: 52 current mean IoU: 0.8538\n",
            "best mean IoU: 0.8594 at epoch: 50\n",
            "----------\n",
            "epoch 53/100\n"
          ]
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            "epoch 53 average loss: 0.1281\n",
            "----------\n",
            "epoch 54/100\n"
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            "epoch 54 average loss: 0.1251\n"
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          "text": [
            "current epoch: 54 current mean IoU: 0.8582\n",
            "best mean IoU: 0.8594 at epoch: 50\n",
            "----------\n",
            "epoch 55/100\n"
          ]
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            "epoch 55 average loss: 0.1260\n",
            "----------\n",
            "epoch 56/100\n"
          ]
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            "epoch 56 average loss: 0.1235\n"
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          "text": [
            "current epoch: 56 current mean IoU: 0.8492\n",
            "best mean IoU: 0.8594 at epoch: 50\n",
            "----------\n",
            "epoch 57/100\n"
          ]
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            "epoch 57 average loss: 0.1229\n",
            "----------\n",
            "epoch 58/100\n"
          ]
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            "epoch 58 average loss: 0.1198\n"
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          "text": [
            "current epoch: 58 current mean IoU: 0.8528\n",
            "best mean IoU: 0.8594 at epoch: 50\n",
            "----------\n",
            "epoch 59/100\n"
          ]
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            "epoch 59 average loss: 0.1218\n",
            "----------\n",
            "epoch 60/100\n"
          ]
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          "text": [
            "epoch 60 average loss: 0.1211\n"
          ]
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          "text": [
            "current epoch: 60 current mean IoU: 0.8543\n",
            "best mean IoU: 0.8594 at epoch: 50\n",
            "----------\n",
            "Fold 3\n",
            "----------\n",
            "epoch 61/100\n"
          ]
        },
        {
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            "epoch 61 average loss: 0.1258\n",
            "----------\n",
            "epoch 62/100\n"
          ]
        },
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            "epoch 62 average loss: 0.1268\n"
          ]
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 62 current mean IoU: 0.8924\n",
            "best mean IoU: 0.8924 at epoch: 62\n",
            "----------\n",
            "epoch 63/100\n"
          ]
        },
        {
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          ]
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            "epoch 63 average loss: 0.1229\n",
            "----------\n",
            "epoch 64/100\n"
          ]
        },
        {
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          ]
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          "text": [
            "epoch 64 average loss: 0.1172\n"
          ]
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 64 current mean IoU: 0.8950\n",
            "best mean IoU: 0.8950 at epoch: 64\n",
            "----------\n",
            "epoch 65/100\n"
          ]
        },
        {
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          ]
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            "epoch 65 average loss: 0.1159\n",
            "----------\n",
            "epoch 66/100\n"
          ]
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          ]
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            "epoch 66 average loss: 0.1155\n"
          ]
        },
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 66 current mean IoU: 0.8898\n",
            "best mean IoU: 0.8950 at epoch: 64\n",
            "----------\n",
            "epoch 67/100\n"
          ]
        },
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          ]
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            "epoch 67 average loss: 0.1211\n",
            "----------\n",
            "epoch 68/100\n"
          ]
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            "epoch 68 average loss: 0.1169\n"
          ]
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          ]
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        {
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          "name": "stdout",
          "text": [
            "current epoch: 68 current mean IoU: 0.8875\n",
            "best mean IoU: 0.8950 at epoch: 64\n",
            "----------\n",
            "epoch 69/100\n"
          ]
        },
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            "epoch 69 average loss: 0.1156\n",
            "----------\n",
            "epoch 70/100\n"
          ]
        },
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            "epoch 70 average loss: 0.1122\n"
          ]
        },
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          ]
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          "name": "stdout",
          "text": [
            "current epoch: 70 current mean IoU: 0.8862\n",
            "best mean IoU: 0.8950 at epoch: 64\n",
            "----------\n",
            "epoch 71/100\n"
          ]
        },
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            "epoch 71 average loss: 0.1105\n",
            "----------\n",
            "epoch 72/100\n"
          ]
        },
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            "epoch 72 average loss: 0.1092\n"
          ]
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          "text": [
            "current epoch: 72 current mean IoU: 0.8860\n",
            "best mean IoU: 0.8950 at epoch: 64\n",
            "----------\n",
            "epoch 73/100\n"
          ]
        },
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          ]
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            "epoch 73 average loss: 0.1080\n",
            "----------\n",
            "epoch 74/100\n"
          ]
        },
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            "epoch 74 average loss: 0.1071\n"
          ]
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          ]
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          "text": [
            "current epoch: 74 current mean IoU: 0.8863\n",
            "best mean IoU: 0.8950 at epoch: 64\n",
            "----------\n",
            "epoch 75/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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            "epoch 75 average loss: 0.1082\n",
            "----------\n",
            "epoch 76/100\n"
          ]
        },
        {
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        {
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          "text": [
            "epoch 76 average loss: 0.1051\n"
          ]
        },
        {
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          ]
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        {
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          "name": "stdout",
          "text": [
            "current epoch: 76 current mean IoU: 0.8798\n",
            "best mean IoU: 0.8950 at epoch: 64\n",
            "----------\n",
            "epoch 77/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 22.98it/s]\n"
          ]
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        {
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          "name": "stdout",
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            "epoch 77 average loss: 0.1076\n",
            "----------\n",
            "epoch 78/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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          "text": [
            "epoch 78 average loss: 0.1046\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 55.14it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 78 current mean IoU: 0.8796\n",
            "best mean IoU: 0.8950 at epoch: 64\n",
            "----------\n",
            "epoch 79/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 79 average loss: 0.1062\n",
            "----------\n",
            "epoch 80/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.89it/s]\n"
          ]
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        {
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          "text": [
            "epoch 80 average loss: 0.1034\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 56.02it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 80 current mean IoU: 0.8794\n",
            "best mean IoU: 0.8950 at epoch: 64\n",
            "----------\n",
            "Fold 4\n",
            "----------\n",
            "epoch 81/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 81 average loss: 0.1106\n",
            "----------\n",
            "epoch 82/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 82 average loss: 0.1114\n"
          ]
        },
        {
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            "Validation: 100%|██████████| 97/97 [00:01<00:00, 55.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 82 current mean IoU: 0.9061\n",
            "best mean IoU: 0.9061 at epoch: 82\n",
            "----------\n",
            "epoch 83/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 83 average loss: 0.1077\n",
            "----------\n",
            "epoch 84/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 84 average loss: 0.1044\n"
          ]
        },
        {
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 84 current mean IoU: 0.9064\n",
            "best mean IoU: 0.9064 at epoch: 84\n",
            "----------\n",
            "epoch 85/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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          "text": [
            "epoch 85 average loss: 0.1070\n",
            "----------\n",
            "epoch 86/100\n"
          ]
        },
        {
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          ]
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        {
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          "text": [
            "epoch 86 average loss: 0.1028\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 55.66it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 86 current mean IoU: 0.9017\n",
            "best mean IoU: 0.9064 at epoch: 84\n",
            "----------\n",
            "epoch 87/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.38it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 87 average loss: 0.0998\n",
            "----------\n",
            "epoch 88/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.20it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 88 average loss: 0.1035\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 58.08it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 88 current mean IoU: 0.9019\n",
            "best mean IoU: 0.9064 at epoch: 84\n",
            "----------\n",
            "epoch 89/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.51it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 89 average loss: 0.1010\n",
            "----------\n",
            "epoch 90/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.63it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 90 average loss: 0.0999\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 55.65it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 90 current mean IoU: 0.9017\n",
            "best mean IoU: 0.9064 at epoch: 84\n",
            "----------\n",
            "epoch 91/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 91 average loss: 0.0999\n",
            "----------\n",
            "epoch 92/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 92 average loss: 0.0991\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 57.25it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 92 current mean IoU: 0.9023\n",
            "best mean IoU: 0.9064 at epoch: 84\n",
            "----------\n",
            "epoch 93/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.59it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 93 average loss: 0.0989\n",
            "----------\n",
            "epoch 94/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.82it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 94 average loss: 0.0979\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 57.22it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 94 current mean IoU: 0.8938\n",
            "best mean IoU: 0.9064 at epoch: 84\n",
            "----------\n",
            "epoch 95/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.05it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 95 average loss: 0.0972\n",
            "----------\n",
            "epoch 96/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.87it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 96 average loss: 0.0935\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 57.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 96 current mean IoU: 0.9007\n",
            "best mean IoU: 0.9064 at epoch: 84\n",
            "----------\n",
            "epoch 97/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.50it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 97 average loss: 0.0932\n",
            "----------\n",
            "epoch 98/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.03it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 98 average loss: 0.0914\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 57.70it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 98 current mean IoU: 0.8962\n",
            "best mean IoU: 0.9064 at epoch: 84\n",
            "----------\n",
            "epoch 99/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.52it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 99 average loss: 0.0898\n",
            "----------\n",
            "epoch 100/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:04<00:00, 23.38it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 100 average loss: 0.0944\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:01<00:00, 57.67it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 100 current mean IoU: 0.8963\n",
            "best mean IoU: 0.9064 at epoch: 84\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/6.88k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b97d38968aac4574823ab943106d9407"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/15.0M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "172e95d3d2504680aa69d79ec948b268"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of SegformerForSemanticSegmentation were not initialized from the model checkpoint at nvidia/segformer-b0-finetuned-ade-512-512 and are newly initialized because the shapes did not match:\n",
            "- decode_head.classifier.bias: found shape torch.Size([150]) in the checkpoint and torch.Size([1]) in the model instantiated\n",
            "- decode_head.classifier.weight: found shape torch.Size([150, 256, 1, 1]) in the checkpoint and torch.Size([1, 256, 1, 1]) in the model instantiated\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of parameters: 3714401\n",
            "----------\n",
            "Fold 0\n",
            "----------\n",
            "epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:10<00:00,  9.69it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 average loss: 0.9753\n",
            "----------\n",
            "epoch 2/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 12.09it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 average loss: 0.9368\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 23.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 2 current mean IoU: 0.2824\n",
            "best mean IoU: 0.2824 at epoch: 2\n",
            "----------\n",
            "epoch 3/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 12.01it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 average loss: 0.7988\n",
            "----------\n",
            "epoch 4/100\n"
          ]
        },
        {
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            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.94it/s]\n"
          ]
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        {
          "output_type": "stream",
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          "text": [
            "epoch 4 average loss: 0.6025\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.20it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 4 current mean IoU: 0.4995\n",
            "best mean IoU: 0.4995 at epoch: 4\n",
            "----------\n",
            "epoch 5/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.78it/s]\n"
          ]
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        {
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          "text": [
            "epoch 5 average loss: 0.4808\n",
            "----------\n",
            "epoch 6/100\n"
          ]
        },
        {
          "output_type": "stream",
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          ]
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        {
          "output_type": "stream",
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          "text": [
            "epoch 6 average loss: 0.4329\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.54it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 6 current mean IoU: 0.5823\n",
            "best mean IoU: 0.5823 at epoch: 6\n",
            "----------\n",
            "epoch 7/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.79it/s]\n"
          ]
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        {
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          "text": [
            "epoch 7 average loss: 0.3910\n",
            "----------\n",
            "epoch 8/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.86it/s]\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "epoch 8 average loss: 0.3681\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 8 current mean IoU: 0.6311\n",
            "best mean IoU: 0.6311 at epoch: 8\n",
            "----------\n",
            "epoch 9/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.77it/s]\n"
          ]
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        {
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          "text": [
            "epoch 9 average loss: 0.3545\n",
            "----------\n",
            "epoch 10/100\n"
          ]
        },
        {
          "output_type": "stream",
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            "Training: 100%|██████████| 97/97 [00:08<00:00, 10.99it/s]\n"
          ]
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        {
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          "text": [
            "epoch 10 average loss: 0.3415\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.29it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 10 current mean IoU: 0.6347\n",
            "best mean IoU: 0.6347 at epoch: 10\n",
            "----------\n",
            "epoch 11/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.60it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 11 average loss: 0.3295\n",
            "----------\n",
            "epoch 12/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.84it/s]\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "epoch 12 average loss: 0.3201\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.13it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 12 current mean IoU: 0.6436\n",
            "best mean IoU: 0.6436 at epoch: 12\n",
            "----------\n",
            "epoch 13/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.71it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 13 average loss: 0.3170\n",
            "----------\n",
            "epoch 14/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.72it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 14 average loss: 0.3065\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.81it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 14 current mean IoU: 0.6475\n",
            "best mean IoU: 0.6475 at epoch: 14\n",
            "----------\n",
            "epoch 15/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.51it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 15 average loss: 0.3079\n",
            "----------\n",
            "epoch 16/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 16 average loss: 0.3006\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 27.30it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 16 current mean IoU: 0.6662\n",
            "best mean IoU: 0.6662 at epoch: 16\n",
            "----------\n",
            "epoch 17/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 17 average loss: 0.2935\n",
            "----------\n",
            "epoch 18/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.76it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 18 average loss: 0.2869\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.99it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 18 current mean IoU: 0.6712\n",
            "best mean IoU: 0.6712 at epoch: 18\n",
            "----------\n",
            "epoch 19/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.50it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 19 average loss: 0.2899\n",
            "----------\n",
            "epoch 20/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.51it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 20 average loss: 0.2845\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 20 current mean IoU: 0.6730\n",
            "best mean IoU: 0.6730 at epoch: 20\n",
            "----------\n",
            "Fold 1\n",
            "----------\n",
            "epoch 21/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:09<00:00, 10.57it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 21 average loss: 0.2942\n",
            "----------\n",
            "epoch 22/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 22 average loss: 0.2849\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 26.03it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 22 current mean IoU: 0.7060\n",
            "best mean IoU: 0.7060 at epoch: 22\n",
            "----------\n",
            "epoch 23/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.86it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 23 average loss: 0.2799\n",
            "----------\n",
            "epoch 24/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.79it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 24 average loss: 0.2844\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.34it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 24 current mean IoU: 0.6945\n",
            "best mean IoU: 0.7060 at epoch: 22\n",
            "----------\n",
            "epoch 25/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.68it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 25 average loss: 0.2716\n",
            "----------\n",
            "epoch 26/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.90it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 26 average loss: 0.2691\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.24it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 26 current mean IoU: 0.7043\n",
            "best mean IoU: 0.7060 at epoch: 22\n",
            "----------\n",
            "epoch 27/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.66it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 27 average loss: 0.2678\n",
            "----------\n",
            "epoch 28/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.60it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 28 average loss: 0.2569\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 27.71it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 28 current mean IoU: 0.7051\n",
            "best mean IoU: 0.7060 at epoch: 22\n",
            "----------\n",
            "epoch 29/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.65it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 29 average loss: 0.2618\n",
            "----------\n",
            "epoch 30/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 30 average loss: 0.2529\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 29.12it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 30 current mean IoU: 0.7104\n",
            "best mean IoU: 0.7104 at epoch: 30\n",
            "----------\n",
            "epoch 31/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.61it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 31 average loss: 0.2554\n",
            "----------\n",
            "epoch 32/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.76it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 32 average loss: 0.2589\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 32 current mean IoU: 0.7048\n",
            "best mean IoU: 0.7104 at epoch: 30\n",
            "----------\n",
            "epoch 33/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.50it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 33 average loss: 0.2584\n",
            "----------\n",
            "epoch 34/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.73it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 34 average loss: 0.2500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.24it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 34 current mean IoU: 0.7118\n",
            "best mean IoU: 0.7118 at epoch: 34\n",
            "----------\n",
            "epoch 35/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.84it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 35 average loss: 0.2601\n",
            "----------\n",
            "epoch 36/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.84it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 36 average loss: 0.2529\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.10it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 36 current mean IoU: 0.7089\n",
            "best mean IoU: 0.7118 at epoch: 34\n",
            "----------\n",
            "epoch 37/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.78it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 37 average loss: 0.2473\n",
            "----------\n",
            "epoch 38/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 38 average loss: 0.2453\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.35it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 38 current mean IoU: 0.7137\n",
            "best mean IoU: 0.7137 at epoch: 38\n",
            "----------\n",
            "epoch 39/100\n"
          ]
        },
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            "epoch 39 average loss: 0.2435\n",
            "----------\n",
            "epoch 40/100\n"
          ]
        },
        {
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            "epoch 40 average loss: 0.2409\n"
          ]
        },
        {
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          ]
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        {
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          "text": [
            "current epoch: 40 current mean IoU: 0.7120\n",
            "best mean IoU: 0.7137 at epoch: 38\n",
            "----------\n",
            "Fold 2\n",
            "----------\n",
            "epoch 41/100\n"
          ]
        },
        {
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            "epoch 41 average loss: 0.2482\n",
            "----------\n",
            "epoch 42/100\n"
          ]
        },
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        {
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          "text": [
            "epoch 42 average loss: 0.2450\n"
          ]
        },
        {
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 42 current mean IoU: 0.7573\n",
            "best mean IoU: 0.7573 at epoch: 42\n",
            "----------\n",
            "epoch 43/100\n"
          ]
        },
        {
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            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.79it/s]\n"
          ]
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            "epoch 43 average loss: 0.2478\n",
            "----------\n",
            "epoch 44/100\n"
          ]
        },
        {
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          ]
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        {
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          "text": [
            "epoch 44 average loss: 0.2449\n"
          ]
        },
        {
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            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.59it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 44 current mean IoU: 0.7586\n",
            "best mean IoU: 0.7586 at epoch: 44\n",
            "----------\n",
            "epoch 45/100\n"
          ]
        },
        {
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          ]
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            "epoch 45 average loss: 0.2421\n",
            "----------\n",
            "epoch 46/100\n"
          ]
        },
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          ]
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          "text": [
            "epoch 46 average loss: 0.2418\n"
          ]
        },
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            "Validation: 100%|██████████| 97/97 [00:03<00:00, 27.71it/s]\n"
          ]
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        {
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          "text": [
            "current epoch: 46 current mean IoU: 0.7549\n",
            "best mean IoU: 0.7586 at epoch: 44\n",
            "----------\n",
            "epoch 47/100\n"
          ]
        },
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            "epoch 47 average loss: 0.2390\n",
            "----------\n",
            "epoch 48/100\n"
          ]
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          "text": [
            "epoch 48 average loss: 0.2371\n"
          ]
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          "text": [
            "current epoch: 48 current mean IoU: 0.7544\n",
            "best mean IoU: 0.7586 at epoch: 44\n",
            "----------\n",
            "epoch 49/100\n"
          ]
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            "epoch 49 average loss: 0.2357\n",
            "----------\n",
            "epoch 50/100\n"
          ]
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          "text": [
            "epoch 50 average loss: 0.2344\n"
          ]
        },
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          "text": [
            "current epoch: 50 current mean IoU: 0.7538\n",
            "best mean IoU: 0.7586 at epoch: 44\n",
            "----------\n",
            "epoch 51/100\n"
          ]
        },
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            "epoch 51 average loss: 0.2399\n",
            "----------\n",
            "epoch 52/100\n"
          ]
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            "epoch 52 average loss: 0.2355\n"
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          "text": [
            "current epoch: 52 current mean IoU: 0.7551\n",
            "best mean IoU: 0.7586 at epoch: 44\n",
            "----------\n",
            "epoch 53/100\n"
          ]
        },
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            "epoch 53 average loss: 0.2330\n",
            "----------\n",
            "epoch 54/100\n"
          ]
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            "epoch 54 average loss: 0.2293\n"
          ]
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          "name": "stdout",
          "text": [
            "current epoch: 54 current mean IoU: 0.7570\n",
            "best mean IoU: 0.7586 at epoch: 44\n",
            "----------\n",
            "epoch 55/100\n"
          ]
        },
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            "epoch 55 average loss: 0.2310\n",
            "----------\n",
            "epoch 56/100\n"
          ]
        },
        {
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          "text": [
            "epoch 56 average loss: 0.2291\n"
          ]
        },
        {
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          ]
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        {
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          "name": "stdout",
          "text": [
            "current epoch: 56 current mean IoU: 0.7560\n",
            "best mean IoU: 0.7586 at epoch: 44\n",
            "----------\n",
            "epoch 57/100\n"
          ]
        },
        {
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          ]
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        {
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            "epoch 57 average loss: 0.2293\n",
            "----------\n",
            "epoch 58/100\n"
          ]
        },
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          ]
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          "text": [
            "epoch 58 average loss: 0.2312\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 58 current mean IoU: 0.7530\n",
            "best mean IoU: 0.7586 at epoch: 44\n",
            "----------\n",
            "epoch 59/100\n"
          ]
        },
        {
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          ]
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        {
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            "epoch 59 average loss: 0.2262\n",
            "----------\n",
            "epoch 60/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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          "text": [
            "epoch 60 average loss: 0.2258\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 60 current mean IoU: 0.7548\n",
            "best mean IoU: 0.7586 at epoch: 44\n",
            "----------\n",
            "Fold 3\n",
            "----------\n",
            "epoch 61/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 61 average loss: 0.2292\n",
            "----------\n",
            "epoch 62/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
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        {
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          "name": "stdout",
          "text": [
            "epoch 62 average loss: 0.2296\n"
          ]
        },
        {
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 62 current mean IoU: 0.7731\n",
            "best mean IoU: 0.7731 at epoch: 62\n",
            "----------\n",
            "epoch 63/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
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          "text": [
            "epoch 63 average loss: 0.2275\n",
            "----------\n",
            "epoch 64/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 64 average loss: 0.2273\n"
          ]
        },
        {
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 64 current mean IoU: 0.7773\n",
            "best mean IoU: 0.7773 at epoch: 64\n",
            "----------\n",
            "epoch 65/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 65 average loss: 0.2275\n",
            "----------\n",
            "epoch 66/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 66 average loss: 0.2360\n"
          ]
        },
        {
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 66 current mean IoU: 0.7668\n",
            "best mean IoU: 0.7773 at epoch: 64\n",
            "----------\n",
            "epoch 67/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 67 average loss: 0.2257\n",
            "----------\n",
            "epoch 68/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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          "text": [
            "epoch 68 average loss: 0.2228\n"
          ]
        },
        {
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 68 current mean IoU: 0.7746\n",
            "best mean IoU: 0.7773 at epoch: 64\n",
            "----------\n",
            "epoch 69/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
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        {
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          "text": [
            "epoch 69 average loss: 0.2183\n",
            "----------\n",
            "epoch 70/100\n"
          ]
        },
        {
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          ]
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        {
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          "text": [
            "epoch 70 average loss: 0.2191\n"
          ]
        },
        {
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 70 current mean IoU: 0.7762\n",
            "best mean IoU: 0.7773 at epoch: 64\n",
            "----------\n",
            "epoch 71/100\n"
          ]
        },
        {
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          ]
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        {
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          "text": [
            "epoch 71 average loss: 0.2157\n",
            "----------\n",
            "epoch 72/100\n"
          ]
        },
        {
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          ]
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        {
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            "epoch 72 average loss: 0.2196\n"
          ]
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        {
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          ]
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        {
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          "name": "stdout",
          "text": [
            "current epoch: 72 current mean IoU: 0.7752\n",
            "best mean IoU: 0.7773 at epoch: 64\n",
            "----------\n",
            "epoch 73/100\n"
          ]
        },
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          ]
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            "epoch 73 average loss: 0.2154\n",
            "----------\n",
            "epoch 74/100\n"
          ]
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            "epoch 74 average loss: 0.2162\n"
          ]
        },
        {
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          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 27.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 74 current mean IoU: 0.7724\n",
            "best mean IoU: 0.7773 at epoch: 64\n",
            "----------\n",
            "epoch 75/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.88it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 75 average loss: 0.2121\n",
            "----------\n",
            "epoch 76/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.84it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 76 average loss: 0.2114\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.50it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 76 current mean IoU: 0.7722\n",
            "best mean IoU: 0.7773 at epoch: 64\n",
            "----------\n",
            "epoch 77/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.69it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 77 average loss: 0.2099\n",
            "----------\n",
            "epoch 78/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.71it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 78 average loss: 0.2102\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.32it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 78 current mean IoU: 0.7743\n",
            "best mean IoU: 0.7773 at epoch: 64\n",
            "----------\n",
            "epoch 79/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.70it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 79 average loss: 0.2107\n",
            "----------\n",
            "epoch 80/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.82it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 80 average loss: 0.2119\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.65it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 80 current mean IoU: 0.7731\n",
            "best mean IoU: 0.7773 at epoch: 64\n",
            "----------\n",
            "Fold 4\n",
            "----------\n",
            "epoch 81/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.79it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 81 average loss: 0.2139\n",
            "----------\n",
            "epoch 82/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.64it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 82 average loss: 0.2128\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.66it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 82 current mean IoU: 0.7851\n",
            "best mean IoU: 0.7851 at epoch: 82\n",
            "----------\n",
            "epoch 83/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.78it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 83 average loss: 0.2151\n",
            "----------\n",
            "epoch 84/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.76it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 84 average loss: 0.2131\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.31it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 84 current mean IoU: 0.7835\n",
            "best mean IoU: 0.7851 at epoch: 82\n",
            "----------\n",
            "epoch 85/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.67it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 85 average loss: 0.2096\n",
            "----------\n",
            "epoch 86/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.69it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 86 average loss: 0.2098\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.96it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 86 current mean IoU: 0.7824\n",
            "best mean IoU: 0.7851 at epoch: 82\n",
            "----------\n",
            "epoch 87/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.66it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 87 average loss: 0.2063\n",
            "----------\n",
            "epoch 88/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.74it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 88 average loss: 0.2082\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 27.77it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 88 current mean IoU: 0.7812\n",
            "best mean IoU: 0.7851 at epoch: 82\n",
            "----------\n",
            "epoch 89/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.66it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 89 average loss: 0.2095\n",
            "----------\n",
            "epoch 90/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.80it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 90 average loss: 0.2087\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.99it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 90 current mean IoU: 0.7812\n",
            "best mean IoU: 0.7851 at epoch: 82\n",
            "----------\n",
            "epoch 91/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.65it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 91 average loss: 0.2093\n",
            "----------\n",
            "epoch 92/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.71it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 92 average loss: 0.2068\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 92 current mean IoU: 0.7794\n",
            "best mean IoU: 0.7851 at epoch: 82\n",
            "----------\n",
            "epoch 93/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.61it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 93 average loss: 0.2030\n",
            "----------\n",
            "epoch 94/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.73it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 94 average loss: 0.2066\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 27.80it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 94 current mean IoU: 0.7812\n",
            "best mean IoU: 0.7851 at epoch: 82\n",
            "----------\n",
            "epoch 95/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.70it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 95 average loss: 0.1997\n",
            "----------\n",
            "epoch 96/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.59it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 96 average loss: 0.2023\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 96 current mean IoU: 0.7804\n",
            "best mean IoU: 0.7851 at epoch: 82\n",
            "----------\n",
            "epoch 97/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.74it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 97 average loss: 0.2049\n",
            "----------\n",
            "epoch 98/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.72it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 98 average loss: 0.2028\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.46it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "current epoch: 98 current mean IoU: 0.7799\n",
            "best mean IoU: 0.7851 at epoch: 82\n",
            "----------\n",
            "epoch 99/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.89it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 99 average loss: 0.2014\n",
            "----------\n",
            "epoch 100/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:08<00:00, 11.77it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 100 average loss: 0.2021\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:03<00:00, 28.66it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 100 current mean IoU: 0.7750\n",
            "best mean IoU: 0.7851 at epoch: 82\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of parameters: 51145311\n",
            "----------\n",
            "Fold 0\n",
            "----------\n",
            "epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:20<00:00,  4.68it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1 average loss: 0.9550\n",
            "----------\n",
            "epoch 2/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:12<00:00,  7.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 2 average loss: 0.8909\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:05<00:00, 17.91it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 2 current mean IoU: 0.2697\n",
            "best mean IoU: 0.2697 at epoch: 2\n",
            "----------\n",
            "epoch 3/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:12<00:00,  7.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 3 average loss: 0.7783\n",
            "----------\n",
            "epoch 4/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.42it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 4 average loss: 0.6306\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.17it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 4 current mean IoU: 0.5641\n",
            "best mean IoU: 0.5641 at epoch: 4\n",
            "----------\n",
            "epoch 5/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 5 average loss: 0.4992\n",
            "----------\n",
            "epoch 6/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 6 average loss: 0.4384\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.10it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 6 current mean IoU: 0.6059\n",
            "best mean IoU: 0.6059 at epoch: 6\n",
            "----------\n",
            "epoch 7/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.41it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 7 average loss: 0.3942\n",
            "----------\n",
            "epoch 8/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:12<00:00,  7.47it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 8 average loss: 0.3584\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.33it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 8 current mean IoU: 0.6428\n",
            "best mean IoU: 0.6428 at epoch: 8\n",
            "----------\n",
            "epoch 9/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.42it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 9 average loss: 0.3311\n",
            "----------\n",
            "epoch 10/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.39it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 10 average loss: 0.3089\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.27it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 10 current mean IoU: 0.6523\n",
            "best mean IoU: 0.6523 at epoch: 10\n",
            "----------\n",
            "epoch 11/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.42it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 11 average loss: 0.2832\n",
            "----------\n",
            "epoch 12/100\n"
          ]
        },
        {
          "output_type": "stream",
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          ]
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        {
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          "text": [
            "epoch 12 average loss: 0.2757\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 20.71it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 12 current mean IoU: 0.6710\n",
            "best mean IoU: 0.6710 at epoch: 12\n",
            "----------\n",
            "epoch 13/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.42it/s]\n"
          ]
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          "text": [
            "epoch 13 average loss: 0.2726\n",
            "----------\n",
            "epoch 14/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:12<00:00,  7.47it/s]\n"
          ]
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        {
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          "text": [
            "epoch 14 average loss: 0.2627\n"
          ]
        },
        {
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          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.29it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 14 current mean IoU: 0.6763\n",
            "best mean IoU: 0.6763 at epoch: 14\n",
            "----------\n",
            "epoch 15/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:12<00:00,  7.48it/s]\n"
          ]
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        {
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          "text": [
            "epoch 15 average loss: 0.2458\n",
            "----------\n",
            "epoch 16/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.45it/s]\n"
          ]
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        {
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          "text": [
            "epoch 16 average loss: 0.2419\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.08it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 16 current mean IoU: 0.6983\n",
            "best mean IoU: 0.6983 at epoch: 16\n",
            "----------\n",
            "epoch 17/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.42it/s]\n"
          ]
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        {
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          "text": [
            "epoch 17 average loss: 0.2283\n",
            "----------\n",
            "epoch 18/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.46it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 18 average loss: 0.2273\n"
          ]
        },
        {
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          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.09it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 18 current mean IoU: 0.6914\n",
            "best mean IoU: 0.6983 at epoch: 16\n",
            "----------\n",
            "epoch 19/100\n"
          ]
        },
        {
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          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.43it/s]\n"
          ]
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          "text": [
            "epoch 19 average loss: 0.2158\n",
            "----------\n",
            "epoch 20/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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          ]
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        {
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          "text": [
            "epoch 20 average loss: 0.2061\n"
          ]
        },
        {
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            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.06it/s]\n"
          ]
        },
        {
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          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 20 current mean IoU: 0.7251\n",
            "best mean IoU: 0.7251 at epoch: 20\n",
            "----------\n",
            "Fold 1\n",
            "----------\n",
            "epoch 21/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "Training: 100%|██████████| 97/97 [00:16<00:00,  5.90it/s]\n"
          ]
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        {
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          "text": [
            "epoch 21 average loss: 0.2203\n",
            "----------\n",
            "epoch 22/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
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          ]
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        {
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          "text": [
            "epoch 22 average loss: 0.2158\n"
          ]
        },
        {
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          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 19.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 22 current mean IoU: 0.7880\n",
            "best mean IoU: 0.7880 at epoch: 22\n",
            "----------\n",
            "epoch 23/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.41it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 23 average loss: 0.2009\n",
            "----------\n",
            "epoch 24/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.38it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 24 average loss: 0.2007\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 20.75it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 24 current mean IoU: 0.7852\n",
            "best mean IoU: 0.7880 at epoch: 22\n",
            "----------\n",
            "epoch 25/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.40it/s]\n"
          ]
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        {
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          "text": [
            "epoch 25 average loss: 0.1912\n",
            "----------\n",
            "epoch 26/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.41it/s]\n"
          ]
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        {
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          "text": [
            "epoch 26 average loss: 0.1835\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.20it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 26 current mean IoU: 0.7956\n",
            "best mean IoU: 0.7956 at epoch: 26\n",
            "----------\n",
            "epoch 27/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.36it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 27 average loss: 0.1826\n",
            "----------\n",
            "epoch 28/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.39it/s]\n"
          ]
        },
        {
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          "text": [
            "epoch 28 average loss: 0.1779\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 20.93it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 28 current mean IoU: 0.7923\n",
            "best mean IoU: 0.7956 at epoch: 26\n",
            "----------\n",
            "epoch 29/100\n"
          ]
        },
        {
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          "name": "stderr",
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          ]
        },
        {
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          "text": [
            "epoch 29 average loss: 0.1753\n",
            "----------\n",
            "epoch 30/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 30 average loss: 0.1695\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.09it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 30 current mean IoU: 0.7783\n",
            "best mean IoU: 0.7956 at epoch: 26\n",
            "----------\n",
            "epoch 31/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 31 average loss: 0.1613\n",
            "----------\n",
            "epoch 32/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 32 average loss: 0.1597\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.23it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 32 current mean IoU: 0.7868\n",
            "best mean IoU: 0.7956 at epoch: 26\n",
            "----------\n",
            "epoch 33/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 33 average loss: 0.1565\n",
            "----------\n",
            "epoch 34/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 34 average loss: 0.1570\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.02it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 34 current mean IoU: 0.7821\n",
            "best mean IoU: 0.7956 at epoch: 26\n",
            "----------\n",
            "epoch 35/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 35 average loss: 0.1508\n",
            "----------\n",
            "epoch 36/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 36 average loss: 0.1414\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 20.80it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 36 current mean IoU: 0.7909\n",
            "best mean IoU: 0.7956 at epoch: 26\n",
            "----------\n",
            "epoch 37/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:12<00:00,  7.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 37 average loss: 0.1383\n",
            "----------\n",
            "epoch 38/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.46it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 38 average loss: 0.1352\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 20.61it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 38 current mean IoU: 0.7907\n",
            "best mean IoU: 0.7956 at epoch: 26\n",
            "----------\n",
            "epoch 39/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 39 average loss: 0.1424\n",
            "----------\n",
            "epoch 40/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:12<00:00,  7.48it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 40 average loss: 0.1344\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.02it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 40 current mean IoU: 0.7864\n",
            "best mean IoU: 0.7956 at epoch: 26\n",
            "----------\n",
            "Fold 2\n",
            "----------\n",
            "epoch 41/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.43it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 41 average loss: 0.1525\n",
            "----------\n",
            "epoch 42/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.43it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 42 average loss: 0.1435\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.21it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 42 current mean IoU: 0.8786\n",
            "best mean IoU: 0.8786 at epoch: 42\n",
            "----------\n",
            "epoch 43/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.41it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 43 average loss: 0.1441\n",
            "----------\n",
            "epoch 44/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.39it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 44 average loss: 0.1389\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.00it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 44 current mean IoU: 0.8778\n",
            "best mean IoU: 0.8786 at epoch: 42\n",
            "----------\n",
            "epoch 45/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.35it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 45 average loss: 0.1353\n",
            "----------\n",
            "epoch 46/100\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
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          ]
        },
        {
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          "name": "stdout",
          "text": [
            "epoch 46 average loss: 0.1417\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
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          ]
        },
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          "text": [
            "current epoch: 46 current mean IoU: 0.8460\n",
            "best mean IoU: 0.8786 at epoch: 42\n",
            "----------\n",
            "epoch 47/100\n"
          ]
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            "epoch 47 average loss: 0.1375\n",
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            "epoch 48/100\n"
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            "epoch 48 average loss: 0.1319\n"
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            "current epoch: 48 current mean IoU: 0.8702\n",
            "best mean IoU: 0.8786 at epoch: 42\n",
            "----------\n",
            "epoch 49/100\n"
          ]
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            "epoch 49 average loss: 0.1251\n",
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            "epoch 50/100\n"
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            "epoch 50 average loss: 0.1240\n"
          ]
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            "current epoch: 50 current mean IoU: 0.8663\n",
            "best mean IoU: 0.8786 at epoch: 42\n",
            "----------\n",
            "epoch 51/100\n"
          ]
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            "epoch 51 average loss: 0.1189\n",
            "----------\n",
            "epoch 52/100\n"
          ]
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            "epoch 52 average loss: 0.1189\n"
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            "current epoch: 52 current mean IoU: 0.8642\n",
            "best mean IoU: 0.8786 at epoch: 42\n",
            "----------\n",
            "epoch 53/100\n"
          ]
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            "epoch 53 average loss: 0.1187\n",
            "----------\n",
            "epoch 54/100\n"
          ]
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            "epoch 54 average loss: 0.1136\n"
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          "text": [
            "current epoch: 54 current mean IoU: 0.8626\n",
            "best mean IoU: 0.8786 at epoch: 42\n",
            "----------\n",
            "epoch 55/100\n"
          ]
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            "epoch 55 average loss: 0.1157\n",
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            "epoch 56/100\n"
          ]
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            "epoch 56 average loss: 0.1139\n"
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          "text": [
            "current epoch: 56 current mean IoU: 0.8602\n",
            "best mean IoU: 0.8786 at epoch: 42\n",
            "----------\n",
            "epoch 57/100\n"
          ]
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            "epoch 57 average loss: 0.1146\n",
            "----------\n",
            "epoch 58/100\n"
          ]
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            "epoch 58 average loss: 0.1149\n"
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          "text": [
            "current epoch: 58 current mean IoU: 0.8505\n",
            "best mean IoU: 0.8786 at epoch: 42\n",
            "----------\n",
            "epoch 59/100\n"
          ]
        },
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            "epoch 59 average loss: 0.1112\n",
            "----------\n",
            "epoch 60/100\n"
          ]
        },
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          "text": [
            "epoch 60 average loss: 0.1115\n"
          ]
        },
        {
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          "text": [
            "current epoch: 60 current mean IoU: 0.8497\n",
            "best mean IoU: 0.8786 at epoch: 42\n",
            "----------\n",
            "Fold 3\n",
            "----------\n",
            "epoch 61/100\n"
          ]
        },
        {
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            "epoch 61 average loss: 0.1225\n",
            "----------\n",
            "epoch 62/100\n"
          ]
        },
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            "epoch 62 average loss: 0.1121\n"
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 62 current mean IoU: 0.9207\n",
            "best mean IoU: 0.9207 at epoch: 62\n",
            "----------\n",
            "epoch 63/100\n"
          ]
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            "epoch 63 average loss: 0.1125\n",
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            "epoch 64/100\n"
          ]
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            "epoch 64 average loss: 0.1114\n"
          ]
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          ]
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          "text": [
            "current epoch: 64 current mean IoU: 0.9158\n",
            "best mean IoU: 0.9207 at epoch: 62\n",
            "----------\n",
            "epoch 65/100\n"
          ]
        },
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            "epoch 65 average loss: 0.1059\n",
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            "epoch 66/100\n"
          ]
        },
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            "epoch 66 average loss: 0.1027\n"
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          "text": [
            "current epoch: 66 current mean IoU: 0.9147\n",
            "best mean IoU: 0.9207 at epoch: 62\n",
            "----------\n",
            "epoch 67/100\n"
          ]
        },
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            "epoch 67 average loss: 0.1030\n",
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            "epoch 68/100\n"
          ]
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            "epoch 68 average loss: 0.0995\n"
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          "text": [
            "current epoch: 68 current mean IoU: 0.9101\n",
            "best mean IoU: 0.9207 at epoch: 62\n",
            "----------\n",
            "epoch 69/100\n"
          ]
        },
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            "epoch 69 average loss: 0.1019\n",
            "----------\n",
            "epoch 70/100\n"
          ]
        },
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            "epoch 70 average loss: 0.1032\n"
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          "text": [
            "current epoch: 70 current mean IoU: 0.9070\n",
            "best mean IoU: 0.9207 at epoch: 62\n",
            "----------\n",
            "epoch 71/100\n"
          ]
        },
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            "epoch 71 average loss: 0.0987\n",
            "----------\n",
            "epoch 72/100\n"
          ]
        },
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            "epoch 72 average loss: 0.0961\n"
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          "text": [
            "current epoch: 72 current mean IoU: 0.9066\n",
            "best mean IoU: 0.9207 at epoch: 62\n",
            "----------\n",
            "epoch 73/100\n"
          ]
        },
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            "epoch 73 average loss: 0.0983\n",
            "----------\n",
            "epoch 74/100\n"
          ]
        },
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            "epoch 74 average loss: 0.0962\n"
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          "text": [
            "current epoch: 74 current mean IoU: 0.9028\n",
            "best mean IoU: 0.9207 at epoch: 62\n",
            "----------\n",
            "epoch 75/100\n"
          ]
        },
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            "epoch 75 average loss: 0.0968\n",
            "----------\n",
            "epoch 76/100\n"
          ]
        },
        {
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            "epoch 76 average loss: 0.0910\n"
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          "text": [
            "current epoch: 76 current mean IoU: 0.9007\n",
            "best mean IoU: 0.9207 at epoch: 62\n",
            "----------\n",
            "epoch 77/100\n"
          ]
        },
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            "epoch 77 average loss: 0.0881\n",
            "----------\n",
            "epoch 78/100\n"
          ]
        },
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            "epoch 78 average loss: 0.0907\n"
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          "text": [
            "current epoch: 78 current mean IoU: 0.8937\n",
            "best mean IoU: 0.9207 at epoch: 62\n",
            "----------\n",
            "epoch 79/100\n"
          ]
        },
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            "epoch 79 average loss: 0.0893\n",
            "----------\n",
            "epoch 80/100\n"
          ]
        },
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            "epoch 80 average loss: 0.0883\n"
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          "text": [
            "current epoch: 80 current mean IoU: 0.8957\n",
            "best mean IoU: 0.9207 at epoch: 62\n",
            "----------\n",
            "Fold 4\n",
            "----------\n",
            "epoch 81/100\n"
          ]
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            "epoch 81 average loss: 0.0999\n",
            "----------\n",
            "epoch 82/100\n"
          ]
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 82 average loss: 0.0936\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 20.75it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "saved new best metric model\n",
            "current epoch: 82 current mean IoU: 0.9386\n",
            "best mean IoU: 0.9386 at epoch: 82\n",
            "----------\n",
            "epoch 83/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.41it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 83 average loss: 0.0953\n",
            "----------\n",
            "epoch 84/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 84 average loss: 0.0943\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.01it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 84 current mean IoU: 0.9351\n",
            "best mean IoU: 0.9386 at epoch: 82\n",
            "----------\n",
            "epoch 85/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.38it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 85 average loss: 0.0899\n",
            "----------\n",
            "epoch 86/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.43it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 86 average loss: 0.0927\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.02it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 86 current mean IoU: 0.9300\n",
            "best mean IoU: 0.9386 at epoch: 82\n",
            "----------\n",
            "epoch 87/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 87 average loss: 0.0905\n",
            "----------\n",
            "epoch 88/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.39it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 88 average loss: 0.0868\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 20.78it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 88 current mean IoU: 0.9304\n",
            "best mean IoU: 0.9386 at epoch: 82\n",
            "----------\n",
            "epoch 89/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.38it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 89 average loss: 0.0859\n",
            "----------\n",
            "epoch 90/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.42it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 90 average loss: 0.0892\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 20.81it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 90 current mean IoU: 0.9243\n",
            "best mean IoU: 0.9386 at epoch: 82\n",
            "----------\n",
            "epoch 91/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 91 average loss: 0.0892\n",
            "----------\n",
            "epoch 92/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 92 average loss: 0.0889\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 21.07it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 92 current mean IoU: 0.9282\n",
            "best mean IoU: 0.9386 at epoch: 82\n",
            "----------\n",
            "epoch 93/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.42it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 93 average loss: 0.0841\n",
            "----------\n",
            "epoch 94/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 94 average loss: 0.0828\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 20.73it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 94 current mean IoU: 0.9194\n",
            "best mean IoU: 0.9386 at epoch: 82\n",
            "----------\n",
            "epoch 95/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 95 average loss: 0.0819\n",
            "----------\n",
            "epoch 96/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 96 average loss: 0.0816\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 20.95it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 96 current mean IoU: 0.9232\n",
            "best mean IoU: 0.9386 at epoch: 82\n",
            "----------\n",
            "epoch 97/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 97 average loss: 0.0820\n",
            "----------\n",
            "epoch 98/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 98 average loss: 0.0796\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 20.74it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 98 current mean IoU: 0.9194\n",
            "best mean IoU: 0.9386 at epoch: 82\n",
            "----------\n",
            "epoch 99/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.37it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 99 average loss: 0.0777\n",
            "----------\n",
            "epoch 100/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 97/97 [00:13<00:00,  7.40it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 100 average loss: 0.0775\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Validation: 100%|██████████| 97/97 [00:04<00:00, 20.94it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "current epoch: 100 current mean IoU: 0.9175\n",
            "best mean IoU: 0.9386 at epoch: 82\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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iiRMnasGCBaynBtBq8bmiuk6dOmnixIn63//+5+y0/dZbb8nDw6PamuZt27bpqquuUlBQkAIDAxUaGupsmnqm1wRHn47PPvtMb775pi677LIan0ukyr+7nJwcvfjiizWun9OmTZOkatfQxYsXa/DgwbJarerUqZNCQ0O1bNmyWuP99fvqSLAb2uskIyNDRUVFdf6d2u12HT58uN6P19AvedA+saYaTaauf3R+3UDDsZfgDz/8oE8++USff/65brvtNj3zzDP64Ycfqs0yNoWT1yed6pikWhuDtUaNfX2GYejtt99WYWFhrcl7enq6CgoK5O/vr5EjRyo2NlbvvvuubrzxRn3yyScqLi7W5MmTneMdTU9ef/11RURE1Hg8D4/q/wR5e3vXmIW32Wy68MILlZWVpXvvvVd9+/aVn5+fUlJSdOutt1ZrrFJfdrtdYWFh1b4kOJmjoU1zuO666zRu3Dj973//0xdffKGnnnpKTz75pJYuXaqLL75YkvTMM8/o1ltv1UcffaQvvvhCf/zjHzV37lz98MMPtX4JAQBtQXv6XHHzzTfr008/1aeffqrLL79cH3zwgXOduCTl5OTonHPOUWBgoB555BH16NFDVqtVGzdu1L333tuoa+HJIiMjde655+qZZ57R999/X2fHb8fz3HzzzZo6dWqtYxxr1d944w3deuutuvLKK3XPPfcoLCxMFotFc+fO1b59+2qc547PZlarVZJqbXwqVfaKcYwBToWkGqcVGhqqwMBA/fLLL6cc5/hGMScnx1mKJZ34FvjXRo4cqZEjR+of//iH3nrrLd1000165513dPvtt9d5IXXMBO/atavGfTt37lRISEiL2oLJbrdr//79ztlpSdq9e7ckORufdO3aVStXrlR+fn61b4UdpVyO19yjRw/Z7XZt3769RnOPpvLNN9/oyJEjeuSRR5wz5Q7Z2dm644479OGHHzq/Gb/uuuv07LPPKi8vT0uWLFFsbGy1LUF69OghSQoLC9P48eMbFdPWrVu1e/duLV68WFOmTHEe//LLL6uNc7xPe/furfEYvz7Wo0cPrVy5UmPGjKlWTtdUGvp7GhkZqd///vf6/e9/r/T0dA0bNkz/+Mc/nEm1JA0aNEiDBg3SAw88oDVr1mjMmDFauHAh+4cDaHX4XFHT5ZdfroCAAL311lvy9PRUdnZ2tdLvVatW6fjx41q6dGm1xmcHDhxoshhuvPFG3X777QoODtYll1xS65jQ0FAFBATIZrOd9rr+/vvvq3v37lq6dGm193/OnDlNFnNdMfr6+tb5d2o2mxUTEyOp+t//ycvmHHbv3t1kFRVo2yj/xmmZzWZdeeWV+uSTT7R+/foa9zu+QXQkUN9++63zvsLCQi1evLja+Ozs7BrfOjqSREeplqPrc05OTrVxkZGRiouL0+LFi6vd98svv+iLL76o8yLgTs8995zzz4Zh6LnnnpOnp6cuuOACSdIll1wim81WbZwk/etf/5LJZHImVldeeaXMZrMeeeSRGt9IN9W3uI7S73vuuUfXXHNNtduMGTPUq1evGiXgpaWlWrx4sVasWFFj642JEycqMDBQjz/+eK3r3DIyMk4bk+Ob65Nfo2EYevbZZ6uNi4qK0sCBA/Xaa69VW+f9zTffaOvWrdXGXnfddbLZbHr00UdrPF9FRUWN37uGqu/vqc1mq1ECFxYWpqioKOf/C3l5eaqoqKg2ZtCgQTKbzfUqbQSAlobPFTX5+Pjoqquu0vLly/XCCy/Iz89PV1xxhfP+2q6FZWVlev7555sshmuuuUZz5szR888/X2f3dYvFoquvvloffPBBrV+KnHxdry3mH3/8UWvXrm2ymOuKccKECfroo4+qbbuWlpamt956S2PHjlVgYKAkKT4+XmFhYfrvf/9b45r64YcfKiUlpdoX3EBdmKlGvTz++OP64osvdM455+iOO+5Qv379dOzYMb333ntavXq1goODNWHCBHXp0kXTp0/XPffcI4vFokWLFik0NFTJycnOx1q8eLGef/55XXXVVerRo4fy8/P10ksvKTAw0Hnx8vHxUf/+/bVkyRL17t1bHTt21MCBAzVw4EA99dRTuvjiizVq1ChNnz7dufVFUFBQtT0nXSklJUVvvPFGjeP+/v668sornT9brVatWLFCU6dOVUJCgj777DMtW7ZMf//7350lXZMmTdJ5552n+++/XwcPHtSQIUP0xRdf6KOPPtKf//xn54eKnj176v7779ejjz6qcePG6Te/+Y28vb31008/KSoqSnPnzj2j11RaWqoPPvhAF154YZ2lTpdffrmeffZZpaenKywsTMOGDXPGVVpaWq30W5ICAwP1wgsv6JZbbtGwYcN0/fXXO38fli1bpjFjxtT4MuHX+vbtqx49euivf/2rUlJSFBgYqA8++KDWNVaPP/64rrjiCo0ZM0bTpk1Tdna2nnvuOQ0cOLBaon3OOefot7/9rebOnavNmzdrwoQJ8vT01J49e/Tee+/p2Wef1TXXXHPa92zevHnOD2oOZrNZf//73+v1e5qfn6/OnTvrmmuu0ZAhQ+Tv76+VK1fqp59+cu5v+tVXX2nmzJm69tpr1bt3b1VUVOj11193frABgNaIzxU13XzzzXrttdf0+eef66abbqo2Qz569Gh16NBBU6dO1R//+EeZTCa9/vrrTVoaXd/X+8QTT+jrr79WQkKCZsyYof79+ysrK0sbN27UypUrlZWVJUm67LLLtHTpUl111VW69NJLdeDAAS1cuFD9+/evdk12hccee0xffvmlxo4dq9///vfy8PDQf/7zH5WWluqf//ync5yXl5eefvppTZ06VWeddZYmT56sTp06adOmTVq0aJEGDx6sO+64w6Wxoo1ozlbjaN0OHTpkTJkyxQgNDTW8vb2N7t27G3fddVe17RI2bNhgJCQkGF5eXkaXLl2MefPm1dj6YuPGjcYNN9xgdOnSxfD29jbCwsKMyy67zFi/fn2151uzZo0RHx9veHl51dgGY+XKlcaYMWMMHx8fIzAw0Jg0aZKxffv2aufXte3R1KlTDT8/vxqv7+StJU7lVFtqnbztguN59u3bZ0yYMMHw9fU1wsPDjTlz5tTYEis/P9+4++67jaioKMPT09Po1auX8dRTT1XbKsth0aJFxtChQw1vb2+jQ4cOxjnnnGN8+eWX1eK79NJLa31955xzTp2v64MPPjAkGS+//HKdYxzbUz377LPOY/fff78hyejZs2ed53399dfGxIkTjaCgIMNqtRo9evQwbr311mp/53X9vRiGYWzfvt0YP3684e/vb4SEhBgzZswwfv75Z0OS8corr1Qb+8477xh9+/Y1vL29jYEDBxoff/yxcfXVVxt9+/at8bgvvviiER8fb/j4+BgBAQHGoEGDjL/97W/G0aNH63wthnHid6u2m8VicY473e9paWmpcc899xhDhgwxAgICDD8/P2PIkCHG888/7xyzf/9+47bbbjN69OhhWK1Wo2PHjsZ5551nrFy58pQxAkBLx+eK6ioqKozIyEhDkrF8+fIa93///ffGyJEjDR8fHyMqKsr429/+Znz++eeGJOPrr7+uFk9Dt9SqS23bVRmGYaSlpRl33XWXERMTY3h6ehoRERHGBRdcYLz44ovOMXa73Xj88ceNrl27Gt7e3sbQoUONTz/9tEZ8ji21nnrqqRrP/+u/p4bEuHHjRmPixImGv7+/4evra5x33nnGmjVran2Mzz77zDjvvPOMwMBAw9PT0+jWrZuRmJhoZGdnn/K5AQeTYbSRzkxAC3Prrbfq/fffd/m3sTi9uLg4hYaG1liHDQAAAJwp1lQDaDPKy8trrD9etWqVfv75Z5177rnuCQoAAABtGmuqAbQZKSkpGj9+vG6++WZFRUVp586dWrhwoSIiIvS73/3O3eEBAACgDSKpBtBmdOjQQfHx8frvf/+rjIwM+fn56dJLL9UTTzyhTp06uTs8AAAAtEGsqQYAAAAAoJFYUw0AAAAAQCORVAMAAAAA0EitYk213W7X0aNHFRAQIJPJ5O5wAADtnGEYys/PV1RUlMxmvp9uClzrAQAtTX2v960iqT569KhiYmLcHQYAANUcPnxYnTt3dncYbQLXegBAS3W6632rSKoDAgIkVb6YwMBAN0cDAGjv8vLyFBMT47w+4cxxrQcAtDT1vd63iqTaUQYWGBjIhRYA0GJQptx0uNYDAFqq013vWQgGAAAAAEAjkVQDAAAAANBIJNUAAAAAADQSSTUAAAAAAI1EUg0AAAAAQCORVAMAAAAA0Egk1QAAAAAANBJJNQAAAAAAjURSDQAAAABAI5FUAwAAAADQSCTVAAAAAAA0Ekk1AAAAAACNRFINAAAAAEAjkVQDAAAAANBIJNUAAAAAADQSSTUAAAAAAI3UrpLqorIKfbs7Q5/8fNTdoQAAAABAo+zPKNAvKbmy2w13hwJJHu4OoDml5ZVqyqJ18vG06LLBkTKZTO4OCQAAAADqLT2vRJf833cqKberg6+nxvYK1bheIRrXK0SRQT7uDq9daldJdVSwVSaTVFxu0/HCMoX4e7s7JAAAAACot6Sd6Sopt0uSsovK9cnPR52VuD3D/DWuV4jO7hWqUT06yeppcWeo7Ua7Sqq9PSwKD7AqNa9ER7KLSaoBAAAAtCpf70yXJP3x/J46u3eovt2Tqe/2ZOjnwznam16gvekFeuX7gwry8dS18Z1188iuig3xc3PUbVu7SqolqXMHn6qkukhxMcHuDgcAAAAA6qW0wqbVezMlSRf2j9CgzkEaHttRiRf2Vm5Rudbsy9S3ezK1ale6juWW6L+rD+i/qw/o7N6humVkV53fN0wWM0tgm1q7S6pjOvpq/aFsHc4qdncoAAAAAFBv6w5kqajMptAAbw2ICqx2X5Cvpy4eFKmLB0XKZje0ale6Xv/hkL7ZnaFvq27RwT66MaGLrhseo9AAbxmGodIKu0rL7SqpsKmk3KaScrssZqlHqD89qOqp3SXVnTtULt4/kl3k5kgAAAAAoP6+3pkhSTqvT6jMp5hxtphNuqBfuC7oF67k40V6c90hvfvTYaXkFOupz3dp3pe75WE2qbTCXudjDIgK1B/O76UJ/cNP+Vxoh0l1TAdfSdKRbGaqAQAAALQeX++qXE99ft+wep/TpZOvZl3cT3eP763lW4/p9R8OaVNyjmy/2o7LYjbJ6mGW1dOigtIKbTuap9+9sUF9wgP0hwt66uKBkacsHc8uLNPKHWn6cnuaPC1mJU7orR6h/o17oa1Mu0uqHTPVh5mpBgAAANBKHMgs1IHMQnlaTBrTM6TB51s9LfrNsM76zbDOOpZbLJvdkLeHRVbPykTa02J2js0qLNOi1Qe0eM1B7UrL18y3NqlH6G794fxeumxwpDyqxh7NKdYX21L1+bY0rTuYVS1R/2pnumZP6q/rz4pp82Xk7TCprpypTskulmEYbf4vGAAAAEDr5+j6fVZsRwVYPc/osU63n3VHPy/9dWIfzRjXXa+sOaBFqw9oX0ah/rxks+av3K2LBkZqzb5MbTmSW+28vhEBmjggQusPZen7vcc1a+lWfbUzXU9ePVgd/bzOKOaWrN0l1ZHBVplNUmmFXRn5pQoLtLo7JAAAAAA4pcaUfp+pIF9P/Xl8b00f202vrT2k/363XwePF2nhN/skSSaTFN+lgyYOiNDEARHq0qlyAtNuN/Ty6gP65+c79eX2NG0+/K2euXaIzu4dWudz5ZWUa9WuDH23O0Md/Lw0vl+4hnUJds6Kt2TtLqn2tJgVGeSjlJxiHc4uJqkGAAAA3MRmN9jiqR4KSyv04/4sSdK5fZovqXYIsHrqrvN66tbRsXrzx0PadjRPCd06aXz/MIUF1MynzGaTZpzdXaN7dtKf3tmsvekFmrJonW4b001/u6iPrJ4WSdKx3GJ9ub1yHfYP+4+r3HaifPzFb/erg6+nzusbpgv7hWtc71D5e7fM9LVlRuVi0R0qk+oj2UWK79rB3eEAAAAA7c6nW45q5lubNKRzkK4f0UWThkS12KTJ3b7fm6kym11dOvqqR6if2+Lw8/bQHWf3qPf4AVFB+mTmWM39bIdeW3tIi74/oDX7MjVxQIS+2pmurSnVy8d7hPppfL9wZeSX6qtd6couKtfSjSlaujFFXhazRvXopPH9wzU0JljdQ/3k69Uyfl9aRhTNLKaDr9YdyKIDOAAAAOAmn21NlST9fCRXPx/Zqkc/3a5Jg6M0eUSMhsYE0/voJCeXfre298XHy6JHrhioc/uE6m/vb9HO1HztTM2XdKJ8/ML+4bqwf7i6n9QtvMJm1/pD2Vq5PU1f7kjToeNF+mZ3hr7ZneEcEx3so+6hfuoR6q8eYf7qEeqnnqH+Cg3wbtb3qV0m1exVDQAAALjXztQ8SdI18Z21MTlb+zMKtWT9YS1Zf1h9wgM0+awYXTU0Wh3acIOr+jAM48T+1M24nrqpnd83XJ/96WzN+3K3sgvLdF7fUJ3fN1yhAd61jvewmDWyeyeN7N5J91/aT/syCvTl9nSt2pWuPekFyiosU0pOsVJyivXdnsxq504eHqMnrxncHC+rMtZme6YWJKZj5QL6w1nMVAMAAADNraTcpgOZhZKkeyb2UViAt9Yfytbb65K1bMsx7UrL1yOfbtcjn25XkI+nOvl5qZO/lzr5eVf+199bIf5e6tLRV2N6hlTbDqqt2XEsX6l5JfLxtCihW0d3h3NGQgO8Nfc3gxp8nslkUs+wAPUMC9Cd51aWn2cVlml/RoH2ZRRoX0ah9qVX/jk5q8jZMK25tMukmplqAAAAwH32phfIbkjBvp4KqyrVPSu2o86K7ag5kwbo480penvdYW0/lqfc4nLlFpdrf1US/msd/bx0RVyUrh7WWQOiAltdefTpOEq/x/Ts5Gzwhcq/945+HTU8tvoXDaUVtmoNz5pDu06qU3KKZbcbMtNxEAAAAGg2jjW1fcIDaiTBQT6eumVUrG4ZFauswjIdLyhVZkGZjheWKquwrPLPBaXKLCjVhkM5yiwo1SvfH9Qr3x9U34gAXT2ss64YGlVrV+rW6Kuq/albc+l3c/L2sKi5+921y6Q6ItAqD7NJ5TZDafklp938HAAAAEDT2VW1nrpvRMApx1XORnqpV3jt91fY7Pp2T4Y+2JCiL7enaWdqvv6xfIeeWLFTZ/cK0aQhURrVo1Or/byfXVimTcnZktyzlRbqp10m1R4WsyKDrTqcVawj2cWt9n8yAAAAoDVyzlRHBJ7R43hYzDq/b7jO7xuu3KJyfbLlqD7YeESbknP09a4Mfb2rssFXTEcfJXTrpIRuHZXQrZNiOvq0ijLxb/dkyG5UfvkQHUzO0lK1y6RakjoH+1Yl1UU6K7Z1L/gHAAAAWpNdzqT61DPVDRHk66mbR3bVzSO7al9GgZZuPKLv9mTql5RcHc4q1uGsI3p/wxFJUmSQVSO6dVTXTn7y9jCfdLPI27Pqz54WDYgKdGsZOaXfrUO7TapjOvpo7X46gAMAAADNKbuwTOn5pZKaNqk+WY9Qf90zsa/umSjll5Rrw6Fs/XggS+sOZGnLkRwdyy3RR5uPnvZxTCYpLiZYE/pHaMKAcPU4aR9lV7PZDeeezOdR+t2itdukunOHyjbrdAAHAKCmBQsW6KmnnlJqaqqGDBmif//73xoxYkStY8vLyzV37lwtXrxYKSkp6tOnj5588klddNFFzRw1gNbAUfrduYOP/Juho1SA1VPn9glzrkkuLrNpU3K21h3M0vGCMpVW2FRWYVep82ZTabldeSXl2p1WoE3JOdqUnKMnV+xU91A/Z4Id1znYpQ2PNx/OVk5RuYJ8PDWsS7DLngdnrt0m1TEdK9ckMFMNAEB1S5YsUWJiohYuXKiEhATNnz9fEydO1K5duxQWVnO25IEHHtAbb7yhl156SX379tXnn3+uq666SmvWrNHQoUPd8AoAtGT1bVLmKj5eFo3uGaLRPUNOOzYtr0Rfbk/TF9vTtHZfpvZnFGrhN/u08Jt9CvH31uDOQeoV5q8eYf7qFeavnmH+CrB6NkmcX++snKU+u3eoPNrwPtxtQbtNqp0z1TnMVAMAcLJ58+ZpxowZmjZtmiRp4cKFWrZsmRYtWqT77ruvxvjXX39d999/vy655BJJ0p133qmVK1fqmWee0RtvvNGssQNo+XalVc5U9z3DJmXNITzQ6lynnVdSrm92ZeiL7WlatTNdmQWl+mpnunPds0NEoFW9wv3VNyJAU0bFKqajb6Oe2/G45/cNPePXAddqx0l15Uz1sZwSVdjsfPsDAICksrIybdiwQbNmzXIeM5vNGj9+vNauXVvrOaWlpbJaqzfy8fHx0erVq+t8ntLSUpWWljp/zsvLO8PIAbQWO13QpKw5BFo9NWlIlCYNiVJZhV2bkrO1O71Ae9PytTejQHvSCpSeX6rUvBKl5pXouz2ZWrzmkG4dE6u7zuupIJ/6z2Cn5pZo+7E8mUzS2b1Iqlu6dptUhwdY5Wmp3Ks6Na/EOXMNAEB7lpmZKZvNpvDw6pvChoeHa+fOnbWeM3HiRM2bN09nn322evTooaSkJC1dulQ2m63O55k7d64efvjhJo0dQMtntxvaneqYqW5dSfXJvDzMSujeSQndO1U7nltcrr3pBdqXXqCPfz6q1Xsz9eK3+/Xe+sP68/jeujGhizzrMZm3alflLHVcTLA6+Xu75DWg6bTb6Vmz2eTc6+1INuuqAQBorGeffVa9evVS37595eXlpZkzZ2ratGkym+v+mDFr1izl5uY6b4cPH27GiAG4S0pOsQrLbPKymBUb4ufucJpckI+n4rt20HVnxej16SP0yrSz1DPMX9lF5Zrz8TZNnP+tVm5Pk2EYtZ5fVFahrUdy9eHmFEnS+XT9bhXa7Uy1VLmu+uDxIpJqAACqhISEyGKxKC0trdrxtLQ0RURE1HpOaGioPvzwQ5WUlOj48eOKiorSfffdp+7du9f5PN7e3vL2ZvYFaG8cpd89wvzrNWPbmplMJp3XJ0zjeobonZ8O619f7tb+jELd/tp6jereSdPHdlNGQan2phc4byk51fMS9qduHdp1Un2iAzjNygAAkCQvLy/Fx8crKSlJV155pSTJbrcrKSlJM2fOPOW5VqtV0dHRKi8v1wcffKDrrruuGSIG0JrsPObezt/u4GEx6+aRXXVFXJSeX7VPL68+oLX7j2vt/uO1ju/o56Weof46p0+oBkS1/GZuaOdJ9Ym9qpmpBgDAITExUVOnTtXw4cM1YsQIzZ8/X4WFhc5u4FOmTFF0dLTmzp0rSfrxxx+VkpKiuLg4paSk6KGHHpLdbtff/vY3d74MAC3QzrTW2aSsKQRYPXXvRX11U0IXzftitzYdzlFMR1/1DK3cistx6+jn5e5Q0UDtPKmumqnOZqYaAACHyZMnKyMjQ7Nnz1Zqaqri4uK0YsUKZ/Oy5OTkauulS0pK9MADD2j//v3y9/fXJZdcotdff13BwcFuegUAWqpdrbTzd1Pq3MFX8ybHuTsMNKF2nVQ7GpUdy2WmGgCAk82cObPOcu9Vq1ZV+/mcc87R9u3bmyEqAK1ZaYVNBzILJbWv8m+0fW27O8BpOEorcgrL3RwJAAAA0LbtTS+QzW4o0OqhiEDr6U8AWol2nVQH+1Ym1fmlFSq32d0cDQAAANB27XLuTx0ok8nk5miAptOuk+pA64nq99xiZqsBAAAAV2E9Ndqqdp1Ue1jMzsQ6p4ikGgAAAHCVnSTVaKPadVItnSgBzykqc3MkAAAAQNt1ovybpBptS7vu/i1JHXw9lZzFTDUAAAAa56PNKVq9J1MBVk8F+ngo0OqpQB9PBVo9qv7rqe6hfrJ6WtwdqtvkFpUrNa9EktSbpBptTLtPqoOqZqqzmakGAABAA5Xb7Lrn/S0qqzh109u+EQFa/sdxMpvbZ4Ounal5kiq3tA20ero5GqBptfukuoNv5f/UNCoDAABAQx3NKVZZhV1eHmbdNqab8krKlVdcrvySCuefk7OKtDM1X9/vy9S4XqHuDtktdqVR+o22q90n1cE+lUk15d8AAABoqEPHiyRJXTv66r6L+9Y6ZvZHv+i1tYf0zrrD7TappkkZ2jIalVH+DQAAgEZKzqpKqjv51jnm+rO6SJK+2J6q4wWlzRJXS8N2WmjLSKqryr9zKP8GAABAAzmS6piOdSfV/aMCNbhzkMpthpZuTGnQ49vshpJ2pGlfRsEZxelOhmGc1Pk70M3RAE2P8m9HUs1MNQAAABro0PFCSZXl36dy/VldtOXIVr3zU7JuH9dNJlP9GpYt+Hqv5n25W5LUPdRPF/YP14T+4Roa06HVND07kl2sgtIKeVpM6h7q5+5wgCbHTLVzn2pmqgEAANAwzjXVnU6dLE4aEikfT4v2ZRRq/aHsej12dmGZXvx2vyTJbJL2ZxTqP9/s19UvrNWIx1fq3ve3aOX2NJWU287sRbiYY5a6R6i/PC3tPv1AG8RMNY3KAAAA0AiGYehwPcq/JSnA6qlJQyL17vojemfdYZ0V2/G0j/+fb/eroLRC/SID9c4dI/Xt7gx9uT1NX+9KV2ZBmZasP6wl6w/L28OsPhEB6h0eoD7hAeodUfnf8EDves+Iu5Kj8zfrqdFWtfukuoNzpprybwAAANTf8cIyFZbZZDJJMR19Tjv++hFd9O76I1q29ahmT+qvIJ+692tOzy/Rq2sOSJL+cmFvBfl4atKQKE0aEqWyCrvWHcjSl9tT9eX2NB3NLdGWI7naciS32mMEWj3UJyJAPcMC1LmDj/MWHeyrsADvZisfp/M32rp2n1Q71lQXltmcewwCAAAAp+Mo/Y4MtMrbw3La8UNjgtU73F+70wr08c9HdcvIrnWOff7rfSopt2tITLAu6BdW7T4vD7PG9grR2F4heujyATqQWajdafnalVqg3Wn52pmap4PHi5RXUqGfDmbrp4M1y809LSZFBvkoOthHsSF+GhgdqEHRQeoTEVCv19IQu1LzJLFHNdqudp9UB1o9ZTJJhiHlFJcpLMDq7pAAAADQCiRnVTYp63KK7bROZjKZdP1ZXfTIp9v1zrrkOpPqoznFeuvHZEnSPRP6nLKE22QyqXuov7qH+uuigSeOl5TbtD+jMtnel1GglOxiHckpVkp2sVLzSlRuM5ScVaTkrCKt3X/ceZ6nxaTe4QEaGBWkgZ2DNCg6SP0jAxs98VRWYdf+jMr3qQ+dv9FGtfuk2mw2KcjHUzlF5copKiepBgAAQL0kHy+WJHU5zXrqk101NFpPfLZT247maeuRXA3qHFRjzL+/2qsym10J3TpqTM9OjYrN6mlR/6hA9Y+qmchW2OxKyy/VkawipeQUa096gX5JydXWlFzlFJVr29E8bTuapyXrD0uSooN9tOCmYYqLCW5wHPsyClRhNxRg9VBUEJ+z0Ta1+6RaqlxX7UiqAQAAgPo4VDVTfbrO3yfr4OeliwZG6OOfj+qdn5I1qPOg6o95vFDvVSWzfznNLHVjeVjMig6uLP0+mWEYSskpdibYW1Py9PPhHKXkFOvahWs0e9IA3ZzQpUExndifOqBFNE0DXIEFxJKzSUQ2zcoAAABQT8lVa6obMlMtSdefFSNJ+mjzURWVVVS779mVe1RhN3R271CN6Hb6DuFNyWQyqXMHX100MFL3TOyr124boe/uPU8XDYhQuc3Qgx/+osR3f64R86nQpAztAUm1TjQry2WmGgAAAPWUnOXYo7phSfXI7p3UtZOvCkortGzLMefxPWn5+t/mFEnSXyf0brpAz0Cg1VMv3DxM91/STxazSf/blKIrF3yv/RkF9Trf0aSM9dRoy0iqddK2WsXMVAMAAOD0istsSs8vldTwmWqz2aTrhlfOVr/z02Hn8fkr98gwpAn9wzW4c3CTxXqmTCaTZpzdXW/dnqDQAG/tTivQ5c99r8+2HjvtuSeXfwNtFUm1Ti7/ZqYaAAAAp+eYpQ60eii4aoKmIa6N7yyL2aQNh7K1Jy1f247matnWYzKZpMQWMkv9awndO2nZH8dqRLeOKiit0J1vbtRjn25Xuc1e6/jc4nIdzS2RJPUOJ6lG20WjMp00U01SDQAAgHo4dLzhTcpOFhZo1QV9w/TF9jS989NhHcysfLxJg6PUtwWXSocFWPXW7Ql66vNd+s+3+/Xf1Qf08vcHFODtoUAfTwVaPRXo46EgH085cu2oIKtzEgtoi0iqdWJNdQ6NygAAAFAPjpnqhpZ+n+z6ETH6Ynua3voxWcXlNplN0p/H92qqEF3Gw2LWrEv6aWiXDpq1dIuyi8qVV1KhvJIKScU1xg+IrrltGNCWkFTr5KSamWoAAACcnjOpbmCTspOd0ztMEYFWpeZVlkhfPayzuof6N0l8zeGigREa3y+sKqkuV15xZXKdW+z4c7lKyu36zdBod4cKuBRJteRcB8OWWgAAAKiPQ1XbaXU9g5lqi9mk64Z31v99tVeeFpP+eEHLn6X+NQ+LWaEB3goN8HZ3KIDbkFRL6uDYUquYmWoAAACcXlPMVEvSlNGx+uFAlib0D1fMGSToANyHpFpSsA8z1QAAAKgfm93QkewzX1MtSSH+3nr3t6OaIiwAbsKWWpKCqmaqS8rtKim3uTkaAAAAtGTHcotVbjPkaTEpMsjH3eEAcDOSalXuL2gxmyTRrAwAAACnlly1njqmg6/zMySA9oukWpLJZHLunZdTTAk4AAAA6uZYT80aaAASSbUT22oBAACgPg5VJdVdz7BJGYC2gaS6SrBjpppmZQAAADgFR/n3mTYpA9A2kFRX6VC1VzUz1QAAADiVQ1mFkqSunfzcHAmAloCkuoqjA3g2STUAAABOgZlqACcjqa7inKmmURkAAADqkFNUprySCkkk1QAqkVRXca6pLmSmGgAAALU7VDVLHRbgLR8vi5ujAdASkFRXCfZjphoAAACnRudvAL/WqKR6wYIFio2NldVqVUJCgtatW3fK8fPnz1efPn3k4+OjmJgY3X333SopKWlUwK7imKlmTTUAAADqcpg9qgH8SoOT6iVLligxMVFz5szRxo0bNWTIEE2cOFHp6em1jn/rrbd03333ac6cOdqxY4defvllLVmyRH//+9/POPim5NinOpekGgAAAHU4dLyq83dHOn8DqNTgpHrevHmaMWOGpk2bpv79+2vhwoXy9fXVokWLah2/Zs0ajRkzRjfeeKNiY2M1YcIE3XDDDaed3W5ujkZl2exTDQAAgDo41lRT/g3AoUFJdVlZmTZs2KDx48efeACzWePHj9fatWtrPWf06NHasGGDM4nev3+/li9frksuuaTO5yktLVVeXl61m6sFORqVFZfLMAyXPx8AAABaH8q/AfyaR0MGZ2ZmymazKTw8vNrx8PBw7dy5s9ZzbrzxRmVmZmrs2LEyDEMVFRX63e9+d8ry77lz5+rhhx9uSGhnrENVo7KyCruKy23y9WrQWwMAAIA2rrTCpmN5lX2BmKkG4ODy7t+rVq3S448/rueff14bN27U0qVLtWzZMj366KN1njNr1izl5uY6b4cPH3Z1mPLzssjDbJIk5bCuGgAAAL9yOKtYhlH5ubFT1YQMADRoOjYkJEQWi0VpaWnVjqelpSkiIqLWcx588EHdcsstuv322yVJgwYNUmFhoe644w7df//9Mptr5vXe3t7y9vZuSGhnzGQyKdjXS5kFpcopKldUsE+zPj8AAABatuSsyiZlXTr5yWQyuTkaAC1Fg2aqvby8FB8fr6SkJOcxu92upKQkjRo1qtZzioqKaiTOFotFklrc2mVHB/AcmpUBAADgV5KrmpR16cjkC4ATGrxwODExUVOnTtXw4cM1YsQIzZ8/X4WFhZo2bZokacqUKYqOjtbcuXMlSZMmTdK8efM0dOhQJSQkaO/evXrwwQc1adIkZ3LdUnTwPdGsDAAAADjZoSxH52+20wJwQoOT6smTJysjI0OzZ89Wamqq4uLitGLFCmfzsuTk5Goz0w888IBMJpMeeOABpaSkKDQ0VJMmTdI//vGPpnsVTSTIh221AAAAULsTM9U0KQNwQqNaXM+cOVMzZ86s9b5Vq1ZVfwIPD82ZM0dz5sxpzFM1qxPl38xUAwAAoLrkLJJqADW5vPt3a9KBNdUAAACohd1uOJNqttMCcDKS6pME+1aWfzNTDQAAgJOl55eqtMIui9nELjEAqiGpPomj/DubpBoA0M4tWLBAsbGxslqtSkhI0Lp16045fv78+erTp498fHwUExOju+++WyUlJc0ULeB6h45XbqcVHewjTwsfoQGcwL8IJwnyqUyq8+j+DQBox5YsWaLExETNmTNHGzdu1JAhQzRx4kSlp6fXOv6tt97Sfffdpzlz5mjHjh16+eWXtWTJEv39739v5sgB12E9NYC6kFSfJMBamVTnl1a4ORIAANxn3rx5mjFjhqZNm6b+/ftr4cKF8vX11aJFi2odv2bNGo0ZM0Y33nijYmNjNWHCBN1www2nnd0GWhNnUs16agC/QlJ9En/vymboBaXMVAMA2qeysjJt2LBB48ePdx4zm80aP3681q5dW+s5o0eP1oYNG5xJ9P79+7V8+XJdcskldT5PaWmp8vLyqt2AluxQ1XZaXZmpBvArjdpSq60KsFYl1SXMVAMA2qfMzEzZbDaFh4dXOx4eHq6dO3fWes6NN96ozMxMjR07VoZhqKKiQr/73e9OWf49d+5cPfzww00aO+BKh+j8DaAOzFSf5MRMdYUMw3BzNAAAtA6rVq3S448/rueff14bN27U0qVLtWzZMj366KN1njNr1izl5uY6b4cPH27GiOEK247m6t9Je7R6T6ZKym3uDqfJHa5KqmOYqQbwK8xUn8S/aqa63GaotMIuq6fFzREBANC8QkJCZLFYlJaWVu14WlqaIiIiaj3nwQcf1C233KLbb79dkjRo0CAVFhbqjjvu0P333y+zueZ3+N7e3vL29m76FwC3+OTno/rrez+rtMIuSfLyMCu+SweN7RWi0T06aVB0kDxaccfs/JJyZRWWSZK6dvJzczQAWhqS6pP4eZ14OwpKK0iqAQDtjpeXl+Lj45WUlKQrr7xSkmS325WUlKSZM2fWek5RUVGNxNliqbyGUvnVttnthp5N2qNnk/ZIkgZGByozv0ypeSVau/+41u4/LkkK8PbQyB6ddNGACP1mWLRMJpM7w24wx3rqTn5ezspGAHDgX4WTWMwm+XlZVFhmU0FJhUL8+QYdAND+JCYmaurUqRo+fLhGjBih+fPnq7CwUNOmTZMkTZkyRdHR0Zo7d64kadKkSZo3b56GDh2qhIQE7d27Vw8++KAmTZrkTK7R9hSX2fTX937Wsq3HJEkzxnXTfRf3k9kk7cso1Jp9mfp+b6bW7juuvJIKfbk9TV9uT1NyVpHuvrB3s8a6P6NAb/6YrJJymzzMJlnMZnlYTDKbTFU/m+Tv7aHz+oaqZ1hAjfMp/QZwKiTVv+Jv9ahMqtlWCwDQTk2ePFkZGRmaPXu2UlNTFRcXpxUrVjiblyUnJ1ebmX7ggQdkMpn0wAMPKCUlRaGhoZo0aZL+8Y9/uOslwMVSc0s047X12pqSK0+LSf+4cpCuOyvGeX/PMH/1DPPXlFGxstkN/ZKSq2Vbj+nFb/fr2aQ9igiy6oYRXVwep2EYemtdsh77dIeK67HO+x/Ld6hvRIAmDYnSZYMjnaXeNCkDcComoxXUZeXl5SkoKEi5ubkKDAx06XNd8Mwq7cso1NszRmpUj04ufS4AQOvUnNel9oL31P0y8kv19/9tVbCPp4bHdlB81w7qHuIvs7l6qfaWIzma8dp6peWVqoOvpxbeHK+E7vX7zPTMF7v076/2ymI26cVb4nVBv/DTn1TFbjdkMqnepeOZBaW674MtWrkjXZI0qnsnJXTvKJvdUIXdkL3qv5U/25WSXazVezNVbjvx0Xhw5yBdNjhSm5Jz9Nkvqfrj+T2VOKFPvWMG0LrV99rETPWv+Fs9JYmZagAA0K68u/6wvtxe2aDuvQ1HJEnBvp4a1qUywY7v2kFpeSW694MtKim3q1eYv16eepa6NGD2NvHC3jqWW6L3NxzRXW9t1NszRmpolw6nPe+Tn49qzsfb5Odt0dXDOuvqYZ1PWYr91c40/e39LcosKJOXxay/XdRHt43pVuMLgl/LLSrX59tS9cmWo1qz77i2HMnVliO5zvu70KQMQC1Iqn8lwLmtVrmbIwEAAGg+v6RUJo+juneS3TD085Ec5RSV66ud6fpqZ3q1sef1CdX/3TBUAVWTEfVlMpk09zeDlJFfqm92Z2j64vX64M7R6hZSe7KaU1SmBz/apk9+PipJyiqU5q/co/kr92h0j066dnhnXTQgUj5elWv3i8ts+sfy7Xrjh2RJUp/wAM2/Pk79IutX/RDk66nrzorRdWfFKLOgVJ/9kqpPfj6qnw5myWwyKS4muEGvF0D7QFL9K869qkuYqQYAAO3H1qqk+g8X9NToHiEqt9m1/WieNhzK1oZD2Vp/KEvZheWaMqqrZl3ST5bTzPrWxdNi1vM3DdP1L/6grSm5mrponT64c7RCA6o3iP16V7rufX+L0vNLZTGbdNd5PdUtxFfvrT+iNfuOO28Pem/TZYMjNaZniP61crf2ZxRKkqaP7aZ7JvZp9G4uIf7eumVkV90ysqvS8kpUUm5jOy0AtSKp/pWAqr2q8yn/BgAA7UR2YZmOZBdLkgZGB0mqTH6HxARrSEywbhvbTYZRuQbZswn2m/bz9tCiW8/Sb174XslZRZq++Ce9PWOk/Lw9VFhaoX8s36G3fqycbe4e6qd/XRenIVWzxFcN7awj2UX6YEOK3t94WIezivXOT4f1zk+HJUnhgd565to4je0VcsZxOoQHWpvssQC0PSTVv+JvZaYaAAC0L78crZylju3kq8A6SrpNJpM8LU23v3RogLdeuy1BV7+wRluO5Oqutzbqt2f30L0fbFFyVbftaWNide9FfWvMNnfu4Ks/je+lP5zfU+sOZum99Uf0xfZUndsnTI9eMUDBvl5NFicAnA5J9a+cWFNNUg0AANoHR+m3Y5a6uXQL8dPLU4frhpd+0KpdGVq1K0OSFB3so6euGazRPU8922w2mzSyeyeN7N5J0pBmiBgAajrz+p02hplqAADQ3jialA1q5qRakoZ26aAFNw6TY4n2NfGd9dmfx502oQaAloKZ6l/x964seWJNNQAAaC/cNVPtcEG/cC39/RiV2+w6K7ajW2IAgMYiqf4VZqoBAEB7kltUrsNZVU3KotyTVEtiuyoArRbl37/iWFOdzz7VAACgHXA0KevS0VdBvg3bdxoAQFJdAzPVAACgPdnqxvXUANAWkFT/ij/dvwEAQDvi7vXUANDakVT/iiOpzmemGgAAtAPu7PwNAG0BSfWvBFSVf5dW2FVWYXdzNAAAAK6TW1yuQ8eLJEkDogLdHA0AtE4k1b/i532iIXohJeAAAKAN21bVpKxzBx918PNyczQA0DqRVP+Kp8Usq2fl28K6agAA0JZR+g0AZ46kuhb+3pXbSbCuGgAAtGVbU/Ik0aQMAM4ESXUtHOuqmakGAABtGTPVAHDmSKprcWJbrXI3RwIAAOAaeSXlOpBZKImZagA4EyTVtXDMVFP+DQAA2qptVaXf0cE+6kiTMgBoNJLqWpyYqSapBgAAbZOj9HtgNFtpAcCZIKmuhb9jTTUz1QAAoI365SjrqQGgKZBU1yKAmWoAANDGbXXOVJNUA8CZIKmuhT9rqgEAQBtWUFrhbFLGTDUAnBmS6lo49qlmphoAALRF21JyZRhSVJBVnfy93R0OALRqJNW1YE01AABoyxyl3wOYpQaAM0ZSXQvWVAMAgLbM0fmb0m8AOHMk1bVwbKmVT1INAADaoK0k1QDQZEiqa3Gi/LvczZEAAAA0rcLSCu2valJG528AOHMk1bXwp/wbAAC0UduP5ckwpIhAq0IDaFIGAGeKpLoWATQqAwAAbdTWI+xPDQBNiaS6Fo6Z6sIym2x2w83RAAAANB1Hk7KB0YFujgQA2gaS6lo41lRLlIADAIC2hSZlANC0SKpr4e1hkZel8q0hqQYAAG1FUVmF9mUUSCKpBoCmQlJdB3/WVQMAgDZmx7E82Q0pLMBbYYFWd4cDAG0CSXUdnM3KStlWCwAAtA2OJmXMUgNA0yGproOjWVk+M9UAAKCN2JqSJ4nO3wDQlEiq68Be1QAAoK050fmbpBoAmgpJdR3YqxoAALQlxWU27UnPl0T5NwA0JZLqOjBTDQAA2pKfj+TIbkgh/t4KD/R2dzgA0GaQVNfB0f2bNdUAAKAt+GjzUUnS2b1DZDKZ3BwNALQdJNV18Pf2lMRMNQAAaP1Kym36dEtlUn3NsM5ujgYA2haS6jqwphoAALQVK3ekKb+kQlFBVo3s3snd4QBAm0JSXQfWVAMAgLbigw1HJElXDYuW2UzpNwA0JZLqOjj3qSapBgAArVh6fom+3ZMpSfoNpd8A0ORIquvg7yz/LndzJAAAAI338eajstkNxcUEq0eov7vDAYA2h6S6DgGUfwMAgDbg/arS76vjmaUGAFcgqa6DP43KAABAK7ftaK52pubLy2LWpMGR7g4HANokkuo6sKYaAAC0dks3pkiSxvcPU7Cvl5ujAYC2iaS6Ds6Z6tIKGYbh5mgAAAAaptxm10ebK5Pq3wyl9BsAXIWkug4B3p6SJMOQispsbo4GAACgYb7dnaHMgjJ18vPSOX1C3R0OALRZJNV1sHqaZanax5FmZQAAoLVxlH5fERctTwsf+QDAVfgXtg4mk0kBVSXg+TQrAwC0MwsWLFBsbKysVqsSEhK0bt26Oseee+65MplMNW6XXnppM0aMk+UWlevL7WmSpKvjo90cDQC0bSTVp+DPtloAgHZoyZIlSkxM1Jw5c7Rx40YNGTJEEydOVHp6eq3jly5dqmPHjjlvv/zyiywWi6699tpmjhwOn2w5qjKbXX0jAtQ/MtDd4QBAm0ZSfQrODuAl5W6OBACA5jNv3jzNmDFD06ZNU//+/bVw4UL5+vpq0aJFtY7v2LGjIiIinLcvv/xSvr6+JNVu9MHGqr2ph3WWyWRyczQA0LaRVJ9CAHtVAwDambKyMm3YsEHjx493HjObzRo/frzWrl1br8d4+eWXdf3118vPz6/OMaWlpcrLy6t2w+ltO5qrz7elqsJmr3PM/owCbUrOkcVs0hVDo5oxOgBon0iqT4G9qgEA7U1mZqZsNpvCw8OrHQ8PD1dqauppz1+3bp1++eUX3X777accN3fuXAUFBTlvMTExZxR3e5CeV6LrFq7Vb1/foEv/b7VW7UqvddtPR4Oys3uFKCzA2txhAkC7Q1J9Cv7Wym21mKkGAKB+Xn75ZQ0aNEgjRow45bhZs2YpNzfXeTt8+HAzRdh6Pf3FLhVWbfO5Ky1ft77yk6YsWqftR0/M8tvthv63qWpv6mHsTQ0AzcHD3QG0ZDQqAwC0NyEhIbJYLEpLS6t2PC0tTREREac8t7CwUO+8844eeeSR0z6Pt7e3vL29zyjW9uSXlFy9t6FynfQr087Smr2ZWrzmkL7bk6nVe7/TtfGd9ZcJfbQvo0ApOcUKsHrowv7hp3lUAEBTYKb6FJxrqkmqAQDthJeXl+Lj45WUlOQ8ZrfblZSUpFGjRp3y3Pfee0+lpaW6+eabXR1mu2IYhh79dLsMQ7p8SJTO6xOm+y/tr5WJ5+jSwZEyDOnd9Ud07lOrNOejbZKkywZHyeppcXPkANA+kFSfAt2/AQDtUWJiol566SUtXrxYO3bs0J133qnCwkJNmzZNkjRlyhTNmjWrxnkvv/yyrrzySnXq1Km5Q27TPt+Wph8PZMnbw6x7L+7rPN6lk68W3DhMH9w5WsO6BKu43KY96QWSpGvYmxoAmg3l36fgmKnOZ001AKAdmTx5sjIyMjR79mylpqYqLi5OK1ascDYvS05Oltlc/Xv5Xbt2afXq1friiy/cEXKbVVph0+PLd0iS7ji7u6KDfWqMie/aQR/cOVqf/ZKqf3+1V106+mhYlw7NHSoAtFsk1acQWNWoLI+kGgDQzsycOVMzZ86s9b5Vq1bVONanT59aO1HjzCxec1DJWUUKC/DW787pUec4k8mkSwZF6pJBkc0YHQBAovz7lBwz1XnFlH8DAIDmdbygVP9O2itJumdiH/l5MxcCAC0RSfUpBPpUzlSzphoAADS3eV/uVn5phQZGB+pqtscCgBaLpPoUKP8GAADusCs1X2+vS5YkPXhpf5nNJjdHBACoC0n1KQT6UP4NAACal2EYemzZdtkN6eKBEUroTjd1AGjJSKpPwVH+XVphV0m5zc3RAACA9uDrXen6bk+mvCxmzbq4n7vDAQCcBkn1Kfh7echUVW3FtloAAMDVym12PbascgutaWNi1aWTr5sjAgCcDkn1KZjNJvl7O/aqpgQcAAC4TkFphZ76fJf2ZxSqk5+X7jq/p7tDAgDUA3sznEag1VP5JRU0KwMAAC5xILNQr609qPfXH1F+aeXnjcQJvZ0NUwEALRtJ9WkE+ngqJaeYZmUAAKDJ2O2Gvt2TocVrDurrXRnO491D/XT72O66YUSMG6MDADREo8q/FyxYoNjYWFmtViUkJGjdunWnHJ+Tk6O77rpLkZGR8vb2Vu/evbV8+fJGBdzcAq1VHcAp/wYAAGeotMKmV78/oPHzvtGtr/ykr3dlyGSSzu8bpsW3jdDKu8/RjQldZDKxhRYAtBYNnqlesmSJEhMTtXDhQiUkJGj+/PmaOHGidu3apbCwsBrjy8rKdOGFFyosLEzvv/++oqOjdejQIQUHBzdF/C4X4NirupjybwAAcGYS3/1Zy7YckyQFeHvo2uExmjKqq2JD/NwcGQCgsRqcVM+bN08zZszQtGnTJEkLFy7UsmXLtGjRIt133301xi9atEhZWVlas2aNPD0rE9TY2Ngzi7oZOfaqplEZAAA4E8u2HNOyLcfkYTbpwcv665r4zvLzZiUeALR2DSr/Lisr04YNGzR+/PgTD2A2a/z48Vq7dm2t53z88ccaNWqU7rrrLoWHh2vgwIF6/PHHZbPVve9zaWmp8vLyqt3cxdEkhPJvAADQWJkFpXrwo18kSb8/r6emjo4loQaANqJBSXVmZqZsNpvCw8OrHQ8PD1dqamqt5+zfv1/vv/++bDabli9frgcffFDPPPOMHnvssTqfZ+7cuQoKCnLeYmLc16wj0IfybwAAcGZmf/SLsgrL1C8yUDPPY6ssAGhLXL5Ptd1uV1hYmF588UXFx8dr8uTJuv/++7Vw4cI6z5k1a5Zyc3Odt8OHD7s6zDrRqAwAAJyJZVuOafnWVHmYTXr62sHy8nD5xy8AQDNqUN1RSEiILBaL0tLSqh1PS0tTREREredERkbK09NTFovFeaxfv35KTU1VWVmZvLy8apzj7e0tb2/vhoTmMs7yb7bUAgAADfTrsu8BUUFujggA0NQa9FWpl5eX4uPjlZSU5Dxmt9uVlJSkUaNG1XrOmDFjtHfvXtntduex3bt3KzIystaEuqU50aiM8m8AANAwjrLvvhEBlH0DQBvV4PqjxMREvfTSS1q8eLF27NihO++8U4WFhc5u4FOmTNGsWbOc4++8805lZWXpT3/6k3bv3q1ly5bp8ccf11133dV0r8KFaFQGAAAao3rZ9xDKvgGgjWpw28nJkycrIyNDs2fPVmpqquLi4rRixQpn87Lk5GSZzScuGjExMfr888919913a/DgwYqOjtaf/vQn3XvvvU33KlyIRmUAAKChfl32PTCasm8AaKsatZfDzJkzNXPmzFrvW7VqVY1jo0aN0g8//NCYp3I7ZqoBAEBDUfYNAO0HdUinEVDV/buozKZym/00owEAQHtH2TcAtC/8K38ajqRakgpoVgYAAE4hPa+Esm8AaGdIqk/Dw2KWn1fldmCUgAMAgLqsO5Cly/69mrJvAGhnGrWmur0J9PFUYZmNZmUAAKAGu93Qi9/t11Of75LNbqhnmL9euDmesm8AaCdIqush0OqpY7klzFQDAIBqcorK9Jd3f1bSznRJ0pVxUfrHVYPk581HLABoL/gXvx4c66rzikmqAQBApc2Hc3TXmxuVklMsLw+zHr58gK4/K0Ymk8ndoQEAmhFJdT049qrOp1EZAADtnmEYWrzmoP6xfIfKbYa6dvLV8zcN04AompIBQHtEUl0PgY6Zasq/AQBo18ptdt29ZLM+3XJMknTxwAg9ec1gBVo93RwZAMBdSKrrwTFTTfk3AADt24ebUvTplmPyMJv090v6adqYWMq9AaCdI6muB8e3z3mUfwMA0K45Zqj/cH4v3Ta2m5ujAQC0BOz1UA80KgMAANmFZfp+b6YkadKQSDdHAwBoKUiq68FZ/s1MNQAA7daKbamqsBvqHxmo7qH+7g4HANBCkFTXw4nyb2aqAQBorz7dclSSdBmz1ACAk5BU10OgD+XfAAC0Z5kFpVq777gk6bJBUW6OBgDQkpBU10OAlX2qAQBozz77JVV2QxrcOUhdOvm6OxwAQAtCUl0PgTQqAwCgXfv056rS78GUfgMAqiOprgdHo7KCsgrZ7YabowEAAM0pLa9E6w5mSZIuHUzpNwCgOpLqenBsqWUYUn4pJeAAALQnn209JsOQhnUJVnSwj7vDAQC0MCTV9eDtYZHVs/KtogQcAID25dMtxyQxSw0AqB1JdT0FsK0WAADtztGcYq0/lC2TSbp0EOupAQA1kVTX04lmZZR/AwDQXizfWjlLfVbXjooIsro5GgBAS0RSXU+OZmX5zFQDANBuOEq/LxvCLDUAoHYk1fUU6Cz/ZqYaAID24HBWkTYfzpHZJF00MMLd4QAAWiiS6npyzFTTqAwAgPZhWVXpd0K3TgoLoPQbAFA7kup6cmyrRaMyAADah2WUfgMA6oGkup6c5d80KgMAoM07mFmorSm5sphNumgApd8AgLqRVNdToE/lTDWNygAAaPscpd+je3RSJ39vN0cDAGjJSKrrKZB9qgEAaDc++fmoJOmywZR+AwBOjaS6nk40KqP8GwCAtmxveoF2pubLw2zSREq/AQCnQVJdTzQqAwCgfXA0KBvbK0TBvl5ujgYA0NKRVNcT5d8AALQPn25xlH5HuTkSAEBrQFJdT0HORmWUfwMA2r4FCxYoNjZWVqtVCQkJWrdu3SnH5+Tk6K677lJkZKS8vb3Vu3dvLV++vJmibTp70/O1J71AXhazLuwf7u5wAACtgIe7A2gtTmypVS7DMGQymdwcEQAArrFkyRIlJiZq4cKFSkhI0Pz58zVx4kTt2rVLYWFhNcaXlZXpwgsvVFhYmN5//31FR0fr0KFDCg4Obv7gz9DKHemSpFE9Oimoqp8KAACnQlJdT45GZXZDKiyzyd+btw4A0DbNmzdPM2bM0LRp0yRJCxcu1LJly7Ro0SLdd999NcYvWrRIWVlZWrNmjTw9K6+XsbGxzRlyk/mqKqm+oF/NLw8AAKgN5d/15O1hlqelcnY6r5h11QCAtqmsrEwbNmzQ+PHjncfMZrPGjx+vtWvX1nrOxx9/rFGjRumuu+5SeHi4Bg4cqMcff1w2m63O5yktLVVeXl61m7tlF5Zp/aEsSdL5fUmqAQD1Q1JdTyaTiWZlAIA2LzMzUzabTeHh1dcTh4eHKzU1tdZz9u/fr/fff182m03Lly/Xgw8+qGeeeUaPPfZYnc8zd+5cBQUFOW8xMTFN+joa45vdGbIbUt+IAHXu4OvucAAArQRJdQM4SsBpVgYAwAl2u11hYWF68cUXFR8fr8mTJ+v+++/XwoUL6zxn1qxZys3Ndd4OHz7cjBHXbuWONEmUfgMAGoaFwQ0Q6NirmvJvAEAbFRISIovForS0tGrH09LSFBERUes5kZGR8vT0lMVicR7r16+fUlNTVVZWJi+vmns9e3t7y9vbu2mDPwPlNru+2Z0hSTq/L12/AQD1x0x1Azhmqin/BgC0VV5eXoqPj1dSUpLzmN1uV1JSkkaNGlXrOWPGjNHevXtlt9udx3bv3q3IyMhaE+qW6KeDWcovqVAnPy/FxQS7OxwAQCtCUt0AAc6Zasq/AQBtV2Jiol566SUtXrxYO3bs0J133qnCwkJnN/ApU6Zo1qxZzvF33nmnsrKy9Kc//Um7d+/WsmXL9Pjjj+uuu+5y10toMEfX73P7hMliZttMAED9Uf7dAI5GZbmUfwMA2rDJkycrIyNDs2fPVmpqquLi4rRixQpn87Lk5GSZzSe+l4+JidHnn3+uu+++W4MHD1Z0dLT+9Kc/6d5773XXS2iwpJ2VSfV41lMDABqIpLoBwgKtkqRjuSVujgQAANeaOXOmZs6cWet9q1atqnFs1KhR+uGHH1wclWvszyjQgcxCeVpMGtsrxN3hAABaGcq/G6BLx8rtNQ5nFbk5EgAA0FSSqkq/R3bvpICqqjQAAOqLpLoBHEl1Mkk1AABtRtLOyk7n5/el9BsA0HAk1Q3gSKpTcopVYbOfZjQAAGjpcovL9dPBbEnSBWylBQBoBJLqBggL8JaXh1k2u6GjOayrBgCgtftmd4ZsdkO9wvzVpZOvu8MBALRCJNUNYDabKAEHAKAN+WpHZen3Bf2YpQYANA5JdQORVAMA0DZU2Oz6eleGJOkCttICADQSSXUDkVQDANA2bEzOUW5xuYJ9PTWsSwd3hwMAaKVIqhsohm21AABoE5KqSr/P6xMmi9nk5mgAAK0VSXUDMVMNAEDbkLSzcn9qSr8BAGeCpLqBSKoBAGj9Dh0v1N70AnmYTTq7d6i7wwEAtGIk1Q0U09FHUuW+lrlF5W6OBgAANEbSjspZ6hHdOirQ6unmaAAArRlJdQP5enkoxN9bErPVAAC0Vkk7K9dTn9+X0m8AwJkhqW6Erp0oAQcAoLXKLynXj/uzJEnj2Z8aAHCGSKobgXXVAAC0Xt/uzlSF3VD3UD/Fhvi5OxwAQCtHUt0IMSTVAAC0Wo7Sb2apAQBNgaS6EbqwVzUAAK3WN7syJLGeGgDQNEiqG4HybwAAWqdym13HC8skSf0iAt0cDQCgLSCpbgRHUp2SU6wKm93N0QAAgPoqKrM5/+zjZXFjJACAtoKkuhHCArzl5WGWzW7oWG6Ju8MBAAD1VFyVVHuYTfLy4GMQAODMcTVpBLPZpJgOPpKkQ8cpAQcAoLUoKquQxCw1AKDpkFQ3EuuqAQBofRzl374k1QCAJkJS3UhdO1Xua0lSDQBA61FcXplU+3iSVAMAmgZJdSPFsK0WAACtjmNNtY+Xh5sjAQC0FSTVjUT5NwAArQ/l3wCApkZS3Ugk1QAAtD7F5ZWNykiqAQBNhaS6kWI6Vnb/zi0uV25RuZujAQAA9eGYqWZNNQCgqZBUN5Kvl4dC/L0lMVsNAEBrUUz5NwCgiZFUn4EuVbPVh7IK3RwJAACojyIalQEAmhhJ9RnoHuovSdqTVuDmSAAAQH1Q/g0AaGok1WegX2SgJGnHsTw3RwIAAOqjuIxGZQCApkVSfQb6RQRIknakklQDANAaFJc7yr9JqgEATYOk+gw4ZqoPZxUrv4QO4AAAtHTsUw0AaGok1Wegg5+XIgKtkqSdqflujgYAAJwO3b8BAE2NpPoM9YusKgFnXTUAAC0e3b8BAE2NpPoM0awMAIDWo6hqTbUv3b8BAE2EpPoMOZLq7cco/wYAoKVzdP+mURkAoKmQVJ8hR1K9KzVPNrvh5mgAAMCpnCj/JqkGADQNkuoz1C3ET1ZPs0rK7Tp4vNDd4QAAgFMoKadRGQCgaZFUnyGL2aQ+4TQrAwCgNXBuqeVJozIAQNMgqW4CNCsDAKDlMwxDxeWUfwMAmhZJdRM4kVTTrAwAgJaqpNwuo6r9CeXfAICmQlLdBJipBgCg5Suq6vwtST5sqQUAaCKNSqoXLFig2NhYWa1WJSQkaN26dfU675133pHJZNKVV17ZmKdtsfpGVq6pPpZbopyiMjdHAwAAauNYT+3tYZbZbHJzNACAtqLBSfWSJUuUmJioOXPmaOPGjRoyZIgmTpyo9PT0U5538OBB/fWvf9W4ceMaHWxLFWj1VOcOPpKk7cxWAwDQIhXT+RsA4AINTqrnzZunGTNmaNq0aerfv78WLlwoX19fLVq0qM5zbDabbrrpJj388MPq3r37GQXcUrGuGgCAlq3Y0fnbi87fAICm06CkuqysTBs2bND48eNPPIDZrPHjx2vt2rV1nvfII48oLCxM06dPr9fzlJaWKi8vr9qtpWNdNQAALZuj/JvO3wCAptSgpDozM1M2m03h4eHVjoeHhys1NbXWc1avXq2XX35ZL730Ur2fZ+7cuQoKCnLeYmJiGhKmW/SPZK9qAABasuLyykZllH8DAJqSS7t/5+fn65ZbbtFLL72kkJCQep83a9Ys5ebmOm+HDx92YZRNwzFTvSetQOU2u5ujAQAAv+acqabzNwCgCTVoUVFISIgsFovS0tKqHU9LS1NERESN8fv27dPBgwc1adIk5zG7vTLh9PDw0K5du9SjR48a53l7e8vb27shobldTAdf+XlZVFhm0/6MQvWJCHB3SAAA4CRFZTQqAwA0vQbNVHt5eSk+Pl5JSUnOY3a7XUlJSRo1alSN8X379tXWrVu1efNm5+3yyy/Xeeedp82bN7eKsu76MptNGhAVJEnaciTHvcEAAIAaillTDQBwgQa3v0xMTNTUqVM1fPhwjRgxQvPnz1dhYaGmTZsmSZoyZYqio6M1d+5cWa1WDRw4sNr5wcHBklTjeFswtGuw1h3M0sbkbF07vO18YQAAQFtwovyb7t8AgKbT4KvK5MmTlZGRodmzZys1NVVxcXFasWKFs3lZcnKyzGaXLtVuseK7dJAkrT+Y7eZIAADAr7FPNQDAFRqV/c6cOVOHDh1SaWmpfvzxRyUkJDjvW7VqlV599dU6z3311Vf14YcfNuZpW7xhXSuT6j3pBcotKndzNAAANN6CBQsUGxsrq9WqhIQErVu3rs6xr776qkwmU7Wb1Wptxmjrp7iM7t8AgKbXPqeUXSTE31uxnXwlSRsPM1sNAGidlixZosTERM2ZM0cbN27UkCFDNHHiRKWnp9d5TmBgoI4dO+a8HTp0qBkjrh/2qQYAuAJJdROL79pRkrTxEEk1AKB1mjdvnmbMmKFp06apf//+WrhwoXx9fbVo0aI6zzGZTIqIiHDeHMvCWpJiun8DAFyApLqJxVeVgG8gqQYAtEJlZWXasGGDxo8f7zxmNps1fvx4rV27ts7zCgoK1LVrV8XExOiKK67Qtm3bTvk8paWlysvLq3ZztRMz1TQqAwA0HZLqJuZIqjcfzlGFze7maAAAaJjMzEzZbLYaM83h4eFKTU2t9Zw+ffpo0aJF+uijj/TGG2/Ibrdr9OjROnLkSJ3PM3fuXAUFBTlvzbHNZlG5o/s3M9UAgKZDUt3EeoX5K8DbQ0VlNu1MzXd3OAAAuNyoUaM0ZcoUxcXF6ZxzztHSpUsVGhqq//znP3WeM2vWLOXm5jpvhw8fdnmcNCoDALgCSXUTM5tNGlo1W70xmRJwAEDrEhISIovForS0tGrH09LSFBERUa/H8PT01NChQ7V37946x3h7eyswMLDazdUcW2rRqAwA0JRIql3AsV8166oBAK2Nl5eX4uPjlZSU5Dxmt9uVlJSkUaNG1esxbDabtm7dqsjISFeF2SiONdW+lH8DAJoQnTpcgGZlAIDWLDExUVOnTtXw4cM1YsQIzZ8/X4WFhZo2bZokacqUKYqOjtbcuXMlSY888ohGjhypnj17KicnR0899ZQOHTqk22+/3Z0vo4YT3b/5+AMAaDpcVVxgSEyQzCbpSHax0vJKFB5odXdIAADU2+TJk5WRkaHZs2crNTVVcXFxWrFihbN5WXJysszmE8Vu2dnZmjFjhlJTU9WhQwfFx8drzZo16t+/v7teQq3YpxoA4AomwzAMdwdxOnl5eQoKClJubm6zrLlqChc/+512HMvTCzcN08WDWlb5GwDgzLTG61JL1xzvae/7P1OZza41952vqGAflzwHAKDtqO+1iTXVLhLfNVgSJeAAALQEFTa7yqq2umRLLQBAUyKpdhHnumo6gAMA4HaOPaolyr8BAE2LpNpF4rt0lCT9kpKrkpMu5AAAoPmVVK2nNpskbw8+/gAAmg5XFReJ6eij8EBvldsMrT/IbDUAAO5UdFLnb5PJ5OZoAABtCUm1i5hMJp3bO0ySlLQzzc3RAADQvtH5GwDgKiTVLnR+v6qkeke6WkGTdQAA2qzi8gpJki9JNQCgiZFUu9DYniHyspiVnFWkfRmF7g4HAIB2yzlTTedvAEATI6l2IT9vD43s0UmS9BUl4AAAuA3l3wAAVyGpdrEL+p4oAQcAAO5R7GxURlINAGhaJNUudn5VUr3+ULZyi8rdHA0AAO3TifJvDzdHAgBoa0iqXSymo696h/vLZjf0zZ4Md4cDAEC7VFzOTDUAwDVIqpvB+X3DJUlf7WBdNQAA7lBcRvdvAIBrkFQ3gwuqttZatTtDFTa7m6MBAKD9oVEZAMBVSKqbwdCYYAX7eiqnqFybDue4OxwAANqdIhqVAQBchKS6GXhYzDq3d6gkuoADAOAOxexTDQBwEZLqZnJ+v6p11exXDQBAsysqd5R/0/0bANC0SKqbyTm9QuVhNml3WoH2pue7OxwAANoVGpUBAFyFpLqZBPl66tw+lSXgSzemuDkaAADaF7bUAgC4Ckl1M/rNsM6SpP9tSpHdbrg5GgAA2o8i1lQDAFyEpLoZnd83TIFWDx3LLdEP+4+7OxwAANqNYmf3b9ZUAwCaFkl1M7J6WnTp4ChJ0tJNlIADANBc2KcaAOAqJNXN7Oph0ZKkz7Yec35rDgAAXIvybwCAq5BUN7P4rh3UpaOvCsts+mJ7qrvDAQCgXaD7NwDAVUiqm5nJZNJVQytnqz+gCzgAAC5nGIZzn2qSagBAUyOpdgNHUr16T4bS80rcHA0AAG1baYVdRtWmG6ypBgA0NZJqN4gN8VN81w6yG9JHm4+6OxwAANq0k3uY0P0bANDUSKrdxDFbTRdwAABcy1H67eVhlsVscnM0AIC2hqTaTS4bHCmL2aQdx/J06Hihu8MBAKDNokkZAMCVSKrdJNjXSyO7d5Qkfb6NLuAAALgK22kBAFyJpNqNJg6IkCR9vi3NzZEAANB2OZNqZqoBAC5AUu1GE/pXJtUbk7OVnk8XcAAAXMHRqIzybwCAK5BUu1FEkFVDYoJlGNKX25mtBgDAFYode1R70vkbAND0SKrdbOKAcEmUgAMA4CqUfwMAXImk2s0c66rX7stUXkm5m6MBAKDtofs3AMCVSKrdrEeov3qG+avcZujrnenuDgcAgDaHmWoAgCuRVLcAJ0rA2VoLAICmxpZaAABXIqluARxdwFftylBJVTMVAADQNJyNypipBgC4AEl1CzC4c5Aig6wqKrPp+72Z7g4HAIA2pahqTbWPF92/AQBNj6S6BTCZTJrQv7IEfOmmFBmG4eaIAABoO4rL7JKYqQYAuAZJdQtxeVyUJGnZlmN6+JPtsttJrAEAaArF5XT/BgC4Dkl1CxHftaMeuWKAJOnVNQf1l/d+VrnN7uaoAABo/WhUBgBwJZLqFmTKqFg9e32cPMwm/W9Tin77+gYVl9G4DACAM+FIqn1ZUw0AcAGS6hbmirhovTglXt4eZn21M10j5ybpoY+3advRXHeHBgBAq1Ts3Keajz0AgKbH1aUFOr9vuF6fnqDoYB/lFpfr1TUHden/rdal//edfkkhuQYAoCGc3b89makGADQ9kuoWakS3jvr2b+dp8W0jdOngSHlZzNp2NE/TXv1Jqbkl7g4PAIBWo7iMfaoBAK5DUt2CWcwmndM7VAtuHKYf/n6B+kYEKCO/VHe8vl4l5ay1BgCgPorKSaoBAK5DUt1KdPTz0ktThquDr6e2HMnVfR9sYT9rAADq4cSaapJqAEDTI6luRWI6+mrBTcNkMZv04eajeum7/e4OCQCAFs1mN1RaUblFJd2/AQCuQFLdyozuEaLZl/WXJD3x2U79uP+4myMCAKDlKj5puRTl3wAAVyCpboWmjOqqq4d1lt2Qnvlit7vDAQCgxXJ0/jaZJG8PPvYAAJoeV5dWyGQy6Z6JfeRlMWvdwSytO5Dl7pAAAGiRnOupPS0ymUxujgYA0BaRVLdSEUFWXR3fWZL03Nd73RwNAAAtUxHbaQEAXIykuhW785wesphN+nZ3hrYcyXF3OACANmTBggWKjY2V1WpVQkKC1q1bV6/z3nnnHZlMJl155ZWuDbCeiuj8DQBwMZLqVqxLJ19dPiRKkvT81/vcHA0AoK1YsmSJEhMTNWfOHG3cuFFDhgzRxIkTlZ6efsrzDh48qL/+9a8aN25cM0V6eiWOPao96fwNAHANkupW7vfn9pAkrdiWqj1p+W6OBgDQFsybN08zZszQtGnT1L9/fy1cuFC+vr5atGhRnefYbDbddNNNevjhh9W9e/dmjPbUmKkGALgaSXUr1ys8QBcNiJAkPb+K2WoAwJkpKyvThg0bNH78eOcxs9ms8ePHa+3atXWe98gjjygsLEzTp0+v1/OUlpYqLy+v2s0VHN2/WVMNAHAVkuo24K7zekqSPv75qF5fe9BZ6gYAQENlZmbKZrMpPDy82vHw8HClpqbWes7q1av18ssv66WXXqr388ydO1dBQUHOW0xMzBnFXZeTu38DAOAKJNVtwKDOQbpoQIRsdkMPfrRNY5/8Wgu/2af8knJ3hwYAaOPy8/N1yy236KWXXlJISEi9z5s1a5Zyc3Odt8OHD7skPsq/AQCuRteONmL+9XFa8tNhvfjtfqXkFOuJz3bq5dUH9NFdYxQV7OPu8AAArURISIgsFovS0tKqHU9LS1NERESN8fv27dPBgwc1adIk5zG73S5J8vDw0K5du9SjR48a53l7e8vb27uJo6+puJwttQAArsVMdRth9bRo6uhYrbrnXD197RDFdPRRRn6pHlu23d2hAQBaES8vL8XHxyspKcl5zG63KykpSaNGjaoxvm/fvtq6das2b97svF1++eU677zztHnzZpeVddfXiTXVzCMAAFyDK0wb42kx65r4zhoQFajL/r1ay7emavWeTI3tVf+SPABA+5aYmKipU6dq+PDhGjFihObPn6/CwkJNmzZNkjRlyhRFR0dr7ty5slqtGjhwYLXzg4ODJanGcXcoLqucNaf8GwDgKsxUt1H9IgN1y8iukqQ5H/+isgq7myMCALQWkydP1tNPP63Zs2crLi5Omzdv1ooVK5zNy5KTk3Xs2DE3R1k/xeVVM9U0KgMAuAgz1W3Y3Rf21qdbjmpfRqFe+f6AfntOzTVtAADUZubMmZo5c2at961ateqU57766qtNH1Aj0agMAOBqzFS3YUE+nrr3or6SpP9L2qPU3BI3RwQAQPMiqQYAuBpJdRt39bDOGtYlWIVlNt3z/s/6+Oej2pScrcyCUhmG4e7wAABwKcc+1XT/BgC4CuXfbZzZbNIjVwzU5c+t1nd7MvXdnkznfeN6hei/U4fL24MPGgCAtsnR/dvHk488AADXYKa6HRgYHaRFt56lq4d11ojYjooMsspkkr7bk6l5X+x2d3gAALhMETPVAAAX42vbduLcPmE6t0+Y8+cvtqXqjtc36MXv9uucPqEa3YMttwAAbU9JOUk1AMC1mKlupyYMiNANI2JkGNJf3v1ZuUXldY49mlOsWUu3aNmWY6zDBgC0KjQqAwC4Gkl1O/bgZf3VLcRPx3JLdP+HW2tNmA3D0L0fbNHb6w7rrrc26pqFa7UpOdsN0QIA0HAnGpVRnAcAcA2S6nbM18tD8yfHyWI26dMtx/S/TSk1xqzalaHv9mTK02KSj6dFGw5l66rn1+hP72xSbnHds9sAALibYRgqqir/9vFkphoA4Bok1e3ckJhg/fmCXpKk+//3i35JyXXeV26z69Fl2yVJ08Z009d/PVfXxHeWySR9tPmoHv5km1tiBgCgPspsdtnslVVYlH8DAFyFpBr6/Xk9dXbvUBWX23T74vVKzyuRJL35wyHtzyhUJz8vzTy/pyKCrHr62iF6c3qCJOl/m1K0/WieO0MHAKBOjtJviUZlAADXIamGLGaTnrtxqHqG+Ss1r0QzXluv1NwS/WvlHklS4oTeCrR6OseP7hmiSwdHyjCkJ1fsdFfYAACckqNJmafFJE8LH3kAAK7BFQaSpECrp16eOlzBvp76+UiuLn72W+UWl6tPeIAmD4+pMf5vE/vI02LSN7sz9P3eTDdEDADAqRWznhoA0AwalVQvWLBAsbGxslqtSkhI0Lp16+oc+9JLL2ncuHHq0KGDOnTooPHjx59yPNynayc/Lbw5Xh5mk7Krtth64LJ+8qjl2/2unfx0U0JXSdLcz3bIbmerLQBAy0LnbwBAc2hwUr1kyRIlJiZqzpw52rhxo4YMGaKJEycqPT291vGrVq3SDTfcoK+//lpr165VTEyMJkyYoJSUmp2m4X4ju3fS41cNkskkXTIoQuN6hdY59g/n95S/t4d+ScnTJ1uONmOUAACcXpEzqWamGgDgOiajts2JTyEhIUFnnXWWnnvuOUmS3W5XTEyM/vCHP+i+++477fk2m00dOnTQc889pylTptTrOfPy8hQUFKTc3FwFBgY2JFw0UmpuiTr5e512Ddq/k/bomS93q3MHH/11Qh95Wszy8jCrW4ifeob5N1O0ANC8uC41PVe8p6t2pevWV35S/8hALf/TuCZ5TABA+1Hfa1OD6qHKysq0YcMGzZo1y3nMbDZr/PjxWrt2bb0eo6ioSOXl5erYsWOdY0pLS1VaWur8OS+PDtPNLSLIWq9x08d10+s/HNKR7GL9ecnmavc9fe0QXRPf2QXRAQBwesXMVAMAmkGDyr8zMzNls9kUHh5e7Xh4eLhSU1Pr9Rj33nuvoqKiNH78+DrHzJ07V0FBQc5bTEzNRlloGXy9PDR/cpzG9wvX2J4hGhHbUX0jAiRJs5Zu0dp9x90cIQCgvXKUf7NHNQDAlZq1c8cTTzyhd955R6tWrZLVWvdM6KxZs5SYmOj8OS8vj8S6BRvdM0Sje4Y4f7bbDf3xnU36dMsx/e6NDVr6+9HqEUopOACgeRWVM1MNAHC9Bs1Uh4SEyGKxKC0trdrxtLQ0RUREnPLcp59+Wk888YS++OILDR48+JRjvb29FRgYWO2G1sNsNunpa4doaJdg5RaX67ZXf1JWYZm7wwIAtDMldP8GADSDBiXVXl5eio+PV1JSkvOY3W5XUlKSRo0aVed5//znP/Xoo49qxYoVGj58eOOjRath9bTopSnDFdPRR4eOF2nGa+tVWFrh7rAAAO0I5d8AgObQ4C21EhMT9dJLL2nx4sXasWOH7rzzThUWFmratGmSpClTplRrZPbkk0/qwQcf1KJFixQbG6vU1FSlpqaqoKCg6V4FWqQQf2+9cutZCrR6aMOhbN388o/Krdr/GgAAVysqr/wy19eTpBoA4DoNTqonT56sp59+WrNnz1ZcXJw2b96sFStWOJuXJScn69ixY87xL7zwgsrKynTNNdcoMjLSeXv66aeb7lWgxeoZFqDXpicoyMdTm5JzdP1LPyizoPT0JwIAcIaKmakGADSDRi0ymjlzpmbOnFnrfatWrar288GDBxvzFGhD4mKCteS3I3Xzf9dpx7E8XfeftXpjeoKign3cHRoAoA2j/BsA0Bzo3IFm0TciUO/+dqRu/u+P2p9RqNFPfKVOfl6KCvZRZJBV43qH6vqzYuRpaXDxBAAAtXLuU035NwDAhchg0Gy6h/rrvTtHq39kZTf344Vl2pqSqy+2p+nBD3/Rxc9+p292Z7g5SgBAW1FUVrWmmu7fAAAX4iqDZhUd7KNlfxyr3OJyHc0p0bHcYu1OK9BL3+3X3vQCTV20Tuf3DdMfzu+puJhgmUwmd4cMAGilKP8GADQHkmo0O5PJpGBfLwX7eql/VKAu6BeuGxO66P+S9mjxmoP6ame6vtqZrqggqyYOjNBFAyJ0VmxHmc0k2ACA+ispd+xTTVINAHAdyr/RIgT5eOrBy/prxZ/P1uVDouTrZdHR3BK98v1BTX7xB93w0g86mlPs7jABAK0IM9UAgObATDValJ5h/vq/G4aqpNym7/ZkasUvqfrsl2P68UCWLn72Oz159SBdNDDS3WECAFoBZ1JNozIAgAsxU40Wyepp0YX9w/XMdUO0/I/jNKRzkHKLy/W7NzZq1tKtzo6uAADUpdhZ/s0cAgDAdUiq0eLFhvjpvd+N1u/O6SGTSXp7XbJue/UnEmsAwCmd6P7NTDUAwHVIqtEqeHmYdd/FffX6bQny9/bQ2v3HSawBAHWy2w2VlNslsaYaAOBaJNVoVcb2CtHi20Y4E+vpi0msAQA1OUq/JWaqAQCuRVKNVie+awctvu0s+XlZtGZfZWK9Zl+mcovK3R0aAKCFODmptnqQVAMAXIfOHWiV4rt21GvTR2jKy+u0Zt9xrdl3XJLUuYOP4mKC9Zth0Tqnd5gs7G0NAO1S8Umdv81cCwAALkRSjVYrvmtHvX3HSL2wap9+OZqrw1nFOpJdeft0yzFFBll13fAYndc3TCXlNuWXVCivuFw9wvwVFxPs7vABAC7EHtUAgOZCUo1WbXDnYL1wc7wkKbeoXNuO5eqrHen6YOMRHcst0bNJe/Rs0p4a580Y101/u6ivPC2sgACAtsjR+Zs9qgEArkZSjTYjyNdTo3uEaHSPEN1zUR99vi1N76xL1t70AvlbPRRo9ZSXxax1B7P00ncHtCk5R/++cagig3xcEk9xmU25xeWKCLK65PEBAHVzlH/TpAwA4Gok1WiTvD0sunxIlC4fElXjvhW/pOqe93/W+kPZuvT/Vuv/rh+qsb1CmvT5DcPQ1FfWacOhbP3f9UN16eDIJn18AMCpFZFUAwCaCbWvaHcuGhihT/8wVgOiApVVWKbbXv1JPx3MatLnWHcgS+sOZMlmN3T3ks1aW9VIDQDQPIrKWVMNAGgeJNVol7p28tMHd47Whf3DVWaz647X1utgZmGTPf5L3x2QJPl7ezgff8exvCZ7fADAqZU4Z6opygMAuBZJNdotq6dF/3f9UA3uHKTsonLd9upPyikqO+PH3Z9RoKSdaZKkd387SiO6dVR+aYWmLlqnI9lFKrfZlZFfqt1p+corYW9tAHAFZ6MyZqoBAC5GUo12zcfLov9OHa7oYB/tzyzUb1/foLIKe73OzS0q14pfjqmkqsTQ4eXVB2QY0gV9w9Q/KlAvTRmuPuEBSs8v1blPrVKv+z/TWf9YqQn/+lajHk/Sa2sPym43Tvt85bb6xQUAOKn8m+7fAAAXI6lGuxcWYNWiW8+Sv7eHfjyQpZv++4OWbTmm0gpbneccOl6oKxas1u/e2KgpL69TQWnljEhWYZne33BEkjTj7O6SpCAfT71621nq2slXFVXJs8lUWRpeWGbT7I+26dr/rNXe9Pxan8tuN/TvpD3qP3uFJv9nrdY38fpvAGiL6P4NAGguLDQCJPWJCNCCm4bp9sU/6aeD2frpYLY6+HrqqqGdde3wzuoXGegcu+VIjm579SdlFlSWiq87mKVbF63TK9PO0hs/HFJphV2DooOU0K2j85zIIB8lJZ6jw9nFCvbxVKCPp0yS3vjxkJ78bKc2HMrWJc+u1rSxsZo2uptzG678knL95d2f9cX2ynLyHw9k6ZqFa3V+3zD9ZUJvDYgKar43CQBaEUf3b8q/AQCuZjIM4/R1p26Wl5enoKAg5ebmKjAw8PQnAI2UfLxIS9Yn6/0NR5SWV+o8PiAqUNfEd1ZYgFX3vP+zisps6h8ZqMQLeyvx3c3KK6nQsC7BSs4qUmZBmZ69Pk5XxEXX6zmP5hTrgQ9/0Vc70yVJHmaTLhscqUsGRerJFTu1L6NQXhaz7ru4r/ak5+vd9Udkq5rxnjKqq2Zd3I8PjUAz47rU9Jr6PZ21dKveXpesu8f31p/G92qCCAEA7U19r00k1UAtKmx2fbcnU++uP6yVO9JUbqv+v8nYniF64eZhCrB66peUXN303x+VW1zZdCwqyKpv/naePC31X11hGIZW7kjXS9/u17pflXdHBFq18JZ4xcUES5IOZBbqX1/u1sc/H5UkdQ/x078mx2lI1f0AXI/rUtNr6vf0z+9s0oebj+r+S/o5l+MAANAQ9b02saYaqIWHxazz+obphZvjte7v4/Xw5QM0KLqy1PqqodFadOtZCrB6SpIGRgfpzdsTFOxb+fNtY7s1KKGWJJPJpAv7h+vd343SJzPH6sq4KHmYTRrVvZM++cNYZ0ItSd1C/PR/NwzV69NHKDzQW/szC/WbF9Zo/srdqqCZGQBIkorZpxoA0EyYqQYaILeoXEFVyfOvHcws1Oq9mbr+rBh5NDCprk1JuU3eHmaZTKY6x+QUlenBj7bpk6pZ67N7h+rfNwxVkE/tMQJoGlyXml5Tv6e3vPyjvtuTqXnXDdFvhnVugggBAO0NM9WAC9SVUEtSbIifbh7ZtUkSaqlyH+1TJdSSFOzrpX/fMFTPXh8nH0+Lvt2dod88/70OZhY2SQwA0Fo5un+zpRYAwNVIqoE24Iq4aL33u1GKDLJqX0ahrnz+e63ek6lWUIgCAC5B928AQHNhSy2gjRgYHaSPZo7RHa9t0ObDObr55R/l42lR106+6tLRV0NignXr6Fj5efO/PYC2z7Gm2teLf/MAAK7FTDXQhoQFWPXOHSN13fDOsphNKi63aWdqvr7YnqanPt+lCf/6Vkk70twdZp2OF5Tq1lfW6YnPdjLLDrjZggULFBsbK6vVqoSEBK1bt67OsUuXLtXw4cMVHBwsPz8/xcXF6fXXX2/GaGsqKquQJPkyUw0AcDG+vgXaGKunRf+8Zogeu3KQUnKKdeh4ofZnFOrl1QeUklOs6YvX65JBEbrrvJ7y9jDLbkiGIVnMkpfFIm9Ps7w9zAry8Tztmu6mZLcb+ut7P2vVrgyt2pWhEH8v3T6ObXAAd1iyZIkSExO1cOFCJSQkaP78+Zo4caJ27dqlsLCwGuM7duyo+++/X3379pWXl5c+/fRTTZs2TWFhYZo4caIbXgHl3wCA5kP3b6CdKCqr0LMr9+i/qw/IZj/9//ZDOgfpH1cN0sCqrcRc7b/f7ddjy3bIbJLshmQxm/T2jJEa0a1jszw/0BBt/bqUkJCgs846S88995wkyW63KyYmRn/4wx9033331esxhg0bpksvvVSPPvpovcY39Xva6/7lKrcZWjvrfEUG+Zzx4wEA2h+6fwOoxtfLQ7Mu6adPZo7VyO4dFeTjqWBfT3Xw9VRHPy8F+XjKx9Mii7lydvrnI7m6YsH3enz5DmcZ5ZHsIr27/rAe+nibXvn+gH4+nKPyX+2NXVJuU2puifZnFGjb0VytP5il3Wn5p4xty5EcPblipyTp4csH6Iq4KNnshu56a6PS80pc8G4AqEtZWZk2bNig8ePHO4+ZzWaNHz9ea9euPe35hmEoKSlJu3bt0tlnn13nuNLSUuXl5VW7NZVym13ltsovD309KcoDALgWVxqgnekfFah37hh1yjHpeSV65NPt+nTLMb347X59+vNReVjMSs4qqjHW6mlWr7AAFZZWKKOgVPklFbU+5vCuHXTH2d01vl+4zOYTZeX5JeX6w9ubVG4zdNGACN08squuju+sncfytSstX3e9tVFvzRgpD7NJJeV2lVbYFGj1rPYYAJpOZmambDabwsPDqx0PDw/Xzp076zwvNzdX0dHRKi0tlcVi0fPPP68LL7ywzvFz587Vww8/3GRxn8xR+i1JVi/mDwAArkVSDaCGsECrnrtxmH4zLE0PfrhNKTnFkipLsod0DtKQmGAdzCzUxuQc5RaXa2tKbrXzLWaTfD0t8vGyyNfLoqM5JVp/KFvrX9+g7qF+umRgpGyGodJyu7am5OjQ8SJFB/voyasHy2QyydfLQy/cPExXPPe9fjqYrUEPfa7SCrsci1W8PMzq0tFXsZ181SPUXzeP7KqYjr7N/TYBOElAQIA2b96sgoICJSUlKTExUd27d9e5555b6/hZs2YpMTHR+XNeXp5iYmKaJBbHHtUWs0leFpJqAIBrkVQDqNP5fcOVcHcnrfglVR39vHRWt47yP2lLLrvd0P7MQu3LKFCQj6dC/L0V6u+tQB+Pak3O0vNK9Mqag3rjh0Pan1Go577eW+15LGaTnr0+TkG+ns5j3UP99cx1Q/S7NzaopLx6iXlZhV170wu0N71AK3ek6/UfDmnWxX11U0JXZrCBMxQSEiKLxaK0tOo7BaSlpSkiIqLO88xms3r27ClJiouL044dOzR37tw6k2pvb295e3s3Wdwnc3b+9rQ0a8NFAED7RFIN4JT8vD10dXznWu8zm03qGeavnmH+p3yMsECr7r2or+46r6feW39Ye9ILZPU40Wn8nN6hGtqlQ43zJgyI0NpZF6ik3CYfL4v8vDzk5WFWam6JDh4v1MHjRfpk81GtO5ilBz/apuVbU/XPawYzaw2cAS8vL8XHxyspKUlXXnmlpMpGZUlJSZo5c2a9H8dut6u0tNRFUZ4anb8BAM2JpBpAs/H39tC0Md0adE54oLXGsZiOvorp6KtxvaSbRnTRa2sP6skVu7R2/3FN+Ne3unZ4Z92Y0EV9I9peV2agOSQmJmrq1KkaPny4RowYofnz56uwsFDTpk2TJE2ZMkXR0dGaO3eupMr10cOHD1ePHj1UWlqq5cuX6/XXX9cLL7zglviLyyuTavaoBgA0B5JqAK2a2WzSrWO66by+Ybrn/S1adyBLr609pNfWHlJ81w66dFCkDEnFZRUqKrOpo5+XrhoarU7+NctO7XZDKTnF2pOerz1pBTp4vEhxMUG6elhnebAuE+3I5MmTlZGRodmzZys1NVVxcXFasWKFs3lZcnKyzOYT/08UFhbq97//vY4cOSIfHx/17dtXb7zxhiZPnuyW+E/MVPMxBwDgeuxTDaDNsNsNrdl3XG+tO6QvtqWpoo79uL08zLoyLkrTxnRTZJBV3+zOUNKOdH2zO0O5xeU1xvcK89ffL+mnc/uEttj1mXa7wXryZsR1qek15Xu64pdU/e6NDYrv2kEf3Dm6iSIEALQ39b028RUugDbDbDZpbK8Qje0VovS8Er27/rB+PpIrq6fF2Y18U3K2fj6Sq3fXH9G764/IbJJOzr29LGZ1D/VTr/AAhQd464ONR7QnvUDTXv1JY3uG6I6zu2tk907y8qg+c51VWKb9GQXqHRGgQKunmtObPx7S3OU7ddPILrpnQh9m1dHuFZdXNirz8aT8GwDgeiTVANqksECrZp7fq8ZxwzC0MTlHi74/oBW/pMpmN9QnPEDn9wvTBX3DFBcTXC0p/cMFvbTg67169fuDWr03U6v3ZirA6qHx/cI1pmeIdqfl6/u9mdp2NE+SZDZJg6KDNLJ7J/WPClRRmU05ReXKKS5TB18vTRnVVb7/396dR0dZ33sc/0wyyWRCNpJAFsiGYQ1LKWENvdSKglqr1ipSUBRve2yxAq2oRw62Vy+Fez14LdUW63U51gUFhSrYepFNQQIkhj2G9bJmEbKTkISZ3/2Dy9iRqJOHkJkk79c5c054nl8m3/kS8uGb55nnacVTUvedqta/vbdPjS63nt94WDuPV2rx5KHqHnnpe9GBzoILlQEA2hJDNYBOxWazaVhaVw1L66oztQ1qdLmVFO382vXRzhA9dkN/3TUqTUs2HtKHe0t1urZBKwpOakXBSa+18REOna5t0M4TVdp5oqrZ51uWd1yLJw9VVnL0Zb+Wc00uzXqrQI0ut4b0jNahL84q93C5frh4k/7jJ4PlchntOlGpnSeqVFbToMQoh5JjnEqOcWpAcpS+3ydwT2cHLsfF+1RzoTIAQFtgqAbQaTV3sbKvkxIbrvm3DtITNw/UZ8cq9OGeEuUdrVCfhAjlZMZrzFXx6hbpUHFVvXIPn1HuoXIdOX1WUU67op2hinLatXpXsQ59cVa3PvepHrm+n6bnpF/WUPvUh0XaX1qr+IhQvXjPcFXVN+n+v+ZfOF395e2XrC8s9v7zd1JiNPfG/hqeHivpwlH8otIa/c/eUoXag5RzVbyykqN4rzbanTqGagBAG2KoBoAWCA6yaXh6rGcQ/aqkaKduHdpTtw699N7ev/pBbz28fJc+KizVk6v2ac2+Ek0dlabx/RMU9v/v/WxyufXpoTNa/3mZgmw29U2MUJ+ESPVOiFSE48sf2ZsOnNaLm45Ikv7zJ4MVH+FQfIRDK2fkaN7KPfpgT7FSY8M1uGeMhvSMVo+uTpVWN6i4sl4nKur1j70l2nG8Urcv2aJrByQoKzlKq3cV60BZrVfNXcNDNOaqeP1wcJKuy0pUMAM22gHP6d8h/DcHAHDlkTYA0EZiu4TqhbuH6bXco/r31YXKPVyu3MPlinDYNXFgooyRPiosbfYK5NKF08t7xISpR1entv9vhSRp6qhU/aBfgmdNF4ddT0/6jhbdMeQbj4I/WnNOz3x0QEu3HdOafaVas69U0oULtY3r203GSLmHz6iirkmrdxdr9e5ipceF61+/10s/GdbT80uAi4wx2nqkXH/dclQfH/hCLrfRxa8eFGSTMyRY4aHBCgsJVkpsuGaN790qp8ADzalvvHChMo5UAwDaArfUAgA/OHrmrN7OO66VBad0srLea198RKiuy0qUMyRY+0trVFRSo7Kahkueo1d8F61+8HuXdTGmg2U1enbdQdU2uDRxYKKuy0rwXL28yeXWrhOV+qiwTG9sPeYZ9uO6hGpYWlclxziVGB2mYJtNy/KPa39p7Td9KS9BNmnyiFQ9dF1fde0Sarl+fyGXWl9r9vTh5Tv1dt4JzZnQVzOuzmylCgEAnY2v2cRQDQB+5HYb5R2t0Ae7ixVks2lCVoKy02MvOc26qq5JxyvqdLKyXicr6lV+tlG3DeupjPgubVLn2YbzejvvuP77kyOX/BLgImdIsG4Z2kOThqcoNvzCoGxkdN5tVN/o0rkml842urQ8/4Te33lK0oULwd03NkMDe0Qps1ukenR1totTzMml1teaPX3gjc+0alexHv/hAE0fm9FKFQIAOhvuUw0A7UBQkE0jMmI1IqP592hfFB0eoujwaA3s4Z9Tprs47Lo3J0N3jUrT5kNndOzMWZ2qOqfiynpV1jfpX3p3023Deira+e336B7Xp5umjkzVb9/bq89LavT0mv2efQ57kFJiw5UUHabkaKeSYsIU4wxReKhdzv8/fbzJ5VZtw3nVNZxXo8utgT2ilZ0We8m9w9F5cfVvAEBbYqgGAPjMHhykcX26Sep2Wc8zslecVv1qrN757IQ+OXBaB8tqdfj0WTWcd+tgWa0Olvl+KrkkRTjsGpsZr6v7ddOoXnFKjQ3ndmGdGPepBgC0JYZqAIBf2IODNGl4qiYNT5UkudxGJyrqdKKiXqcq63Wq8pyKq+pVc+686ptcqms8r/omtxzBQeriCFa4wy4ZaeuRMzpd26h/7C3RP/aWSJK6Rzo0PD1WQ1Ki1cVhl8MerFB7kEKDgzQsrau6Rfp+OzW0P3VNF49U898cAMCVR9oAAAJCcJBNaXFdlBbXsveJu91Gu09WaX1RmT7e/4V2n6xSWU2D56rlX/XKvcP1/b7dW6tsBCCu/g0AaEsM1QCAdi0oyKYhKTEakhKjWeP76FyTSzuOV2r7kXIVldao4bxbjefdajjvUuN5t2Lb4dXG0TJZyRfOUODvGgDQFhiqAQAdSlhIsEb1itOoXnH+LgV+8l+TvuPvEgAAnQiXSgUAAAAAwCKGagAAAAAALGKoBgAAAADAIoZqAAAAAAAsYqgGAAAAAMAihmoAAAAAACxiqAYAAAAAwCKGagAAAAAALGKoBgAAAADAIoZqAAAAAAAsYqgGAAAAAMAihmoAAAAAACxiqAYAAAAAwCKGagAAAAAALGKoBgAAAADAIoZqAAAAAAAsYqgGAAAAAMAihmoAAAAAACyy+7sAXxhjJEnV1dV+rgQAgC/z6GI+4fKR9QCAQONr3reLobqmpkaSlJKS4udKAAD4Uk1NjaKjo/1dRodA1gMAAtW35b3NtINfs7vdbp06dUqRkZGy2Wwt+tzq6mqlpKTo+PHjioqKukIVdjz0reXomTX0zRr61nKt2TNjjGpqapScnKygIN5J1RpakvV8/1tH76yhb9bRO2vom3X+yPt2caQ6KChIPXv2vKzniIqK4hvSAvrWcvTMGvpmDX1rudbqGUeoW5eVrOf73zp6Zw19s47eWUPfrGvLvOfX6wAAAAAAWMRQDQAAAACARR1+qHY4HPrtb38rh8Ph71LaFfrWcvTMGvpmDX1rOXrWcfB3aR29s4a+WUfvrKFv1vmjd+3iQmUAAAAAAASiDn+kGgAAAACAK4WhGgAAAAAAixiqAQAAAACwiKEaAAAAAACLOvxQ/dxzzyk9PV1hYWEaOXKktm3b5u+SAsaCBQs0fPhwRUZGqnv37rrllltUVFTktebcuXOaMWOG4uLiFBERodtuu02lpaV+qjjwLFy4UDabTbNmzfJso2fNO3nypKZOnaq4uDg5nU4NGjRIeXl5nv3GGD3++ONKSkqS0+nU+PHjdeDAAT9W7H8ul0vz5s1TRkaGnE6nrrrqKj355JP65+tL0jfp448/1k033aTk5GTZbDatXLnSa78vPSovL9eUKVMUFRWlmJgY3XfffaqtrW3DV4GWINu/GfneOsj4liHnW46c913AZ73pwJYuXWpCQ0PNSy+9ZPbu3Wt+9rOfmZiYGFNaWurv0gLChAkTzMsvv2z27NljduzYYW644QaTmppqamtrPWvuv/9+k5KSYtauXWvy8vLMqFGjzJgxY/xYdeDYtm2bSU9PN4MHDzYzZ870bKdnlyovLzdpaWnmnnvuMVu3bjWHDx82H374oTl48KBnzcKFC010dLRZuXKl2blzp/nRj35kMjIyTH19vR8r96/58+ebuLg4s2rVKnPkyBGzbNkyExERYf7whz941tA3Yz744AMzd+5c8+677xpJZsWKFV77fenRxIkTzZAhQ0xubq755JNPTGZmppk8eXIbvxL4gmz/duT75SPjW4act4ac912gZ32HHqpHjBhhZsyY4fmzy+UyycnJZsGCBX6sKnCVlZUZSWbjxo3GGGMqKytNSEiIWbZsmWdNYWGhkWS2bNnirzIDQk1Njendu7dZs2aNGTdunCdw6VnzHnnkETN27Niv3e92u01iYqJ56qmnPNsqKyuNw+Ewb775ZluUGJBuvPFGM336dK9tP/7xj82UKVOMMfStOV8NWl96tG/fPiPJbN++3bPm73//u7HZbObkyZNtVjt8Q7a3HPneMmR8y5Hz1pDz1gRi1nfY078bGxuVn5+v8ePHe7YFBQVp/Pjx2rJlix8rC1xVVVWSpNjYWElSfn6+mpqavHrYr18/paamdvoezpgxQzfeeKNXbyR69nXee+89ZWdn6/bbb1f37t01dOhQvfDCC579R44cUUlJiVffoqOjNXLkyE7dtzFjxmjt2rXav3+/JGnnzp3atGmTrr/+ekn0zRe+9GjLli2KiYlRdna2Z8348eMVFBSkrVu3tnnN+HpkuzXke8uQ8S1HzltDzreOQMh6+2U/Q4A6ffq0XC6XEhISvLYnJCTo888/91NVgcvtdmvWrFnKycnRwIEDJUklJSUKDQ1VTEyM19qEhASVlJT4ocrAsHTpUn322Wfavn37JfvoWfMOHz6sP//5z/r1r3+txx57TNu3b9eDDz6o0NBQTZs2zdOb5v69dua+Pfroo6qurla/fv0UHBwsl8ul+fPna8qUKZJE33zgS49KSkrUvXt3r/12u12xsbH0McCQ7S1HvrcMGW8NOW8NOd86AiHrO+xQjZaZMWOG9uzZo02bNvm7lIB2/PhxzZw5U2vWrFFYWJi/y2k33G63srOz9fvf/16SNHToUO3Zs0dLlizRtGnT/Fxd4Hr77bf1+uuv64033lBWVpZ27NihWbNmKTk5mb4B8An57jsy3jpy3hpyvuPosKd/x8fHKzg4+JIrMpaWlioxMdFPVQWmBx54QKtWrdL69evVs2dPz/bExEQ1NjaqsrLSa31n7mF+fr7Kysr03e9+V3a7XXa7XRs3btTixYtlt9uVkJBAz5qRlJSkAQMGeG3r37+/jh07Jkme3vDv1ducOXP06KOP6s4779SgQYN01113afbs2VqwYIEk+uYLX3qUmJiosrIyr/3nz59XeXk5fQwwZHvLkO8tQ8ZbR85bQ863jkDI+g47VIeGhmrYsGFau3atZ5vb7dbatWs1evRoP1YWOIwxeuCBB7RixQqtW7dOGRkZXvuHDRumkJAQrx4WFRXp2LFjnbaH11xzjXbv3q0dO3Z4HtnZ2ZoyZYrnY3p2qZycnEtu57J//36lpaVJkjIyMpSYmOjVt+rqam3durVT962urk5BQd4/poODg+V2uyXRN1/40qPRo0ersrJS+fn5njXr1q2T2+3WyJEj27xmfD2y3TfkuzVkvHXkvDXkfOsIiKy/7EudBbClS5cah8NhXnnlFbNv3z7z85//3MTExJiSkhJ/lxYQfvGLX5jo6GizYcMGU1xc7HnU1dV51tx///0mNTXVrFu3zuTl5ZnRo0eb0aNH+7HqwPPPVwY1hp41Z9u2bcZut5v58+ebAwcOmNdff92Eh4eb1157zbNm4cKFJiYmxvztb38zu3btMjfffHOnvGXEP5s2bZrp0aOH51Yb7777romPjzcPP/ywZw19u3Cl3oKCAlNQUGAkmaefftoUFBSYo0ePGmN869HEiRPN0KFDzdatW82mTZtM7969uaVWgCLbvx353nrIeN+Q89aQ874L9Kzv0EO1Mcb88Y9/NKmpqSY0NNSMGDHC5Obm+rukgCGp2cfLL7/sWVNfX29++ctfmq5du5rw8HBz6623muLiYv8VHYC+Grj0rHnvv/++GThwoHE4HKZfv37mL3/5i9d+t9tt5s2bZxISEozD4TDXXHONKSoq8lO1gaG6utrMnDnTpKammrCwMNOrVy8zd+5c09DQ4FlD34xZv359sz/Lpk2bZozxrUdnzpwxkydPNhERESYqKsrce++9pqamxg+vBr4g278Z+d56yHjfkfMtR877LtCz3maMMZd/vBsAAAAAgM6nw76nGgAAAACAK42hGgAAAAAAixiqAQAAAACwiKEaAAAAAACLGKoBAAAAALCIoRoAAAAAAIsYqgEAAAAAsIihGgAAAAAAixiqAXyrDRs2yGazqbKy0t+lAACAK4CsB6xjqAYAAAAAwCKGagAAAAAALGKoBtoBt9utBQsWKCMjQ06nU0OGDNHy5cslfXm61urVqzV48GCFhYVp1KhR2rNnj9dzvPPOO8rKypLD4VB6eroWLVrktb+hoUGPPPKIUlJS5HA4lJmZqRdffNFrTX5+vrKzsxUeHq4xY8aoqKjoyr5wAAA6CbIeaL8YqoF2YMGCBXr11Ve1ZMkS7d27V7Nnz9bUqVO1ceNGz5o5c+Zo0aJF2r59u7p166abbrpJTU1Nki4E5B133KE777xTu3fv1u9+9zvNmzdPr7zyiufz7777br355ptavHixCgsL9fzzzysiIsKrjrlz52rRokXKy8uT3W7X9OnT2+T1AwDQ0ZH1QDtmAAS0c+fOmfDwcPPpp596bb/vvvvM5MmTzfr1640ks3TpUs++M2fOGKfTad566y1jjDE//elPzbXXXuv1+XPmzDEDBgwwxhhTVFRkJJk1a9Y0W8PFr/HRRx95tq1evdpIMvX19a3yOgEA6KzIeqB940g1EOAOHjyouro6XXvttYqIiPA8Xn31VR06dMizbvTo0Z6PY2Nj1bdvXxUWFkqSCgsLlZOT4/W8OTk5OnDggFwul3bs2KHg4GCNGzfuG2sZPHiw5+OkpCRJUllZ2WW/RgAAOjOyHmjf7P4uAMA3q62tlSStXr1aPXr08NrncDi8wtYqp9Pp07qQkBDPxzabTdKF94ABAADryHqgfeNINRDgBgwYIIfDoWPHjikzM9PrkZKS4lmXm5vr+biiokL79+9X//79JUn9+/fX5s2bvZ538+bN6tOnj4KDgzVo0CC53W6v920BAIC2QdYD7RtHqoEAFxkZqYceekizZ8+W2+3W2LFjVVVVpc2bNysqKkppaWmSpCeeeEJxcXFKSEjQ3LlzFR8fr1tuuUWS9Jvf/EbDhw/Xk08+qUmTJmnLli169tln9ac//UmSlJ6ermnTpmn69OlavHixhgwZoqNHj6qsrEx33HGHv146AACdAlkPtHP+flM3gG/ndrvNM888Y/r27WtCQkJMt27dzIQJE8zGjRs9FxZ5//33TVZWlgkNDTUjRowwO3fu9HqO5cuXmwEDBpiQkBCTmppqnnrqKa/99fX1Zvbs2SYpKcmEhoaazMxM89JLLxljvrx4SUVFhWd9QUGBkWSOHDlypV8+AAAdHlkPtF82Y4zx51AP4PJs2LBBV199tSoqKhQTE+PvcgAAQCsj64HAxnuqAQAAAACwiKEaAAAAAACLOP0bAAAAAACLOFINAAAAAIBFDNUAAAAAAFjEUA0AAAAAgEUM1QAAAAAAWMRQDQAAAACARQzVAAAAAABYxFANAAAAAIBFDNUAAAAAAFj0f/3lwjgSF1b1AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for model_type in model_list:\n",
        "    os.makedirs(os.path.join(root_dir, model_type), exist_ok=True)\n",
        "    out_dir = os.path.join(root_dir, model_type)\n",
        "    loss_values, metric_values = train_model(model_type, df_model_par, out_dir, loss_function, metric, device, lr=1e-4, attention_gate_flag=True)\n",
        "    plot_train_curve(loss_values, metric_values, model_type, out_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ln9hSN9aMuMe"
      },
      "source": [
        "Testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "Lj7Kyu9TJfdm",
        "outputId": "9df9e4ce-2b58-40c9-a952-0bc8b1edde2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/MyDrive/Stroke_Segmentation/custom'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "test_loader = DataLoader(\n",
        "    test_set, batch_size=8, num_workers=4\n",
        ")\n",
        "out_dir"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "quqO3p2uNN7T",
        "outputId": "2ddd9630-28ac-4979-f5cb-04ecf564a483"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/MyDrive/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "root_dir"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "zzvocOArO0ah"
      },
      "outputs": [],
      "source": [
        "model_list = [\"resnet\", \"unet\", \"segformer\", \"custom\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "G2y8BPMVM6WO",
        "outputId": "a3b70079-448e-4106-cfe8-089868027e67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cache found in /root/.cache/torch/hub/mateuszbuda_brain-segmentation-pytorch_master\n",
            "Some weights of SegformerForSemanticSegmentation were not initialized from the model checkpoint at nvidia/segformer-b0-finetuned-ade-512-512 and are newly initialized because the shapes did not match:\n",
            "- decode_head.classifier.bias: found shape torch.Size([150]) in the checkpoint and torch.Size([1]) in the model instantiated\n",
            "- decode_head.classifier.weight: found shape torch.Size([150, 256, 1, 1]) in the checkpoint and torch.Size([1, 256, 1, 1]) in the model instantiated\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 18 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "mean_test_metric = pd.DataFrame(columns=['model_tye', 'mean_test_iou'])\n",
        "plt.figure(\"check\", (12, 6))\n",
        "for j in range(len(model_list)):\n",
        "    model_type = model_list[j]\n",
        "    model = get_model(model_type, device, attention_gate_flag=True)\n",
        "    model.load_state_dict(torch.load(os.path.join(root_dir, model_type, \"best_metric_model.pth\")))\n",
        "    model.eval()\n",
        "    test_metric_values = []\n",
        "    with torch.no_grad():\n",
        "        for i, test_data in enumerate(test_loader):\n",
        "\n",
        "            test_outputs = model(test_data[\"image\"].to(device))\n",
        "            test_labels = test_data[\"label\"].to(device)\n",
        "            if model_type == \"segformer\":\n",
        "                test_outputs = torch.nn.functional.sigmoid(torch.nn.functional.interpolate(test_outputs.logits,128,mode=\"bilinear\",align_corners=False))\n",
        "\n",
        "            test_outputs = test_outputs.float() > 0.5\n",
        "            iou = metric(test_outputs, test_labels)\n",
        "            test_metric_values.append(iou.item())\n",
        "\n",
        "\n",
        "            # for j in range(10):\n",
        "            if i >= 30 and i < 33:\n",
        "                k = i - 30\n",
        "                if j == 0:\n",
        "                    plt.subplot(3, 6, 1 + 6 * k)\n",
        "                    plt.title(\"original image\")\n",
        "                    plt.axis(\"off\")\n",
        "                    plt.imshow(test_data[\"image\"][5, 0, :, :], cmap=\"gray\")\n",
        "                    plt.subplot(3, 6, 2 + 6 * k)\n",
        "                    plt.title(\"label\")\n",
        "                    plt.axis(\"off\")\n",
        "                    plt.imshow(test_data[\"label\"][5, 0, :, :])\n",
        "                plt.subplot(3, 6, j + 3 + 6 * k)\n",
        "                plt.title(f\"{model_type} output\")\n",
        "                plt.axis(\"off\")\n",
        "\n",
        "                plt.imshow(test_outputs.detach().cpu()[5, 0, :, :])\n",
        "    mean_test_metric.loc[j] = [model_type, sum(test_metric_values) / len(test_metric_values)]\n",
        "plt.savefig(os.path.join(root_dir, f\"model_comparison.png\"))\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "edB1ZxOaWdNr",
        "outputId": "c6a07bc3-3871-4b68-c758-affd59fbb386"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   model_tye  mean_test_iou\n",
              "0     resnet       0.767503\n",
              "1       unet       0.786753\n",
              "2  segformer       0.698115\n",
              "3     custom       0.764607"
            ],
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              "      <th>model_tye</th>\n",
              "      <th>mean_test_iou</th>\n",
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              "      <td>resnet</td>\n",
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              "      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-b80eceb6-febf-4e22-b233-9e41d6795971 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-b80eceb6-febf-4e22-b233-9e41d6795971');\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",
              "\n",
              "\n",
              "<div id=\"df-f468879e-9057-4e33-9907-141fd2ae3363\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f468879e-9057-4e33-9907-141fd2ae3363')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f468879e-9057-4e33-9907-141fd2ae3363 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f3fcb308-d095-4c0e-91c4-9e8977b0d7dc\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('mean_test_metric')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f3fcb308-d095-4c0e-91c4-9e8977b0d7dc button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('mean_test_metric');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mean_test_metric",
              "summary": "{\n  \"name\": \"mean_test_metric\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model_tye\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"unet\",\n          \"custom\",\n          \"resnet\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_test_iou\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0386889412061843,\n        \"min\": 0.6981148238886487,\n        \"max\": 0.7867528852845026,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.7867528852845026,\n          0.7646066068617765,\n          0.7675032179710294\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "mean_test_metric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "1Crcx9d0Wkgl"
      },
      "outputs": [],
      "source": [
        "df_model_par['parameters_num_millions'] = df_model_par['parameters_num'] / 1_000_000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Y-NvH_RHpuzs",
        "outputId": "420ab582-c21d-4e0f-8f72-80b25bdd5014"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  model_type  parameters_num  parameters_num_millions\n",
              "0     resnet        24436369                24.436369\n",
              "1       unet         7763041                 7.763041\n",
              "2  segformer         3714401                 3.714401\n",
              "3     custom        51145311                51.145311"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4027c1d4-a1a0-4631-84a2-0b2008de2ee6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model_type</th>\n",
              "      <th>parameters_num</th>\n",
              "      <th>parameters_num_millions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>resnet</td>\n",
              "      <td>24436369</td>\n",
              "      <td>24.436369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>unet</td>\n",
              "      <td>7763041</td>\n",
              "      <td>7.763041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>segformer</td>\n",
              "      <td>3714401</td>\n",
              "      <td>3.714401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>custom</td>\n",
              "      <td>51145311</td>\n",
              "      <td>51.145311</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4027c1d4-a1a0-4631-84a2-0b2008de2ee6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4027c1d4-a1a0-4631-84a2-0b2008de2ee6 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-4027c1d4-a1a0-4631-84a2-0b2008de2ee6');\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",
              "\n",
              "\n",
              "<div id=\"df-30c11b52-89c4-40f0-933e-ce4b970aa866\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-30c11b52-89c4-40f0-933e-ce4b970aa866')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-30c11b52-89c4-40f0-933e-ce4b970aa866 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_0bea86b2-d1fb-40d6-9f0f-02301267eb33\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_model_par')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0bea86b2-d1fb-40d6-9f0f-02301267eb33 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_model_par');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_model_par",
              "summary": "{\n  \"name\": \"df_model_par\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model_type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"unet\",\n          \"custom\",\n          \"resnet\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parameters_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21542344,\n        \"min\": 3714401,\n        \"max\": 51145311,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7763041,\n          51145311,\n          24436369\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parameters_num_millions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.54234486381526,\n        \"min\": 3.714401,\n        \"max\": 51.145311,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7.763041,\n          51.145311,\n          24.436369\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "df_model_par"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "B86yF8b7qjmk",
        "outputId": "177ef441-9a3b-469d-fe23-29b469750384"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.set(style=\"whitegrid\")\n",
        "plt.figure(figsize=(8, 4))\n",
        "for idx, row in df_model_par.iterrows():\n",
        "    plt.plot([row['model_type']], [row['parameters_num_millions']], label=row['model_type'], marker='o', linestyle='-', linewidth=2)\n",
        "plt.title('Model Parameters Comparison (in Millions)', fontsize=16)\n",
        "plt.xlabel('Model Type', fontsize=14)\n",
        "plt.ylabel('Number of Parameters (Millions)', fontsize=14)\n",
        "plt.legend(title='Model Type')\n",
        "plt.savefig(os.path.join(root_dir, \"model_parameters_line_plot.png\"), dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Assuming 'model' is your model instance\n",
        "model = get_model(\"custom\", device, attention_gate_flag=True)\n",
        "\n",
        "# print(model)\n"
      ],
      "metadata": {
        "id": "vrGLLqcfT_a_"
      },
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {
        "id": "TPq9uemorRQz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "9a72236a-a2dd-4ea3-bc2c-f10c3965b6d6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "\n",
        "# Assuming 'model', 'test_loader', and 'device' are already defined and configured\n",
        "model.eval()\n",
        "\n",
        "# Function to attach a hook and capture attention weights\n",
        "def attention_hook(module, input, output):\n",
        "    global attention_weights\n",
        "    # output is a tuple of (output, attn_weights), where attn_weights is what we need\n",
        "    attention_weights = output[1].detach()  # Detach to avoid any gradient computation\n",
        "\n",
        "# Attach the hook to the first self-attention block of the first transformer in the first encoder layer\n",
        "handle = model.encoder.layers[0]['transformers'].blocks[0]['self-attention'].attention.register_forward_hook(attention_hook)\n",
        "\n",
        "test_metric_values = []\n",
        "plt.figure(figsize=(18, 6))  # Adjust the figure size as needed\n",
        "with torch.no_grad():\n",
        "    for i, test_data in enumerate(test_loader):\n",
        "        test_outputs = model(test_data[\"image\"].to(device))\n",
        "        test_labels = test_data[\"label\"].to(device)\n",
        "\n",
        "        # Assuming you have a function `metric` that calculates IOU or another relevant metric\n",
        "        iou = metric(test_outputs, test_labels)\n",
        "        test_metric_values.append(iou.item())\n",
        "\n",
        "        if i == 32:  # Visualize the results for one batch index to check the attention mechanism\n",
        "            attention_map = attention_weights[0][0]  # First head's attention weights of the first example\n",
        "            attention_map = attention_map.cpu()  # Move the attention map to CPU\n",
        "            # Reshape the attention map to its square form if it's flat (assuming it's a square matrix)\n",
        "            side_length = int(attention_map.shape[0] ** 0.5)\n",
        "            attention_map = attention_map.view(side_length, side_length)\n",
        "            attention_map = (attention_map - attention_map.min()) / (attention_map.max() - attention_map.min())\n",
        "\n",
        "            img = test_data[\"image\"][0].cpu().numpy().transpose(1, 2, 0)\n",
        "            img = (img - img.min()) / (img.max() - img.min())  # Normalize image for displaying\n",
        "\n",
        "            plt.subplot(1, 3, 1)\n",
        "            plt.imshow(img)\n",
        "            plt.title(\"Input Image\")\n",
        "            plt.axis(\"off\")\n",
        "\n",
        "\n",
        "\n",
        "            label_img = test_data[\"label\"][0].cpu().numpy().squeeze()\n",
        "            plt.subplot(1, 3, 2)\n",
        "            plt.imshow(label_img, cmap='gray')\n",
        "            plt.title(\"Label\")\n",
        "            plt.axis(\"off\")\n",
        "\n",
        "            plt.subplot(1, 3, 3)\n",
        "            plt.imshow(attention_map.numpy(), cmap='viridis')\n",
        "            plt.title(\"Attention Map\")\n",
        "            plt.colorbar()\n",
        "            plt.axis(\"off\")\n",
        "\n",
        "plt.show()\n",
        "handle.remove()  # Clean up the hook after use to prevent memory leak\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # # Assuming 'model' is your model instance\n",
        "model = get_model(\"segformer\", device, attention_gate_flag=True)\n",
        "# Assuming 'model', 'test_loader', and 'device' are already defined and configured\n",
        "# model.eval()\n",
        "# print(model)\n",
        "#"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uyQ2-qBhBeqy",
        "outputId": "01d60a13-3c96-4765-afe0-071f2d54fcae"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of SegformerForSemanticSegmentation were not initialized from the model checkpoint at nvidia/segformer-b0-finetuned-ade-512-512 and are newly initialized because the shapes did not match:\n",
            "- decode_head.classifier.bias: found shape torch.Size([150]) in the checkpoint and torch.Size([1]) in the model instantiated\n",
            "- decode_head.classifier.weight: found shape torch.Size([150, 256, 1, 1]) in the checkpoint and torch.Size([1, 256, 1, 1]) in the model instantiated\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision.transforms.functional import to_pil_image\n",
        "\n",
        "# Assuming 'model' and 'test_loader' are properly set up and 'device' is defined\n",
        "model.eval()\n",
        "\n",
        "# Function to attach a hook\n",
        "feature_maps = []\n",
        "def get_feature_maps(module, input, output):\n",
        "    feature_maps.append(output.detach())  # Append the detached output of the layer\n",
        "\n",
        "# Attach the hook to the desired layer\n",
        "handle = model.segformer.encoder.patch_embeddings[0].proj.register_forward_hook(get_feature_maps)\n",
        "\n",
        "# Prepare to visualize\n",
        "plt.figure(figsize=(15, 5))\n",
        "\n",
        "with torch.no_grad():\n",
        "    for i, batch in enumerate(test_loader):\n",
        "        if i == 32:  # Assuming we visualize for the 30th batch\n",
        "            inputs = batch[\"image\"].to(device)\n",
        "            labels = batch[\"label\"].to(device)\n",
        "            outputs = model(inputs)\n",
        "\n",
        "            # Assuming the feature map is from the first image in the batch and visualizing the first feature map\n",
        "            if feature_maps:\n",
        "                feature_map = feature_maps[0][0, 0, :, :]  # Select the first feature map for the first image in the batch\n",
        "                # Normalize feature map for better visualization\n",
        "                feature_map = (feature_map - feature_map.min()) / (feature_map.max() - feature_map.min())\n",
        "\n",
        "            # Convert to PIL images for visualization\n",
        "            input_img = to_pil_image(inputs[0].cpu())\n",
        "            label_img = to_pil_image(labels[0].float().cpu(), mode='L')\n",
        "            feature_map_img = to_pil_image(feature_map.cpu(), mode='L')\n",
        "\n",
        "            plt.subplot(1, 3, 1)\n",
        "            plt.imshow(input_img)\n",
        "            plt.title('Input Image')\n",
        "            plt.axis('off')\n",
        "\n",
        "            plt.subplot(1, 3, 2)\n",
        "            plt.imshow(label_img, cmap='gray')\n",
        "            plt.title('Label')\n",
        "            plt.axis('off')\n",
        "\n",
        "            plt.subplot(1, 3, 3)\n",
        "            plt.imshow(feature_map_img, cmap='hot')\n",
        "            plt.title('Feature Map')\n",
        "            plt.colorbar()\n",
        "            plt.axis('off')\n",
        "\n",
        "            break  # We only visualize the first relevant batch\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# Don't forget to remove the hook after visualization to clean up\n",
        "handle.remove()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "2Dus6LtCq-fM",
        "outputId": "6f9dc064-7e69-4729-c6db-4786b4ab31e2"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # # Assuming 'model' is your model instance\n",
        "# model_list = [\"resnet\", \"unet\", \"segformer\", \"custom\"]\n",
        "model = get_model(\"unet\", device, attention_gate_flag=True)\n",
        "# Assuming 'model', 'test_loader', and 'device' are already defined and configured\n",
        "# model.eval()\n",
        "# print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KlqFEsqZqYpV",
        "outputId": "efab11f2-a0bb-4a7c-fb42-4a63fae3b437"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cache found in /root/.cache/torch/hub/mateuszbuda_brain-segmentation-pytorch_master\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision.transforms.functional import to_pil_image\n",
        "\n",
        "# Assuming 'model', 'test_loader', and 'device' are properly set up\n",
        "model.eval()\n",
        "\n",
        "# Function to attach a hook\n",
        "feature_maps = []\n",
        "def get_feature_maps(module, input, output):\n",
        "    feature_maps.append(output)  # Append the output of the layer\n",
        "\n",
        "# Attach the hook to the first convolutional layer of the first encoder block\n",
        "handle = model.encoder1.enc1conv1.register_forward_hook(get_feature_maps)\n",
        "\n",
        "# Setup the plot\n",
        "plt.figure(figsize=(15, 5))\n",
        "\n",
        "with torch.no_grad():\n",
        "    for i, batch in enumerate(test_loader):\n",
        "        if i == 32:  # Visualize for the 30th batch\n",
        "            inputs = batch[\"image\"].to(device)\n",
        "            labels = batch[\"label\"].to(device)\n",
        "            outputs = model(inputs)\n",
        "\n",
        "            # Access the feature map\n",
        "            if feature_maps:\n",
        "                # Extract the first feature map from the first image in the batch\n",
        "                feature_map = feature_maps[0][0, 0, :, :]\n",
        "                feature_map = (feature_map - feature_map.min()) / (feature_map.max() - feature_map.min())\n",
        "\n",
        "                # Convert to PIL image for visualization\n",
        "                input_img = to_pil_image(inputs[0].cpu())\n",
        "                label_img = to_pil_image(labels[0].float().cpu(), mode='L')\n",
        "                feature_map_img = to_pil_image(feature_map.cpu(), mode='L')\n",
        "\n",
        "                # Plotting\n",
        "                plt.subplot(1, 3, 1)\n",
        "                plt.imshow(input_img)\n",
        "                plt.title('Input Image')\n",
        "                plt.axis('off')\n",
        "\n",
        "                plt.subplot(1, 3, 2)\n",
        "                plt.imshow(label_img, cmap='gray')\n",
        "                plt.title('Label')\n",
        "                plt.axis('off')\n",
        "\n",
        "                plt.subplot(1, 3, 3)\n",
        "                plt.imshow(feature_map_img, cmap='hot')\n",
        "                plt.title('Feature Map')\n",
        "                plt.colorbar()\n",
        "                plt.axis('off')\n",
        "\n",
        "            break  # Only visualize the first matching batch\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# Remove the hook after visualization to clean up\n",
        "handle.remove()  # Remove the hook\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "RiONJ84vsM9q",
        "outputId": "c1439f34-e2e8-4abe-f58e-918915bec18e"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model_list = [\"resnet\", \"unet\", \"segformer\", \"custom\"]\n",
        "model = get_model(\"resnet\", device, attention_gate_flag=True)\n",
        "# Assuming 'model', 'test_loader', and 'device' are already defined and configured\n",
        "# model.eval()\n",
        "# print(model)"
      ],
      "metadata": {
        "id": "1tljcvebsYYT"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Set the model to evaluation mode\n",
        "model.eval()\n",
        "\n",
        "# Define a hook to capture the outputs of a specific layer\n",
        "feature_maps = []\n",
        "def hook(module, input, output):\n",
        "    feature_maps.append(output.detach())\n",
        "\n",
        "# Register the hook to the first BasicBlock after the first downsampling in layer1\n",
        "handle = model.encoder.layer1[0].register_forward_hook(hook)\n",
        "\n",
        "# Prepare the figure for plotting\n",
        "fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n",
        "\n",
        "# Process a single batch from your data loader\n",
        "with torch.no_grad():\n",
        "    for i, batch in enumerate(test_loader):\n",
        "        if i == 32:  # Just an example to process a specific batch\n",
        "            inputs = batch[\"image\"].to(device)\n",
        "            labels = batch[\"label\"].to(device)\n",
        "            outputs = model(inputs)\n",
        "\n",
        "            # Check if feature maps are captured\n",
        "            if feature_maps:\n",
        "                # Select a feature map from the first item in the batch, for example, the first channel\n",
        "                feature_map = feature_maps[0][0, 0, :, :]  # Adjust indices as needed\n",
        "                # Normalize the feature map for better visualization\n",
        "                feature_map = (feature_map - feature_map.min()) / (feature_map.max() - feature_map.min())\n",
        "\n",
        "                # Plotting the input image\n",
        "                ax[0].imshow(inputs.cpu().data[0].permute(1, 2, 0))\n",
        "                ax[0].set_title(\"Input Image\")\n",
        "                ax[0].axis('off')\n",
        "\n",
        "                # Plotting the label\n",
        "                ax[1].imshow(labels.cpu().data[0].squeeze(), cmap='gray')\n",
        "                ax[1].set_title(\"Label\")\n",
        "                ax[1].axis('off')\n",
        "\n",
        "                # Plotting the feature map as a heatmap\n",
        "                # ax[2].imshow(feature_map.cpu(), cmap='hot', interpolation='nearest')\n",
        "                # ax[2].set_title(\"Feature Map\")\n",
        "                feature_map_image = ax[2].imshow(feature_map.cpu(), cmap='hot', interpolation='nearest')\n",
        "                ax[2].set_title(\"Feature Map\")\n",
        "                ax[2].axis('off')\n",
        "                plt.colorbar(feature_map_image, ax=ax[2], orientation='vertical')\n",
        "\n",
        "                ax[2].axis('off')\n",
        "\n",
        "                plt.show()\n",
        "                break  # Stop after processing the specified batch\n",
        "\n",
        "# Don't forget to remove the hook after you're done\n",
        "handle.remove()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "fIaMVx--tDUi",
        "outputId": "0e6e9f08-d1b3-44c6-d735-6083ed048fc4"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 4 Axes>"
            ],
            "image/png": 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