x-beagle-aws-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4b7e6300",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thu May 23 21:28:54 2024       \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 535.161.08             Driver Version: 535.161.08   CUDA Version: 12.2     |\n",
      "|-----------------------------------------+----------------------+----------------------+\n",
      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                      |               MIG M. |\n",
      "|=========================================+======================+======================|\n",
      "|   0  Tesla T4                       On  | 00000000:00:1E.0 Off |                    0 |\n",
      "| N/A   34C    P0              32W /  70W |   7281MiB / 15360MiB |      0%      Default |\n",
      "|                                         |                      |                  N/A |\n",
      "+-----------------------------------------+----------------------+----------------------+\n",
      "                                                                                         \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                            |\n",
      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
      "|        ID   ID                                                             Usage      |\n",
      "|=======================================================================================|\n",
      "|    0   N/A  N/A     29845      C   ...conda3/envs/pytorch_p310/bin/python     7272MiB |\n",
      "+---------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e659b11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/SageMaker\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "HOME = os.getcwd()\n",
    "print(HOME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "89866fb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.0.196 🚀 Python-3.10.14 torch-2.1.0 CUDA:0 (Tesla T4, 15102MiB)\n",
      "Setup complete ✅ (16 CPUs, 62.0 GB RAM, 87.5/135.0 GB disk)\n"
     ]
    }
   ],
   "source": [
    "# Pip install method (recommended)\n",
    "\n",
    "!pip install ultralytics==8.0.196\n",
    "\n",
    "from IPython import display\n",
    "display.clear_output()\n",
    "\n",
    "import ultralytics\n",
    "ultralytics.checks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c533a902",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ultralytics import YOLO\n",
    "\n",
    "from IPython.display import display, Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "90bccb01",
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import os\n",
    "import io\n",
    "from zipfile import ZipFile\n",
    "from sagemaker import get_execution_role\n",
    "\n",
    "role = get_execution_role()\n",
    "bucket='x-beagle'\n",
    "data_key = 'aws_dataset.zip'\n",
    "data_location = 's3://{}/{}'.format(bucket, data_key)\n",
    "\n",
    "s3 = boto3.client('s3')\n",
    "\n",
    "# S3에서 ZIP 파일 다운로드\n",
    "zip_obj = s3.get_object(Bucket=bucket, Key=data_key)\n",
    "zip_body = io.BytesIO(zip_obj['Body'].read())\n",
    "\n",
    "# 압축 해제\n",
    "with ZipFile(zip_body, 'r') as zip_file:\n",
    "    zip_file.extractall('./aws_data')\n",
    "\n",
    "# 압축 해제된 데이터 디렉토리 경로\n",
    "data_dir = './aws_data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4ca1764",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile dataset_aws.yaml\n",
    "train: /home/ec2-user/SageMaker/aws_data/aws_dataset/train/\n",
    "val: /home/ec2-user/SageMaker/aws_data/aws_dataset/val/\n",
    "test: /home/ec2-user/SageMaker/aws_data/aws_dataset/test/\n",
    "nc: 42\n",
    "names: ['Hammer','SSD','Alcohol','Spanner','Axe','Awl','Throwing Knife','Firecracker','Thinner','Plier','Match',\n",
    "        'Smart Phone','Scissors','Tablet PC','Solid Fuel','Bat','Portable Gas','Nail Clippers','Knife','Metal Pipe',\n",
    "        'Electronic Cigarettes(Liquid)','Supplymentary Battery','Bullet','Gun Parts','USB','Liquid','Aerosol','Screwdriver',\n",
    "        'Chisel','Handcuffs','Lighter','HDD','Electronic Cigarettes','Battery','Gun','Laptop','Saw','Zippo Oil','Stun Gun',\n",
    "        'Camera','Camcorder','SD Card']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "de30c494",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "New https://pypi.org/project/ultralytics/8.2.20 available 😃 Update with 'pip install -U ultralytics'\n",
      "Ultralytics YOLOv8.0.196 🚀 Python-3.10.14 torch-2.1.0 CUDA:0 (Tesla T4, 15102MiB)\n",
      "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8m.pt, data=/home/ec2-user/SageMaker/dataset_aws.yaml, epochs=300, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, stream_buffer=False, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
      "Overriding model.yaml nc=80 with nc=42\n",
      "\n",
      "                   from  n    params  module                                       arguments                     \n",
      "  0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 \n",
      "  1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                \n",
      "  2                  -1  2    111360  ultralytics.nn.modules.block.C2f             [96, 96, 2, True]             \n",
      "  3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               \n",
      "  4                  -1  4    813312  ultralytics.nn.modules.block.C2f             [192, 192, 4, True]           \n",
      "  5                  -1  1    664320  ultralytics.nn.modules.conv.Conv             [192, 384, 3, 2]              \n",
      "  6                  -1  4   3248640  ultralytics.nn.modules.block.C2f             [384, 384, 4, True]           \n",
      "  7                  -1  1   1991808  ultralytics.nn.modules.conv.Conv             [384, 576, 3, 2]              \n",
      "  8                  -1  2   3985920  ultralytics.nn.modules.block.C2f             [576, 576, 2, True]           \n",
      "  9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 \n",
      " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 12                  -1  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 \n",
      " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 15                  -1  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 \n",
      " 16                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              \n",
      " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 18                  -1  2   1846272  ultralytics.nn.modules.block.C2f             [576, 384, 2]                 \n",
      " 19                  -1  1   1327872  ultralytics.nn.modules.conv.Conv             [384, 384, 3, 2]              \n",
      " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 21                  -1  2   4207104  ultralytics.nn.modules.block.C2f             [960, 576, 2]                 \n",
      " 22        [15, 18, 21]  1   3800014  ultralytics.nn.modules.head.Detect           [42, [192, 384, 576]]         \n",
      "Traceback (most recent call last):\n",
      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/multiprocessing/queues.py\", line 239, in _feed\n",
      "    reader_close()\n",
      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/multiprocessing/connection.py\", line 177, in close\n",
      "    self._close()\n",
      "  File \"/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/multiprocessing/connection.py\", line 361, in _close\n",
      "    _close(self._handle)\n",
      "OSError: [Errno 9] Bad file descriptor\n",
      "Model summary: 295 layers, 25880638 parameters, 25880622 gradients, 79.2 GFLOPs\n",
      "\n",
      "Transferred 469/475 items from pretrained weights\n",
      "Freezing layer 'model.22.dfl.conv.weight'\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/ec2-user/SageMaker/aws_data/aws_dataset/train.cache... 4379 images, 0 backgrounds, 0 corrupt: 100%|██████████| 4379/4379 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /home/ec2-user/SageMaker/aws_data/aws_dataset/val.cache... 546 images, 0 backgrounds, 0 corrupt: 100%|██████████| 546/546 [00:00<?, ?it/s]\n",
      "Plotting labels to runs/detect/train/labels.jpg... \n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01, momentum=0.9) with parameter groups 77 weight(decay=0.0), 84 weight(decay=0.0005), 83 bias(decay=0.0)\n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
      "Starting training for 300 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      1/300      7.39G      1.129      2.459      1.212         87        640: 100%|██████████| 274/274 [02:08<00:00,  2.13it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.05it/s]\n",
      "                   all        546       3040      0.542      0.466      0.459       0.35\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      2/300      7.58G      0.893      1.127      1.056        118        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.22it/s]\n",
      "                   all        546       3040      0.695      0.592      0.645      0.506\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      3/300      7.54G     0.8955     0.9841      1.048        141        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.17it/s]\n",
      "                   all        546       3040      0.711      0.641      0.694      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      4/300      7.55G     0.9245     0.9477      1.068        136        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.19it/s]\n",
      "                   all        546       3040      0.728      0.664      0.726      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      5/300      7.55G     0.8928     0.8648      1.055        104        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.24it/s]\n",
      "                   all        546       3040      0.685      0.695      0.734      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      6/300      7.53G     0.8653     0.8126      1.047        105        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.24it/s]\n",
      "                   all        546       3040      0.748      0.716      0.788      0.614\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      7/300      7.53G     0.8364     0.7588      1.026        101        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.22it/s]\n",
      "                   all        546       3040       0.75       0.68       0.74      0.568\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      8/300      7.57G     0.8296     0.7365      1.028         97        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.21it/s]\n",
      "                   all        546       3040       0.73      0.775      0.807      0.637\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      9/300      7.54G     0.7974     0.6931      1.015        104        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.843      0.726      0.822      0.661\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     10/300      7.53G     0.7855     0.6741      1.007        103        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.23it/s]\n",
      "                   all        546       3040      0.785      0.745      0.816      0.657\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     11/300       7.6G     0.7678     0.6442     0.9995        148        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.22it/s]\n",
      "                   all        546       3040      0.776      0.797      0.833       0.67\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     12/300      7.53G     0.7627     0.6294     0.9961        130        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.881      0.779      0.852      0.692\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     13/300      7.56G     0.7433     0.6138     0.9902        114        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.24it/s]\n",
      "                   all        546       3040      0.815      0.792      0.856      0.703\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     14/300      7.56G     0.7379     0.6026     0.9832        112        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.819      0.818      0.853       0.69\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     15/300      7.53G     0.7234     0.5847     0.9813        103        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.879      0.801      0.866      0.713\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     16/300      7.55G     0.7098     0.5741     0.9731        117        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.24it/s]\n",
      "                   all        546       3040      0.818      0.812      0.876      0.723\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     17/300      7.53G      0.707     0.5624     0.9755         89        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.07it/s]\n",
      "                   all        546       3040       0.88        0.8      0.878      0.728\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     18/300      7.55G      0.701     0.5503     0.9721        102        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.24it/s]\n",
      "                   all        546       3040      0.893      0.834      0.888      0.742\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     19/300      7.56G     0.6913      0.547     0.9641         95        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.861      0.839       0.89      0.743\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     20/300      7.64G     0.6843      0.535     0.9655        113        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.06it/s]\n",
      "                   all        546       3040      0.842      0.793      0.862       0.72\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     21/300       7.6G      0.685     0.5331     0.9651         74        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.841      0.873      0.905      0.739\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     22/300      7.56G     0.6722      0.519     0.9576         89        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.882      0.848      0.902      0.762\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     23/300      7.56G     0.6668     0.5175     0.9554        105        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.834      0.849      0.893       0.75\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     24/300      7.56G     0.6603     0.5061     0.9521         95        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.23it/s]\n",
      "                   all        546       3040      0.854      0.869      0.913      0.766\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     25/300      7.54G     0.6534     0.4974     0.9466        112        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.24it/s]\n",
      "                   all        546       3040      0.869      0.884      0.914      0.772\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     26/300      7.54G     0.6519     0.4943     0.9471        122        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.23it/s]\n",
      "                   all        546       3040      0.879      0.854      0.899      0.756\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     27/300      7.51G     0.6429     0.4871      0.942         97        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.85it/s]\n",
      "                   all        546       3040      0.871      0.897      0.926       0.78\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     28/300      7.56G     0.6385     0.4761     0.9396         96        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.861      0.849      0.896      0.759\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     29/300      7.56G     0.6358     0.4801     0.9408        107        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.899      0.843      0.892      0.762\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     30/300      7.57G     0.6291     0.4704     0.9354         89        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.14it/s]\n",
      "                   all        546       3040      0.906      0.864      0.911       0.77\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     31/300      7.52G     0.6241     0.4689     0.9386         85        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.922       0.86      0.911      0.781\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     32/300      7.57G     0.6199     0.4619     0.9368        136        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.903       0.87      0.911      0.775\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     33/300      7.56G     0.6138      0.454     0.9278        121        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.90it/s]\n",
      "                   all        546       3040      0.908      0.842      0.902      0.773\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     34/300      7.56G     0.6121     0.4506       0.93        126        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.24it/s]\n",
      "                   all        546       3040      0.909      0.855      0.908      0.775\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     35/300       7.5G     0.6094     0.4497     0.9307        103        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.884      0.879      0.915       0.78\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     36/300      7.56G      0.608     0.4495     0.9274        127        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.95it/s]\n",
      "                   all        546       3040      0.916      0.854      0.926      0.792\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     37/300      7.51G     0.6057     0.4421     0.9207        117        640: 100%|██████████| 274/274 [02:07<00:00,  2.14it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.24it/s]\n",
      "                   all        546       3040       0.91      0.874      0.912      0.792\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     38/300      7.54G     0.5979     0.4374     0.9216        172        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.899      0.867      0.913      0.782\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     39/300      7.55G     0.5987     0.4376     0.9216        119        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.78it/s]\n",
      "                   all        546       3040      0.885       0.87      0.905      0.781\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     40/300      7.57G     0.5968     0.4333       0.92        119        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.901       0.89      0.935      0.806\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     41/300      7.57G      0.583     0.4239     0.9171         93        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.873      0.907      0.935        0.8\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     42/300      7.55G     0.5815     0.4246      0.914        108        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.82it/s]\n",
      "                   all        546       3040      0.843      0.912      0.927      0.802\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     43/300      7.55G     0.5873     0.4289     0.9213        105        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.898      0.872      0.917      0.789\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     44/300      7.57G     0.5836     0.4193     0.9131         78        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.897      0.894      0.926      0.793\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     45/300      7.56G     0.5812     0.4189     0.9146         76        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.02it/s]\n",
      "                   all        546       3040      0.896      0.901      0.931      0.799\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     46/300      7.56G     0.5741      0.411     0.9125         99        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.24it/s]\n",
      "                   all        546       3040      0.911      0.887      0.915      0.791\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     47/300      7.54G      0.567     0.4071     0.9093        119        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.868      0.905      0.927      0.801\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     48/300      7.57G     0.5663     0.4088     0.9079        108        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.20it/s]\n",
      "                   all        546       3040      0.885      0.897      0.925      0.804\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     49/300      7.56G     0.5716     0.4108      0.915        106        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040       0.91      0.865       0.91      0.799\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     50/300      7.55G      0.567     0.4055     0.9099         87        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.921      0.886       0.92      0.793\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     51/300      7.54G     0.5642     0.4053     0.9091         80        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.932      0.874      0.921      0.806\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     52/300      7.53G      0.563     0.4018     0.9084        134        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.917       0.88      0.926      0.807\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     53/300      7.53G     0.5566     0.3954      0.904        112        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.23it/s]\n",
      "                   all        546       3040      0.845      0.926      0.929      0.807\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     54/300      7.54G     0.5569     0.3957     0.9052         89        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.908      0.903       0.94      0.815\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     55/300      7.55G     0.5504       0.39     0.9004         97        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.93it/s]\n",
      "                   all        546       3040      0.929      0.859      0.918      0.798\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     56/300      7.56G     0.5472     0.3847     0.8998        109        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.929      0.883      0.923      0.805\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     57/300      7.54G     0.5489     0.3888     0.9033         85        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.886      0.912      0.936      0.821\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     58/300      7.54G     0.5465     0.3847     0.8997         98        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.82it/s]\n",
      "                   all        546       3040      0.906      0.911      0.935      0.815\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     59/300      7.55G     0.5417      0.382      0.899         83        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.932      0.896      0.941      0.823\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     60/300      7.57G     0.5431     0.3837     0.8969         94        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.943      0.886      0.943      0.829\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     61/300      7.55G     0.5354     0.3794     0.8956        118        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.82it/s]\n",
      "                   all        546       3040      0.925      0.881      0.921      0.811\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     62/300      7.55G     0.5375     0.3807     0.8979         95        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.945      0.888      0.937      0.821\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     63/300      7.56G      0.541     0.3806     0.9008        108        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.928      0.899      0.923      0.814\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     64/300      7.54G     0.5329     0.3746     0.8937        100        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.81it/s]\n",
      "                   all        546       3040      0.924      0.897       0.94      0.827\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     65/300      7.56G     0.5289     0.3728     0.8928        114        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.23it/s]\n",
      "                   all        546       3040      0.929      0.879       0.92      0.804\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     66/300      7.58G      0.529     0.3711     0.8918        100        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.929      0.886      0.925      0.814\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     67/300      7.55G     0.5223     0.3661     0.8916        133        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.81it/s]\n",
      "                   all        546       3040       0.95      0.885      0.941      0.823\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     68/300      7.54G     0.5256     0.3681     0.8925        138        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.895      0.915      0.939      0.823\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     69/300      7.54G     0.5242      0.369     0.8937         94        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.924      0.892      0.934      0.821\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     70/300      7.54G     0.5263      0.368      0.896         85        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.83it/s]\n",
      "                   all        546       3040      0.926      0.881       0.92      0.813\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     71/300       7.5G     0.5209     0.3652     0.8902        139        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.908      0.888      0.926      0.811\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     72/300      7.52G     0.5175     0.3614     0.8912        123        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.946      0.879      0.932      0.816\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     73/300      7.54G     0.5189     0.3641     0.8899         85        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.85it/s]\n",
      "                   all        546       3040      0.926      0.895       0.93      0.821\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     74/300      7.53G     0.5189     0.3624     0.8881        125        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.929      0.899      0.931      0.819\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     75/300      7.55G     0.5158     0.3606     0.8892        100        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.917      0.903       0.93      0.824\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     76/300      7.56G     0.5126     0.3567     0.8855        104        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.84it/s]\n",
      "                   all        546       3040      0.926      0.901      0.944      0.833\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     77/300      7.55G     0.5124     0.3577     0.8872        159        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.934      0.898      0.936      0.826\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     78/300      7.55G     0.5041     0.3517     0.8838        131        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.925        0.9       0.93      0.819\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     79/300      7.56G     0.5088     0.3562     0.8855        111        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.933      0.911      0.943       0.83\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     80/300      7.54G     0.5094     0.3532     0.8843         82        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.912      0.905      0.939      0.828\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     81/300      7.56G     0.4996     0.3452     0.8824         97        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040       0.87      0.941      0.934      0.823\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "     82/300      7.56G     0.5011     0.3444     0.8826        121        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.927      0.908      0.934      0.823\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     83/300      7.57G     0.4992     0.3477     0.8813        106        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.90it/s]\n",
      "                   all        546       3040      0.934      0.906      0.941      0.824\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     84/300      7.56G     0.5013     0.3473     0.8848         72        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.886      0.935      0.943       0.83\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     85/300      7.55G      0.499     0.3464     0.8832        108        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040       0.92       0.91      0.937      0.831\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     86/300      7.57G     0.4951     0.3441     0.8799        112        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.938        0.9      0.949       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     87/300      7.57G      0.493     0.3411      0.877        110        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.87it/s]\n",
      "                   all        546       3040      0.937      0.906      0.946      0.839\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     88/300      7.55G     0.4915     0.3421     0.8805        111        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.932      0.909      0.944      0.837\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     89/300       7.5G     0.4934     0.3419     0.8783        108        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.875      0.936      0.943      0.836\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     90/300      7.55G     0.4866     0.3369     0.8763        122        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.929       0.91      0.943      0.833\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     91/300       7.5G     0.4903     0.3391     0.8811         78        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.889      0.931      0.936      0.829\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     92/300      7.55G     0.4926     0.3398     0.8818         87        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.937      0.907      0.943      0.838\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     93/300      7.56G     0.4863     0.3367     0.8756        131        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.944      0.898      0.944      0.834\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     94/300      7.58G     0.4876     0.3356     0.8744         77        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.90it/s]\n",
      "                   all        546       3040      0.892      0.935      0.944      0.834\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     95/300      7.54G     0.4823     0.3303     0.8743        103        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040       0.93      0.903      0.931      0.826\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     96/300      7.55G     0.4842     0.3336     0.8762         97        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.922      0.902      0.925       0.82\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     97/300      7.55G     0.4798     0.3306     0.8728         83        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.23it/s]\n",
      "                   all        546       3040      0.947      0.907      0.944      0.834\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     98/300      7.54G     0.4847     0.3349     0.8752        117        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.931      0.917      0.943      0.837\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "     99/300      7.56G      0.477     0.3283     0.8723        122        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.929      0.898       0.94       0.83\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    100/300      7.57G     0.4794     0.3324     0.8736        136        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.947      0.888      0.932      0.829\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    101/300      7.55G     0.4742     0.3267     0.8738        100        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.94it/s]\n",
      "                   all        546       3040      0.937      0.903      0.941      0.829\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    102/300      7.54G     0.4679     0.3206     0.8701        115        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.904      0.936      0.943      0.834\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    103/300      7.53G     0.4712     0.3229     0.8707         81        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.24it/s]\n",
      "                   all        546       3040      0.906      0.939      0.944      0.835\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    104/300      7.54G     0.4709     0.3214     0.8714        155        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.939       0.91      0.945      0.834\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    105/300      7.56G     0.4746     0.3255     0.8734        125        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.95it/s]\n",
      "                   all        546       3040      0.946      0.908      0.944      0.843\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    106/300      7.55G     0.4651     0.3196     0.8649        132        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.936      0.908       0.94      0.839\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    107/300      7.54G     0.4692     0.3233      0.871         90        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.921      0.924      0.944       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    108/300      7.52G     0.4622     0.3167     0.8681        134        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.947      0.906      0.942      0.842\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    109/300       7.6G     0.4639     0.3182     0.8682        106        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.10it/s]\n",
      "                   all        546       3040       0.95      0.908      0.937      0.841\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    110/300       7.6G     0.4622     0.3149     0.8676        116        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.951      0.908      0.949      0.845\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    111/300      7.55G     0.4633     0.3165     0.8702        113        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.941      0.924      0.946      0.846\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    112/300      7.55G     0.4608     0.3161     0.8688        117        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.945      0.917      0.947      0.845\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    113/300      7.53G     0.4605     0.3168     0.8667        133        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.919      0.923      0.941      0.838\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    114/300      7.54G     0.4615     0.3143     0.8706        105        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.932      0.906      0.944      0.836\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    115/300      7.56G     0.4614     0.3153      0.865        116        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.941      0.902      0.944      0.842\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    116/300      7.55G      0.458     0.3127     0.8673        106        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.929      0.904      0.943      0.837\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    117/300      7.54G     0.4536     0.3084     0.8654        112        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.90it/s]\n",
      "                   all        546       3040      0.937      0.911      0.945      0.841\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    118/300      7.53G     0.4606     0.3136     0.8686        115        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.933      0.918      0.948      0.841\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    119/300      7.52G     0.4557     0.3114     0.8653        118        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.925      0.927      0.947      0.844\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    120/300      7.57G     0.4555     0.3087     0.8652        145        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.907      0.938      0.945      0.846\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    121/300      7.51G     0.4544     0.3083     0.8654        105        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.94it/s]\n",
      "                   all        546       3040      0.931      0.928      0.948      0.845\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    122/300      7.55G     0.4489     0.3064     0.8633         96        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.909      0.937      0.946      0.845\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    123/300      7.53G     0.4461     0.3041     0.8616        111        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.934      0.927      0.945      0.842\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    124/300      7.55G      0.449     0.3049     0.8637        102        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.926      0.927      0.944       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    125/300      7.51G     0.4435     0.3034      0.861        117        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.03it/s]\n",
      "                   all        546       3040      0.896      0.936      0.944       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    126/300      7.56G     0.4463     0.3043     0.8646        111        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040       0.95        0.9      0.942      0.839\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    127/300      7.54G     0.4485     0.3075     0.8659        121        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.945      0.902      0.944       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    128/300      7.56G     0.4477     0.3024     0.8615         89        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.905      0.936      0.945      0.842\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    129/300      7.53G     0.4416     0.2987     0.8592         94        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.13it/s]\n",
      "                   all        546       3040      0.934      0.924      0.948      0.842\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    130/300      7.54G     0.4422     0.3015     0.8627        113        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.899      0.935      0.946      0.843\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    131/300      7.57G     0.4395     0.2989     0.8588        115        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.918      0.934       0.95      0.849\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    132/300      7.55G     0.4396     0.2993     0.8601        120        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.945      0.915      0.949      0.848\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    133/300      7.55G     0.4374     0.2971     0.8591        147        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.96it/s]\n",
      "                   all        546       3040      0.946      0.913      0.949      0.845\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    134/300      7.55G     0.4396     0.2985     0.8599         99        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.953      0.913      0.944      0.843\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    135/300      7.52G     0.4359     0.2949     0.8584        132        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.24it/s]\n",
      "                   all        546       3040       0.95      0.912       0.94      0.844\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    136/300      7.58G     0.4367     0.2977     0.8597        111        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.955      0.914      0.941      0.841\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    137/300      7.55G     0.4318     0.2923     0.8578         96        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.04it/s]\n",
      "                   all        546       3040      0.953      0.913      0.951      0.846\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    138/300      7.53G     0.4368      0.297     0.8609        105        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.952      0.915      0.948      0.842\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    139/300      7.51G     0.4329      0.295      0.858        122        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.948      0.915      0.948      0.846\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    140/300      7.54G     0.4278     0.2911     0.8577         96        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.949      0.906      0.943      0.845\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    141/300      7.54G     0.4293      0.291     0.8575        147        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.953      0.902      0.942      0.845\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    142/300      7.56G     0.4289     0.2907     0.8575        122        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.19it/s]\n",
      "                   all        546       3040      0.949       0.91      0.943      0.845\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    143/300      7.56G     0.4242     0.2891     0.8545        113        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.912      0.942      0.943      0.844\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    144/300      7.55G     0.4266     0.2889     0.8557        129        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.895      0.945      0.944      0.846\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    145/300      7.54G     0.4266     0.2882     0.8557        103        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.949      0.912      0.936       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    146/300      7.52G     0.4264      0.289     0.8545        112        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.06it/s]\n",
      "                   all        546       3040      0.948      0.913      0.937      0.842\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    147/300      7.54G     0.4219      0.287     0.8548        135        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040       0.94      0.916      0.944      0.843\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    148/300      7.52G     0.4253     0.2867     0.8545         76        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.941      0.923      0.947      0.849\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    149/300      7.55G     0.4212     0.2849     0.8547        110        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.952      0.915      0.949      0.849\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    150/300      7.54G     0.4216     0.2852     0.8533        128        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.90it/s]\n",
      "                   all        546       3040      0.948      0.918       0.95      0.852\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    151/300      7.54G      0.419     0.2853      0.853        124        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040       0.95      0.914      0.948      0.848\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    152/300      7.55G     0.4186     0.2859     0.8521         93        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.955      0.911      0.947      0.845\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    153/300      7.53G     0.4218     0.2868     0.8551        130        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.13it/s]\n",
      "                   all        546       3040      0.947      0.913      0.942      0.844\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    154/300      7.55G     0.4149     0.2815      0.851        109        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.943      0.908      0.939      0.842\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    155/300      7.52G     0.4185     0.2856     0.8529        100        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040       0.95      0.906      0.939       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    156/300      7.57G      0.415     0.2805     0.8536         96        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.932      0.923      0.942       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    157/300      7.57G     0.4094     0.2766     0.8501         81        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.95it/s]\n",
      "                   all        546       3040      0.931      0.922      0.939       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    158/300      7.54G     0.4152     0.2815     0.8525         91        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040       0.95      0.904      0.941      0.841\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    159/300      7.57G      0.409     0.2775     0.8505        122        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.951      0.904      0.938      0.842\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    160/300      7.57G     0.4084     0.2765     0.8513        102        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.952        0.9      0.938      0.844\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    161/300      7.54G     0.4115     0.2787     0.8515         81        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.95it/s]\n",
      "                   all        546       3040      0.953        0.9      0.937      0.842\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    162/300      7.54G     0.4081     0.2766     0.8507        120        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.953      0.901      0.939      0.843\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    163/300      7.57G     0.4057     0.2743     0.8511        142        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.944       0.91      0.936      0.841\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    164/300      7.56G      0.407     0.2749     0.8508        132        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.953      0.903      0.936      0.841\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    165/300      7.54G     0.4076     0.2775     0.8501        137        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.91it/s]\n",
      "                   all        546       3040      0.946      0.907      0.936      0.842\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    166/300      7.55G     0.4065     0.2735     0.8517         96        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.939      0.908      0.935      0.842\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    167/300      7.53G     0.4017     0.2725     0.8479         83        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.957      0.892      0.935      0.843\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    168/300      7.53G     0.4039     0.2742     0.8495        134        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.955      0.892      0.935       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    169/300      7.56G     0.4037     0.2729      0.849         90        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.95it/s]\n",
      "                   all        546       3040      0.954      0.892      0.935      0.841\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    170/300      7.57G     0.4026      0.272     0.8474         72        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.949      0.895      0.933      0.839\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    171/300      7.51G     0.4014     0.2712     0.8469         96        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.946      0.896      0.933      0.838\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    172/300      7.56G     0.3997     0.2691     0.8467        122        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.90it/s]\n",
      "                   all        546       3040      0.945      0.898      0.933      0.838\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    173/300      7.56G     0.3964     0.2702      0.847        122        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040       0.95      0.897      0.933      0.837\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    174/300      7.59G     0.3939     0.2671     0.8483         71        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.946      0.903      0.935      0.839\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    175/300      7.56G     0.3963     0.2668     0.8483        111        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.952      0.905      0.937      0.841\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    176/300      7.58G     0.3991     0.2717     0.8486        106        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.957      0.898      0.938      0.843\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    177/300      7.52G     0.3931     0.2684     0.8456        161        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040       0.94      0.907      0.939      0.844\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    178/300      7.55G     0.3979     0.2678     0.8466        126        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.945      0.905      0.942      0.845\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    179/300      7.52G     0.3958      0.267     0.8478        108        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.20it/s]\n",
      "                   all        546       3040      0.948      0.905      0.939      0.844\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    180/300      7.56G     0.3904     0.2638     0.8446        120        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.949      0.903      0.939      0.844\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    181/300      7.52G     0.3908     0.2652     0.8449         91        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.949      0.905      0.938      0.843\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    182/300      7.55G     0.3919     0.2637     0.8453        116        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.89it/s]\n",
      "                   all        546       3040      0.956      0.905      0.939      0.843\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    183/300      7.55G      0.388     0.2632      0.843        146        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.957      0.906      0.939      0.843\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    184/300      7.53G     0.3916      0.264     0.8474        137        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.28it/s]\n",
      "                   all        546       3040      0.958      0.904      0.939      0.841\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    185/300       7.5G     0.3841     0.2609     0.8445        112        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.28it/s]\n",
      "                   all        546       3040      0.956      0.901      0.937      0.841\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    186/300      7.57G     0.3891      0.262     0.8449        120        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.14it/s]\n",
      "                   all        546       3040      0.954      0.902      0.932      0.837\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    187/300      7.53G      0.384     0.2602     0.8435         84        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.955      0.903      0.939      0.841\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    188/300      7.56G     0.3863     0.2596     0.8422        117        640: 100%|██████████| 274/274 [02:07<00:00,  2.15it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.954      0.904      0.933      0.839\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    189/300      7.55G      0.383     0.2587     0.8438         93        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.24it/s]\n",
      "                   all        546       3040      0.949      0.906      0.932       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    190/300      7.56G     0.3835     0.2584     0.8425         93        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.27it/s]\n",
      "                   all        546       3040      0.952      0.906      0.932      0.839\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    191/300      7.61G     0.3826     0.2592     0.8456        132        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.948      0.908      0.932      0.838\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    192/300      7.53G     0.3774     0.2551     0.8406         66        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.947      0.908      0.932      0.838\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    193/300      7.55G     0.3773     0.2544     0.8418        135        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.951      0.906      0.932      0.839\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    194/300      7.55G      0.382     0.2574     0.8437         96        640: 100%|██████████| 274/274 [02:06<00:00,  2.17it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.96it/s]\n",
      "                   all        546       3040      0.957      0.901      0.933      0.841\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    195/300      7.55G     0.3781     0.2561     0.8435        128        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.953      0.908      0.933       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    196/300      7.57G     0.3762     0.2531     0.8428        145        640: 100%|██████████| 274/274 [02:07<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.958      0.904      0.933       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    197/300      7.56G     0.3768     0.2552     0.8383        132        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.957      0.904      0.933      0.839\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    198/300      7.54G      0.372     0.2522     0.8415        111        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.98it/s]\n",
      "                   all        546       3040      0.958      0.904      0.934      0.838\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    199/300      7.54G      0.371     0.2508     0.8375         77        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.26it/s]\n",
      "                   all        546       3040      0.958      0.904      0.934       0.84\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "    200/300      7.55G     0.3743      0.253      0.841         90        640: 100%|██████████| 274/274 [02:06<00:00,  2.16it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.25it/s]\n",
      "                   all        546       3040      0.956      0.904      0.934       0.84\n",
      "Stopping training early as no improvement observed in last 50 epochs. Best results observed at epoch 150, best model saved as best.pt.\n",
      "To update EarlyStopping(patience=50) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.\n",
      "\n",
      "200 epochs completed in 7.453 hours.\n",
      "Optimizer stripped from runs/detect/train/weights/last.pt, 52.1MB\n",
      "Optimizer stripped from runs/detect/train/weights/best.pt, 52.1MB\n",
      "\n",
      "Validating runs/detect/train/weights/best.pt...\n",
      "Ultralytics YOLOv8.0.196 🚀 Python-3.10.14 torch-2.1.0 CUDA:0 (Tesla T4, 15102MiB)\n",
      "Model summary (fused): 218 layers, 25864078 parameters, 0 gradients, 78.8 GFLOPs\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:06<00:00,  2.94it/s]\n",
      "                   all        546       3040      0.948      0.918       0.95      0.852\n",
      "                Hammer        546        129          1      0.976      0.995      0.952\n",
      "                   SSD        546         91      0.964      0.874      0.919      0.807\n",
      "               Alcohol        546          6          1      0.918      0.995      0.878\n",
      "               Spanner        546        136      0.967      0.993      0.995      0.964\n",
      "                   Axe        546         81      0.978      0.975      0.993      0.917\n",
      "                   Awl        546         32      0.883      0.943      0.919      0.724\n",
      "        Throwing Knife        546          8       0.96          1      0.995       0.86\n",
      "           Firecracker        546         44      0.826      0.727      0.844      0.627\n",
      "               Thinner        546          4      0.872       0.75      0.775      0.698\n",
      "                 Plier        546         31      0.967          1      0.995      0.934\n",
      "                 Match        546          2          1          0      0.512      0.307\n",
      "           Smart Phone        546        271      0.993      0.987      0.993      0.961\n",
      "              Scissors        546        294      0.986      0.969      0.992      0.929\n",
      "             Tablet PC        546         49      0.992      0.918      0.957      0.919\n",
      "            Solid Fuel        546          4      0.901          1      0.995      0.845\n",
      "                   Bat        546         16          1       0.99      0.995      0.934\n",
      "          Portable Gas        546         15      0.935      0.966      0.987      0.945\n",
      "         Nail Clippers        546         19      0.936          1      0.995      0.919\n",
      "                 Knife        546        296      0.983      0.981      0.986       0.91\n",
      "            Metal Pipe        546         28          1      0.979      0.995      0.938\n",
      "Electronic Cigarettes(Liquid)        546         10      0.877          1      0.977      0.908\n",
      " Supplymentary Battery        546        117       0.98          1      0.995      0.945\n",
      "                Bullet        546         34      0.985      0.941      0.978      0.869\n",
      "             Gun Parts        546         52      0.969      0.981      0.987      0.902\n",
      "                   USB        546        242      0.904      0.857      0.925      0.676\n",
      "                Liquid        546         37      0.985          1      0.995      0.926\n",
      "               Aerosol        546         22      0.994          1      0.995      0.983\n",
      "           Screwdriver        546         71      0.948      0.901      0.975       0.83\n",
      "                Chisel        546         50      0.959      0.947      0.965      0.882\n",
      "             Handcuffs        546          6      0.947          1      0.995      0.905\n",
      "               Lighter        546        234      0.987      0.991      0.995      0.959\n",
      "                   HDD        546        165      0.979          1      0.988      0.933\n",
      " Electronic Cigarettes        546         14          1      0.987      0.995      0.901\n",
      "               Battery        546         33      0.974      0.909      0.967      0.853\n",
      "                   Gun        546         56      0.966          1      0.995      0.982\n",
      "                Laptop        546         27      0.951          1      0.981      0.971\n",
      "                   Saw        546        106      0.961      0.934      0.961      0.851\n",
      "             Zippo Oil        546          6      0.935      0.833      0.922      0.889\n",
      "                Camera        546         64      0.953      0.957       0.98        0.8\n",
      "             Camcorder        546         22      0.745      0.955      0.892      0.687\n",
      "               SD Card        546        116      0.734        0.5      0.649      0.313\n",
      "Speed: 0.2ms preprocess, 5.8ms inference, 0.0ms loss, 0.8ms postprocess per image\n",
      "Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# 데이터셋 설정 파일 경로\n",
    "dataset_yaml = '/home/ec2-user/SageMaker/dataset_aws.yaml'\n",
    "\n",
    "# YOLOv8 모델 학습\n",
    "model = YOLO('yolov8m.pt')\n",
    "\n",
    "results = model.train(data=dataset_yaml, epochs=300, imgsz=640, batch=16, lr0=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ef54c0a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "args.yaml\t\t\t PR_curve.png\t   val_batch0_labels.jpg\r\n",
      "confusion_matrix_normalized.png  R_curve.png\t   val_batch0_pred.jpg\r\n",
      "confusion_matrix.png\t\t results.csv\t   val_batch1_labels.jpg\r\n",
      "F1_curve.png\t\t\t results.png\t   val_batch1_pred.jpg\r\n",
      "labels_correlogram.jpg\t\t train_batch0.jpg  val_batch2_labels.jpg\r\n",
      "labels.jpg\t\t\t train_batch1.jpg  val_batch2_pred.jpg\r\n",
      "P_curve.png\t\t\t train_batch2.jpg  weights\r\n"
     ]
    }
   ],
   "source": [
    "!ls {HOME}/runs/detect/train/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4b59d0ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/SageMaker\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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gbaj84DqrflSmZNvU/lRdBcQNyR2pN5DUzzY93LAUgmiJwHFK47otUVH50fZx+dLuB/iFVcQ7OKaBgAAAAdAO1IXQdWFMa4XGI/mP6UrgV58G7x/dUfqa0oxhaoLGcbm+8xAP1rSUYAogiWwI4qOHq3+8akPSo4erf7xrUknFFJnA5qH5p24OE/nSbAe06Dvk+1NFyv8AdapFjVegFOwKNQ1GLOjd8fWpAQelMaNG6qPrURiaLlCSPSjUQt02IwPU1zl2Dc3aRj7pbcx9h/k1s3k4CZbjArMtv+PiRj/CoUZrGerMKj1JQOMdvSlcnafTvUxAPOBmoZkJQBSMk4qHEzMa8B85s8k4P6Co+1WNQG28kHpt/kKr16cP4SX9bH0mF/jw/wCva/M6UZU4JrG13V3tl+x2pxcyLkv/AM819fr6Va1PUYLG3PmN+8YfIi8sx9AP61xiPc3UvzEPe3D8+mfT6Afyrmw1Hnd5bI8BvlXmXtE077ZqCqT/AKPbkFh/fbqB+HU/hXaDjjHSq1hZx6fZxwoOFHJPUnufzqcNnnAqK9X2kr9Asoqw4/L0qlqo/wBEU/8ATQfyNXOvWqeq8Wi/74/kaml8SOnBf7xD1OR8Z/8AI2X3/bP/ANFrWDW94z/5Gy+/7Z/+i1rBrYQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFX9D/wCQ/pv/AF9Rf+hiqFX9D/5D+m/9fUX/AKGKANjxb/yNF7/wD/0BayAOK2PFo/4qi8/4B/6AtZFR1BsglHz59qaKklGQDUaHvUPcye42ReKgZRwc9KtdaNme1S43NoVbKxm3dsLqBo/MKk8g+lFrbi2gEfmbvU+9aJiHZRTPLHQgflStK1hqcebmtqVeiqN2cU4gSRNFn7wIz6VZ8pe4H5UojUdBSUGipVbq1hlrdx2pkEgY5x90elPN5YZ3fZeR38taq3UW7gMVJ6EGsK31tv7SFhdWqpJv2F1b7x9celJU5S2PNngqM6jqTum+zOo+3WOf+PY5/wBxaYb2wZjGbMkjsYl/xqkw25GR7Vla80jWccMe/MpKu0bYOAKmMHOSiaPLaEI8zcv/AAJm+mo6aQdtnwDjIjX/ABrc0w215psyRxBYnZkZSgwcgZyO9ef+H7RrWCW4lDASABVK46d61TMW4TABrmx+FVRezjLZ72OvL8NSpS9rZ7NayfU6UeGdLCFRY2YU9QLZcGlTwzpcRzHY2aH1W2UVy7KrIyNyWGMjtWQ0l5hrfY67WztHesoYXGT2r/8AkqFVw2X0v+XL/wDA5f5no/8AZMH92P8A79imXOjrNY3FtHIIvOjZNyp0JGM4zXHW1hcuqSSjaw7HrV5bJu5H4Cp+rY1PSt/5Kio4TL5JOVJ/+By/zLyeDY8RGW7EkkeMP5WOcYz96rS+GgpyLrn/AK5//XrNWzUdealWBR2qvqmNerrf+So7PbYaKsqf4sunw0M5+1YPqI//AK9KvhzH/L3k/wDXP/69VNmB0ppBA6U/qWL/AOf/AP5Kh/WaNv4f4svDw9jpdf8AkP8A+vTl0AA83JP/AAD/AOvXFme/1W5WFoHhC7s5JQE/j14roLOFre2SN33sOprSWAxcVf6xr/hRmsXRk/4X4s1xoSj/AJb/APjn/wBepdRjEenwx5yFZVz9Aayu1FRHA13UjOrV5uV3typfkDxVNQlGELX8woooAJ+leocIDJNSKtCr+VP6VSQ0FGaKKYxaQ9KKQ0AIelJb9ZP97+lBNJb9ZP8Ae/pQgJmpppW602gApGpaQ9KQIrzKHZVIyD1FQBHTgHI7Zqy3My+wppU5NZNXZrBkIL91BpjM2T+7/Wp2GATVaRypGMn6VD0NE1uL827PldaXLf8APLFIJvZv++DUsb7zwP0xQrBzEeCT/qqXaT/yzI+tWMGlp8oczKhBBw0WP5Uu31TirWKMUcgcxVKr/cP5VEXiYkKGyOcEcVacYqBYdgLZJJ7VMkVFir5JUMI8Z9qZtiBOE/SrKKNgFOC0+W4uaxCoTA/d/pThtH/LP9KlowaqwrkRZc/cP5Vf0Jl/t/TsL1uou3+0Kqbav6IMa7p3/XzH/wChChLUTehhfEn/AJH/AFP/ALZf+ikrlK6v4k/8lA1P/tl/6KSuUr9UwH+6Uv8ADH8kePP4mbmiXNpHaXMMoH2mRk8rMAkyB1HIOPwxVy81G0tLeWGC0SJnDACW1UkfdA5YZ/vVzUUrQyLIuNw6Zp0k8kqgOc4JOfr/APqqpYdSnzPYXNoRk0lFFdJIUUUUAdT8P/8AkZh/1xf+ldQetcx8P/8AkZx/1xf+ldOetfk/HH+/R9Dys22h8wooor4k8YtWEsEU7G5TchQjGM88VoNfacro0cQDBstmMEEHtzWLRn860jVcVZGsK0oKyRqottFNHPLFOO77ohtBPtVpr7S8KNilehXyhxz1zisV7h3BBxz6VFVqu46RRaxDjpFIfKVaVig+Uk447UyiisDnbuFOh/4/LX/rsv8AOm06H/j8tf8Arsv861w/8WPqjbDfxoeqNTUP+P2b/eNVqs6h/wAfs3+8arVz1fjfqftVL4F6BU1q8STBpl3JgjGM9qhpKmLs7ltXVjUa6shtMceHDAklARjoRzUcYgZhNIkvzBtxEY2jr6Vn1IszqMDGMYrZVrv3kZeystGXI5bNI1RhvAPJCc/XP5cVWupIpJiYV2pjgYqGkqJVXJctio00ncWiikrM0FrqYP8AUp/uiuWrpo9RSCJI3udhCrwWx16V72S/b+X6njZt9j5liqd7pNjqOftdusvy7eSRxnParf28f8/Q/wC+6Z/asZu/sv2v9/t3bN3OK908Uy5PC2jSyrI1iu5em12A/IHB6/jVhtC014JIGtFMUilWTJxjOcYzxzWp58v/AD0b86PPl/56N+dMCCOJIY1jjQKijCgdhT6k8+X/AJ6N+dHny/8APR/zoAjoqTz5f+ejfnR58v8Az0b86AI6hhtLe2Z2ggSMucsVXGatefL/AM9G/Ojz5f8Ano350CI6Kk8+X/no/wCdHny/89G/OgCOipPPl/56N+dHny/89G/OgZHUMFpb2xcwwpGXOW2rjJq158v/AD0b86PPl/56N+dAEdFSefN/z0b86PPl/wCejfnQBHRUnny/89G/Ojz5f+ejfnQIo3+m2up24gu4y8YOcByv6gj8qqJ4a0hIxH9iVlD+YA7s2GznIyeK2fPl/wCejfnR58v/AD0b86BmVNoWn3DxvJAxMbblAlcDOSegODyT1q7BbxW0KQwoEjQYVR2FWPPl/wCejfnS+fIFZmmKqoySTQBFSH+tUh4o01hdMmqI6WilrhkJYRgeuKmg1m1v4IpLW+W5ilOFaN9w9f6UCOlY/LXMTPLPrVqBKQUmXIzwRmunPNUP7NjW+WdeMEHH41xYqnOdrbXOvDVIwvfsWZf9aaZT5f8AWGo67jlDrUbLt5A4qSikAzggEU0ginFSpyvTuKMg0hjKjkCPGUYZDDBBqY/rTSAeDSYiMHA9qeAHHNIYs0m0jpQAjIV9xTDUu4jgk1Gyk9DSY7jfelHJxTN2ODinRvg5xn0qQLHCrzTOWPH/AOqk5bljgU5Ru4UHH86vcQ1xhNo796niQKlCxAHNSU0gGle4o+b6U6imA0plSM9arnMfysOBVvvTSARyOKTQFbYjjIH5U0wjsSKnNuh5wQfY037MP77/AJ1PKVzEIjOfvGkaM4+/UxtFPcn6mmNaqFPyZpcrHcrbCuRvHHSpI0IB+YfnTHhjDphRnOPxqdbZAOVqUhsTY/8AeH50u2QdCKX7PH6UeQnv+dVYkT956UfPj7tL5A7Fvzo+zE+v4tRZjK437mBXpSDcc/KOtPlt1jcHnJ4609LSNSFA6deamzuO9hnllv4Vo+zkj7oH41Z+yp6frS/ZU9SPxquUm5TaOFJkid0EkmSqk8sB1xUwiRBya5nXfC+pah4tsdRtLsxwQWzRnLkbWLZyMdcj+Vdatsg681XJpcV9SONfMkDAfIvT3q1QABR2qkrCDtUUPV/941Kaih6v/vGmA6UnCoOrGpFUKAB0qPrcfRalpIQtFFFMYUUUUAZuqQeYgA4LGqNtbyRF9wyCc1sXK+ZtTvnNRx4LMHGDWTj7xlKF5XKm0+hppUmVB75q8UAPIpTACNy0+UXszm7/AP5Ccn4fyFV7mMRzlR2A/kKtXwVNYfzDtUFcnHsKgvZElu3eM5Q4wcewrri/dSPocNCXtYStpyL8zl7e9+2TPdXgnucZjYo4RuAMdMev510+gafal3v0sngdxhfMcs2PXrxniqGgaA6kC4t2gghbAjbq7D+a/wA67CNRjOKnFSg5Wpv/ACPnY8yRBOxChR1NICcBe9LImZC2fpSbTz3NcDMnqx4OOeo+tU9UYNZr/wBdB/I1PK3lxF/QVUvW32KnGMOAfrg1pSfvI68C39Yh6nLeM/8AkbL7/tn/AOi1rBrqfFulajc+J7yaCwupYm2YdIWYH5FHUCsX+w9W/wCgXe/+A7/4VuUUKKv/ANh6t/0C73/wHf8Awo/sPVv+gXe/+A7/AOFAFCir/wDYerf9Au9/8B3/AMKP7D1b/oF3v/gO/wDhQBQoq/8A2Hq3/QLvf/Ad/wDCj+w9W/6Bd7/4Dv8A4UAUKKv/ANh6t/0C73/wHf8Awo/sPVv+gXe/+A7/AOFAFCir/wDYerf9Au9/8B3/AMKP7D1b/oF3v/gO/wDhQBQoq/8A2Hq3/QLvf/Ad/wDCj+w9W/6Bd7/4Dv8A4UAUKKv/ANh6t/0C73/wHf8Awo/sPVv+gXe/+A7/AOFAFCir/wDYerf9Au9/8B3/AMKP7D1b/oF3v/gO/wDhQBQoq/8A2Hq3/QLvf/Ad/wDCj+w9W/6Bd7/4Dv8A4UAUKKv/ANh6t/0C73/wHf8Awo/sPVv+gXe/+A7/AOFAFCir/wDYerf9Au9/8B3/AMKP7D1b/oF3v/gO/wDhQBQoq/8A2Hq3/QLvf/Ad/wDCj+w9W/6Bd7/4Dv8A4UAUKKv/ANh6t/0C73/wHf8Awo/sPVv+gXe/+A7/AOFAFCir/wDYerf9Au9/8B3/AMKP7D1b/oF3v/gO/wDhQBQoq/8A2Hq3/QLvf/Ad/wDCj+w9W/6Bd7/4Dv8A4UAUKKv/ANh6t/0C73/wHf8Awo/sPVv+gXe/+A7/AOFAFCir/wDYerf9Au9/8B3/AMKP7D1b/oF3v/gO/wDhQBQoq/8A2Hq3/QLvf/Ad/wDCj+w9W/6Bd7/4Dv8A4UAUKKv/ANh6t/0C73/wHf8Awo/sPVv+gXe/+A7/AOFAFCir/wDYerf9Au9/8B3/AMKP7D1b/oF3v/gO/wDhQBQoq/8A2Hq3/QLvf/Ad/wDCj+w9W/6Bd7/4Dv8A4UAUKKv/ANh6t/0C73/wHf8Awo/sPVv+gXe/+A7/AOFAFCir/wDYerf9Au9/8B3/AMKP7D1b/oF3v/gO/wDhQBQoq/8A2Hq3/QLvf/Ad/wDCj+w9W/6Bd7/4Dv8A4UAUKKv/ANh6t/0C73/wHf8Awo/sPVv+gXe/+A7/AOFAFCir/wDYerf9Au9/8B3/AMKP7D1b/oF3v/gO/wDhQBQoq/8A2Hq3/QLvf/Ad/wDCj+w9W/6Bd7/4Dv8A4UAUKKv/ANh6t/0C73/wHf8Awo/sPVv+gXe/+A7/AOFAFCr+h/8AIf03/r6i/wDQxR/Yerf9Au9/8B3/AMKu6Po+qRa3YSSabeIi3MbMzQMAAGGSTigC94sH/FT3h/3P/QFrGrpPE2m30/iG6lhsrmSNtmHSJiD8g7gVkf2Pqf8A0Drv/vw3+FSIokZGKjC47Vpf2Pqf/QOu/wDvw3+FH9j6n/0Drv8A78N/hRYVjOFFaB0bUj/zDbv/AL8N/hSHRdT7adef9+G/wpWAoUjLkVf/ALG1T/oG3n/fhv8ACk/sfVP+gbef9+G/wpWGZuCDRmtE6Lqh/wCYbef9+G/wpDoepn/mG3n/AH4b/Cpsx6GXMMoeOlVCqmQPtUsOhxyPxrdOh6p/0Dbw/wDbBv8ACj+wtTxxpd5/34b/AAqXFthdGL5Tt2rG1sGG5ti5Kx7G6KSM5HpXZjRdV/6Bl5/34b/Cl/sXVP8AoGXn/fhv8KqmuSXMVP3o8pg2dqHsoGJbBQcdKmFpGvRB+NbQ0bVP+gZef9+G/wAKP7G1T/oG3n/fhv8ACk4Ju9hqckkjJWJV4AFPCx7w5Rd4GN2Oa0jompn/AJht5/34b/CkOh6p/wBA28/78N/hT5bbCbvuUgV7U7jtVn+xNV/6Bl7/AN+G/wAKP7F1YdNMvf8Avw3+FGpNivSYq2uj6tjnTLz/AL8N/hTv7H1X/oGXn/fhv8KYWKVNIq9/Y2q/9A28/wC/Df4UHRdV/wCgZef9+G/woKRi3lu0ksMqEKytgn2q4DlQcYyKvf2Lqv8A0DLz/vw3+FH9japj/kG3n/fhv8KbbasRy2baKNFXv7F1TH/INvP+/Df4UDRdU76bef8Afhv8KViSkBnk9KkC+vSro0XUxydOu8/9cG/wp39j6n/0Drv/AL8N/hVJDsUqKu/2Pqf/AEDrz/vw3+FH9j6n/wBA68/78N/hTGUqKu/2Pqf/AEDrz/vw3+FH9j6n/wBA68/78N/hQBSpDV7+x9T/AOgdef8Afhv8KQ6Pqf8A0Drz/vw3+FAGeaLbrJ/vf0q8dG1T/oG3n/fhv8KS30bVFMmdNvBlu8Den0oQFdutNq+2kan/ANA67/78N/hTf7H1P/oG3n/fhv8AChgUqKu/2Pqf/QNvP+/Df4Uf2Pqf/QNu/wDvw3+FAGWRifJ7jFOK5NaD6JqTjnTrv/vw3+FM/sTVx00+7P8A2wb/AAqLFKRR21DKgz0H41pf2PrAP/IMvP8Avw3+FMfRdXbppl5/34b/AAqWtCmzN2e35NSxrh/8Tmr/APYer/8AQKuv/Adv8KVdF1dWz/ZV3+EDf4VKQr2K2DRg1d/snVv+gXe/9+G/wpf7J1X/AKBd7/34b/CrsVco8+lJ+FX/AOydV/6Bd7/4Dv8A4Uv9kap/0C73/wAB2/wosFzNZdwqN42OOOla39kap/0C73/wHf8AwpP7I1T/AKBd7/4Dv/hScbjUrGaqkClAOa0hpGp/9Ay9/wDAd/8ACj+x9T/6Bl7/AOA7f4UcocxQx60YFaH9j6p/0DL3/wAB2/wo/sbU/wDoGXn/AIDt/hTsK5QwPSr2jf8AIc0//r5j/wDQhS/2Nqn/AEDbz/vw3+FXNJ0nUY9ZsZH0+7RFuI2ZmhYAAMOScU0hXOU+JEUjePtTZY3IPlchT/zySuV8ib/nk/8A3ya9112/1mHWbiO00yWaAbdsiwuwPyjPI465rO/tTxF/0BZ//Ad6+oocRTpUo01TT5Ulv2+RySoXbdzxvyJv+eT/APfJo8ib/nk//fJr2T+1PEX/AEBZ/wDwHej+1PEX/QFn/wDAd61/1mn/AM+19/8AwBfVvM8b8ib/AJ5P/wB8mjyJv+eT/wDfJr2T+1PEX/QFn/8AAd6P7U8Rf9AWf/wHej/Waf8Az7X3/wDAD6t5njfkTf8APKT/AL5NHkTf88n/AO+TXsn9qeIv+gLP/wCA70f2p4i/6As//gO9L/Waf/Ptff8A8APq3mcH4BikTxKGaN1HkvyVPtXQtPFk/N+lbf8AaniL/oCz/wDgO9H9qeIv+gLP/wCA718vnS/tSsq0vdsrdzlxWWrEWvK1jD8+L+9+lL58X979K2/7U8Rf9AWf/wAB3o/tTxF/0BZ//Ad68b+x4/z/AIHH/YUP539xifaIv736UefF/f8A0rb/ALU8Rf8AQFn/APAd6P7U8Rf9AWf/AMB3o/saP8/4B/YUP539xiefF/e/Sjz4v7/6Vt/2p4i/6As//gO9H9qeIv8AoCz/APgO9H9jR/n/AAD+wofzv7jE8+L+/wDpR58X9/8AStv+1PEX/QFn/wDAd6P7U8Rf9AWf/wAB3o/saP8AN+Af2FD+d/cYnnxf3/0p9vIj31qqnJ85e1bH9qeIv+gLP/4DvR/aniL/AKAs/wD4DvVQymMJKXNsXTyWMJqXPt5DdQdft03zD7x71V3r/eX86uf2p4i/6As//gO9H9qeIv8AoCz/APgO9ZyyVSk3z/gfYxzWUYpcv4lPev8AeH50b1/vD86uf2p4i/6As/8A4DvR/aniL/oCz/8AgO9T/Ykf5/wK/taX8n4lPev94fnRvT+8Pzq5/aniL/oCz/8AgO9H9qeIv+gLP/4DvR/Ykf5/wD+1pfyfiU96/wB5fzo3r/eX86uf2p4i/wCgLP8A+A70f2p4i/6As/8A4DvR/Ykf5/wD+1pfyfiU96/3h+dG9f7w/Orn9qeIv+gLP/4DvR/aniL/AKAs/wD4DvR/Ykf5/wAA/taX8n4lPev94fnWzc6Ja6kqPO0ozGFZUfAbHTI9RmqX9qeIv+gLP/4DvR/aniL/AKAs/wD4DvXdg8CsLezvc48Vi3iLXVrE1r4Q0m0lEiJMzBAg3yFhwMdD3rQXSrZL9bwb/NVNgG75frj1rJ/tTxF/0BZ//Ad6P7U8Rf8AQFn/APAd67rHGdJRXN/2p4i/6As//gO9H9qeIv8AoCz/APgO9FgOkorm/wC1PEX/AEBZ/wDwHej+1PEX/QFn/wDAd6LBY6Siub/tTxF/0BZ//Ad6P7U8Rf8AQFn/APAd6LAdJRXN/wBqeIv+gLP/AOA70f2p4i/6As//AIDvRYLHSUVzf9qeIv8AoCz/APgO9H9qeIv+gLP/AOA70WCx0lFc3/aniL/oCz/+A70f2p4i/wCgLP8A+A70WCx0lFc3/aniL/oCz/8AgO9H9qeIv+gLP/4DvRYLHSUVzf8AaniL/oCz/wDgO9H9qeIv+gLP/wCA70WCx0lFc3/aniL/AKAs/wD4DvR/aniL/oCz/wDgO9FgsdJRXN/2p4i/6As//gO9H9qeIv8AoCz/APgO9FgsdJUF7Al3YTWskImjlG1kLlQR9RWF/aniL/oCz/8AgO9H9qeIv+gLP/4DvRYDP03wDZ6XZaraWqSRQahGVZfO3FcjHBK/zzVrw54Os/DUcUdlbbPm3SSvOXZ+COmAO9Tf2p4i/wCgLP8A+A70f2p4i/6As3/gO9OwHoVGM4+tcL/wkHin/oETf+Az/wCFT2Ou+JJtQtop9LlSF5VWRjbuNqkjJz24pcoHWS/601HUsisZCQpI+lM2N/dP5UwG0U7Y390/lRsb+635UgG0mAadsb+6fypdj/3T+VADNoppSpdjf3T+VGxv7p/KiwEG0jvRg1OY2P8ACfypvkMezflSsBAwzxTfKFWhCR/Cx+tL5ZHRT+VHKBUNuG7GnrBj0qzsbP3T+VGxv7p/KjlQEIiUcnk+9SAYHFO2N/dP5UbH/un8qdgGg0U7a390/lRsb+635UANo707Y390/lRsb+6fyoAbRTtjf3T+VGxv7p/KgBtFO2v/AHT+VGxsfdP5UAN7UU7Y390/lRsb+6fyoAz7j5b6M9iKvVVu4JGljZY2OD2Bq2qMVBKt+VTHdjYlH4U7Y2fun8qNjf3T+VUIbRTtjf3T+VGxv7p/KgClfD5EPcHIxU0Aym7uTTLqKR8ARufoDU0CSCIAqwP0qV8Q+g6inBG/un8qNjf3T+VUIbS0uxv7p/Kgo390/lQA2inbG/un8qCrf3T+VADTUUPVv941MUfH3T+VRxRuC2UYfMe1MA6XH1FS1HIjhlcIxwegFS7W/un8qSAKKXa390/lRtb+6fypgJRS7W/un8qZJvC4VGJPHAoAjX552bsvFNCbpXHepo4mRAMHP0puxhOTtbBHXFKwrETKV4xSJJtbn8qtFCRyp/KoXtmI+UHP0ot2BpkMum2d1KZpItzt1O4jP61jXmjXAun+ywZh42/OPTnqc9a20W4jPCMR6YqQi5cY2bR601No6aOMq0tnfydzn9urKduP/QaCdWVTnp7ba6NINo5Uk/SmvbE9Afyov5IbxT39nH7jmgup9QD/AOO0v/EzP+VrbktJf4Ub8qj+zzf885P++TUuduiM3i2v+XUfuMOY32Asp4POPlqUWOp3FuqCLdGTuAyo5q3NaXEtyB5Eu3hQdpx71vRRlAg2kAY7U4S12RVLGtSuqcV8jzHxl4y1/SvFd7ZWV/5VvHs2J5MbYyik8lSepNYX/CwvFP8A0FP/ACXi/wDiaPiF/wAjzqP/AGy/9FJWZrWg3WhSQpdSQuZVLL5RJxj1yBVGJp/8LC8U/wDQU/8AJeL/AOJo/wCFheKf+gp/5Lxf/E1zFak2g3UOhRau0kJt5W2qoJ39SOmMdvWgDT/4WF4p/wCgp/5Lxf8AxNH/AAsLxT/0FP8AyXi/+JrmKKAOn/4WF4p/6Cn/AJLxf/E0f8LC8U/9BT/yXi/+JrmK3tM8H6tqlus8aRwxMMq0zEbh6gAE0AWf+FheKf8AoKf+S8X/AMTR/wALC8U/9BT/AMl4v/iazdW8O6lowD3UQMROBLGdy59Pb8az7a2mvLhLe3jaSVzhVUcmgDov+FheKf8AoKf+S8X/AMTR/wALC8U/9BT/AMl4v/iaUeAdZ8nfm23Y/wBX5h3fyx+tc7d2lxY3L29zE0UqdVagDof+FheKf+gp/wCS8X/xNH/CwvFP/QU/8l4v/iaydJ0O/wBakZbOLKr96RjhV/GtDUPBer6fbNOyRTxoMt5LElR64IFAE3/CwvFP/QU/8l4v/iaP+FheKf8AoKf+S8X/AMTXMVv6d4O1fUrdZ0jjhiYZUzMRuHrgAmgCx/wsLxT/ANBT/wAl4v8A4mj/AIWF4p/6Cn/kvF/8TWZq/h7UNFKm7iBjY4WVDlSfT2/GsugDp/8AhYXin/oKf+S8X/xNH/CwvFP/AEFP/JeL/wCJrmK6Sz8D6zd24mKwwBhkLM5DH8ADj8aAH/8ACwvFP/QU/wDJeL/4mj/hYXin/oKf+S8X/wATWRqmi32jSql5DtDfddTlW+hqtZ2VxqF0ltaxNLK/RR/nigDoP+FheKf+gp/5Lxf/ABNH/CwvFP8A0FP/ACXi/wDiaV/AWspD5g+zO2M+Wsh3fqMfrXNSxSQTPFKjJIhwysMEGgDpP+FheKf+gp/5Lxf/ABNH/CwvFP8A0FP/ACXi/wDia5inIjyyLHGpZ2OFVRkk+lAHS/8ACwvFP/QU/wDJeL/4mj/hYXin/oKf+S8X/wATRD4D1mWESN9niJGdjyHd+gI/WsPUNNu9LufIvIWjfGRnkMPUHvQBuf8ACwvFP/QU/wDJeL/4mj/hYXin/oKf+S8X/wATWJpul3mrXPkWcJkfGSegUepNbVz4F1m3gMqrBNgZKROS35EDNAC/8LC8U/8AQU/8l4v/AImj/hYXin/oKf8AkvF/8TXMkEEgggjqDW3pXhLVNWgFxCkcULfdeZsBvpgE0AW/+FheKf8AoKf+S8X/AMTR/wALC8U/9BT/AMl4v/iaztX8OajooV7qJTExwJYzlc+nt+NZkMMlxMkMKM8jnCqoySaAOk/4WF4p/wCgp/5Lxf8AxNH/AAsLxT/0FP8AyXi/+JpV8A6y0PmE2ytj/VmQ7v5Y/Wueu7O4sLl7a6iaKVOqtQB0H/CwvFP/AEFP/JeL/wCJo/4WF4p/6Cn/AJLxf/E1zFFAHT/8LC8U/wDQU/8AJeL/AOJo/wCFheKf+gp/5Lxf/E1zFTWtrNe3UdtboXlkbaqigDof+FheKf8AoKf+S8X/AMTR/wALC8U/9BT/AMl4v/iaxNU02bSNQksrho2kQAkxkkcjPcD1plhp91qdyLe0haWQ84HQD1J7CgDe/wCFheKf+gp/5Lxf/E0f8LC8U/8AQU/8l4v/AImnN4A1kRbw1qzY+4JDn+WP1rnLq1nsrh7e5iaKVDhlbqKAOh/4WF4p/wCgp/5Lxf8AxNH/AAsLxT/0FP8AyXi/+JrmKKAOn/4WF4p/6Cn/AJLxf/E0f8LC8U/9BT/yXi/+JrmKt6Zptzq18lpaqDI3JJ6KPUn0oA3P+FheKf8AoKf+S8X/AMTR/wALC8U/9BT/AMl4v/ia5+8tXsr2a1kKl4XKMV6Eg44rc07wXqOp6fFeQTWqxyglQ7sDwSOcKfSgCT/hYXin/oKf+S8X/wATR/wsLxT/ANBT/wAl4v8A4mpv+Feat/z8WX/fb/8AxNYutaHc6FcRw3UkLtIu8eUxOBnHcCgDU/4WF4p/6Cn/AJLxf/E0f8LC8U/9BT/yXi/+JrmkRpJFRFLOxwqgZJNXtX0a60WeKG6Me+SMSAIScAkjB468UAa//CwvFP8A0FP/ACXi/wDiaP8AhYXin/oKf+S8X/xNcxRQB0//AAsLxT/0FP8AyXi/+Jo/4WF4p/6Cn/kvF/8AE1zFFAHT/wDCwvFP/QU/8l4v/iavaJ468SXevadbT6jvhmuoo5F8iMZUsARkL6VxVafhv/kadI/6/Yf/AEMUAeyaprM9pqUsKXSIq4wpC8ZAPeqf/CQ3P/P6n5L/AIVzfiqR28Xaghjwq+Xh8/e/drVW/wBNm05kWZo2LgkbCT/MVIjrv+Ehuf8An9T8l/wo/wCEhuf+f1PyX/CuGq1Np88Wlrf5Ro24VATuJ59sdqBHX/8ACQ3P/P6n5L/hR/wkNx/z+p+S/wCFecPdXycCw3kY5EoANXFJKgkYOORQB3f/AAkNz/z+p+S/4Uf8JDc/8/qfkv8AhXDVo2eiXl5EJEVUQ9Gc4zQB1H/CQ3P/AD+p+S/4Uf8ACQ3P/P6n5L/hXDanY6xpkpLaes1v2ljlHXsCD071Dpxu70BHtGjmLYCBg2fegDv/APhIbn/n9T8l/wAKP+Ehuf8An9T8l/wrnh4avym4mEH+6WOf5VmXNrNaSmKeMo38/pRqB2n/AAkNz/z+p+S/4Uf8JDc/8/qfkv8AhXn9w8yAGFA/XOaebXV0077dJawpE7lI0Mh3t6Me2CAe/wCFAHe/8JDc/wDP6n5L/hR/wkNz/wA/qfkv+FecW8988yrNbKqEZLBs49vzrooPD1/PGHKpGDyA7YP6CgDpT4iuB1vo/wAl/wAKT/hI5/8An+j/ACX/AArCsdOmsrmcXUQAKrtbqD16GrUlxZRKWdogB1OBSbtuBp/8JHP/AM/0f5L/AIUf8JHP/wA/0f5L/hWRb3tjdEiLy2A74GK0VsC67hFGPTIpKSewoyUldMm/4SOf/n+j/wDHP8KP+Ejn/wCf6P8AJf8ACsS+uW065EdxZDY/3GQA7qmtbi2u5FjjiG8jIUpzUqrG9r6iU05cq3NR/EdwEYi+jzjjhP8ACkTxHdFQTep+S/4VVv8ASpJbCVIo4zIRwBwa+fNV8UeILDVru0N08RhlZPLaJcrg9ORXp4HL6uNk402lbuEpcu59If8ACQ3P/P6n5L/hR/wkNz/z+p+S/wCFfMn/AAmfiD/oIH/v2n+FH/CZ+IP+ggf+/af4V6X+reK/mj97/wAifao+m/8AhIbn/n9T8l/wo/4SG5/5/U/Jf8K+d9A1bxd4k1eLTdPvN00nJZo1Coo6sxCnAqvqfiPxLpOq3Wnz6krTW0rROY0UqSpwcZUcVH+r+I5/Z88b72u/8g9qtz6Q/wCEhuf+f1PyX/Cj/hIbn/n9T8l/wr540LU/GfiS/wDselzvNIBuc7ECoPViRxXVX/hL4k2No1wl7a3ZUZMVuQX/AADIM/QVjVyedKahUqwT9X/kNTvqkeu/8JDc/wDP6n5L/hR/wkNz/wA/qfkv+FfM3/CY+IVk2tfMCDggxLx+les2Wi+IdUBmt2jityBtebgNx2wCa48zwNXL+Xn97m7eXrYyniOVK0W/Q9A/4SG5/wCf1PyX/Cj/AISG5/5/U/Jf8K8z1iy1/RAr3RUxMcCWPDLn06cfjWZDqOpXMyQws0kjnCqqAkmvElmEIy5ZRd/68zknmlOEuWUZJ+n/AAT1/wD4SG5/5/U/Jf8ACj/hIbn/AJ/U/Jf8K4dfC3ihofMM1srYz5Zb5v5Y/WucvLvVbC6e2ui0UydVKiqnjVTV5xa/r1LqZiqSvOEl8v8Agnrf/CQ3P/P6n5L/AIUf8JDc/wDP6n5L/hXjv9r3v/Pb/wAdFH9r3v8Az2/8dFZf2nS7P+vmY/2xR7P8P8z2L/hIbn/n9T8l/wAKP+Ehuf8An9T8l/wrx3+170/8tf8Ax0V19j4e1y7s4ZX8qEtGpIlODnHPABxWkMdGafJFv+vU7sDioYyTjF2t3/pnZ/8ACQ3P/P6n5L/hR/wkNz/z+p+S/wCFcDqVhqmlYNyo8tjgSJypNV7Nb+/uBBbKZJDzgAcD1J7VlLNIRnyOEr9rf8E9dZfNx5lJW9T0b/hIbn/n9T8l/wAKP+Ehuf8An9T8l/wrk28L62I9wltyf7obn+WKwrh7y1naGcGORTgqRVVcxVJXnCS+X/BFDAyqaQmn8/8AgHpP/CQ3P/P6n5L/AIUf8JDc/wDP6n5L/hXmX2uf+/8ApR9sm/v/AKVj/bNHs/w/zNP7Lrd1+P8Akem/8JDc/wDP6n5L/hR/wkNz/wA/qfkv+FeaR3FxLKkat8zEKOO5rqYvC+oiNRLNbF+5DN/8TXXhcbHEt8iehz4jCzoW5mtTov8AhIbn/n9T8l/wo/4SG5/5/U/Jf8K5e+0W5sLfzpXiK5C/KTn+VY9tHq15dNDbacJQG+95oUKM9TXZqcp6B/wkNz/z+p+S/wCFH/CQ3P8Az+p+S/4Vyx8Paui7pIbZh6RTEn8io/nWeQVJBBBHBB7Uagdz/wAJDc/8/qfkv+FH/CQ3P/P6n5L/AIVzFpod7dxiRVVEPIMhxmmXuj3linmSIrR92Q5Ao1A6r/hIbn/n9T8l/wAKP+Ehuf8An9T8l/wrzl7m8UnbYlxjIPmAd+n8qswvJJCrSReW5HKbs4/GgDvf+Ehuf+f1PyX/AAo/4SG5/wCf1PyX/CuGooA7n/hIbn/n9T8l/wAKmtNduJr2CI3aMHkVSMLzk/SvNLm4u4pQILPzkxndvA9f/rVf8OT3U2vWYuLQwhbmPad4bcNwoA1vF3i7XNM8T3lnZ33lW8ezanlI2MopPJUnqTWL/wAJ74l/6CX/AJAj/wDiaPHv/I6ah/2z/wDRa1m6volzorxJcvE5lBK+WSenrkCqKNL/AIT3xL/0Ev8AyBH/APE0f8J74l/6CX/kCP8A+Jrm60pdEuYdFi1VniMEhwFBO7qR0xjt60AaX/Ce+Jf+gl/5Aj/+Jo/4T3xL/wBBL/yBH/8AE1zdFAHSf8J74l/6CX/kCP8A+Jo/4T3xL/0Ev/IEf/xNc3W1pvhbU9TgE8aJFE33WlbG76AAmgC1/wAJ74l/6CX/AJAj/wDiaP8AhPfEv/QS/wDIEf8A8TVDVPD2oaQgkuI1aInHmRnK59+4qjZWkl/exWsO0SSttBboPrQBu/8ACe+Jf+gl/wCQI/8A4mj/AIT3xL/0Ev8AyBH/APE1J/wgeqf897P/AL7b/wCJrN1jw/d6KkT3MkLiUkDy2J6fUCgC9/wnviX/AKCX/kCP/wCJo/4T3xL/ANBL/wAgR/8AxNZWmaNe6vIy2sWVX7zscKv41evvCOqWFu07LHMijLeUxJA9cECgCf8A4T3xL/0Ev/IEf/xNH/Ce+Jf+gl/5Aj/+JrG03T5tUvktIGRZHBILkgcDPYGt3/hA9U/572f/AH23/wATQBH/AMJ74l/6CX/kCP8A+Jo/4T3xL/0Ev/IEf/xNLN4I1KCCSV57Tailjh26AZ/u1zVAHSf8J74l/wCgl/5Aj/8AiaP+E98S/wDQS/8AIEf/AMTXN1uaf4T1TUIFmVI4Y2GVMzYyPXABNAFj/hPfEv8A0Ev/ACBH/wDE0f8ACe+Jf+gl/wCQI/8A4ms3VNCv9I2m5jBjY4EiHKk+lU7S0nvrlbe2iMkrdFFAG9/wnviX/oJf+QI//iaP+E98S/8AQS/8gR//ABND+CNXWHePs7tj7iyHP6jH61z0sUkMrRSoySIcMrDBBoA6H/hPfEv/AEEv/IEf/wATR/wnviX/AKCX/kCP/wCJrm6ciNI6oilnY4CgZJNAHRf8J74l/wCgl/5Aj/8AiaP+E98S/wDQS/8AIEf/AMTSQ+CdXlhEjeRGSM7Hc5/QEVjX+nXWm3HkXcRjfqO4I9Qe9AG1/wAJ74l/6CX/AJAj/wDiaP8AhPfEv/QS/wDIEf8A8TWNp+m3WqXHkWkRdsZJ6BR6k1rXHgvVoITIohmwMlI3Jb9QKAH/APCe+Jf+gl/5Aj/+Jo/4T3xL/wBBL/yBH/8AE1zhBBIIwR1BrY03wvqWqQCeJEjiP3XlbAb6YBNAFv8A4T3xL/0Ev/IEf/xNH/Ce+Jf+gl/5Aj/+JrO1TQL/AEgB7mNTETgSRnK59Pas+KKSeZIokZ5HOFVRkk0AdD/wnviX/oJf+QI//iaP+E98S/8AQS/8gR//ABNC+CNXaHefs6tj7hkOf5Y/WsG6tZ7K4e3uY2jlTqpoA3v+E98S/wDQS/8AIEf/AMTR/wAJ74l/6CX/AJAj/wDia5uigDpP+E98S/8AQS/8gR//ABNH/Ce+Jf8AoJf+QI//AImubresvCGq3tuswWKFGGV81iCR9AD+tAE3/Ce+Jf8AoJf+QI//AImj/hPfEv8A0Ev/ACBH/wDE1lano97pEqpdxbQ33XU5VvoaZpemT6tei1tyiuVLZckAAfQGgDY/4T3xL/0Ev/IEf/xNH/Ce+Jf+gl/5Aj/+JqT/AIQPVP8AnvZ/99t/8TWRq+jXGizxw3LxMzruBjJIxnHcCgDT/wCE98S/9BL/AMgR/wDxNH/Ce+Jf+gl/5Aj/APia5uigDpP+E98S/wDQS/8AIEf/AMTR/wAJ74l/6CX/AJAj/wDia51EaR1RFLOxwFAySa6GLwTq8sIdvIjJGdjuc/oCKAF/4T3xL/0Ev/IEf/xNH/Ce+Jf+gl/5Aj/+JrFvtPutNuPIu4jG+MjuCPUHvS6fpt1qlx5FpEXbGSegUepNAGz/AMJ74l/6CX/kCP8A+Jo/4T3xL/0Ev/IEf/xNMuPBerQQmRRDNgZKRuS36gVi2trJd3kVqmFkkcIN/ABJxzQBu/8ACe+Jf+gl/wCQI/8A4mj/AIT3xL/0Ev8AyBH/APE1J/wgeqf897P/AL7b/wCJo/4QPVP+e9n/AN9t/wDE0AR/8J74l/6CX/kCP/4mj/hPfEv/AEEv/IEf/wATXOuhjkZCQSpIyOlNoA6T/hPfEv8A0Ev/ACBH/wDE0f8ACe+Jf+gl/wCQI/8A4mubooA6T/hPfEv/AEEv/IEf/wATV3RvGviG71zT7efUN0UtzHG6+TGMqWAIyF9K46tLw9/yMulf9fkP/oYoA7XxR4o1nTvEd3aWl55cEezavlIcZRSeSM9SayP+E28Q/wDQQ/8AIMf/AMTR42/5G++/7Z/+i1rO1TSLjSXjWd4mMgJHlknp9QKANH/hNvEP/QQ/8gx//E0f8Jt4h/6CH/kGP/4mufq/JpFxFpMepM8XkyHAUE7upHpjt60AaP8Awm3iH/oIf+QY/wD4mj/hNvEP/QQ/8gx//E1z9FAHQf8ACbeIf+gh/wCQY/8A4mj/AITbxD/0EP8AyDH/APE1z9a1h4c1DUIRNGiRxt91pDjP070AWv8AhNvEP/QQ/wDIMf8A8TR/wm3iH/oIf+QY/wD4mqOo6HfaYoedFaMnHmIcjP8ASqMEEtzMsMKM8jHAUd6ANz/hNvEP/QQ/8gx//E0f8Jt4h/6CH/kGP/4mkXwfqZj3FoFP90uc/wAsVkXllcWE5huYyjjnnoR6g96ANj/hNvEP/QQ/8gx//E0f8Jt4h/6CH/kGP/4mszT9Ku9TcrbR5C/edjhR+NXLzwxqNnA0xWOVFGW8piSB9CBQBP8A8Jt4h/6CH/kGP/4mj/hNvEP/AEEP/IMf/wATXP05EaR1RFLMxwAOpNAG9/wm3iH/AKCH/kGP/wCJo/4TbxD/ANBD/wAgx/8AxNZ2p6TcaU0S3DRkyLkBCTj68VQoA6D/AITbxD/0EP8AyDH/APE0f8Jt4h/6CH/kGP8A+Jrn627bwrqdzEJCscIIyBKxB/IA0AS/8Jt4h/6CH/kGP/4mj/hNvEP/AEEP/IMf/wATWdqOjXumYNxGNhOA6nKk1WtrWa8nWC3jLyN0AoA2v+E28Q/9BD/yDH/8TR/wm3iH/oIf+QY//iaa3hDU1i3jyGOPuB+f5Y/WsOSN4pGjkUq6nDKRgg0Ab3/CbeIf+gh/5Bj/APiaP+E28Q/9BD/yDH/8TXP0oBYgAEk8ACgDf/4TbxD/ANBD/wAgx/8AxNH/AAm3iH/oIf8AkGP/AOJpkPhLU5og5EURIztdjn9Aay72xudPn8m5jKPjI7gj1BoA1/8AhNvEP/QQ/wDIMf8A8TR/wm3iH/oIf+QY/wD4msiysLnUZ/Jtoy7dSegA9zWnP4T1OGIyARS4GSsbHP6gUASf8Jt4h/6CH/kGP/4mj/hNvEP/AEEP/IMf/wATXPkEEgjBFaun+Hb/AFGETRqkcR+60hxn6d6ALf8Awm3iH/oIf+QY/wD4mj/hNvEP/QQ/8gx//E1Q1HRL3SwGuEBjJwJEORmqMUTzSrHEhd2OFUDkmgDd/wCE28Q/9BD/AMgx/wDxNH/CbeIf+gh/5Bj/APiaavhDUzFvPkK2PuF+f5Y/Wsa5tprSdoZ4yki9VNAG3/wm3iH/AKCH/kGP/wCJo/4TbxD/ANBD/wAgx/8AxNc/RQB0H/CbeIf+gh/5Bj/+Jo/4TbxD/wBBD/yDH/8AE1z9SW8Et1OkEKlpHOABQBuf8Jt4h/6CH/kGP/4mj/hNvEP/AEEP/IMf/wATWRf2MunXj2szIzpjJQkjkZ71FBby3UywwRs8jdFFAG5/wm3iH/oIf+QY/wD4mj/hNvEP/QQ/8gx//E0n/CIan5e7MGf7m85/lj9axJ4JbWdoZ0KSKcFTQBuf8Jt4h/6CH/kGP/4mj/hNvEP/AEEP/IMf/wATXP0UAdB/wm3iH/oIf+QY/wD4mj/hNvEP/QQ/8gx//E1gIjSOqIpZmOAAMkmt2PwjqckQc+TGT/Az8/oMUAO/4TbxD/0EP/IMf/xNH/CbeIf+gh/5Bj/+JrHvLG5sJ/JuYij9R6EeoNV6AOg/4TbxD/0EP/IMf/xNH/CbeIf+gh/5Bj/+JrP07RrrU4pZYdixxDlnJAJ9Bgdaz6AOg/4TbxD/ANBD/wAgx/8AxNH/AAm3iH/oIf8AkGP/AOJqlp+gX+pR+bCipEejyHAP070uo6Bf6bH5syK8XQvGcgfXvQBc/wCE28Q/9BD/AMgx/wDxNH/CbeIf+gh/5Bj/APia5+igDoP+E28Q/wDQQ/8AIMf/AMTR/wAJt4h/6CH/AJBj/wDia5+igDoP+E28Q/8AQQ/8gx//ABNW9J8X67c6zY2819uiluI0dfJQZBYAjha5StDQv+Rh0z/r7i/9DFAEPxC/5HnUf+2X/opKv/EX/j60/wD65t/MVQ+IX/I86j/2y/8ARSV1HivxJeaDNapaxQOJUJbzVJ6Y6YIoA8wrtr//AJJjY/8AXQf+hNVb/hYerf8APvZf98P/APFVp+IL+XU/ANteTKiySygsEBA4LDjOfSgDz+iiigC9otsl5rdlbyjMbzKGHqM8it7x1qdxJrDaersltAq4RTgMSAcn88VzFtcSWt1FcRHEkTh1PuDmu1vv7A8VpHePqKadehQsiy4wfzIz9QaAI/BU8mqWt/o92xltmiyobnZzjj9D+FYeha0PD1zcyG0E07L5aMX27PXsc9vTpXYeFF0qxvprDTpmu5TH5k9zjC8EAKPzJ/zx51cuJLqZ16M5I/OgCxDPqV7qSywSXEt4zZDISWzXSeP3jM+nI5U3iwnz9v4Y/XdWbpPi2/0e1jtYI4GhViSGU5bJ9c1oeKLC0u9ItvENmjRm4YCZCc8nPP4EEUAWNWmfQ/BOm2lqxikuxvlZTgnIyRn8QPoKyvB2qT2evQQCRjBcNsdCeCT0P1zitHxr8+jaDIv3DCf1VMVz3h5S3iLTgOv2hD+RoA2hocJ+IRsNg+ziXzSuONu3fj6dqfria14k1i5is4JZLS2kMSAHamRwTkkAnNa8ci/8LQlHGfI2/jsB/lWH4n8Q36avPZ2c8lrb27lQsJ2EnuSR6nNADbjUtR0vQptE1ewlcS/6mWST7nTGODnB965eu70u8k8S+FNStdQPmzWq745SOc4JH48EfQ1wlAFrTbuOx1KC6lg89Ym3eWW25I6c4PfBq1rGs3Wt6i0771UkCOENkIPb3rLrT0fXLvRHle0WItKACZFzwPTmgDptQMyfDpE1Xd9qaQeQJfv43fn93P4YqLwvjS/Cuq6woHn8xxsR0wBj9W/Sp3aDxloF1dywiLUbJSdyE7WGM4x74NV7H958Mb9V6rNz/wB9If5UAcvb6ne21+L2O4k+0BtxcsTu+vqK6Tx3bxvNYanGu37XD82PUAEH8mx+FchXceKYGm0rw3ZggSyIqDd0B2oOfzoA4eus+H9rHNrkszgEwwlkz2JIGfyz+dL/AMK81b/n4sv++3/+Jqhpd5L4T8SOs+JAmYphGc5HHIzj2NAFDVNWu9T1GS5mlfO47Fzwg7AeldRcNJrfw8+1XR33NnJhZD1IyAcn6H9KZe6L4bv52vrfXYraKQ72hYAkE9cAkEfTBrQkubE+AtQTT0cWkJ8lHfrIcrlvxLUAUbSRtD+HrXdudlzeybd46jkj+QP51zuh6tc6bq8E6TPtaQCUE8OpPOa39V/efDbS2Xoswz/4+P51x8Kl541HUsAPzoA6vxNpCHxrBAg2pesjNjsS2G/ln8aPHWoSf2kmmQsY7W3jX92pwCSM/oMVreIZFXx5ooPUBP1cgVzfjVSPFd2T0IQj/vgUAa3g+eTV9O1DRbpzJEYt0W452duPocGovAtvHC+o6nKmTaRfLntkEn9Fx+NJ8O1P9sXTdhb4P/fQ/wAKn8LMJdA8Rqn3mjYj8VbFAHJzanez35vnuZPtBbcHDEEfT0FdR4qxqfhnStZYDzyBFIQOuQf6qfzrjK7PUv3fw001W6tNx+bmgDjKKKKACu+0q/0PRr61sdLU3d1cSLHLct0UEjIB/wAPzNcDWl4f/wCRi07/AK+E/nQBe8bf8jVdf7qf+gCnaLr9vo2iX0USSjUZzhJVUbVGOOc5z949PSm+Nv8Akarr/dT/ANAFYABJAAyT0AoAnhvbuG7FzFcSi4zkOGJYn+tdX4/VSdLmkULdPEwlA9sY/UtRpOi22gWy6zrvyuObe2/iLdiR6+3bvXN6xqs+s6i93PxnhEHRF7CgChRRRQBLaxxy3cMcsnlxvIqu/wDdBPJr0LQNT0e11WHSNGhLrJuM10/VsKTx6/oK84rofBP/ACNVr/uv/wCgmgDP1/8A5GHUf+vh/wD0I1nVo6//AMjDqP8A18P/AOhGqUEL3FxHBGMvIwRR6knAoA6rwpYQWdnP4iv1zDbg+Qp/ib1/Pge/0rm9R1CfU76W7uGzJIc47KOwHsK6nxrOlhaWGhW5xHDGHfHc9Bn9T+NcZQB1+m3+h6BZ288Cm81WVFJ3fdhJHI//AFc/Sj4if8hq2/69x/6E1cnF/rk/3hXWfET/AJDVt/17j/0JqAOQqSCLzriOLON7hc46ZNR1PY/8hC2/66r/ADFAHTy+BZYbuRZdQiis4wM3Mq7QSewGf61V1TwhPZWJvrO7ivrVRlni6gevUgj8atfEC7lk1mK1LEQxRBgvbcc5P8qd8Pp3OpXVkx3W8sBZkPTIIH8iaAMfQNBbXXuUWfymhj3gBN24+nUYrf0bwmNP1bT7i91S1iuop45Raggs2GB29RyfYGl8CoLfWNUReRGuB+DGue0KeS58YaZPKxaSS/iZmPcmQUAdp4v06VPFdxd5kCShSFXG1sIo59xj9a1dX0t9QlifzUijjU7maneOP9ZGe+//ANlFJrWqT6aYBCkbb92d4J6Y9D70hGUfDrs6eRdRzRFsM6jlffGf61c1O2Nt4djtwS5STGcYzye1M0/X7q6vooJI4QjnBKg5/nVrxCxbSCT/AM9QP1NAHICCQSl90hBGNmOB+maf5b/3G/KkoqRD1Rtw3I23PPFb3iMTsIPJDG12cbOmff8ADFYdtC1zcxwqcF2C59K37vUl0UixtI95QAs0rEjJ9qYxumib+wb1boP5O0iPd16dvxxVDSLyPTzPJJDI0rLtjIXIH1/StKG4TxDbvbzAxXCDcpVjtP4VzbAqxU8EHBoAleS5ebzWaQyZzu5zW3q++fQ7KWZSbgkducEH/AVX0/S0SIX2oHy7deVRur/5/Wqmqai+o3G7BWJOEX0H+NAD7G304QGfUEaV0fKQ4zkjkGrviGM6t4btnIaL9+HCp2xuAFYPNbt1/wAinaf9dP8A4qgDn9Is00q6SYxvPtOSrcZ9O1aF/d3OoXRkZXA/hQZIWqWeCTk4GcCsm08b3WnW19dQaTPmKVYvLkX53BI5A/GgDv1Wc+HAl0GMpPyh+uM9/wAK4u8uLiK7itEdzNnJCRgrj3rsNH1JPEGnyXL2z29yigMj8HGMjjt3qg9jJLfsQOOhPtXPXXvKVrmdWl7RaOz/AK0ItFjSfVIUJBIJ3hB8pI5/pW9qXmTb1jbDLwuaTTLSK2mUIoBAIFSTD98/+8adOlywafUmnGSjaW5mm1ur/wAP3drcPmaE+ZDIeowM4/mPoai8K2JsbC8uDK0kjPsXdzswBwPxP6VuwKGt5wehXB/I1DZWwttLkjBz85OfyrTleiCNPlkn5GfqNzcW0BnhY7k53Z9+9eW/GfT4RqOla3CgU38BEmO5XaQT74YD/gNep6t/yC5/93+tcB8U7V7zTPBumoVWe4/drvOACRGvPtkivZyKThjIu+97+ln/AJFTXus8bqzp8MNzqVrBczCCCWZEklP8CkgFvwHNeg/8KQ8S/wDP9pP/AH9k/wDjdcHrGlz6Jq91ply8bzWz7HaMkqT7ZAP6V9tRxlDENxpTu/IwcWtz2zwTrnhOx1yLw54YiaQybjNeyg7ptqseDjnGB1wOTivJ/HBh/wCEx1kIp8z7dNuJPX5v/wBdanwm/wCSi6f/ALkv/otqxvGn/I765/1/Tf8AoZrgwuGjQx8km3eKbberd2VJ3id7ps7+Evgk+pWbGK/1WfZ5y8Mo3FRg/wC6rEehbNcN4R8SX2heJrS8iuZdjzKtwpYkSIThs+px39a7TxH+++A3h94+VS5Xdj281T+teX2qNJdwxoMs0igfUmrwVOFWnWc1fmlJP5aL7gk7NWPR/H3hmE/FmytIl2Ras8MjheMFn2uf/HS3411nj3UZTqiaZE5S2t41zGpwCxGf5Yqt42njT4yeFFYjIWPP/ApGA/WovHCkeLbsnowjI/74FfD8R1qksFh030/z/wAkcOaScaD5erNjwbcSaxpuo6JduZIjFui3HOztx9DtNQ+AreOGTUtTmXJtIvlz2yCT+i4/Gk+HCn+2bpv4RbkH/vof4VP4VYTeH/Eip95o2I/FGxXz1D3lSk91zfhscuH95UZy3XN+G33HO3Gp+de/bvtVybssTuyRt44wfr2rp/Ey2up+HdM1iYnzSPKZ8bdxweoGcDKn864Gu21M7Phjpqt1abj83P8AKuehNyjUUu1/nc5cNUc4VVLa1/ndHH3IgEn+jlin+19ahoorjepwN3ZoaJdWtjrFtdXsbyQRNvKoASSBx1I74rc1jV31e8+0q0ggZQ0SNxtUj0HGa5Ouu0DQ59Vht3J8q1SFPMlPT7o4HvWlqlSk6UFu1+p9VwtWUKk1LZK5uaTLLd+D9SW8YvFGreUz88gZA/A4/Os3R9Zt9L0y5iCyi7mbAkUDCr9c5z17elSa5rFu1qmlaYNtnH95h/y0I/pnnPeufAJIAGSelKtiXTnFU3dxVr/5enQ+ypUeeMnNWUnexejuhFOtxFcT/aMk7s85xxz35rf8YxxM9hLIVSdoyHGCD2x0z3JqPTdKg0aBdU1f5XHMNv8AxE9sj1/l3rC1TUptVvXuZeM8Ko6KvpTk3Rw7hU3lbTt5v1FFKpWUobRvr+hWk8r5fKLHjndTKKK81u52pWNLw/B9o16zTGQJN5/4Dz/Sui1GwvtQ1O4lit2KBtoJIGcccZ+lctpuoy6Xdi5hSNnClQJASBn6EVbuPFWoKizyPcuzDdtgBx1PAA/rX0GTzhyOHXc8bM4y5lLoWLqynQPBNFIhYEdP5Vp6LYPbeFr+CxlnW5eQtvLFnHA6E89AcU8aida8JXN5JBIJ7RmUb02liuDkexBp2lFNP0WPVpkfzpgNkW7GM9j+Rr2zyjM0DTrq21WAwS3IAYebu53LzkNke/6CruqiL+3nbYzRB1LhR16Zq1B4leSXy7mFBE3BMZIK/rVDWLAafe7EYmNxuXPUe1AD9W1CS/nHlLKsCgBUIxz68Vf8PNLJFcwT7jb7P4ugzWRYafPqE2yIYUfec9FrRv72CytDp1gc54ll9fWgDCaCQyK4MgC/wgcH68VLHC7yKmGG4gZx0qOpLf8A4+Yv98fzpCNdvDkiTsrXKpAoH711xk+gGahvNClt4DPBKtxEOpTqKm8TTu18kOTsRAQPc96XwzKxu5oDzG8ZJU9M5A/rTGULLSX1JZkEzwsi7gVXJPtzWppmiG0vbSa4u4xLHIj+VwWbBBx9aPDqiO+vFHIUY/I1m6XI8viCykcks11GSf8AgQoAq/EK1mh8WXM7riKcIY29cIoP6it3xJoMmsT28huI7eCGM75H7ZP+e9M+JoH+jnuJCP8Ax0VL4o1660Q2oto4X80Nu8wE9MYxgj1qhnO/8Ic0skRs9Rgurdn2ySRAEp74B5/OtPX7P+z/AAVFaeZ5nlTbd2MZ+Zu1R6P4wv7/AFa3tZobYRytglFYEce5q/4yYv4cJP8Az3A/IkUAedUUUUAOTbvXf93PP0rsPGzXLLZm3LHTjGMeX93Pvj2xj8a5SytWvb6C1Q4aVwmT2z3rsNQ1xPC5XStOi8xowC7zsWAJGeBn8eMCgBmhtOPCOp/2hu+ybD5PmeuD0z74x71m+CrfzvEKvjiGNn/9l/rWtbXkXjO0lsrlTBdxL5kZjY7D2zj6kfnXNaXrF1oU85gjiMjjY3mAnGD2wRQBcu9C1jUr25vY7GQxyyM67iFOCeOCc9KxJ4JbaZop42jkXqrDBFXz4h1cz+d/aE+7OcBvl/756Vv68yav4Ts9XdFW5VtrEDqMkH9RmgBNUlfRvB+n2tsxjkuhvkZeCcjJ/mB9BWZ4U1Ke01qCHzGME7bHQngk9D9c1oeL/n0jRJF+6Yj+qpWDoSltesAP+e6H9aAH+IbNbHXbuCMYQNuUDsCM4/Wqdnay315FbQjMkjBR/jWt4wYN4luAOyoD/wB8ir/g6CO2hvtYmGUt0Kp9cZP44wPxoAPEl3FpVlFoFicKqhrhx1Ynsfr1/KuTqW5uJLq5luJTmSRizH3NRUAanh2yS/121gkGY9xdwe4Azj9MVraymr+INVuI7SGV7W3cxKAdqZHBOTgE1W8FkDxEgPUxuB+VSeItdvU1Wa0tJntoIGKhYjtye5JHvmgBJ9Qv9N0WXR9UsZHEn+qkd/ufTg5wferfhvGm+GtS1ZQPP5jQkdOmP1b9Km026k8Q+GNQtr4+ZLbLvSUjnOCR/I/gags/n+HN6q9Vl5/76U/yoA5uDUbu3vReRzyeeGyWLE7vr610HjWCN5rHUY1x9qi+b6jBB/I/pXK12XiWFptM8P2gIEsiBBu6A7VHP50AcbXT+BraObWZJXAJhiJTPYkgZ/LP50v/AAgeqf8APez/AO+2/wDiapabdyeGPEDrNiQJmKYIeo9s/hQBS1LU7nUb97iWV87jsXPCDsB6V0c7PrHgP7Tc/PcWr4WQ9SMgHn6H9KZd6R4fvpmvINait43O5oiASD3wCQR+Rq/JcWR8E36WKsLWE+Ujv1kOVy34k0AUrWRtG8CNdQHZcXcmN46jkj+QP51g6Nqdxp+qwzLK21nAlBPDKTzmtzU/n+H2nMvRZRn/AMfH865SJS0yKOpYAUAdN4i0pD4wghQbUvGRmx2ycN/LP40eNL6T+0E06JiltBGv7teBkj/DFaeuyKPG2kA9QF/ViBXP+L1I8TXRPcIR/wB8CgDU8KTPqmn3+kXLmSMx5j3HO3t/PBqLwZAkT3+oyrk2sXy57cEn9B+tHgIH+1rluwgwf++h/hU3hthLomvqnUoxH4q2KAOYl1G7mvTeNcSeeW3BwxBH09BXSeJcaj4d0zVmA844jkIHXg/1B/OuRrrtQ+T4eaerdWl4/NzQByNFFFAGp4cs0vtftYZBmPcXYHuFGcfpVnxVqM13rk8RkbyYG2IgPAI6n65zTvBrAeJIQe6OB/3yaz9bUrrt+D/z8Of/AB40AdFp8r614Nv7e5YySWnzxu3JAAyP5EfjUPghPLl1C92lvJgwAB1zz/7LT/CfyaHrcjfd8r+StWNpfiC80i1mhtVhHmnJdlJYcY45x+lADj4a1uSMzmxkIPJyRu/LOaynR43ZHUq6nBVhgg1pQ+ItXhuBML+ZyDkq7Eqfw6Vs+MYoriz07VUQJJcIN49cqCPy5oA5KiiigDQ0bUk0nUVu3tvPKKQq79uCe/Q9s0y6vrzU9QactK8ztlFUklfQCqVauleIbzR4njtlhKu25t6kk+3WgDc8UMw8N6XHenOoZBOfvbcHOf8Ax2ktZG0bwI11AdlxdyY3jqOSP5A/nSarDba94ebXI4zFdxHbKASQ2CB/UGk1P5/h9pzL0WUZ/wDHx/OgDD0bU7jT9VhmWVtrOBKCeGUnnNXvGVmlrrzOgws6CUgevIP8s/jWFEpaZFHUsAK6jx6wOrWy9xBk/wDfRoA5ZEaSRY0Us7EBQO5Nddqjr4Z0KPTLdgL25XdPIOoH+eB+NU/Blitxq7XUgHl2qb8npuPT+p/CsjVr9tS1Se6YnDt8oPZRwB+VAFKnwx+dPHFnG9gufTJplT2X/H9b/wDXVf50AdFJ4LkhupBLfRRWkYGbiVdoJPYDP9aral4VmtLI3tpdRXtsoyzR9QPXqcj8as+ObqWTVo7YsfKjjDBe2TnJ/lTvAs7nULmzY7oJISzIemQQP5E0AZOiaKdae4VZvLaGPeBs3bj6dRW7pHhgWGqWM95qVtFcxzRyi2BBZsMDjqOT7A0eC0EGq6kg5Ea4H4MawtEmkuPFmmzSsWke9iZie5LigDoPHVtLF4ouJnXEcwQofXCKDWvr+jPqk0EhnjghiQ73f3o+IgG6E9/M/wDZRTvEWtXOkG2FukTeYGz5gJ6Y6YI9aAMP/hFWlkjNrfQ3EBba8kYBKe+M8/nV/WrX7F4Sitt+/wAuXbuxjPLdqZpfim9vdTgtpYrcJI2CUVgen1q74rYtoRJ/57AfkSKAOEooooAVdu9d33c8/Suq8XG4K2phLfYTHxs+7n3/AAxiuatLdru8ht1ODI4XPpnvXU32sJ4dK6bYReY0YBdpmJAJ56ZoAZo5mHhjUPtu77NsPlb/AKHp+OMe9ZWhapbaUbmWWN2nZNsRUAgeucn6VswXUfiu1ktJ1MN1Eu9CjHaffH5fnXIMpRirDBBwaAJXuriS4M7zSGXOd+45rpNfZp/DWnXFyP8ASSRyepBU5/kKq6VosccI1LVSIrVeVRur+nHp7d6o61qz6tdh8bIU4jT0HqfegDY1GRtK8LWVvAxR7kbpGHBORk/zA/Cs/wANX81tq0MO8+TM2xkJ4yeh/Orvij5tM0hx93yz+qrWNowLa1ZAf89lP60AO1y1Wz1m5hQYQNuUDsCM4/WtHSoY9I086xdKDK3y2sZ7n+9/nt+FXrnTF1XxhOrn9zCqNIP73A4qvrGl61qd4ZBZbYU+WJPMT5V/OgA8YsWksWPUxk/yrmK7HxPpl5drbSQQ7khiPmHcBj8zXHUAW9MngtdSgnuUZ4o23FVAJJxx198VLq+qSanfPLvfyc/u0b+EfT1rPrU0jRZtUkLk+Vap9+U9PoPegDX0uWS68J6gt2xeKNW8tm55AyB+eKZoOLDw/qGpKB533EJ7dMfqf0qrrWrQNbLpenDbaR/eYfxn/DPPvVm1+fwJdheok5/76U0AYMN/dQXYukmfzs5LE5z9fWtrxbCjS2d8gx9oj+b8MY/Q/pXOV1WvxNLp+iWwIEjqFGfXCj+tAHK1u+E7VbjWQ7jIhQuM+vAH8/0qX/hDNR/57Wv/AH03/wATVrwtbtZa3e2shUyJHglTxwR/jQBg6nqE17qUs5kbAc+WAfujtitu7dtV8HJdTHdPbPguepGQP5EflXLuCrsD1Bwa6ay+TwNelujScfmooAdbudK8Gm4hO2e5fG8dRyR/IH86xdK1Cex1KKVZG2s4Egz94E85rY1D5/A9gV6CQZ/8eFc1EC0qKOpYAUAdDrumofFMMSjC3RRjjtk4P8s0eLLx/ty2MbFIIUHyLwM//qxWhrLqPF2lg9gv6sRWJ4oBHiC4J7hCP++RQBo+G5n1GyvNLuGLoY8x7udv+Tg1H4ThSNr2+kXJt4+M9upP8v1pPBYP9pXDdhDj9RUugMJNI1pV6lGI/FWxQBz0l/dS3Zumnfzichgen0rf8QYvtC0/UyB5p+RyO/B/qD+dcxXT3vyeBrIN1MnH5saAOYooooAK6/T7vStMu4LSwU3FxM6pJO3QAnnH/wBauQq9o/8AyGrL/rsv86ALfin/AJGC4+if+giruh/8S7Qb7U1UGcny48jOOn9T+lUvFP8AyMFx9E/9BFbOjXK2vhCW42K7QuzAHkbsjH8xQBjJpmvGQXwhn8zO7eWG78s5qLW9T/tO4jd7XyJo12Pls5/DAx3pqa/qiXPn/bJGOclSflPtjpWn4qijlisdRRArXEfzj14BH86AOaooooAvaTfppt8t09v5xUEKu7bgnv0PvTLi8utQvjMWkaVmyqqSSvoBVStLTdcutKiaO3WIhm3HepOf1oA1/ETMNB05Ls/6dkE5+9jHOf0rA0+xl1G8S2hHLdT2Udya3tSig1rQ21iOMx3MZ2yjOQeg/qDVm30q90/QtthD5l5dD95IGA8tfQZP+fyoAvafc2wF3p1mo8i1ixv/ALzHOTXD2Vv9rvoLftJIFJ9ATXWeHdIvrBb0XMGzzIwE+dTk8+hrG0+wudN8Q2KXcXlszggbgc9uxoA0NdF9fX50zT4XNvbqoKpwucZ5PTpVaG51DQrSa11Cykkt5htAZ+F9cEZH4VZ8T6tc21+bS1kaBQA7snDMx9/pin+Hb6XVo7nTb5jMhj3Bm5I5x1/EGgDkqfEnmzJHnG5gufTNI6lHZD1UkVJaf8fkH/XRf50AbsnhOSK5cSXkcdsgGZ5F2gk9gM/1qvf+G5bW0N3bXEd3AOWaPqB69TxVjxjcyPqcduWPlxxghe2Tnml8HTMb6e1PMMkRYqemQQP5GgDN0jSDqzTqsvlmJNwGzdu9uorZ0vw8LLUrKa6v4I545UkFuCCzYIOOvX86PCaiHUtQQchFwPwNY+jyvP4n0+WRizveRsxPc7xQBqeMvCmoan4rvbuBofLk8vG5sHhFHp7VP4v8P3msz2j2pjAiRg29sdSK6/U/+QjL+H8hVSgDzb/hBdW/vW//AH2f8K6W68P3kvgq20tTH9ojcEkt8vUnr+NdJQOTSEebf8ILq3963/77P+FH/CC6t/et/wDvs/4V6Z5Teoo8pvUUxnm8PgnVoZ45CLVwjBtrNkNg9Dx0rb1zwSuo3CXOmm3tSVAeErtUH1G0f0rrGjKjJIptAHO6d4cm0LRruO1kSbUbldm8/KqD2+mSffiofDGg32lG7tr1YntLpNrBXyQeR+RBNdRRSEcK/wAOrn7URHfQ/Zs8MwO8D6dP1rX17Qrm40Wz0jTRGLeEhmeRsFiM/wCJJro6KAOd/wCEdn1LwtDpl86R3Nsf3UqncMDpn8Dj8Aah8PeDX0i/F9ezxyvGD5SRZxkjGSTiuoooA4yPQdcTxL/bJ+z7vOLlPMP3Txt6enFWvEHgxtWv2vrKeOJ5ceZHJnGfUEZrqaKAOetvD02keG7qys3SW9uhtkkY7VAIxx9Bn8TXNx+AdWkJHm2q4/vOf6CvRaKAOH0Twvq+kaxBefuGWMkOofqpGD296t654Hk1DUpLyxuIkEx3OkmRhu5BAPWutooAwLXw/Lo3h26srN0mvboEPIx2qMjHH0BP41B4c0C7sbK907UBG1rdL1R+VOMH9MflXTUUAcTB8PJVvgZ72JrQNk7QQ7D0x0H51a8UaBqWt6hG1uIUtoU2Rhnwfc4x/nFdZRQB5t/wgurf3rf/AL7P+Faeg+Eruw1RJb6C0uLYqVdGw+M9CAR6121FAHE3nw9mkv3e0u4EtXYsAwO5Ae2BwcfhWjq3h+5/sC20bTCnlI2+aSRsFz1/nz+ArpaKAOc0vw/cN4bn0XUmQKW3QyRtnb3/AJ/zNUtI8ByWepx3N7dRPFCwdUjzliOmc9P1rsKKAON1rw/rOpa82oxGBAjL5IL8qF6dvXn8av8AiLws+vtDeQyRwXYQJIjklT+I/HtXR0UAc9pnhyfQtFvI7eRJdQuV27s4VOwx9Mk/lVXwtoOo6LfS/aRC9tOmyQK2T7H+Y/GurooA4ib4dym+JhvYxaFsjcCXUemOh/OtDxJoN5f21lYaeI1tLVcfO/LHGB+n8zXT0oGTjvTGea/8ILq3963/AO+z/hR/wgurf3rf/vs/4V6Z5Teoo8pvUUAeZ/8ACC6t/et/++z/AIVd0nwdqdnq9ncyNBsimV2w/OAfpXfMhXk4ptIRxniXwrqGp67PdwGERuFA3Ng8KB6Vb8M+EX0x5L28WKW6X/ULn5VPqTjr/KuoooA4rU/B+v6teNc3d7Zs56De+FHoBt4FZ8ngDVo8fvrVs/3Xb+or0WlVSxwKYzzX/hBdW/vW/wD32f8ACj/hBdW/vW//AH2f8K9M8pvUUeU3qKAPM/8AhBdW/vW//fZ/wrX8NeFdQ0vXIbucwmNAwO1snlSPSu0ZSvWm0hHBar4N1O81e8uY2g2SzM65fnBP0qbQfB19Z63a3N0YTFExYhWycgHHb1xXb0UAcTr/AIV1TVNbubtGh8tyAgZ+QAAPT2rM/wCEF1b+9b/99n/CvSaKAPOI/A2qrIrFrfAIP3z/AIVv+LvDl7rGpQz2xiCJCEO9sc5J/rXUUUAecx+AtWdtvmWy+5c/0FSQeB9UgvYnZ7cqkgJIc8gH6V6FSjqKAMDxR4V/tyaO4t5kiuUXYQ/3WXPHToetM0Lw3L4etrmcSRz6hImxAOEX8frjP0rpJfv/AIUymM5nwp4fvtJubyW7aM+dHtBVs8571l6J4M1O11/TriRoNkV1E7YfnAYE9q7qprX/AI/IP+ui/wA6QFPxdaSXcyrHjKtk5P8Asimatpq6jCieYElTlCeh9RVrWf8AkKzf8B/9BFRLDHNEuwhXHUE9aBGfpWhmxuhPcSo0gyEVP51Z1i2kuNNMaY3eZnk+5qzHCLdvMkccdAKrMTJKT0LGgDnf7Guv9j86P7Guv9j/AL6rqPscn95fzo+xSf3l/OlYWpzltpt3bXMUyhCUYNjd1rU1LRxqU32m3lVZGADK/wD9arkls8SbmK49qioAi0/Tf7ISWZnWS4ZdqqvQVW0nRWNy1xdhWCnKr1y3vV2ii4XK2oaRqeoTl5JoAg+4gZsKPyrPm8O3kKbi8Lc4wGP+FbVFAXOf/sa6/wBj/vqta4sZn8O29uNu9XyeeO/+NWqKAMjT7G7sr6KfCEKfmAbqD1qxqHhqK9upLi2MGJTudXXv69Kv0UBcjs7GLRbF4rdUkmkPzbcKKWFLmYkCKNcf3pP/AK1PooAiSa4jlDeQvB/56f8A1qvTRGciSAoSeoZsY/Q1WooAmnc29t5USiSRvvfNgCmWczndFPGERhwQ+eaZRRcB0+nGdHjmZBbnqwbkjP0/rXJeMfCUPi3UIJZXkWG2j8uJUcAcnJOMfT8hXUSf6pvoabF9wfStKVadKXPTdmJ6nm//AAqOw/vzf9/R/hR/wqOw/vzf9/R/hXplLXT/AGnjP+fj+8XLHscb4S+HlpoHiO31GJpS8auBukyOVI9PeqWtfDCy1LXL6+d5d1xO8hxIAMk59K7+ip+v4nn5+d32vfoHKrWsc7p3g2ybwdN4XvHkNoz74n3gshzu4OPXn8TWbpHwi0nTNVt7+eeSRbeQSom/IZgcjPHTPau0opRx2JimlN676jsuxyGv+DRrniga28sqTxMhh2yABAnI4x68/jW3rvheDxFLFeLKYbkIEdc8H9PetSiuetOVeKhV1S2v0JnThNWmrozdN8PR+H9Mu47eUyXdyu0sT90c4x+ZNVvDeiNol1NuYvDOmyQFgfoen1/Otuk3L/eH51gqdONrLbYUMPDTljtt5HNyfDq3a4LR3jC3JyMnkD8uau6/oi6haWen27FbW1XC4b7xxgdvT+ZrW3r/AHh+dKpVmA3qM9yalUaKTSitQWCgk0qe/kcX/wAIP/00b/vsf4Un/CDn/no3/fY/wrvvscn95fzo+xyf3l/Oj6rR/lRl9SofyI4mz8Bxy3SLNK4izlsOM4/Kul1TRLq7tYrGylitrGNQojViM49eOlXpYGiALEHPpUVUqFKKcVHc2o04UHemrM5xvBFyqlvtKHAzwx/+JrS0Dw2lkzXcuJZhxErHhT69K0aKiOEoRfMoK51PE1WrOTMe/wDC2o6ldNcXN7GzHoAxwo9ANtUJvB1xBtzOrZ/ut/8AWrp6fHGZX2qRn3olhKEndwTYLE1UrKTOQ/4RWb/nof8Avof4Uf8ACKzf89D/AN9D/Cu0+xyf3l/Oj7HJ/eX86n6lh/5EP61X/mZz+g6XNpGoGZm3RupRhuHHfPT2qaXwuTKTaTxiBiSA2crk9K1pYWiIDEc+lR1tCnCmrRVjKdSU3eTuQXNobfSf7PtCGZjmRycUQWf2rRxYXLBHQ5Rwc/T+eKnorS5FzPg8NOkwa5nj8oHJC5yfzp+p2c+qaimzasQAUEnnHc1dooC4t3p1yLNLPT2ihhx85YkM35Csj/hGL3/nrb/99H/Ctaii4XMKLQruWTYGiB9Sx/wpyaNdRXSAmM7XGSD71t0qffX60AM1jSRqEqvFIqTKuMN0YUyx046RDLIXWS6ddqgdBV28/wBcP92q9DHcraFZTW01w0pX50xwe9U9K0q5j1eykbZhZ4yefRhWrU9l/wAf9v8A9dV/mKBGR8Telv8A9df/AGUVY8R6Vb6xFDbm5jhu0y0W8/eB6j+Vct49/wCR01D/ALZ/+i1qe20TS9b0i3OnzRW9+gAmSRydxxzwTx68VRRoaF4cj0fVIpr68ga5ORDFGc5ODk889M9qseMP+Rbb/r5/9maqOm6HD4buf7R1S9g3RA+XHGSSSRjvgmuVuZn1PVZJQArXE2QD0GTxQBUorqP+ED1T/nvZ/wDfbf8AxNH/AAgeqf8APez/AO+2/wDiaAOesbprK+gulGTE4fHrg9K6/UtItfFEo1HTL2FZXUCSKQ4IIGOccg9qxdT8K32k2Rup5bdowQuI2Ynn6gVh0AdvZW1p4Pgnurq6imvnTYkUZ/H69QOfasvwvrEFrc3NtfsBb3gwzHoDz19jk1zlFAHWN4JdpTJFqNsbMnIkJ5A/kfzqLxHqVmlhb6Lpz+ZBBy8gOQx+vfqSa5iigDr9PktPEPh6PSri4WC8tz+5Z+jDt+nGPbNTadocHhuc6lql5CTED5UcZzkke+MmuKooAs3949/fz3TjDSuWx6DsPyrppP8AQ/hzGF4a5k+b/von+SiuQooAK0tI0S51qWWO2eJDGoY+YSOv0BrNooAuafePpeqQ3KjcYX5A7joR+Wa6nUdCt/Ec/wDaOlXsIaUAyRyHGD+GSD+FcVRQB2Ny9r4Y0G4sIrhJ7+6GJCnRRjH4YGffmqXhfUrWOK60u/cJbXQwHJwFbGOvbtz7VzdFAHXR+CxFcCa51C3+wKclwcFh/IfnWZ4o1aPVNSH2f/j2gXZGemfU/wCfSsSigArS0K6tLTVY3voY5bZgUcSIGC574NZtFAHXXHgz7RcPcWV/a/YnO4En7oPbjg/nVfXr6ztNKg0PTpRKkZ3TSjox9Pz5/AVzNFAHVaBeWd/o02hX0wh3NuhkboDnOPz/ADzViy8Kx6XdpfanfW4t4TvUKT8xHTOf5DNcbRQBqaxq7X+uNfxZUIw8rPYL0/x/Gujv7C28XRw31jcxRXYQLLDIf8OfxxzXEUUAdpttvCWj3Mf2lJtSuV24Q/cH/wBbJPvWR4W1aLTNRdbk4trhdjnsPQn26/nWFRQB1z+ChJcGW31G3+wE5Dk5YD+R+uaqeKNTtpltdNsGDWtouNwOQxxj8cDv7mucqxY2cl/exWsRVZJTgFzgUAV6K6j/AIQPVP8AnvZ/99t/8TR/wgeqf897P/vtv/iaAMHTrxtP1GC7QZMTgkeo7j8q6vUdBg8RXH9o6VewfvQDJHISMHHtnB9qwdX8O3eiwxy3MkDrI20eWxJ/UCsigDr9SltNA8PvpFtcLPd3BzMy9FHf9BjH1NQeGtQs5NOutF1CQRRTkmOQnABPbPboCK5eigDrE8EtHL5lzqNstmDkyA4JH48D86p+KNXgv5oLSy/49LVdqkdGPA49gBXP1d0vTJtXvPstu0avtLZkJAwPoDQBSorqP+ED1T/nvZ/99t/8TR/wgeqf897P/vtv/iaAM/w1qyaRqollz5Ei+XIR2Hr+YrWuPBwu7hrjTb62a0c7hlj8oPbjOf0rD1fRLnRZYo7l4nMgLDyyT0+oFZtAHW6xeWWk6ANEsZx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QjL+H8hVMnNXNU/5CM34fyFU6ACiilpAJRRRQAUEgdTTS3pTOpzQA4v2HNJyepo6Cmls9KQGE9pc3N1cfZ7eWYLIc+WhbHJ9KX+ydR/6B91/35b/CpYtVvdNubkWk/lh5Du+QHOCfUVo6drOu6lfR20V5gsfmbyk+Udz0rntFs+PUMPObjLm5m+iXf1MWawvLePzJ7SeJM43PGVH5mtzLdqd4q1fzpBp0T7o4iPMc4yzj/D+dMQ4+Q9R0q4pJtI9nK6cKVWrCm7pW+/UnD4AyKduFMB9qOtanrklFRcj7ppQzD3p3AkpaYHB9RTgwPQ5oAWikpaACiiloASilpOaACjpRRQAUUCigA7UUUUAFFHSigApc0lFABS00kDqaY0yLQBJRUH2lfQ0faV9CPwpXQyeiq/2lScbgDQZV6lxRzILC3JDxGMHk0yyIWHB6g4NMeWMKTvHNJHJHt3Bxzzmpvrcq2hd3Ck3ioRMmPvCjz0/vVVxWJtwo3ioPPT1P5UnnL6N+VFwsWdw9aXPoaqecD/Cx/Cjzv9h6XMFi3VMjde5P3R0+tL55AOVaoFnDKXCsOcjjrSckNRLqfIxHY9KlqmLgFc4NOFwCOjU1JCaLY60lVvOPZWNHmydkNPmQrFhmCqSxAA7mmRTxTgmKRXAOCVOeawfFkeoXXhq7jsI91zgFVP8AFz0qh4G0bUdC0mWG8VlMkm9YywJTIGenvmnpa4up2NTWn/H5B/10X+dUlmOcMpq5ZMGu4CDn94v86Exiax/yFZv+A/8AoIrPf7jfStDWP+QrP/wH/wBBFZ7fcb6UdR0/jXqVq3Lr/kVLX/rp/Vqw6vS6j5mlRWPlY8tt2/d169se9dDR9JWhKThboyTQtn9rw78d9ufXFeWnWLHQviLrt9fact+EurkQxPjAk83hjnPQZ7GvQVZkcOpIZTkEdjXjmvSNL4i1ORzlnu5WJ9y5rswkVJyT7HkZxBpxqfI9W8N+KNR8U+HfF91fuoVLQiKJBhYwY5OB/ia8ZHB6Z9q9M+G3/IneMP8Ar1/9pyVxvh+wtr77R9oi37Nu35iMZz6fSuzDU17ScI+X5HgYzExw9H209Uv87D/E3iu/8VT20l7HbxC3jKRx26sqgE9cEnnp+Qrp/hDYXJ8Q3Gq5MVjbQMssjHCknHy59sZ9sCs/+wdN/wCfb/yI3+NbIvLhdCOio/l6cRtMMahNw75YcnPfJ5710VMHN0+SFkeQuIsLe7jL7l/mY2hzwaj461a7ijZoZmmljAOODICP0Ndc2IWEnA3N91ecVzdjY22mzma0j8uQrtJ3E8de/wBBV37TNjBfPfkCvOxuU4itK9Nq2m7f+Q4cQYK95xl9y/zOiEyscYNNlBGHX7ynIrCF9cKMCT/x0Vvqdy+9eTjMDVwqSqNO/by+SPVwWY0MepOlFrlte9ut+zfYuxuHQMOhFOqpaNtLRn6irdZwlzK53oWikoqgFooooAKKSloAiuP+PeT/AHTRH9wfSi4/49pP900RfcH0oDqRrxzUwqMDDFfenKex6is46GktSSoZnwNo696lZtq5qmxJOTROVtCUIRTgowOKSnjoKeHim3c9LLoxnKSkrjdi+lIY0YYI/Wug0jQ/O/fXkZEZHyoSQT7msNhhiB6108kH0R6MPYTk4xitPJEQgjUcL+tUtYGLNB/tj+RrqLDTrYWJvr92EWcKi/xVhaklpO7Kz+VCZC0YLAHHbr7GqpOFKvCaWifRHn5rSjiMFWo0kk2rXeiv6mHp+nXOqXa21rGWc9T2UepPYVoeJdGh0S8gt4ZJJN8Idi+PvZI4x24rWtdZSz08WVnJbQAgAyR43t7k+tR+IP8AiYXkcmpf6NKse1V+5lcnnDfjXrf2tesrpqOvR3Z8K+GHHCyUZxc9NeZWXl/W5yddzpekvqlycztFBGvz7MbiT0xkH0NYNrBp9rcpN5kU2w58uVgVP1FbumXd3a3DfZIzKzJ80e0sMZHOB6f1rizPGwrVKbjF6X3XfyZ7HD2UV8Jh66nNXk4fDLazfVbXv89jDvbiWGxikR8OSATgc8GnPcvvIRuBxn1NQanJEbNI45FYq44DAnoajgmjniDxnKenpXm42ny4eElG13LpbtY6Xjaqxs6aqNpRhs7q9nf/AIJaW5lU5Y7h6YGauRypIuRzms6nRErMpXjLDI9a8yE2nqdtPEVVJXk7eprR/d/GndVNQCQK2096fLKFXA6mu2Mlyl4tP28vUliORUlV7YlgzdicCrFawd0cstwoooqiQqupMjnHSpnO2Nj6CooRiMVEtWkXHRXJAAp9TS5wKQUtNCKzys3HSo6fKNr8dDUeK55XvqWtgpsYxE49zTwKCMRvj0NRI7ME/fa8mVJVyQ2Ovyn29P61XVAoCqoC+gGKtspeMgYyRx9arHse1c9RHFUtF3YnWrFnnzSO22q+4Z2jknsKs2iuJCWAA29O9TD4jfBtutExvHv/ACOmof8AbP8A9FrXN10nj3/kdNQ/7Z/+i1rm69o5wooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK0vD3/ACMulf8AX5D/AOhis2tLw9/yMulf9fkP/oYoA97PWkpT1pKkQVg+M7ia18J301vNJDKvl7XjYqw/eKOCK3q53x3/AMiZqH/bP/0YtRV+B+h1YFJ4qkn/ADL80eU/8JBrX/QXv/8AwJf/ABo/4SDWv+gvf/8AgS/+NdXBp2j+FNBtb/V7IX2oXY3RwP8AdUYzjB44yMnB5NSW9toPjWyuIrHT003VIU3oseNrD8AARnAPGRkV53s5bc2vY+2eMw6vP2X7tO3NZW7bb28xvw81TUL3X547u+uriMWrMFlmZwDuTnBPXk16XXlfw1Vk8TXSsCGW0cEHsd6V6pXZhW3T1Pmc/jGOMairKyCiiiug8UKKMikJpXAWmFienAoLe+KZ8x9ahzKWg7fjqc05W3DNJtHU0uAepzQrjbQu4etGaYzqvFNEgJ4YZo5gUSXPNLVd5MfxD86YLgg98UudIfs29i3RUYlB608EHpVqVyHFoWijNFUIKKKKACiiigAooooAKKKKACiiikACmN/ro/xp9Mf/AFsf41QElFFFIBKKKWgAooooAKKKKYBRRRQAUUUUAFFFFABRRRQAUUxpADjvSB2P8NTzIdmSUVHvf+7QJB34o5kFiSijrRVCCiiigDndU/5CUv4fyFU6uap/yEpfw/kKp0gCiimlscd6AFJFNLZpPc9aKAA9KTOOhzTHbLBR+NA4HFK4hT15pMjOMijIxVJmIJIP41LdhpGXc/8AH1N/vn+ddLpAGj+G7jVCB58/yRZ/Ifrk/hXMSZad/Usf5103is/ZbPTtPX7saZI9cAAf1rGOl5HyVD93KrX6x29W7HLkliSSSTySa6CR9rrgZNc9XRtEHbJODTp9TvyL/l58v1HrKG5p+QaqmJkbIOQevtS5ZTyK2v3PfLNHvUAlwetSCQGi4WHU2mXFxFbW8txK22OJC7nBOABknFYX/CbeHv8AoIf+QZP/AImrjTnP4U2K6W50IZh3p4kPcVzf/CbeHf8AoI/+QZP/AIml/wCE38O/9BH/AMgyf/E1fsKv8r+4XNHudJ5g96duB7iua/4Tfw7/ANBH/wAgyf8AxNH/AAm/hzvqH/kGT/4mn7Gr/K/uFzLudNmis7TNVs9WtmuLCcyxK5QttK/NgHGCB6iroYis2mnZlXJOtFM8xe/FIJlPr9cUrgSUUAgjiigApc80lFAB2o70tJQAtRySbeO9SVVZh5pJpNgG1nPJpwQDtTPMPYUnznvSKsS8Cmll9RmmbPWlES0rlWHfJjtSHy/QUmxQOopDsXksKBEb+USowOuTQDGCQFGD7U1ShYndnNKWj25zUgSqY8fdpdydlpium3Ip29B6/lVCHbx/dpPM/wBmm709D+VHmjspP4UXAdvPYCjc+OlN8xs8Rml3Sk8IPxouAjlyuPWmZf0BFObzj0VRTQsx43KPpSZSEBcZ6GpAz+1M8uTP+sxThAxHMhoVwF3v7UvmsPTFN+zjPLH86d5Kn/8AXT1CyAT/AEp3mjNM+zp70htgOUYg0aisSh1brUtlmPUbfH3TIv8AOqWWQ4ccdmFXLBt17bg/89VI/MUJiaLGs/8AIVn/AOA/+gis9vuH6Voayf8Aiaz/APAf/QRWU06sSqHOOtXezFBpTXqCSBE9TnpTGYsMseKYzhBkmoGdn5PA9KirL3mh46aVeS8xzSluF4HrXkOsf8hu/wD+vmT/ANCNeqXFwsEe49egHrXn+n2UWqeJr+O5Teu6RzyRzv8Ab610YDERpOpOWtkcqpycVJ6Jmx4I8QaXpHhrxJaX115U95b7IE8tm3nY4xkAgckdcVk+Ff8Al7/4B/7NXRf8Ixo+B/of/kV/8anl0ix0uOI2UHlGYZf52bOMY6k+pr1cux1GtXtBO7727ep5ebxc8vqtbRt+MkXNH0S71m5EcCFYgf3kpHyqP6n2q94t0u00m/t4LSMqpgDMSxJY5Izz9KjbxLfSWNtp0O23t41VG8v7z+uT71oeP/8AkM23/XuP/Qmr0eet9Zip6J309O5817LDfUajp6yTjdvzvov61OTrsPDXhA3X+lapCwgI/dwklS3uccgf5+vNabfHTdQiuxEspiJIVuhOCP65rsPCer3ur6/cS3cu4CA7UHCr8y9BRj51o026eitv19F/mLJ6eGnXiq3vNuyXT1f+X3nDOAJGA6Amt+OUbwOmawJP9a/+8a3UhYyghvkA+7jqfXNeTxBf918/0PZ4VcV7dP8Au/8AtxYztYMOoNXlYMoI6GqRFWLZsoV9K8Om7Ox9MiaiiitigooooAKKRmCKWY4Aqsbos2EwB70nJIlySJrj/j2k/wB00RfcH0qrKzmJ9zE8Vai+4PpSTuCd2K685pnuKmpjJg5FKS6miYcOuDUDxlD7etOd1iRndgiqMkscACnpIsiAghlIyCDkGpa5tx7PQr1Zs7g2lzHOEDlOQD06VHJERyvSmjpWmHVm7npZclKUk+x0miX1xfanK875xEcKOAOR0Fc4/wB9vrW14Y/4/wCX/rl/UViv99vrXStzuoxUa00lpZfqb9ig1XRvsR3I8LZV9pKn8fxrlvFWmS6cLTzXRjJvxszxjb/jXU6q72uj2UVuSsTr8xXjPAP65NcVrZJEAzx839K7Mu/3mL9fyZ83xEpPKa807RbWn/byMuL/AFyf7wrqfH//ACGbb/r3H/oTVysZCyKT0BBrf8YanZ6pqUM1lN5sawhSdpXByT3A9a92pFvEQaWln+h+cUJxWCrRb1bjZfec9XoXhn/kMN/17t/6Etee12+k6nZ6XqXm3s3lI0LKDtLZOVPYH0rzc3i5VaSirv3vyR9DwzOMMJipTdknT3/xM4qT/Wv/ALxrN8+4sp1aPKDHAP3X9j71pOQZGI6EmqXmbdySKGKsAAOeeD/WtM1i3GGmmt/wPJyadpzSeun6m5bXSXKFgNrj7yE8irUfEicdWFY+mQO8pun4XovvWzCNsisepICj0FfHYilGFXlifdYKU6nLzb3LExzIoHpmoyWeUKuSx4FSzcH3IqWyh6ykdeF+lEYuTsejjJWqSLSKEQKO1PooruRwhRRRQIZLzC/0qOP7gqVxlCPaoITlOe1Zy+I0XwkwNLimin4qkSRyrlPcVV71exVWRdrEVnUj1GmMxRkAUtNNZNGtOq6UuaI0SR5wFxj2pMxf88x/3yKjl+V93Y0delZnY8TJ62X3D1aFOFjA+iiplAwGAAyKrbCeatxIz7ERSzEAAAZJrfD01NtSOrCVfaN3S0LXiGytZdduXktoXc7cs0YJPyisz+zrH/nzt/8Av0v+Fd3/AGXDJqs11Psk3gbI2GRgAAkiuOb75+tehGzLws6dSKilskVf7Osf+fO3/wC/S/4Uf2dY/wDPnb/9+l/wrpLK1tLTTBqF5H5pc4SPt/ng1LbSabq7m2NmttKQSjJj+gFF0OVaCu1C6W70PPfElpbQadG0NvFGxlAyiAHGD6Vy9dl4wha3sxC33kuNp/ANXG1EtzzMdb2vu7WQUUUVJxhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVpeHv+Rl0r/r8h/9DFZtaXh7/kZdK/6/If8A0MUAe9nrSUp60lSIKwvGSCXwtdRnozwqfxlSt2uf8buY/CF669VMRH/fxaip8D9DqwN3iqdv5l+aON+J0h/tyzh/hW2DAfVmH9BVD4fyMni+2UHh0kU/TaT/AErT+I0Ju/7M1iEFraaAJvHQfxDP13H8qqfDmykl8QteFSIbaJiznoCRgD8iT+FcMk/rHzPr6UorJnf+Vr56r8zR8PyR2PxE1snhMS9B0zIpr0G0uRc2qT4ID5wD6Z4ryC1W913xLqjaY0ivP5j7kcKQm8Ecn/gNes6XbzWulWlvcMrTRRKjsvQkDHeuqjb2d11bPm85UlilGXSMfyLmfSg/nQaOlaXPKEzRjPWmyMEjaQgnaCcDqa4e71q7S9F55i+RcqoEYlctagZ+YqPX0ojFydiZS5TtZZoIBmWVEH+0cU0Xlqy7hcQ7fXeK5uCLUW+aGCFUI5mu7hiZPfaO31om0q4kGZorCcf3Svy/gMYqrQW7IU5dEdJJe2sYy1xH+DZqJtUtUXcxdV/vNEwH8q5Cfw/qgDSaddRWLHkxxuyo/sQuMUlpdTxutte2cUU6nHm2jsrq/oQ3XP1Oah26B7SS6WOqW/trkExTxP7BqjkMqnK5UVRt72G/YwXKpJIOAzJgP7YPRvaiU/Yr+0RFP2e5fyymSRG2Mgj0HFc8lc6qdZ9i2iYcuhJz1781Kr88oQfWlyochuMcZ9al8rjKnI9ahR7HRzxY+NhjB/DNSBj7iq5RhUqncM1rFvYhrqTrJ61ICD3qrg9jQGZTyDWilYzdNMt0VGkgYc9akrRSuZNNBRRRVCCiiigAooopAFFFFABTG/10f40+mN/ro/xqgJKSlpKQBRS0lAC0UUUAFFFFMAooooAKKKKACiiigBCcUwkucDgUN8xx2HWlz2FZt3KSsACrzRuPYGl2+tLihJiuhu5vSk3A/eGKkpCoIpuLC4wZQ+oqSo+nyHpSpx8ppRfQbH0UUVoSc7qn/ISl/D+Qqn/Kreqf8hKX8P5CqMhwh5pMALZOB+dIBUavxzTt6gcmlcVx3brTCxbp0pNxb/CjNK4EbEjoMsego2y9Q659NvFNRsySMSODtFSg5FBO5G0hCsrDDY/A1UzlvpVm6w0YQ8Enr6e9VVywOcblOGxUyLiUQjvdPsIDKxYZ+tW7576/cT3k6OyLgHGMDr2FVo3CXMrE9M/jzWXd6qXkhkjJ2nKstZLY8TCYGlWpylO+rfXsx017DAYd24+a20FR0PvWsEuyBi4b/vs1xwTkDGAjZFdbZz5RcsORxRojqhlNBXtdfMl8q7PH2k/99mkDXEV3DHJOzAsucOSMZqw8gSMuc4HpVGWdZJ0kG7C4znr1oehx42jRw1uWT5rrq9rm8UQjpTDFjoazV1AL/fqxBqAlbbtOQM81pzRZ6tPG0JyUYzTbIdcVl8P6l/16y/8AoJrxSvatcnVvD+pDubWUf+OGvOvCGgaZrMt1c6xqiWNhZhGkyQGk3Z+VSfoexPtXsZfJRpybLrJtpGVb6JqV1pF1q0NqzWNqwWabcAFJIAGCcnqOmetZ9e16tqOk6j8I9X/sS2Nvp9s6wRAjBbDxkt68k9+fWvHdPS1k1K1S9kMVo0qiZwCdqZG44HPTNd1Gq5qTatZmUo2saOk+Edf1yHztO0yaaHoJCQin6FiAai1fwzrWg7TqenzW6McBzhlJ9NwyM/jXWeNPH8095FY+Gb+S20m3iVUNsGiLHH0BAHTHtW58O9XvPFum6voeuSNeWohBE0vLJnIwT39QTyMVm6tWMfaSSt26j5Yt2RQ+HLhfD1wO/wBqb/0BK64sT1rjfh9keH7gAZH2puR/upXVRryd/UDivGxcv38kdVOF4ItLyPan+1MX7gpw4rFBawo4PFSqc/WovrSg4piJaKQHIyKWmAUUUUAFZ0cgkaSRj8qsVB+lX5DtjY56A1l6YDJZJI3OSSPzqZblItCXP3VJo/en0AqWgetIoj8pyOXpRD6uTUtHenYRH5K55yfrQYkA6VJSMMg0WAhCp/dH5UHaAeBihVI4xSnoQRUlD48FeBTuPSo4htFSZFUiQopM0tAwpRSUUCGsaaDmnOMnFNCEdTSY0HenrytN28inqNoxTQMWkIpaMUCG0U6mkruC7gGIyBnk0DuBGRj1qOwJTVrdPSVcfnUnSmW/y63Zn+86j8mFJgM8SzP/AG3cRA4Uben+6KyoW2SAZ6nGKt+Krny/EF0qjBGzJP8AuCsK0ujJdoBkqWALHvz2rmdRKpr3PMVW1dLz/U17g4cfSq0koVSxPA5qS8YiQD2rhPGWuSxSR6ZaSsrkZmKn1HC11U6NTFYj2FPfv2OjMJqOJm3tc2U1OHU5ZDazxzMjbWCNnZ/n1rI8PqV8WaipPIEoz/wMVF4D0lrYXl/IvzSN5SZ7Acn+n5VY8PjPjHU/T97/AOjBVrCxw0sRCMuayIqYl16aja1jrVUsabq3EVr/ALp/pUxIXAFQ6v8A6u2+h/pWmRf70v66M48xpuOVYhvtH/0uJnRf65P94V1Pj/8A5DNt/wBe4/8AQmrlUO11Y9AQa2vFGs2+t38U9skqIkWwiQAHOSexPrX1lSEnXhJLRJ/ofF0asI4OrBvVuNvlcw663wB/yGLn/r3P/oQrkq3PC+s2+iX809ykrq8WwCMAnOQe5HpRjISnQlGKuxZZUhSxdOc3ZJmLJ/rX/wB410Fo+6IKeo4rnnO52I7nNaVtfIJwuG+dgBXlZ1Qq1fZunG6V7/genkOJpUZz9pK17W/E1qWJ9kw9DxSUxq+Z21PszQopsbh4w3qKdXStSwpaKSgZVuXDSCPPA5NVJ5VxmMZK96geW4lmkIjO3cR17VG4kBByRnqMVxyqN62OCpVbvZFjzHlTlsewrUi+4PpWLASH2GtqL7g+lb0XdG2Hd43JKKKK1OgoavpqatpVzYvI0YmTbvXqtczpvhfX9MtIbOHW0NvEfu7MEjOcD0rte9IRVwqSgrLbzVyZU4yd2V7YSRw4kbcwz74pM5OfWs/XPEVhoMtot4XU3LlVKrkDGMk/nTNf+0NpbPaySIysGLRNggf1HSnRi1q9j0sFWjRU52vZbHSaPfxafcvJKrsrJtGwA9x71nscsT6msXQNZ/tOFra4+S+h4cdNw9RWqdwJyTRUqOlK0kb0cyozbqRT1Nmy1dY7X7JeQCeAdPUVg+KpbGX7J9igeLG/fuPX7uO596kyfU1m6z0g/wCBf0rsyuqp4qKS7/keLxFUp1MsrOKafuvfT4o9CxftaRoX8qAMzgEJGOM+w6CuWTVVu9ZktILd9sYwX27QPc/571NZ6qL+a5jVWBt3IaRuF6kZAz6g9aoXupDDpbnarH944GDIa8mNavXm1re3d2Xmc+Pq0Zu/IortZdDpLFobiMqY4/MXrgA5rQ1CBbiNVJwecH0ritF1EwXe3oD0Hv3H413s0ZkxjHHrUVFWpT5Zyd1sz0stVLFYWpBQWtrq3mcy1k9u7PJMSCcKmc59/pUMcJub0xBSMYJb29a6K4sXmiKjZu7E9qjttNa3VjlDI33jk9ugr0PrreBcZyvPm09LHjSyKtHME6cPc5d/O+3qNjjRQI1XEadv6VNGN8wc+tPNq+0KGWnrAysvI2g5rxkne7PpaGEnC3ukhiM04X+EDLGr4GBgVHDjaT3zUtdtKKSv3ObGP9/IKKKK1OUKTPNLSDuaBh1qvGuyRl/KrNQyDEgaomtmVF9CTFKKQUtUSLUM44DDtUtNblSKUldWGtypmkzQQc49KMVzGlkiORdymiIDb0qQjIqJeHxUtalp3jYkqza3ElrMk0eN6jjI9sVXqQdBXXhlqzuy6zck+x1tsxPie8BJIESge3C1yb/fb611dt/yNF7/ANcl/ktco/32+tdUTpwfxP0j+R0OnRm/0Y21yjJChyk+QAPz/GpbDTreyL3MEwvZUB2rGQMfrVfWN39jWPk58jaN2PXAxn9azdI83+1IPJzncN2P7vf9KVrozUJTpympWTvp/n6nN+MZHltPMkGHa4ywx0OGrjK9A+I+wSHZj/XJux67TXn9TI4MXLmmpWtogoooqTlCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArS8Pf8AIy6V/wBfkP8A6GKza0vD3/Iy6V/1+Q/+higD3s9aSlPWkqRBXO+O/wDkTL//ALZ/+jFrojxXOeOv+RNv/wDtn/6MWs6r9x+h15f/AL3S/wAUfzR59onjK60mxNhcW0V9Y9oZf4fYHnj2IqbVPG893pz6fp9hBptq4w6w9SD1HAAGfpXc+CZUi8F2Bdgo/edT/wBNGrSXU47qXy7fO3vIRgHjPFcsYT5F73Q9rFZnhqWJlehdqT+1o2nva1rnkXhnX/8AhHdSku/s32jfCYtnmbMZIOc4PpXVH4onPGj8f9fP/wBhXaNHGGLsRnH3j1pIbgLHvlkVcDB5yfrgUoU6i92L/A5sRm2ExFT2lfD3f+N/okcb/wALS/6g3/k1/wDYUn/C0f8AqD/+TP8A9hXR3Wv2exstIYRnJA27u3H45/KmJPd6laFozFBA6kJCsm1h9Tj+VauFRby/A5XmGXttLDX/AO35HJ6j8SL26tXis7RbR2GPM8zeR9OBWVo/iW20N99pphMkoJuXlmDNMx6tnbkfyrXK2Vpp1gjwn7VGstpJtw5RzyCfxA5PrWfpB1DW/GIv9UZ7Yxw+VLBv2kKOwHfJq1CrZ+/+CI+vZc98L/5PIntvHktr5iJZsbdjlYjMDs9gSvT2qwnxCVCpXSFUg/NsnwG/DbXRXsCTtElmjrcqwKyJgeWoOee3tzXklt4cubHxDrFtrcEV3lkkhmkiDCQMXyRkdema5a8pU6bqSlt5HVg6mAxNZUo4a3nzy7X7Hc/8LDGedLOPT7T/APY1my+LvNmeT7DjcxbHndM/8Bqpp8MWku76bGlmzjDtbKIyw9DtxmuqtfMnih+9JI6g+pY4qMBXlXlJQla3kj3Vh8LhveVK1/7z/Uz9K1X+0/O/c+V5e3+PdnOfYelaVVvhlaQXM+otMquUEe1GAI53ZOP89atN98/WvVw1Rzppy3MsS4RxM6UFblt+KEorbsrW0tNMGoXkfmlzhI+3+eDUttJpurubY2a20pBKMmP6AVvzHDLEpXai2luypoH/AB/Sf9cj/MV0QIVvauc02EwapNC55RWU4PXBFbixr6DFcVd++cGMSdTm8i2YtwyDSiNs4IGMdc1BEWThWOB2NWlcOuRSjZnDK6ECYpw5pHDbeDzTIQ4HzHnvT2diNXqyYUUUCtEyLBRRRTAKKKKQBRRRTAKY3+uj/GpKjb/XR/jTAkpKWikAlFFLQAUUUUAFFFFMAooooAKKKKACmucCnVFIfmAqZOyGg7ADqakAwKag7+tPpRXUbYUUUVZIUUUUANdcr70wHO0/hUtQDhivvWU9HcpE9FFFaknO6p/yEZfw/kKzpugArQ1U41GX8P5CsuUlnHOBUyFJ6DScDGMmm7e5NOAANMJ3EhRwOvvUGY1pXJ2xrn1Y9BTgpxyefWnKAO3402WVIlzIwAPGTSSYEURYyzKRwGyD9RU6n61AmPtLEMCHQEenHFTqM57U2tQTZBcP8457VBv2zNz1jBPp1NOuWAZ2J4HU+1ZkkhwzFmVmOT7egqGzRSKeqzPDiWM8+d+nNYpO4Fl7EmtbU1Z7UEckOCapwx+TLll+VhyKi+hw5Yr0n6v8ySCIRsTIu5JEBHHer65EahM7kHr1qLaAixEgnqCalJWIoS4BBAU1Nz1lGyLiXBmgKH7xwB+daP8Awi+s/wDPn/5FT/GueS482+aNfk8tuR68Vrf2tqP/AEELr/v83+NUrfaPmc3dH6yvaJ7La3d90W/+EX1n/nz/APIqf41Uigktr2SGZdsiAhhkHB/Cultru50fQTf3lxNNc3AxDHLIWCjscE/j+QrmIZXluZJZGLO+SzHqTmqaStYxo0aNPEUuS/M9bO2i6dBmtf8AID1D/r2k/wDQTXj9ewaxn+wdQJ/59pP/AEE14/Xt5X8EvU+gr7o9M0b/AJIZrf8A19f+zRV5nXpmjf8AJDNb/wCvr/2aKvPtL06fVtUttPtgDNcSBFz0Ge59h1rsouzm33M5dCXRtEv9f1FLHT4DLK3JPRUH95j2Feha1qOnfD/w1N4b0mYXGsXS4vblf4MjB+hwcAds569eiv8AQNa8M+H49H8G6b5k0y5utRMsaOT7bmBz+gHTnkedN8NPGTuzvpJZmOSTdREk/wDfdZe1hVd5SSiul9/UfK47LU3/AIc/8i/cf9fTf+gJXWuu5Djr61xvw/kMWh3CkdLps/8AfK12C3CEc8V5OLt7aR0U21FDkYKAO1ShgRmoN6noRz3oViM8isEx3JzQaakisKfkY60wuIj4bB6Gp6rMKljfcMHqKaAkooopiKmpOVs5ApwWGKjtAFtURRwoxVbVNRtY7qGyedVmlPC/41ats+SBUP4i4kwyadQOlGaYwzRmlooEJmgnilpD0NADM0w07nBpjZAz1qWUOHTrS9RTVyRTqAEyaMn1oooGGT60oY+tIOlKM0ANZiD60u5qRgTijae1AhN5BHpUu41Fs5BzTxQA7caN1N/GimA7dXM6xeyadq4mMLSsWV1bOAkYGGH1zmukxWH4qVE0k3b4xByy8ZZTwRzVws5JSIqX5dDdV1ZQR0IyKEAF/YODyLlB+ZFZPh93fRoFkdWkjGxiCD09x7VrRAG7tAf+fmIj/vsVDGtVcxfF0e/xTebidvyYH/AFrOtgBcRY4+cfzrW8WD/iprz/AIB/6AtZFuR9qhyeN4x7815sn++fr+p46VsT/wBvfqT69fLpsE124BWKLIB7nJwPzryuytbjWtWhc75JJpt8zAcKueT7V1vxCuna8t7L/ln5QkYZ6nJA/lXMWGp3elhhZyiLdwfkVj+oNfX4GlKhSnUh8c9vQ6sfBzxU+yZ6fDBHFGscaBI1GFUcACuT0N/L8YaqQP8AnqP/ACIKyf8AhKdZ/wCfz/yEn+FUoNTvLe8lu4pts8ud77Qc5OTwRjrXDSy6tGFRNq8l5/5BStGScj1CLLjc5wB61DfTrdWNhcJ92WLePoQprz+XxJq80Dwvdny3UqwEajg+4GRWv4d1C6u7U208u+G1RI4V2gbVwRjIGTwB1royrLp4aqpTaf8AwzObOsSnl9aL68tv/AkyTw7o13fWcF9qF7dBZQHWBZWHH+0f8K6h4rW2XdJtVR/eOf51gafrkx0eBpMGQq2+UqAq4JA4HsBWHe6k2oyNsuDMEPOD8tcVWeLxdaUZSaSdvIyao0doq/oWvE+rB5oI7F3iVN25o227s4x0/wA81Ql1S7m3OJnU54VGIrNuSWcOSTkVKDtfHB3frXt0KPs4Ri3do560YShGXKtb9DUtNUuGtrmQu5kiBfZkkHHXnr6fma6izv4dRhsr6BNiyqrMuMbWBwR+Yri4/Nty08a5IB/HPFbvhozPpSiRVVvNYAKAO/oKydlVk0+j6+RxYmMfZJJWvKP5nbJdxsypnJ6H61YrAhYqVPfoT7ituJxJGGr5GjV59z7OtRUIpxLVq2Qy+hqxVGJtk6n14NXq7abujBBRRRVgZ5Xyrl07E7l+hokUMh9RzU16mAko/hOD9DUYORXM1ZuJLXQqhRvVgB6VqRfcH0rOxtkxWjF9wfStKOzJpK1ySiiitTQO9FFFAzL1/RbfW9Ne3niV2X5oy38LUWKONLtVfIcQoGz67RWm5wpqBFDN7darmbg4HXhYpRqS8jj9e0u4sJP7RsflKuGJHHln+qmt3RdYg1uzDcJcJxLH3U/4VryxCRCMA8YIPcelefa1aXPhjUY9UsI5Gt2bBCDdt9VYenoa1p8tWHsp7rZ/ozzZp0pe0ht1R3BQqeay9Z6Qf8C/pUdn4x0e9e3gM+yaZQQpBIDZxtJ7H61LrYx5A/3v6VrlMHHGJev5HPnMlLLKz8o/+lxOf1m0a1BMSBY2Pz46lvUnvWExz1r0HUbRbi2cMONp3fgOtcDPA0EzITkdj6jsa5MvkleD3/MwzGjyT5lsyAHa/cMD+VdpoWqvdxbJpWZzwCT0I6/nXIRWrzsp5Ck4JA6/StMalBp0iRIvHBJB4X6HufWtsbD2tlTV5IywdedCfMnZHX3jSJZuwdgwxgg+9RW07m2Qs7Fjnkk+tRNeLf6MtyhBV1ByOnUVLZLutYyegz/OspQtl17a8/8A7aehCvOWbL3nb2d9/wC8WgzBMlzuPTmpoInA8x2Yg8AE0y3i86Y5+6Ov+FaLIChXpXFTp82p6k6007XK5JX5h1HNWVIYAjoaouW3YPGOtXIhiNfpWtN6tGdRaXY+iiitjEKb0+lOooGJTZBlKdigjIpNXQJ2ZGj7lp1QoCjsp6VN1qIvQuSsxc5oxQOlLVkFeZcNn1qKrUibkPtVasZqzKTCopBghhUtIRkYNQ0XGVnqIDkZqQdBUCHYxRvwqcdK6MLuz08vVpy9DR1LxRYaH4nvFmbdJtVSuGGMqp6gGucPiDSySftPX/pm3+FZfj3/AJHTUP8Atn/6LWubrdSsctPGzp7JdF16fM9IsfG9lZRGEzrLCf4Hjbj6cVZbx/piIwtEht2bqwjY/wDsory6ijmJliuZ8zivx/zOk8Q6ta6jZqsM5ll80O2VYdjzyPeuboopN3Mq1V1Zc0gooopGQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFbdh4f8At1lHc/atm/Py+XnGCR1z7VhiMTSw8eeq7Lbr+hpSpTqvlgrsxKK6T/hFP+n3/wAhf/Xo/wCEU/6ff/IX/wBeuP8AtnBfz/g/8jf6hiP5fxX+ZzdFSXEXkXMsO7d5blc4xnBxUdelGSkk11OVqzswooopiCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK0vD3/Iy6V/1+Q/+his2tLw9/wAjLpX/AF+Q/wDoYoA97NITgZpTTOpqG7CAc8npXPeO/wDkTb//ALZ/+jFro65zx1/yJt//ANs//Ri1lUXuP0OzAf73S/xR/NHP6PGU8IaVeZkIhMo8tf4su1b+mwSyRebzHAACnHO3Gcc++ar+DUR/B2niRA4/eHB6f6xq17yRjaT4bBEbYx24qIz/AHaiZ5hS/wBsqz/vS/NjE8vYZ2JYsMlm6Y9qqvcTopljiigi6KZOC34dvxNEh8rSbfP3IzGrbv7qnH9K5fxf4s+X+z9J8meUOHnnf5o0/wBkep/lVQhKb5Yq5xSSirpli3iubi9OpXVzbmCI8fISkY6jIzyeetXG1m7uWYW9paTRjgzhiv5Kw/WqXgyQa1aTXlxFEYo5BHHGg+QEDqfU12LMsa7nZVAHViKUpWdmhQpScbnHz6Ra3LPIkCLPIv7xYgZBJ/vDg596oXv2qKBTeWU6Pb/6u7GMKPQoK6rUtWs7eNmNxtkCnbJu2gfXPWuQm8YajfK1tFbpOh4MpUooHsTya0pOU3aKuTKko7s6C01tYkWO4t2MhAIldlWJvf61n6lIPEOr2VnDNbtKSVbyju2A4PJ78AmsNrKOXMuqXKyK3Oxf3afiOpP1pFmjtXR9NSW2VchZUBQN67T/AJ61OZYT2WFlUqPtp13R3ZNKTxkYw319PhZ1V7eaDoE5sYtJS9lj4kklIPP1IPP0xUdveeXcLd20axA5aNMcICOB+ANcozS3ExZi8srtySSzMT/M10kcTwRJFIpWRFCsp7EcEVxZTXdWpPRKPTQ+rqYaFOKTd5Pe7bv8iH4Xf8f+o/8AXJP5mrL/AH2+tVvhd/x/6j/1yT+Zqy/32+tejgv4S/rqycV/v9X/ALd/I6HTozf6Mba5RkhQ5SfIAH5/jUthp1vZF7mCYXsqA7VjIGP1qvrG7+xrHyc+RtG7HrgYz+tZukeb/akHk5zuG7H93v8ApXRa6PKUJTpympWTvp/n6k1i7zalPJINsjBiRjocjitqB/kwTyKpz7P+EhuNpA/djd9eKmgPyH1J4964MQ/3pFZqaT20RcDEc1YhPzHHeqQY44qxZtlWJ7niiD1OOcdC4OlFJS9q3RysKBSUo60dQCiloqhCUUUUAFFLSUAFMf8A10f4/wAqfTH/ANdH+NMCSkpaSkAtJRRQAtFFFABRRRTAKKKKACiiikAVXclpwParFVk+a4aoqdEVEsAYFLRRVkhRRRQAUUUUAFV24ugPY/0qxVZub1f9w1FToNFmiiitBHM6x/yEpfw/kKzX+/Wnq/8AyEZfw/kKyifmJz3qJEzA5JGPxoA2/Slo61JKFHIqlqFrJcRr5TYZc1cOe1L2xVJtO6B6lKGJoBbq5A5Kn8ef6VcHGfpUUwwsZ/utnFRyS+ZGUwQD157VxV8xw9GfJVlZ+j/yOijhK1SPNBXXyKU0nmNn+Dqv+0fX6VUuSQFBGe5NX3hD45xgYHFVbmEqy/Nnd7dKwp5hh601CnK7fk/8ip4StSi5zjp6oqSgNEMj3qCSBQWXacjGKukBnx2zUhi3Iz9+TXWeVlb/AHT9X+ZkFvIIEjDZngmq00zsssJwSrbgfT6Vc1GLNozEcpjBqlHCzEYIy3ehI9OUuiJbIFrtZCTnuc9eK6bRbH+0NWggYZjzuf8A3Ryf8PxrFtbfaiSBcEE7uetdj4QURC/vWH+qiAH6k/yFVFXkkfO46kquPpxltb8m2U/FF/8Aa9WaJT+6t/3agevf9ePwrKtv9aeccVE7M7s7HLMck+9PiBLHHpUt3dzgwlV1cdGpLqx+sYOg6hjp9mk/9BNeXWOkz6hE0kUkQCttw5IP8vevSdX3DRL/ALAW8n/oJrm/CdrBcaLL5iEMLhsSKcEfKvf+lerg6/saEp+aPqqy5pKxnppOuR2L2KagUs5Due3WdxGx45K4weg/KobbQ9Ws7pZ7W4EEyE7ZYpWVl7cEDNdU2nXKsGjuBOoP3WOw/wCB/SmM8kRIngljx3KZH5iuuGMhNaM53GSM1G8VN97xZcRn0kvp1/mKtpp/jSQAp4omYH+7qExqxHMkoIjkVsdlbNQXaBLdmVTvHICnaSfrRJ1LXi19w1LuUYPCfiK1QpBqccKltxWOeRQT68DrUp8OeKQM/wBsk/S6l/wqSx1DU7efBuWMaqSyzAlQB/nrWlH4jmVDJJZSSQj/AJawDKn88VyVMRXWrS+42goPcxU0LxO3I1hh/wBvUn+FTJ4d8VyZxrZ49bqX/CuptLqO8s4pkDKsgyA64OPpV+AARbgeprFY2p2X3GjpLocUPDHiwcjXf/JuX/Co9Dl1ez8cwaZf6lPcBd29fPd0bMRYcH6jtXf8gcd64NDj4qgg/wCfIroo15VVNSS+FvYzcUrPzPRfalHynIquspHfP4VMrbhXAnc0uOuRJJZzCBwkpQhGPY44ry+28eX2j6e1hNIb/UJBut5GGOCDnI9sGvUVO0+x615BP4ell+Ihs8iJUZ3ibbxtIOB9OTVrXUWuxsaZouo37xXNxcK6ysLmSdh84YcFQO2MYzXoFvnyxWfHbfYokhV/ux4yec881dt93krk81Dm29TSMLFkUtR8+tHPrRcqxJxRkVHj3oxRcViTcKTcKbijGKLjsKWUA8VHkenFOYcUzgD2pMaSHBgKNwBzTaMcUBZDt1JuBpp6UuKVx2Qu6jdSUcZ60BoLuPWjJo/GkyCOtAhctiky1G4UZxQFw56ZpefWkzzRn2oC5BeyywWcksW0yDGN/Qc964bXLKTVL67t7uUTSwhTviJClDzgLkj2rvZoxPBLC3R1K1ye5be3DlAJFBUg4GfrXXhoptt9DnrtljwlpcunwXEn3YZ9hiGeoA5OOxNdXaj/AE+z/wCu6f8AoQrE0UG2jaxeaORkAkTyzkKp7ZratM/2jZ/9dk/mK55u8rmsH7hleL8/8JNdADJYJ/6AKzbW1/0iOSQ5YMCK3vE6j/hILlv90f8AjorEuLqKyhNxNKscaclj2rznF+2dtdTzuVRq87WidzD8b6TJM0uqE4igt0QAHqxkP6YauN03TL3V71LOwtnnnc8Kg/UnsPc122u6tDqnhS7ktrhZosoCQMYO9aw9D8a6j4c0S50/TY4YpbiUu10Vy4GAMDt2PXPWvrMJWqTpPTVO1npbRHViJ0atV1Kd7S1d+50Xi3wLZ+FvAdrcSKJdWa5VZpw7bQCrHaBnGBgc4z/KuD0vTrjV9UttPtVBnuJAi56DPc+w616L4hnlufgpo088jyyyXe53c5LHMvJNed6Xql5o2oRX9hN5N1FnY+xWxkEHhgR0JrWg5uEru7uzKVro9DvtJ+H3hGUafq6XuqagqgzeUxAQkegZQPpkmkm07w5Dawal4blmNvdllkikbJiZMcc8g/P3J7V5zfXtxqV9NeXcnmXEzl5HwBkn2HFd3p+gXWi+G7Ge8Vo5b2SSVYm6qgCAZHYnk/lW2Hg4VY80nd/5M8vOXfA1LLt/6UjmrCH7fojweUy7dybyODnnI/PB+lVNNsYrGORdxJYZJPpWrHNc22g6PKhBs5GkhnUjoWc7Tn9KJNO8+YosZPmYBOPYfp0rwFX9nWqRk7Rbb+5/0zXE00mlHqYV7GIyoB7nj8v8auwQiTYix7pV5wPSpvEdgLJLM8bnDDgY4GMfjzXSRWkVtmPYvy8Y2jH/ANf8aeOzJww9OVP7V9fTQ7MNlzrpRk7cu/fUy7XSZJM+acJ0AU/1/wAK17W1jtfLijCgZHQfh+PSplPy89ulNb74xxXJlNadTENyf2ZfkPOsJRw+Ej7Nfbjr8yRjhyhP3uVPoRWhYTZ4bjJxj0NUZWXG4AFvvDPqKjWURSrIG4YZP9DXjxnySufQypqcHE6CQHbkdRyKvxuHjVh0IzWSl2jwqxzk9RjpV+yYNbDBzgkV6dGactDxnZScSzRRRXSMbIgkjZD0IxWfHlcq3VTg1pVSu08uYSDo/B+tY1l9oTI3HKt+FXYvuD6VUBytW4/uD6U6XUErElFFFajCilooGVbmQh0jAPzd6fFw5HtUxAPUVB8yuxCn8qcIu7O7C+9CcFvYl3gNiobq2juIXjkUMjjDKe9KSzfw/pQC47H8qXJIX1KfdfeedajpCaD4ghv3tkniUgqSOSB3/wB4V02oTx3VraTxPvjcMyt6jitW+tIb62aCdflboRwVPqKx7uyi06ytbWEsY0LkFiM8nPb617GAnGpWg5fGrr1Vn+J4md4SeHy6u7rlaj1688TddNh9q5XVNJjNztJKoFLrk9R3GfbP5V1vThuRVW/tBPB8qhmU7lz2P+eK+f1TvHQ7cRSVSFmcFqGqwW0IgiZXkK9FGFUVzM8pn3EAkE5B/pVvV7dor918sKN53DngHpmrul6XEbQyMX3N8pyvCnnn26D86+ioqhhKCqb3PAacnY0vDl24tJrRlwuAV9uc4P6/lXX2IJs41UcnP8zXM6VbOlu5aPGz+Mc5ycc/5/nXZaPEPsEbnknOPzNcmJtUwsuXT95/7aXgZyWYrm/59/8AtxPEghXA/wD1mj7QRIEbjPQ+tSy4UA4471BtDyZxkCvJd46I+jVnqyR3Qn5scVYXGBjpWZ5LecTIOAeBWkhBUEdKqnK7dzLVpMdRRRWpIUUUUDCiiigRFMvRh2pVOVp5GRioVGyTafwqHo7lrVWJRS0gNLVIli1UkXaxFW6hnXgNUzV0CIKKKXH6ViUMdAw54I6Gnrwo+lJA0dxEWUnHuMUnnW0VzaW9xcJC11MsEeedzMcDFb0LKV+56GBrwpNubsc549/5HTUP+2f/AKLWubr1vXfBVnq+s3F9LczI8u3KrjAwoH9Kzv8AhXGn/wDP5c/p/hWp555rRXpX/CuNP/5/Ln9P8KP+Fcaf/wA/lz+n+FAHmtFelf8ACuNP/wCfy5/T/CoZvDVhpDi3CLcbhv3TIpI7Y6dOKic+VXNqFF1p8iZ53RXoH9nWP/Pnb/8Afpf8KP7Osf8Anzt/+/S/4Vl9YXY7f7Mn/Mef0V6B/Z1j/wA+dv8A9+l/wo/s6x/587f/AL9L/hR9YXYP7Mn/ADHn9Fegro9jcutv9mhTzTs3rGoK54yOOtWf+Fcaf/z+XP6f4VpCopnJiMO6DSbvc81or0r/AIVxp/8Az+XP6f4Uf8K40/8A5/Ln9P8ACtDnPNaK9K/4Vxp//P5c/p/hR/wrjT/+fy5/T/CgDzWivSv+Fcaf/wA/lz+n+FH/AArjT/8An8uf0/woA81or0r/AIVxp/8Az+XP6f4Uf8K40/8A5/Ln9P8ACgDzWivSv+Fcaf8A8/lz+n+FH/CuNP8A+fy5/T/CgDzWivSv+Fcaf/z+XP6f4Uf8K40//n8uf0/woA81or0r/hXGn/8AP5c/p/hR/wAK40//AJ/Ln9P8KAPNaK9K/wCFcaf/AM/lz+n+FH/CuNP/AOfy5/T/AAoA81or0r/hXGn/APP5c/p/hR/wrjT/APn8uf0/woA81or0r/hXGn/8/lz+n+FMPw708Sbftlx0z0FAHnFFelf8K40//n8uf0/wo/4Vxp//AD+XP6f4UAea13HhuKSfSrWOJC7sWAA/3jWl/wAK40//AJ/Ln9P8KuacIvC8strCjT7F2o7tgjPzHt6n9K8fOowlRgqjtHmV38md2AclOTiru36os/8ACNX/AJe7MWf7u7n+WKy5oJbeVopkKOvUGpI5Lu4ug8bSvOTkFSc5rV8SspktQxBnCHzMfhj+tfNzo0KlCVWknHltu73v8lqerGdSFRQm079uh5JqH/ITuv8Ars/8zVavSh4BsbwfanurhWm/eEDGATz/AFo/4Vxp/wDz+XP5Cvt6H8KPoj5+p8b9TzWivSv+Fcaf/wA/lz+n+FH/AArjT/8An8uf0/wrUg81or0r/hXGn/8AP5c/p/hR/wAK40//AJ/Ln9P8KAPNaK9K/wCFcaf/AM/lz+lH/CuNP/5/Ln9P8KAPNaK9K/4Vxp//AD+XP6f4Uf8ACuNP/wCfy5/T/CgDzWivSv8AhXGn/wDP5c/pR/wrjT/+fy5/T/CgDzWivSv+Fcaf/wA/lz+n+FH/AArjT/8An8uf0/woA81or0r/AIVxp/8Az+XP6f4Uf8K40/8A5/Ln9P8ACgDzWivSv+Fcaf8A8/lz+n+FH/CuNP8A+fy5/T/CgDzWivSv+Fcaf/z+XP6f4Vn654HstL0a5vYrmd3iUEK2MHkD+tAHC1peHv8AkZdK/wCvyH/0MVm1peHv+Rl0r/r8h/8AQxQB7y3XFLjikJAYk0jtgVk3ZthYUmuc8cnPg2//AO2f/oxa6DOBzXN+ODnwjf8A/bP/ANGLWVV+4zswH+90v8UfzQ3wX83g6wX/AK6f+jGrXI3MV7Hg1j+CyF8HWLEgAeZkk/8ATRq0LvU7OA5Egdifuoc81lH4UwzD/e6v+KX5so30AuNDvdI3O1zIGhTBwSDyDn0weTXm0enCOR4b+No7G1bY0cTAeaw7FvT6ZP0rc1PxBKuraj9kkilNwE8sRuS8fygEcAjH1IrFttPubp1N06Qoo2rGpyQB7n1OTXqYSFWUWobM8apJX9Dcj8YQaXb/AGa20wW8JbKhJNxzjHAwPQVDJrN/qb5iX7Oh6yXB3P8Ago4H4n8Kg/0S2GyFFldSAyxIXbJ9cc02WzvruB3kNvaxr9+MyAzY/kPzrWeGwtGV6ru+wKpUcdNiRo9Pgfzru5E83rI24/gP8BTTeTXTCG1RYYzxvkQscewHT8TVeDSrBYsxRvG7AhZZmZQT7spYn8BWhbWEN0dul6TZzyn5WmLTSKnqTuH6YpyxcnHlprlRKh1epqadpGg6eyXOqXqS3B+687cZ9uwo8T3NtqL2Fvp7iZ1LLsRT1O3Hb2p0XhuRkEMulvPkfekuRFHn6L836VQsrc+EdX+0y2cUoBdI0jkIVSPqvUBv84rxsfCnKjJyl2u/mj18pqVViYcsb72XyZsxQWPhC2W4ugtxq0i5jjB4j/z6/lVNrrzkFzO6K0vzsScDJ5rQt9fi1q7cR+Gba5nC7mLyJnAwOrL7iq0hZzcomlxyvKrIsGVxAT0IOMfL04xWWEqRhGUqLvFLRWf46as9+LlFt117/V3X3LXRGT8PLma0n1CWK1kuPkRSEzxyfY+lX2njMckqsGVck7Tn3rG8Ga3/AGRHqCmyF0k4jDAyBQAN3sc5zW9DqNvqUiT2ulwRIrbDbIV2yEHkHgAZ6ciurDTkqaSXR62dvvtY3xk4rHVFpfTqr7Lpe6+4Sx8XiyiMJiaWE/wOBx9Oa6VL2RwyWEFpCxGSykn/ANlFcVql7BrKE2WmW1oLSZoZjFInL4HBwB0/HrW7pqLbajLPGwjie23CMcbuTz7ED+dctHFVnKSn0t0a/M8rGuleEqaV5Xvrf02di7b2E9vctNcOrhgclSSc5+lWjIqkEAcdOasKSYkbPVQeKBk8lcHNXUk5u7OeVZyfvEYV5mVl+VD96rsZ5BHaoVDE9T+NSL8jYx+NOOmpjUldFxc4p1MjOVpxrqWxyPcWkHWilHWmgsLRRRVCEoopaAEopaSgApjf66P8afTH/wBdH+NMCSkpaSkAtFJRQAtFFFABRRRQAUUm4U0v9KV0NJjs4pM5pgbPpinb/TFLmK5RScAmq0G7z5M+gIqR5CxKjp3pAcdAKzk7u40tCfmimCTjtS760uhWY7FGKbvo3/Si6FZjqPxpu76UbvpRdBZilsDNQH/WeZjkcD6UrnPOcU0uQOSKzk7lqJZByKWoElGMZFPEuWA9TWimiOVo5/V/+P8Am/D+QrH6knrWvrJxfT/QfyFY6DC4bmiW5lPcec0D1FR52k5puT1Geai5KdiSWZIV3uSBnHSkguYrgN5TZ2nBBFQXcKXduYZN20/3Tg0zT7OGwjMcTMQTk7jmrTjbzFzFqf8A1Q/3qvQaC7QLNd3MdqrdA/X+Yqk77NjgK21wcHofrT7q6utWugxjLuFwEjUnA+nNfL5k6KxUnUjzOystvyPfwXtHQSi7LW7Jb7R5rOETpIk8B/jTtXLa39t8y2+w3dtFJ837qdMiTp0wQeP613MCPpugXS3nymbIjjJ5yRjp/npXG6rpllqcKR3lusu0/KT95fXB6jpU4ejCljaTiuW6ba7aP+tR15ynhppu9mte+qIrYSMqCZQJdvzqhyA2Oce1WVOEPPU4FQJbG1hRI3O0KFXLEsR05NPMUuMbs+2a+hufIYWVehBwdJvVkd5b+ZaFRkbuTiq1tZ5WMnhkbmroikc7d4z0wSaTy5eTk8c5oOh4usnrRl+A6YL820AUR3E8MZjinlRJR86qxAPbkd6j2sUZt2QOvNQysCpjycle3ag8nF+0xOKXuNOy+6+/6Do2Ek5jHQLuz61MsO7kNx9Kz7GZVvJI5XVTjapxgGtVk4wTwfTvRY9b+ysHb4Pxf+ZnarGDo9+wbKi3kwcdflNY3gwA6RLz/wAvDf8AoK1v6uMaHfYOB9nk/wDQTWB4LH/EpmOD/rz/AOgrXbD/AHSfqhfUMMl7NR0fm/8AM6Lb70hGD1p3GSc0zcOo5rhEsqwn8n4v/MkSLem7OKBHnvUkI2w59cmnZG3HFOxf9k4P+T8X/mUp7QXamIyyR4OQyY5+uQeKz10vV7VWSy1ZBGDkRyQjj+dbEf8Arm/GnSSojndgsRwo5J/CqU5JWFlNOHsGrdWNErRxBpMFgoBYDjpVyK6QqkcQHA6kcfhVJLdpiizHapIwi8/nWukaRjCgDPoKIpno84wxNL993HsDj9K4by1T4p7ByB6/9cK7twcH1rhckfFTk8//AGiu7Cf8vP8ACyKzuk13O0kkMZbGdiIXPtVq3csinP3hnBrLuXLWkxIX944j644zj+VYOv2eqXF/Dd6fLdtAibTFbTbGU+uOhFc0bXJaZ3QOayLvQ45fEdrrKtteGJo3H94HpR4dmv5dKQ6ghWcEjLYBYepA6GtWQ4WqY79Sne8eS47nbiprf/VD61BekiyRv7sin9atQAeWKhLU0vqSUVi3On6xJr73EF+IrExBRH1w3c4qZtM1KQ5fWJF9o4xV2Jcn2NSo0uIZH2pKjH0BzWNqWmzW2kXcv9rXW9ImKlmAAOOOgrnNF0y7uNRjRZUdbJMOfNI3lhjt2xmrjBOLbIc3ex6DRVFLa52/N5atzjDE+wo/s+TIJmHBHYnoOP1rMtMuP93g0zb71UOmcfNMx4UHjrg5/rTBpibSDM5JBGcDucmk7lF3POCeKaZYhjMij8R3qA6dDjBZ+c9/WnrY24YHyxkHufbFLUYx762jBzMDgdBz3xQbyL0k4JH3D260260y3uLaSEAxlhjehwV9MVJLCZLFrbzZE3Js8xThhx1z60C1IG1O0DENKVxjOVIpp1m0GNpdsgHhD/n3qaGzggjVQm8hdu5/mJ+uam2oDgKoyc9OtAWfVlQaqh+5BMxOcfLjnsPxoOpSbhts5SO5/l+taIHHGBS45oswsZn2y8IJFngDrn9af5mokYEUQPr+vr6cVoc56CinZhZGeRqRBA8sZyc+mf8ACk/4mhBIEAz2J6dvT8a0cUuKLA0jOEGolfnlQ+ynb0/xrm9S8P6t/aDGzSCS3k5+eQjYScnPr3rtOv0oxzVQlKDvFkuEXuYPh7w82jefJJOJJJ1QMqrgLjPT866K0GNQtP8Arsv8xTKktf8Aj/tP+uyfzFJtt3ZVkloVvEyZ1u499v8A6CK5fVLG3v8ATpre6bZFjJfP3cd663xEu7Wbkf7v/oIrlNd099R0i6tI5NjSpgN79fyrld41lK9td+xw1oK9zlPsWm2XhPUE0+9W6yyb2B6fMMDFcxW1B4fvdH0TVJrxlBkWONURsg4cHJrFr6nCOL53CfMr7/JGNL4T0zW/+SG6H/19f1lrzRVLMFUEsTgAdTXXaj4qsbv4cad4djiuBeW03mO7KvlkZfoc5/iHauf0bVZNF1aDUYbeCeWA7kSdSybuxwCOR1HvWlGMoxlp1ZtJptHfaP4b0vwRp0Wv+KgJL5vmtNOGCd3Ykev6D3OKxpvGs2t6nc3eqziJTtEEKglY15yBjv0ye9UNX8Ytrt8bzU9E024nKhdxe4AAHYASgD8KzpdVspInRfD+mxsykB0kuMqfUZlIz9QRToqUJe0kry/L0MMXQhiaToydk+2/c7DT1STSoo7MA2ZJKD/gR9eeuasRxzRDCKB69Oa4ux8SXlhZx2sUUBRM4LKSeST6+9WP+Ev1D/njbf8AfLf/ABVcNWOJlUbVGm1d7rX8zznls09K0/8AwL/gGzrbWTeR/ap/veX972z938K0ZVlJDyDr3riL7UrzXJIY2gUum7YsKEk5xnjJ9K9FdPMQqT1GK58di5UKVOM6UHLXTlulr016mlHAOElevUXe0v8AgGb5m0n5uaRpARkntSC2nLbMbcfxGr9tpuSCQWx3NeVTzes7qnRhFvS6jZ6/M9epkdBJOriKkkmnZyutPKxSiV3PyocduOBWhaWZcBSR8vT0xWilooXDU0I8Mg4BHSuKnhOXWR04jGyk7RVkPW0iVRnJx3Jq5Zx7IeBgE5FQojysFIwverwGBivQpwSd0c8Fd3CiiitjQWo5ohNEyHv09jT6KGrqzAy42JyrcMDgitCL7g+lVruErKsyjIPDD+RqzF9wfSsqUXG6YIkpaSitQCloooAKKKKBjSMc0YBFOpCPSlYBjIGGCKwtbj2GDng7v6Vv5qreWEV7s8xnGzONpHf/APVXTgqkKOIjUnsr/kzhzOhUxOEnRp7u34NMj+227DmaMf8AAxUbXkRkAE8eOmdwqF9Ft1HDy/mP8KiGkwtnDSce4/wolTwOzqS+4mNbNN1Sj/4EYXiPTrdnSS02uznGEIYA/wC17d/w96hh0a2ubI2/mtAuMF2bDMc8nFdMNItTn95KCvUEj/Ckj0aGSTAaUIOpJH+FaXwnLGEaktH2OOVLMHNzdKP/AIEQvHbw6SYYpkIjVVUbwWbkc1q6R/yC4f8AgX/oRqD+wbX/AJ6TfmP8Kv21utrbrChJVc4LdeuaurOhHD+ypSbfNfVeVjXDUcVLGfWK8VFcvKrO/W5LTUiRM7Rin0VwWR69xjxh8ZpwAAwKWiiy3C/QKKKKBBRRRQAUUUUAFMkTePcU+ihq407ECuRw1SggikdN3I61H904ORUax3LsmTUjYKkGo8+hOaMf7RNHMKxGE9fyrnPFHiqz0kHTF8xr24Taioudu7gZrqAtVJtG0+51CO/ntI5LqMAJIwyVx6fnV0eSMrzV0RVTcbRI9E09rDS4IJXMkoXLsTnn0+g6VafRrPVNX0qe5QmSzukmhYHGGDA/lwKs4qxZf8f9v/11X+dU5Ny5uolFJcvQ6af/AFzVFUk/+ub/AD2qCY4jOKGaEUt7FCRu3YLBcgZ5NWAwYZBrzvWLjf4peymXXoVkkjWOaF2W3PAzgjgfj3zXXaDH9nsmg86aUJI2HmkLsQTnkmoUtbFOOlzWrD1aF7nVYIYx87oAPzNbmR61h6tcSW2qRTQttdY+DgHufWpq25dTqwF/a+7vZk0tvo2nMIbkS3EwHzbTjH6iorvTbWawa+052KL9+Nu1ZMsrzytLI252OSfU1u26HTfD1w8/yvcAhEPXkY/+vWKaldW0PRnGdJRfM3Jtej76GAAWIABJPQCt210MJp9xcXifP5ZKJkjbgdT/AIVl2N61hO0yIrPtKjd0HvWxpl1Nd2WpyTuXby/y4bpSpqLepeKlVS93Raeu5iWf/H9b/wDXRf511pOBk1yVn/x/W/8A10X+ddTMf3Rwa0obM4sz+OIx7naC235QMk1LHIsiBlPBriNdl8TL4hiis9v9lNGAWhZRIp/iLblIP0GK3dC82NpUlvJ7jOGUyqox2IG1RWilrY89x0ubtFN3D1pdwrQzFopNwpNwoAdRSbhRuFAC0Um4Um4UAOopNwpNwoAdRSbhRuFAC0U3cKNwoAdRTd1G6gB1RN/r/wDgNP3Coycz/wDARQBLRQKRmC9aAHV80vruryOWfVb5mPUtcOT/ADr6R80/3f1r5gRHkdUjVmdjhVUZJPtXoYGnCopKaT23+ZnUlKNrOxpR+JdehTZFrepIvot04H86ifXNXkcs+q3zMepNw5J/WvQdE+Gq23hTVNV1+3P2kWkkltblypiIUkM2D16cHp39vL67YxoVbxUU0vJGblOOt9zUXxLryqFXW9SAAwALp+P1pf8AhJtf/wCg5qX/AIFyf412lt4T8NeGNDtNS8XyXE11drujsoSQQMZxwQcjIycgc4qe18NeC/GlrcR+GmudO1KJN6w3DEhh7gluM8ZB4z0pe1pL7One2guWXcj+HniLVZP7S+03k13jytv2mVpNv384yeM/0Fdv/b11/wA84fyP+Neb+AoZLe41aCZSksbRo6nsQXBFemQ6GjQJLPqFvFv+6Mhs/jmvhM5q4x5lVp4eWit2S+Fdz38HToLDRnVWrv8AmyP+3rr/AJ5w/wDfJ/xrasLh7qyjmcKGbOdvTqRXP6hpU+n7WYrJE33ZF6VsaTIF0yEEH+LoPc1nlVfFPFSpYhvRbP1QY2nRVFTpLr/maNFR+cvofyo81fWvo7nlWH0tMEqH+KlDA9DQFh1JRRQIWikpaACioZ51gUEjcx4CjvVcXr55jUgdQGyaTaAvUUyORZEDqcg0+mAVieL/APkVL/8A3B/6EK2q5/xNcJdaZeaYgYTOoAZvu9Qfr+lZVq9OjHmqOyLp051HaCueRVpeHv8AkZdK/wCvyH/0MVZ/4Re9/wCetv8A99N/hVvStBurHV7K7lkhMcE6SsFJyQrAnHHXiub+08J/z8Rv9Ur/AMrPYj/rTnoOlI55Ue9ZmuXc0M1jb20myWedQf8AdHWtFz84xXTNcuvc5lK7t2FJycVzvjgf8Uffcc5j/wDRi10Nc/44/wCRQv8A/tn/AOjFrGp8LOzAf71S/wAUfzRw+la1ImkW9hAk07oGyijaiZYnljx36DJqRrS5vQTqFx8h58iElU/E9TWVp2om3tlt0CtI2WG9wijnuTWxb29peEDUdVtXB/5YpOqRj6nOWrtoSwdClGcvek0tDHMaOJqYyryxduaXTzZA08MTJb2MHnMw2BIgMD6t0H41mzRS3MhE1wwg/hSL5CPq2ea7+0Xw/bJ5Z1Sw8vAxHHOgC4/GuP022h1CYo2oWdsi/eeeZV/IZ5rz8xzTFzcY0PdvfRfI7spyujKM5YmDdrb3XfsU4oIoUCRy3iR9fLW4KqT74AJ/OpIEtomZmgOTyNrdD9WDGt+bw/pixEw+J9MeT+60qqPz3GudkkijkZDNESpwSsgYfgRwa8mpicdSd5PfyX6HsUsry6t8EP8A0pfmWGa2yrKl0sn8UguBnHovy4X8qmsdTutMO2zmdI+uGOSfqeM/iKuado9ldwLLc69p1ruGQhmRmH1G4YqLU9LtLGLzbfWtPu0HVY5lDj/gOTn8Kp4jMOTnb0+X5ErLctc/ZqGv/b357G3YeI9bvlYQWUU3lgb5EX5lz04z7HtWd4iN8UtDeRCIHfsQHPpknvk/QVP4P1Kxtftn2i9t4d2zG+VVz97pk0ni/UbG5+x/Z723l2mTOyVWxnb6GtalaVTA+98X/BOSjgvY5qvZxaivX+XuxPDe2DWb1E4XyXQY9N61t6aM6nOPZv8A0IVyfhi8htrom7vIQ4gwzvKPmbK55zz3robDV9Ot7+4ka/tQoVsEyrzz9ea6sDK6nfu/zNcXRmoytF/Cuhw2ifcuc9MoP1NdTpuAilRg788evFctog3JOuOSyf8As1dLpIkDSLJtIE+E2+mF6++c17uG/wCRbH5/mzjx3/I7q+i/9JRx7MdPhvrYAIs12ZWfJGMgHHoTWr4curiDUUuLjc1tNOYXcjJB8v5c88Cs46fPfM8gglnjd/MXCHA52nPpyDU1xprz6Yg+/LHc/aQA3YAZHuBgVz140VQiluzwKUpKpc9iiGbaLI/gFIMj5T69aW3cPY27DvGDUneuCx2yb5hvPaplXcOaZipIz61SRLkKp8s+1TZzUTEYx3pyOOh61pF20M7MfnIpAcmiheDVa3EPooorQBKWiigAooooASmN/ro/xqSo2/10f40wJKSlopAJRRRQAUHiiobiXy0JAyR0HqaUpWVwGz3iQDnknoo5JqESzS/M7eWOyqP50sFuFPmP80h6k9vYVMUFY+9Lc1UUiI+7MfxpuzJyCwH1qwEFP2j0pcgWiiAbu1GWHQmp8D0oquQnliUgJcnmkKz4PzH24q3jJoNRyFcsUQxiUIATk079561MnSlqlDQlxRXxJ0yaXbIR94ip80Zo5EHKiDynP8bUeQ2OXNTjrS0+RC5UUHtCx/1jD6Uhsif+WjVdPWk7VPs4l8qKqWhUYV8VLHC6uuXJ5FTL1p46iqjTQtjm9bOL+UfT+QrIjckfMOQSK09bfGqSDjt/IVmkqOTjmrluYS3KuoXsVjb+ZMD1woBwSaZY30V/bJPErqpyMOuCCKzfFmmpqGnwv5hjuI5B5JXqSe3P86PC9kbfT2J80B5CcPIGyRwTkE9SKbiuS/Ulvoav2uI3RtgSZByeKnBwNzEAAdSaoyz28N8doLSnOcdAf8atJC0hDzncB0TGAP8AGlKKViUrlQaj9p1xdNgjL4gMuRyc5AAx78/lWjHLc6fOSu+GUDB3LggH61heH5Wnu7vW0LZnnHk7h0ROB+tdtP8A2brQSdroWtwBhw/Q/njNfNZlQ9piZOnK01ayva/o+572CqclBKSvF38x+l3J1mOa0vlWQhdyyYAI7Vy9ypRwvJIJHFdKLmx0W2kW0m+0XUgxvHQVyl/ci1h88wTTkHASFCzHP0pUppV6Eakk5rmu736aJvqy5xvSqOKtF2svz0IrueG1aLzS2ZpBGOO9WBgTMxOcc4qNATEWKMSTkAqc05VZkZmBBzjp2r2vbU/5l95wqEn0FAO0uQT/AEpf+WZPccHnrTWVypVQeQetQWkd2lrDHdFXn2kSMg460KrT/mX3icJbWZNIB9mY4wTisaVpY5pZAu4HAUAcjjr9K25UP2dgAe2AB71FbQMGMki4xwoI5purTv8AEvvPL9lUePvyu3J28zlcvkk8k961tM1GZiYHiMiKPv56e1TanpmI2nt0ZnJ4jUdT/SodPgZLVS1pMkgY7gyHk/1FW6tO1+ZfedPs6kZbMt6yyvoV8UDbTbyfw/7JrkvD2q2mm+Hrl5pAZVmLJCGAd8qo49v8K6dGuby0mt7i3mjWVWjYhTnaRj0681lT+BrD7JM8Mt35yxlkV2XGQP8Ad5rrw1fDypulOW76BKnNe8kRx+JZ54Nwi0+Fj/DNdgkfkKRNQupWLPrmlRZGQipnB9CS3SsCRfDUNtEzTX7SlQXTgbW7j7hqu0VjKm62sdSfPQ7gQf8AxytvZYb+Z/cZqUux3Nl4is7mwiee5toZeQ6GQcEHGR7HtUp1rTwOL22P/bZf8a47TvDk11EWnt54mLfKu8cL7nHJroP+EI0rp9pus/76/wDxNQ6eFv8AG/uLUpvoaCapp7HnUbVOOT5y/wCNWF1PSI4226haFsdTOuf51ix+CNNeZkM93gZ5Dr/8TUh8CaYFYme74Gfvr/8AE0lTwv8AM/uOTAaU37PVXZtWuq6Wsg3alZgAZz56j+tXDrmkgHOp2X/gQn+Nc1B4E0uWPe094ATxh16f981I/gDTNhKT3mcdTIuP/QapQwq+0/uOuVSa3RsT+IdJeBvL1G3GOT+8GePSuO06+XUviHFdggq+entER/Slk8HIIpmilkdkXco4GcfhUujWek6Xq6XTXU25YTJEB8/JBU5AGehrel7CMZum221YiUptpSOtNwJ28oAcsRjH60omWO7jjjXIBIJH0rnNZ1D7JpcF3ZyM0c8gQyoSu0dSeBkflVG21XUNO1eykM32ixuJAgIcSjJ9GwCPxrzeVmsJaano8KbIwBznmnPwuPfvSqeBQ5wtXayIlq7kF8udNkGO2aksn8y0ifHUU+6GLV/92odM/wCPCMY6Cj7Rqi5S0UVQGfrsPnaFeJnH7sn8uayvD8YXVr8jGTHH/WtvU43m0u7hjTe7xMqrnGTiqGl2pt7iWUWbWsbIqhXcMxI78E1amlBxI5W5XNilqLeaXcSKz5kacrHP0pnFNdm4pAWpNlKOgpYeho3c9KaetKopXHyig5P+NNPI6ilxzQRn/CgQ0rx16Uuwd2pwHNBA4piAU72pmT1p2TQIcOaPpTSTS0xh1paTNB4GaAsGaXvTO+e1PoATmpbX/j/tf+uyfzFRYOOtS2vF9a/9d0/9CFAuguuDdrs6jrhf/QRWJOmCfQ1t63EBrty6thm25/75FZdxDIVJxmsasb30OaUXJNHI+K0ZdGn+Y7fl49fmFZejWlo+kwvJBDJI277yAk/Me+K2vFSMPD91uHTZz/wMVyen6zHaWiQt5vGc7VHqT1zWkKVapgWqN78/T/CcsU430OhNnYj79nbjP/TJf8Kgn/s0KIYtJjuHf7u1QgH1fHFUE1zTiczLdn6Kv891XYvE2iRjH2K4PuUU/wDs1YUsHjoy5rS+/wD4I5ObVnYpSeC57qRpGuI7IHpHCWkx+JIqxB4HtIwvn3t1Ke4DBR/j+tXB4u0YdLS5X/djUf8As1SL4x0Retten/gK/wDxVei55pJcqul/XzMvZSvuS23h3S7YDbaBz6ysX/mcVcXRrE8fYLb/AL8r/hVMeN9EXpaXf/fC/wDxVPHjzSF6W14P+AJ/8VWLwmNlrNv7ylSfWRjw2qQ/ENbeKNY1HRVG0D91npXdLb46kD6Vw+l30WrfEWK7t1dI5c7RIBkYiI7H2r0cW4/iYn9K2xtLWCa1UV+pvGmmUxBEp3EAn3qUc/dUn6CrQhjXoo/Gn1yxppbGvK3uysIpW6gKPel+y5HzOT+FWKKrkQ+VdREQIuBS0UVRQUUUUDFpKWkoENk/1T/Q02L7g+lOk/1T/Q02P7g+lAElFFFAC0UUUDCiiigQUUUUDEIpOadRRYCMqT2pvkDtwfapqKnlQ+ZkIg5yzE1IFCjAp1FNRS2E23uFFFFMAooooAKKKKBBRRRQAUUUUAFFFFAwooooEFIQD1GaWigY3YO1LiloosguJiloooEFT2X/AB/2/wD11X+dQVPZf8f9v/11X+YoGdHc/wCvb8P5VA67lIqa5/4+G/D+Vc7rvi7SdAZ4ruf9+sXmmNMFguQATk+pqijj/G+v65b3v2TRLdHlRkV5EwXDFs7eT3Vc8A8Z6VP4d8Ta/Pm1fR4Vu0GJE80AAZYA43Z6jFV7zdf6VNf6I7G8l2Q2sittLhtsjnnoSSR+AFZ3gfTfFtjqSPPBJFYNKzTxOdm4nJ3Yf5uCR09PrWSXXsaOR102p+L1mAWxsArnADSEH9M07ztRuCDqUUSXA+UCIkgjt1HrmugbqrFckdM1USeG11ZZ7nzJEC7lUAHB7enHeonqrM68H7tS6V3Zk9tZW+lQreah80p5jh759/8APFZl9fzX8/mSnAH3VHRRWrc6no13L5s9rcO+MZzj/wBmqvLcaGYXEVnOshU7SScA9v4qiSVrJqx3UpS5uecW5fKy9NTIrd0P/kGal/1z/o1YVaWnahFZ2d5DIrlpl2qVAwOCOefeoptKWpviYuVO0fL8zNJnAza7Dcf8st/3d/bPtmoJLzxtHKqSWmnsjHBaNyfyzTdRdY9OuCxwDGR+J4H6muZ+yR+rfnXpYHCRq03OcuXW21/1R89xBmdDBVIKr1T2LviLxH4q05xBDpxuJihPyRA7RzjgMc9DVrwX4uub8TNe2scQgcrLtyHQE53lT/DyQeeMVhXWlwXdtJbyNIEcYJUjI/SovD3hq00nX7a9gnuWk3bCHKkENweAorStg1B/u3zedrfqzwqfEeEnJRu9dNj2oHcMjkGlqjpLB9MgwThV289eOP6VernPZCkpaKACiiigAooooAKCQBknFIxCqSegqsN0zn0/lSbGTecvufwpyurdDTPIGOWb86jeNkGQc47+lGoFmlqKKTeMH7wqWmAlLSUtABUY/wCPg/7o/rUlR/8ALwf90fzNAEwqtJIqsxYgY9amdxGu49K5e31yC7gkuoHSaRrp4UR22YwSPT2qZOw4o3jcRnjcfwFeAeGfEU3hjUpL+3t4ZpzC0aeb0Qkj5uPpjt1r1LXdTuLSOJTP5czPkrE2Rtx1zj1/lXilellseaM1LbT9TCs7NWPW/ButahrvhzxleajcvPMbXAzwFHlycAdAK8kBwc16Z8Nv+RO8Yf8AXr/7TkrzOu6ikqk0vL8jOWyNPWvEGqeIbiOfVbr7RJEmxD5apgZz0UAV2vwq0mS2vLnxNeN9n020hdfNfgOe/wBQBn8cVneEfBMN7Ztr3iCb7HokPzZY4afHYd8Z445PQVD4w8btrkaaXpkP2LRIMLHAo2mTHQtjt6Cpn7/7qnt18v8AggtPeZp+E70alrXiC+Vdq3NwJgp7Bmc4/WuwignuW2xRySFR0UE4FcL8OIpJn1FIkZ2Pl/Koyf4672C6urB3WJ2ic8MMc8dua/N+IIxWaVOe/Lpt/hR9Nl7f1SPLvr+bNi6BsvDa2lyw892yqZyVGc/5+tV4riW20USwW7XEqg7YkYAscn1q3BINa0m5+0opngGVlAwemR/Kuck1+LTrZkdFcxgnbzk9/pXfgI8+KjOHw8ll3smt/O5yYmXLRalvza/NPY6qOVXRSflJGSpIyKfkeo/OsOy1eC8t1mCDkkdSMEHFWDeRddin6yf/AFq972U+x5ntEatczq9/rNjrYey0u4vYSoXaLpETpnO0jIPXv2q01+Q+Fjjxj/nsf8KkSCaRlvVunxtH+j/KUBHXnG7PXv8AhUTjKK1RcJJuxlxeItXuNXjs9Q0eWwicHDm4VlGATk4HPOB+NTaLrMl9pa3bSIl0WZfJVsg4YqOvPOK1ShlmF3JF5qOo2jGTGR1H51naTpDWWmtZrEjS73ZZQpG0MxI688ZqQex1CFiilhhiOR6U6mopVFBOSBgn1p1aEmXqsbtc2zAMUXduUH73/wCquXhKadrfiHUr63eCy2wmOUggMAgDH254rupIllTa4yKr/YlPDSOy/wB0mocWNOxzVl4x0a20ffLqttvUtndJ1O7A7fStzTb7+0LJblZIWDMwBhk3qcEjg4Hp6VFq1vZxWmw2kUhPITavOOe/HasyW7vZtLUWEkVhKzMcShSVGTzgZBz1+lC3sU7JXOj/ABP51z2pKz6nIqgsxKgAcknAqe2SSDDyatNKTgnzHTH0ACjimx362urNdNGs5A+XDYGcDnvXj52oyowU3Zcyu/kzty6T9pLlV3b/ACJY/Dd+8e4+Uh/us3P6Cs66tJ7OXyp4yjdR6H6U+e5uL27MpZ2kZsqAScewrX1ssNHsluT/AKX1OeuMc5/SvBdHD1aU5Uk1y9W7p6+mjPTVSrCcVNp3/AuvamfxR57vlIIl2LnoxyD+hq+3+uA/GpFiCzyyAfMxHP4CmuPmz3xX2dRtnzcFZsWqmqaZDrGmzWNw0ixS7dzRkBuCDxkH0q2DSMu5kU52nOcHFRZPRmsZum1OO62OLf4d6EkoQ394DjJUumf/AEGpP+FbaKRkXV8R/wBdE/8Aia1Z7CEZRwGCsN0bjKn35/nToSNN1IRx5FvNyUHRWz+lP6vT7G6zzG31qMyG+G2jhTi5v8/76f8AxNcM1hY/ZlVUuBPn5nMqlSPZduR+de14wa8ftLSe+uUt7aMySueFFeTmHNTcVT63/Q+iyjG1qqm6027W6+pWsdEW+voLaMyFpHC8EdO56elaPiDw7pmkawtrBLcyxKitKGkXdkk8A7eOMdj1rpQbLwbakApc6vIuDjpGP8P1NchPNJczvNM5eRyWZj3NctWcqNPkbvN/h5ep30cTWxFX2kW1BLTzff07FSawtGlJgE6R9leQMfzCj+VbXh3wlZapHd3F7LcxWsC53oyjnqeoPaptD8Pzau5ldvJso+ZJm4+oH+eKua5rtv8AZBpGkL5dknDuOsn/ANb3706MpwXtqz06Lu/8hV8VVm/q9CWvV9l/n5Gb4b8KWOsfavPluV8rZt8tlHXPXIPpWrc/DqwyogvriM858wK+fpgDFTeCG2JqB9PL/wDZq6yIZ+c8sa7sLFVKMXLd/wCZ4+PzLFUsVKMJtJW0+SOCi8BWovFikvZnQttyihT/AFqvceFNMtxqDNPdbLZHZPmXkg4Gfl9672FP35k7AE/ia4jxUzG2uog5UTTFGAONwyTj9P0r0MFhoVJcrRnPNcXyTlzvRGZp1klrGrjcDKoLBiOCB/8AXNa2hOstzMFbdi6Cn2O1K5WG/uEMyiWRgoIAYdPoa6rwta/Z9J0iRt5muQJpWbqzFyM/kBXuYrkp0VSgrLU8bAValfFSrVXdtb/IqzyRpdPBHuHlryoHGD/nNVw8lq0l2qSZjhZFwx5BX0HuKt6lNKdTntGRVjkthJbTYHykNh1/kfxrlbsIuq6gFkyVtX+h+Vf0rwadPmi3fZHNP3all3PaNOctpFm2OsQqxz1NU9HbOh2RH/PMfyq6q5PtSR0zXvMUN2NLtJGcmlC7aUZJIp2Ji0hCAD+FHJIboB+tO25xnGBTlXdz0FVYttEg6UoHOaAKd2rZIwCkpaSqAKKKKAFopKWgAqN/9dH+NSVG3+uj/GmBJRRRSAKKKKACqUzZmVffNXD0qiDm9HPRScfjWVXoioq7LA4FOAz1pBjqadketNFsWikyPWjI9aYhaSjIoyKAI880p6U0Hk0Z4qLlkidKWmIQFpxYetUnoSFLTPMUd6TzVpXQWZJS1F5opTJTug5WITk0tRs3Oc0Z4xms+YqxKpHrTgwyKrqQCRT0Yb1HvVRkJxOb1r/kLz/8B/8AQRWeFHXHNX9ZP/E5uPbb/wCgiqIJA6VT3OXqc7rlxuvGaNt32ZdgQf8APR8AfkPbvWzY2v2SzhgB+4oBPqe9VZNCt5tXW/MkgIbe0QPyswGAa1fwrSck4pIVtSq4tbafzWT94/cDJqwjK6BlO5SMg1XvLIXQXnaR+tWIY1hiWMHO0YzSfLZdxrRmNZaNJpM7LBeu9gwO22kGTGxOchvTrx71heINc1zRGMwisprJ2IjlVGOw5+6/zdffiuwnPz49q8vTWp7XxBqmnSRC6sJ7qUPA5+6dx5GeleRHDU8Rj5qpHmskej7WdLCxcHbVl3TviHPvK6nZxsD917Y7cfUMT/Ool8f6lJcxwRwWLFiQW+bH865LVPKgla3gUkg8LnOPbPem2kYGnMIhm4JweOo9B716aynBJ/w0c313EfzM6u08d63qF/8AZbeLS4mYnY0wkw2O2QetdPp9/rkqA3r6aCzgL5CSEY79T1rg7XQJJdhkUKyj5wOisPf1ratdU1XSEVbm3+1W0YyHVgHX69jWNTJ8PbmhTVioY+rezkzuvtDNKVUDHYmn+cSygLwxrmNL8TaMwEL3uJQD8zggEn3PFb0c8fliaN1eLblWVsiuR5dh09aaOqOLqNX5izLMyBimDt4OfX0pI5mdASBmqo8xLYIx3Z+ZieuSakhkKIo2Z96P7Pwt/gR5yxuJ+u+zc3blv87lhnfcNgX3DUnnbhhfvj7ysMH8KaZlzgsQT3YYpCYJF5O4D+Idvxp/2fhf5EdrxNe91JlS4v7q33M0cZQd8HP480/VNQjsXgEm8eZkB1GQuMdefepJWBASQFlbhXPGfY1neJbKa7jtzEu7Zu3Djocf4VzSw1GjjKPIrX5vyNFWqTw9Rt32/MvKLFkE0sdvgjPmkDa341FLcoGKwoiKP4go5/Ks62sYpdKazuolmt2fIjlAYLjoBmqs/hTRZST9mki9DFM6Y/AGvVujjabWhp73fktu/Gmkktu9PSskeE1g2taanqMKjkBbgsPybNdBZWgtreMSu1xIP+WkgAJ/LipsgSYsMscEUk07hI44yzsewFYN/rmsfYhqdvawQaPkAibJlkUnBbAGBVjxI2+0jgLKkdzdRxSFum0tkj9K1NWMcuj3cLqI7TymjR8/dIHp6cVrBKx5mXP9y/V/madqFW3jAwVKgj6Go9T+1NbqtocEt8x9qq6DqAvdBsptzMxhXcCuDxx0/CtF23EdQPpR8LO9U+YzwHhXO1d23Bx3NZl34bMs4+y3YgGwr5ZiyDk56git4x+Y6gA4HPNORCLncclQvrShJxd0aSp3smc4+laZY6VDo2oXaqQfMSQvsO7Ocj0pdO8IxRapDeNdpKkR3oBEASfdhwfyqDxFptz/AG4dSW1kuIHhWLbHyy468Vf8IQX1vop+1qyFpGMcbHlFzwPatGlyqVyG7NprY6kDjFI2Sw4HWmRbsfNkj1qVRlxjtSWpmLcDMLj/AGTVPTG/0cc9qvuMofoaz9MP7kL6Ch/EbI0RS0gpCaoAIzTfLApwpc0rJju0N2ClCjpR7UvtTsFxj8VH+FPk54pvapY0R/xEjrUn40w8HNKf1pAGOc0tIRS0DCl70mQRSd+TQAEdacKYxPpmnA8UwF9qKTPNLQAUcdaCfaigBKdkYpmcGnKaEDFJqS1P+n2vX/XJ/wChCo6ktT/p1rkf8t0/9CFAuhY1uMNq056H5f8A0EVmmNwMDkVq6x/yFZv+A/8AoIqjVOKMWZF7YNcRtG8SSRnqrrkGsc6JaByp0+2/78r/AIV19JgVj7H+WTRlUpc/WxyyaDaHppluf+2C/wCFTJ4ctcgnTbX8YV/wrpKStFGS+0/vIWHXVmSvh/TSOdMsh/2wX/Cnjw9pPfTbP8Ldf8K1KSteaXdmyijPGg6OP+YVZfjAv+FL/YWkf9Aqx/8AAdP8K0KSnzy7jsipBpOm20yzQafaxSr0eOFVYduCBVuloqW29wEpaKKQCUtFFAxKWiigQUlLSUALRRRQAyT/AFTfQ02L7g+lOk/1T/Q02P7g+lAElFFFAC0UUUDCiiigAooooEFFFFABRRRQMKKKKBBRRRQAUUUUDCiiigQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUDCp7L/j/tv+uq/wAxUFT2X/H/AG//AF1X+dMDorn/AI+G/D+Vcr4m8H6JrkV1dX9szzGAruEjDpyDgHGeBXVXP/Hw34fyqnejNjOP+mbfypso4/StD0+90ZLSJmENvMpV0f5shEPX3HFdG9q0t7BOszRiEOrpsz5gYDv2wQDWZ4YMRsZvJTYvndNm3+Be1bVvcRXduk8LbkYZHGPz96y6mnQbIQMVg+IP+PafjOLduPwNbkgyfbNYevIZLe4jC7i0DAKOc5B4qZHThNZv0ZxSwqxdERdyY3KDkrnpWx4Zs9Mn1SafV7iFLe3XcI5HC+Y3YYJ56dPpWK0V4JF8vTZsblXd5ZHGCSen4U+azu3lZvss/P8A0zP+FfQZe4SnKPNy3W/zR5Uqc1rys6o/EFxN5SaVafYM48oj5tv8v0qn410i0sLm0vLFBHBeoXEY4CkY6DsPmHFS+H9M0HyoZNWW9+0ljmIxN5YGeM4X+tL42iv7vU45A0NxahSsC2p3bB/tAdDXdTqYenioxpPlWt7vR/5+oOnUcLtBp94dE0mC7RYrho03BScrlvX3G78xUWhaFPrdwyq4igj5klIzj2HqahjiC+HY47rdEm4CQkHKr5nJx9K6HSiy/D7UTb583e2/HXHy5/8AHc15LaXtJR3c5a/ccHENCFWth1VWkabdu9ktCMaD4cnm+yW+sv8AaicAsAUJ9uAD+dY50240vxDb2tyBuEyEEdGG4YIrLGcjHXtiu78SRHzfD9xMMTB1WQ98/Kf55qZXjo3e58tQjSxUHVjTUXBx2vZpu2t76+Zb0WZZdPBUYCySJ+KyMp/lWlWboyoloyxnjz5ifqZGJ/nWlXno/QApKWkpiFooooAKKKKAIbhgIwPU0+NdiAd+9RXXJiHYtg1YpdRhRRRTEVj+6uFx0arNV7vhVb0NWB60kMKKKWmAlM/5eD/uj+tPpn/Lc/7o/rQIbcMRjC7iASFz1rkfDqXzTQSXKeVG/nOIzKG53dSNox/30a6yU5ulHopNYWjySytaF4CgMDMT6EkH+tZS3NUtC/f6XbaltS4DYQ7htOPUV85V9H3n277Rb/YzDs8wef5mc7MHp715d8N9RgsLbWt99Z2s8ixeSbqUIpI3+vUDI6V6WCqOnTnJK+xz1YqUkih4R8VWOg6Br1hdRXDy6hD5cRiVSqna4+bJH94dM1ytsYBdQm5Dm33r5oT723POPfFevDxm0d5GLi+04xxg+YsU6MJM9CrZxx+B9u9M8Q+KtMvtG1GK31CB91syqm8AsSpGPc1tHFSUm+R6+f8AwDOUVbct3HiPwt4p0aMS+H9Un0+zbYiLIkSoQB/CJhnAI9cZ+tZklr4Cico/hHVAw7faB/8AH65bw3ZXN1ohls3zNBcufLQ/PgopyB0/hIGeuSK7DTLCK41xIdWuIoURFeYtLgE7R8oY+5/nV0Ix9rODb5YpvRu/Q4MwxVWjTg6SV5SS126/5HK2NmttqepSQWr21nLLm2jdgzKmWwDgnkAjua0a7C48Zi0uGttO0+1Fmh2gFfvgd+OB+tQ+J7Gzl0uy1uzhEAuCFkiHAyQTn9DXq0q0ocsJxsntrf7z5XG0Y4mVSvCopSW6SaVtrq7dzlaK0NJ0a71m4MVqowuN7scBAfWuwvNOstN8FahBZyrMylVmlH8Thl4/DPStK2LhTmobttL0v3OXC5dVr05VXpFJu/ey2X9aHHaNG82rQJGELndgOcD7p9jXWf2ben70Vrj2kP8A8RXM+Gv+Rgtf+B/+gGvQa8bN5yjXSXb9WfUcNQTwkr/zP8kczd6NfSSxtEEUBgW2kfMPTmtS3t1s7CMXICbBlju4Bz/9etBmVAWZgoHUk4rPv5rS/wBGvgLiIRKjq0pAZUIHJweDgjvxxXkTk5LU+kjCKZLpLNb28Fu7lwQQrMck9T/KtTegOCyg/WuYsL62udOT7PfwTGPd+9iYEAgD0PuKrXetxQrpwgUjz7nypC65O0Kxbv7YzURl0HKOp2WaKy9NuBPMxgYvb7eSezeg/CtSrTuiBskqRIXc4Aqt9vXP+qkIxnpUeqg+VCyY8wSDYrdCSO9cvJNcW/jFmkC7204k7RnlXJAHc8ZOOvFJt3Glcu6szX+o3QhbeIbHdGnq7bh/T9ayrfUQbK1kliYPIkceFjxg4x07Dismz8Xf8TlpZ7a9RtjmeFLN3OcLgDjjBz/k12WhXWmavYtdWlqyKz4YSwlGzgdjRTlyu9glHmW5DDzGpwvI71VlIMrYrpDbQBMCBMem0VQtLuPTNac7QsLAKwUdMgHP515meONWjCEnypyWvbRnZlylCpKSV3b9UVbHVrjT42SFYyGbcdwzWjfRw6ppJ1JE2TocSDOQf85zRL4f+0StLZ3UJgY5GT9324pNQuLaw0v+zbaQSuxzI46V4sKdajRnDE/w0nbbfpy/16noSnTnUjKl8V9fTrc6ZuppnUmnMckgHkU1cBq+qlueD1G7cGlK7hg/mKeVzUTk5VBwW/QVNrFN6FS9jaZSoTc6g7WX+Rrlri6MTusqNHMCVdt3HPQ5/Ku4UAAADAFc/wCKtONxaLdRD97GdrAdGU8EH1rWnZvlZz1abtzG3A5mtYZTgl0DHHuK5jw8mnWOhEx6pZ29/cLlpXdSY/QYJHT+dX/C975umrA55TOw/wCzngfh0rz+yto5t8k2/wAmPG/YQDg55GfpXBilONeEYRvJ836foezgHCWHqSnK0Vy3sr9/1Ogk8NabLI0knim0d2OWZtpJP/fdZ02j2UGtWtoNVgnt5AGknUqqoMnIzkjOB+tNv9BmsrdrjYzQCMMJN46llA4x0IJpl21rH4XtTJb4kkl8vzUXDH5s5JHOOMV58cMqlVUuRRe+7fU9SeYeyoe3U3JX5bWS1te+x1+rQ6dqFnFZW2v2VnZxjBiRlO76ncKxP+EW0v8A6Gaz/Jf/AIuuZ04RnUozOFZFUs8Ty4DAAk4xn1FdF4T0S2ea4uJH+0x7vIWORdw67iee/avRxWBXO/aJN+sjzMNmnKuSm5Jekf8AIseCiN1/HkZOwj8N1dY7CON29AajFnbWwZra2ihz1MaBc/lTbsk2bDucCs6FN0aag+hnjK6xFZ1UrXsPtsmLOOtcpqNrFdXcySgkJKzDHY5I/qa6qzP+jc9j1/CubuuL26/32/nXp5d8aIl/DrehhXVpAltOVABVDgk+1dHDF5I0SLIYpbQDI6Hmue1G2W+s2tWUsZ2WMAZzknHauru4lh1iwhX7sYjReMcBsV6WYS/eRj5M5cp+OT8mc74k068vHt7e18hEjiLytIp3FWfBXI6Vzum6UdSu7iJjFbLHEys+AQVDsjYz7AfnXqy6XB9qkuHjLSSIsb5Y4Kgkjjt1qGz8O2dvYy2jRLNHK8jMXHJDNuK8ds140JyUbIJ0U5ORb0qBYbBIlyVj+Vc+mOKvbabDGIY9oGB2FSHmiMbIdSXvNjcAHIHNLtA5IpQKU8CqUTO/UaqjqRThzQB604VSQagKWiirGJRRRQAUUUUAFLSCloAKjb/XR/jUlRt/ro/xpgSUUUUgCsbxL4ktPDOnxXd3krJMkKgerH/JrZrzH43SmHwrYkfe+2pj24PNFr6Bex2GreKbHTblbTBmuGj81kQ42J/eP+FW7XbJN56sGVolKkdwea868A+H4NZW61K/uZriVgA4LfeGTgH2+X9a9GtI8bwoCqFCgAYAxU1lFOK69QpSk3d7EOr3Qs7UTSSyxW4OJHiXLjPTHpzTNE1L+0bNiwZZoW2sG647E/UVYvxbpptwb19ttsIkPPAPHauI0HWobbVI4/tBYeYYGJjK+aucAj371pRpKpCT6oVSfJNdmeg5oqIzwgkGZRjP3jjpzTwyHpKh/wCBfjXOb80e4pNG6mfKTjzE/MUAA/xr+YpXZV49x2T6CkyfakABHEi/mKT5Rg+amD0+Yc0tQvHuOyaQ5xUP2i2x/wAfUXUDhvXp/KoG1SwXH+kg5x0B7nA/WlcOaK6lrBowazJNdsY2hB+1ESuEUiA4BJxz+NS3usafpsIlvLloUL7AWjbk9PSgftI9y+Aad27Vlxa/osykrqUI7/NlcfnUq6zpDEganbZGc/OO3Wnysn2sO5f69xRgeoqp/aemjj7fDx/te2f5Uf2lp5x/pic8fpn+VIOePct/iKcn+sXkdRVJdSsmPyys3TovqM/ypYb+J5olSGXLMoGRjr/9bmmtxOpHuZ2sIDqcxxz8v/oIrM4BwRWxqozqMv4fyFZrpu+tbNGDQwKMDFGPfNNGV4NLikAEHHtSdKd2pkhKxsfbigVim0hedvQdK4zXfDYk1ltTs1J3KfOjXru9R65rsI02sTgAmoWi/elgSOSa8uhNwzCcl2R2VI3wsV5s8fGj6ndXjpHZ3DuzYL+XgKPqcVoWfhPUlfG2JAD1eTr+Ar00Qvs4OTj1rOaCSGPzWXai9favUliZ9DlVPQ5iCHU4Q0MYhmEYIdY3+769ah1C9mgMIvtOn+zK26TaBhx6Z6YrctYfMN88agmR+MHknPOfatJkktvs8W3/AFjYK9RjHIxVPFTtyvYUaKtzGHCPCutq+9YY7iQlsEeWw9BnoarXnhWfR4xcafqkkUROPLbv9McH8q0L/wANaffuCbbyXdvvQ4Ax7jpVZND1XTXiFvMb+3UnZG0hBX8CcVNPllJXeg5tpbalnTL3XVuY7a+EU8EmczAYZeCRnseRitB/EemWV21nczmGVSAS6/KcgHg/jWZYatHLqcdqyOkxZlKsOhCk4/Sr1xpuharNNFcRRPdoR5hVsSDgY6c4xiqxEKcanu7WPPpTk8fr/J/7cbUc8FzEHt5UlQ/xIwNOC4/hFcbP4LeyY3WkanLasPmw5wPxI6/jVK38XavZ5+1tb3katt3Y2lvoR1rJU+b4T05S5X7x3sx3QyKRyFyPYirE4BCgjjrkVykfjbTpg1vPFPaTuApSSMnGfpXVTsF25rzMUmsXQv8A3vyOyi70any/Mz3OxmUjIzwRSkHA4zT5cLISVO0jOPWo8mMYUFkPOPT6V2nP6gd69MY9KuQgPAvtzg1RDtIVwep9KtE4YEfLgdaEPRkFxYwais9pcqTHICMg4IOeCPesWx8LTX4C6jq09zZ8sIAuASDxuPeuhtjm4JJGev61JpGRZBuxJH61pFtI8rLEnTd+7/MfZRmNs+VHGiRiLCjrjp+FXDjIOaTJxikZQxz7U22erGKQ7OCMdKTPA9+aNp8rauCfrSAlThgAAMUiugy4YIobgd81LbxgRgD659az7svPBKQNsSrgZ/i/+tWuowAKqKuc9R30DbgccU+BAC5pp75qWEfITjrVpamS3A8isvTSQ7rjozD9a1D0zWVafu9UuYcng7hn0NKW6N0aamlNNxigk0xi5paZmjcaLhYfmioy1Aai4WBjlqaeaRm+Y4pCwHUjNTcqwpx3pM+1KDkdjRyDgCgLCFh+NGcimSkgEj8KZvY4IOPWp5tSlHQl/Clwe1RGQ+2RUiOHGR170JicRetOFJ701sA5zTCw496Me5pAwI6ilzxTuJoWg0hNITxSuOwGnBgo+YgfU1DPKYoHkEbyMq5CJ1b2FcXfaXr3iW+Se6sILe2UbY45WBZffHPNXBJvVkTlZaI7up7EA39v/wBdUP6is7T7aW00+C3llMskaBS/rWhZsEv7YnvMg/8AHhS6j6FrWf8AkKzf8B/9BFUavax/yFZv+A/+giqNaGAUUUUhBRRRQMKKKKACiiigAooooAKKKKBBRRRQAUUUUAFFFFABRRRQMZJ/qn+hpsX3B9KdJ/qn+hpsf3B9KBElFFFABS0UlAC0UUUAFFFFAwooooAKKKKBBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUDCiiigQUUUUAFFFFAwqey/4/7f/rqv8xUFT2X/AB/2/wD11X+dAHRXP/Hy34fyqrdD/RZR/sH+VWrn/j4f8P5VWuObeQf7J/lVMo5zQbWBpprgqfORgudx4/dp26VqukdjF5NrEqvMzMMDIBPJY/iaydDsYGvbq8ZMzLIqq248Awp2zjvWlLKq6jJI3IiiK4/I/rkflUIpsrM7iMFpWaUggem7HcA44P8A+uqswZnRpC+Dx8+3gc91yB+NWIkVRLPc7WeTEvIyAD0AHrx+tPNql0V88KR/dHG3/E1LitmKFScXzQdmYEF9vuFjkaNSzBNp4Oe/fn/69a2m28V/qOpaeZNlzBGr24yMNlcnPrgkdPWuRgsoV1mJfLDqkyzBegxwAfwxn0+U1Nq0l3B4rlksTItyuwp5QJb7g/Piu7LsHTxE5Qf8t9ejuipY6ul8T3HSalq8V4bR7EC4B2+X5bbs/TPNb2uqdD0KylmI/tOcjdCTlVGMnjrxwOtVR428SpGYmtIy68F2t23D8M4/SsLy9R8RzXF5NcLJJHtDGQ44OcAADAHBr1/qOHdp1IxjGO9ne/8AkjP67iNlNtnRa1ZPdeE7XUNOVpo5VH2lfvFDx0x2BBB61X8P6pfaSJp47SS4sW4mG07R75xgHmjw/B4g02WSOwurYJkl4pSShIOD268dqPD2txab59reRGWyuRiRR1HbNclSNKMPZ0bON29PO2/3Hzud15SxNCpUm4v3te234dy6+ueH4H+02OjH7X1XzThFPrjJH6CrXi6cm00aK9YeYV8ycnjHC5+nf8qhjfwhYyi7ikubl1O5YWU4B7dQP1JrHu9Ul1jXorqYAAyKqp1CrnpXO431S27nmfWXTh7Oc4tzcdIWstU23bq9joPB8lkdPuIbCVZIorqYfKxIGWLdT14Iro64zSFlsfE0kox9lvGlVvZ1cgfmCP8AvmuzrhWx9s3qFFLSUxC0UlUjM8zHyxuUHGc8Um7DSI4tWW41JreFN0SZDSdsjg/rx7/QVoeYn94Vi6bo50xZhHKXMrl2Mhyc/lV7yZj1dfyocuwJdyWVg7jBGF5/GpFk3DrVM2pLEmQ/gKWO0AH32qbsqxcyPWkyvrVb7Iv99qUWqg/eY/jRdiHzFWG3OKckilcFhke9QG0j3d+nrSrax7jwfzNLW47Fjz0HVl/OnrNG3Rx+dVhaxDnH580G2jPUD8qd2KxcBB6Uz/lv/wABFVBvtnGHLRnjntUqRuNTllLZjaFFC+hBck/qPyqk7i2FY/6Q59ENYHh+8jm8i2HmeZFaIWLRsF59CRg9ulblwdjTn0iJrG0u4kiby2tHVRiNCWXBwPrntWb3NVsbIf529sVwbfC/Q94RLnUWbqf3qAAf98Vf17xHe2t6kFrEUJByG2nJ7dz6VX07XNSEcrSq6bn3eZhCDn8elb0ZVI/A7XMajjezIV+FGkKpMt/ej0wyAD/x2q6/DDSmgdku7yRwzABZE4AOOm329a0brWrsOHkklPOABEeP++RkfhzULahLL863DJg5wUkUL6/ex/nsa6faYj+YxfJ0RJo+kQaTZT2umucB90s8xVirYX5RgD06fqOKyLrd9pcuzsTg7n6kEcH6Y6e1XYLvZcNvvNPKMd7IwCsT7nd3+nNX9NhsvEniKVbyRovNQGPyHGCwAGMkHsCa6sum4VpVJ9n+aPEzyi61CFOG7kvyZW0WLQmRn1eedXD4VIwcFfU4GfyNbHi+K4l0+0mtDC2jxgCIQ/wnpk/yrmr/AE250++e1mifeGwp2/fHYj1rqmhfSvh7LDegpLcP+7jbqMkHGPwJr069lUhVjK93t016o8PCc0qFXDzhy8sW21o7rZPvfschBe3NtFLFBO8aS4EgQ43Y/wD1muosP+Scah/12/qlchXX2H/JONQ/67f1StMYklFr+aP5nPlkm5VE3tCf5GN4a/5GC1/4H/6Aa9AxXnnh+QRa5buQSBu4H+6a7U6rGDj7POfcKP8AGvIziLddWXT9WfScMtLCS/xP8kWriH7Rbyw72TzFK7l6jI6isltFt7DS5kgtmnldVD7CEeXBz1yB3Pery6lASciRceqH+lTyyDyGYcjbmvId0tT6RWbOStvB+g36AT6SiTSL5jmTDOGJ7kE5PHrWvD4XhtDYrbxgR2bs8aBuCWGCTnJ7n86saYmNQuOchAij26n+tblKK0FJ6lS1tZIppJpGG5wBtXoAP/11bopatKxIyWJJkKSKGX0NVv7Oi3ZLOeMde3pVuik0mBzUsQi1t0t0QStDIE3ZxnC9avafDqKIW1Ca3aQ44gRlUce5PfNVC+PEaZH3gw/8d/8ArVs5qEX0QY45rl9fdoXvZIzhkjLKcdCFrqRXMeJreVbS/uWTEPlEbsjuuP515mbUpVKcFGN/eXnpqdmCmoTk27aM4P8At7U/+fn/AMhr/hV3RtXvrvXNPt5590UtzHG67FGVLAEZA9KwK0vD3/Iy6V/1+Q/+hiu76lhv+fcfuRz/AFit/O/vZ7vIpLbgcGmhgGwRg1KetNI9RmtZLU5mtboXqKiIP2gHPAX9c0/ZgfIcfqKhld42RzGT8207fQ//AF8UmroHK25NjvVa9QSxCMk4yGP0FS+aM4AbPpilA3Bi3U8Y9BSTCT5lZHK2SGyvyIs/Z/MJcZ4XPH88Gua0bhLhnAMRKo2fU5x/I13UlmyySH7+9PLCBeQM/X9a5jwjYx6hbanbyjKMiD6H5sGsJ1OXG0Zf4vyOvBwbwOIX+D8xZLlm8KX1hI37y0KKpP8AFGXG38un4VseG1LeHrXhSPnGG/3mzXH6lMUUIZAk+TbzJ3bB3fzUGuz8MH/inbUf7/8A6G1Ti6Sp4+y2cbr7zopVHLLbv+e3/kpLbaHplnGFg0y3VRk8Lk8nJqxYWdvZDyrZSsSgttJzgmrQ4PB+tOHIqtWcSkt0tQJBQ9aqTqTEwXJ281b289aQKeaGrjjsVrYbY/qc1zl5xe3X/XRv511cMfyYPTPFcpe8Xt1/10b+dd2XK1RGz1p1X5BoVt9p1uAkZSEGVsjv0H8zWtqf/IwWX/AP/QzT/CtoUsZbpgQZ2+XP90cD9cmk1UY8Q2X/AGz/APQzWuIqe0rt9DPLY8r+TN8DvQcgcYoHJpelcZjqxqg9+tPpozn2p2M04oVrBScmlPSgVVhC0tJS1QwooooAKSlpKACiiigApaSloAKjf/XR/jUlRv8A66P8f5UwJKKKKQBXB/FHSrjWNM0m0toWldtQQkKOi4OTXeUnWjXoHqc/4P05dO8MWkTQ+VPtxLkYYkE4z+FaNoMXF2vo/H0xVoLjd7mqtuQb654PasZO7Vy7WtYkntoru2kt7iMSQyDDoehFYsPg3TLfUo7yNrgCM70hL5VW9RnkfnXQjFOwK0jJpWTCUVLdFSTT7aUYljMn3vvMT1601tLsWLM1qhLZJ/EY/lV3AowKnlQWXYzTpGnb932KLPBzj2x/Km/2Rp2QfsUWQAAcegwP0rSYDNMA5qWkUkjOXRNMRQFsIgFxjAPGOn86WPRdOi2hLGJduNuM8Yzj+Zq8zKvfim+an96p0KUfIgj02yjACWkQAxj5emDkfzqZIIowAkSKPZR9aXzVP8VPGPWjQLWGNuBwvA9BTWUvgMA2ORuGeae3XrSY96CtLEZt4myXt4WJ7lBR9ktuptYDnP8AyzHfrUg+tOGcdaEyWl2Ivstv/wA+8Xf+Ad+tP8mPOfKj/wC+aeB6nilAHrTsKyEwOPlX8hTlyHHA60gxnrTgPmXnvTS1B2MPVP8AkIy/h/IVRYZ5HWruqf8AISl/D+QqnWxiQMozRjB71Kw5zURFJoAI96ilQsmM9akzml7VIXKBiKHJzimlG4OKuTqXQBRk5qLbMOMdPpXjVJyoYyU+SUk0tlc9CEVUoRjzJNN7sgVQAM54oZUfKlQwPVT0NTeXKRjb/KoPMQS+X5iB/TcK0+vt/wDLmf8A4D/wQWHt/wAvI/eVZbRYd0lnEqyM3zEjsTk1A2mFZVnSbLKc4bnGTzj8K1vJcj7v60nkNn7v60vr7/59T/8AAf8AglfVk95x+8osqbmbZgAYX3qnd3MFpZvcvuAj+Ybeea2Gjx95TUbxRuu10BU9iOKSzC3/AC6n/wCA/wDBJlhOb7cfvOejFp9tSWKIRGYkhXPzFiMkgfnVbUfCtnql0bsXMttdMBl1PBI4HH0Aro30+0e4Sdo8yR7ip3HjPXjOKe0VsilmXAAyetN5lrdUp/8AgP8AwTzp5XiFiPbU5QeltW+9+xwWoaf4itLVrN7hr60Yj5omy2PQjrj86h0GzSHVPP1Cyvf3fEC/ZmKA+p4613qy2DKGDDBOM80+X7HCVD8FuR1Oav8AtWXLy+xn/wCA/wDBH9Uxd7qVP/wJ/wCRyfiaQ3EJjg0uS5nkG1ZPIb92PXOOtdjcEDbux+NAt4CAQnX3NLPD5hXOePSuV15YjE0mqcko817rujpjTxVHD1eZwcny2Sb763ukV5ykkY+ZQw9KjVAIlG9c59elV7u6s7KdY55whPJz2HqatrbpLtaJ9yEZDAggivV5XvY891sc1y8kdPMasMYfd5ih/XPFPEi98ZH+1Tfs/P8AFR5CgjJPNBn7XHf8+4/eLb83Rxz16fWrGkpt02PrnLHn61HBHsk+TO48dc4q7HGIE8tQdgq47FZfSnShy1N7t/eKoOf4SO9Md/LjMrYCrkn6VIMZ6UjRo6GNhlSMEUI9J2sU7XUILxmSPcrr1DYzVpkJIyeO9QWem29kZGiDFpCSS1XVXJz6VTs3oRFNL3iCaBpLSWNcbmXC+1XEHygH0pqLUhyBVJGVRq+ghxg1NEMRjNQHkfWrKjCgVUSIjD1IrNuIgmqQTLwzgoT68Vpt1qrPGJJFB6qcg+hpSRqh/wC89qQmXHQUYlB4INBMvoKRQg8zvimu0gHb8qcTMM/ux+dITIwxsAP1pMaG7pPQflS5fGcDNA83b8yDP1oAlH8I/OkMiJlySEz7UDJwfK5xzmpsyj+AfnTQ0mSNgxSsO43c6/8ALLP0pPNYfwN+IqUNJ/dH50hMn90H8adguQtPlCChU9s1D5hY8IVbHIz1q0xkwPkpFL94qlq407Ffzcg/u2BB/E1IsrDnaoJ7Z5qV3fCjZweuKiWVy+wRADpnNGzH0E86YnHl/Wnb3Gfk+hqUb8fdWjY/oPoKdmTdEW985Cc0u+QnOzFPVZj/AHVH60vlyjptxRZjuiLe+fuUu+Qf8s81IUmzxtpCs3qv5UWYXI/MkBH7vmn7pN/ajZNu5dcduKNsvdx+VAnqOw/r+lT2MZOo2pZicTIf1FVzHIf+Wn5CrFhGRqFtlif3qfzFUSZ/ifVJovE15BFOBs2ZXAJGUU/1rK/tW9/57f8Ajq/4VL4rWVfGl+fJXy32fvM4PEa4+ves2tTAu/2re/8APb/x1f8ACkOrXigkz4A5JKrx+lU6CoZSGAIPUHvSAkPiNlGWvo1+oUf0qwNWvCARPkH/AGV/wrLNrE8zb7WEqed5AJJ+mKsAADAGAPSgC7/at5/z2/8AHR/hR/at7/z2/wDHR/hVKloAe3iNklMb30auvVSFGP096kh1ye4TfDdq65xlQp5/Ks+a1hdt/wBlhkcnDFgM4/KpIoIoFKwxJGpOSEXGTTA0P7Vvf+e3/jo/wpf7Vvf+e3/jo/wqnRSAsSa1cw48y42g9PkH+FQDxPksBfISud3yjjHB7VXukd4fkjSRgR8rjPHesxo76O4j8vTbd1IBkcBRyeTjmmBuxeI3mcJHeKzHoNo5/SrP9q3n/Pb/AMdH+FYWnwzbvMuLSKFgNqhVGQPqCfatCkBr6fcXd7JIrXJUIARhF759vatDyLn/AJ/X/wC/a/4Vj6ZI0X2pk+9tTH/j1U7vW51jXcLghzhmTAK/h3/Cs51FF8vUidRQ3Oj8i4/5/m/74X/CgwXAGTfMB/uL/hWLp8kyyrtmeRZOdz8H6VtXRxAJTk7RnAqY1rpu2woT5o3ehFvbzDH/AGkd47bF/wAKlENwel8x/wCAL/hXK3T7laURl3iky6nqVPf8P6VLpV3fT3PlGJ4gvfOVIqVVnbma0M6Vf2k+Xa+xvXy3NvZSyi8Zio6FF/wrzmf4rG0uZbdoJ2MTFCQE5xxXourybdNdCeSvNfNuq/8AIXvP+uz/AMzX0eR4OhjJzVVXtY0nK2x6T/wt4f8APtcf+OUv/C3h/wA+1x/45XlVFfSf2Dgf5fxZl7SR6r/wt4f8+1x/45R/wt4f8+1x/wCOV5ra2El3BNKjooiKjDBssWzgDAPp3wKS5svssKObmF3ZipjQNuXHrlQO/Ymp/sTL78vK7+rH7SR6X/wt4f8APtcf+OUf8LeH/Ptcf+OV5VRVf2Dgf5fxYvaSPVR8XgSB9muP/HK6OXxhPFIUJkJGOir6V4Qv3h9a9PvP+Ppvov8AIV8nxVhqeXwpvDK3M3fr+Zx47FVaVNSg9bnS/wDCaTesn/fK0f8ACaTesn/fK1ydKql3CjqTjmvjPr9fv+B5X9qYnv8Agjq/+E0m9ZP++Vo/4TSb1k/75WufOnSqAXeNAQSCcnoM44HX/Cmw6fNKGPCbTzuB9M9hVfXcTtf8EX/aOLva/wCCOi/4TSb1k/75Wj/hM5vWT/vla5m4t3tpNj4z7H3xUNS8fiF1/Al5nik7N/gjrP8AhNJvWT/vlatr4muGjjfzH+dQwGxeMjNcRWxD/wAett/1xT/0EUp5hXVNyT6rp6nvcP1ZY2tKFfVJeh0H/CSXP/PRv++Vo/4SS5/56N/3ytYVORS7qg6scDNc/wDamK7/AII+t/s/D9vxNv8A4SS5/wCejf8AfK0f8JJc/wB9v++VrKFo+MsyKMEgnPOBnsP84pggckZG3kjJB4qv7Rxff8ET9Rw3b8TY/wCEkuf+ejf98rR/wklz/wA9G/75WsaSJogu7Hzc0ypeaYpaN/ghrL8O9l+Juf8ACSXP/PRv++VqeHWruaJZBKQG9VX/AArnK0rH/jzj+n9a9HLcZWrzaqPZHFjsLTowTgjV/tW9/wCe3/jo/wAKrN4jZGKtfIrA4IYKP6VDIXEbFAC+DtB9axp4r51JbSraQ+Zn7y8j15xzXsHlnQxeIJJm2xXqO3oAuf5VP/at5/z2/wDHR/hXP2sNzDIrmygRjgN5eBj1Oe/QVp0wLv8Aat7/AM9v/HV/wo/tW9/57f8Ajo/wqnRSAlbxE6Ehr6NceoUf0qSPWbqVA8dyGU9CFUg/pWa9rC0i5tYmXGCxAyPTjFToiRoFRQqjoAMCmBe/tW9/57f+Oj/Cj+1L3/nt/wCOj/CqdFICWXxDJA+yW9RG9GCj19vY1d0TXJLvWrONLtJB9ojVwoXuw9qxp7eKUbmt45XAwN4H88Ve0G2hh1yxaOCONmuYt2xQM/MKYHqVz/x8P+H8qry48ps9MVYuf+Phvw/lVab/AFL/AENMZiaE6u15t/56Rn84Y6dqrrDdZYDa0Jc/8AZc/p/Kq/huNo1ui38ZhYfTyUH8wam1h3ctHtVAAVDs3BDKc59s4H1NQipbGXBdrfajaW8qlnWF5HQnhArKFz65IyK1NkDBXGEXOSw+U9+9crBLNHqktzbqGLRbflBJK5GOnJxz1/HFTrcX1uY/KRpIwDjftwmCBwDz+hq5U2YqSXQqxOy6pHI4ziGNUOMFgWKjP4k/gKreIXePxFcuG+YFcEDoNo/pTIbiaK5F5cxSMwWPccDK8n0GPXj3qW6hbX/E3lWOGa4KhC3A4QZJ+mD+Vetk0YqtLn25XftujObvHTuZpvLgs7NMxL4znnoc/hzTrW/ubJZFt5NgkxuG0HOM46j3NdZL4b8MafJ9k1DXJReDhvLHyqffg4/E1xYRihcA7QQCewJzj+Rr36Cw1WLUIWTt9myfbpqZPmjuzTj8Q6pFIXS6wx6ny1/wqC5kePbtOM57Vp+H/DJ1eGa9urlbTT4eHlbufQZ/nU2k6DDrxmgN6tvdoAYEYZEnXPvxgdPWuOs8LSrRSikle9l5enT8DysbS9ri8PGaunz6P0RiW8rySEM2Rj0rv3Vm0zRtv8Kgn6eUa4IWs1lqE1tcJsliyrD3zXoCtt0/Sh/ehx/5CNeZnKiprkWnka5dThTzCrGCsuVbaFSRP+JRLOuA1vfO4J/3yD/OupRt6Kw6EA1zhwnh3US2Pmllxkdy5A/Wt+1GLSEHrsX+VePE917ktFFFUBFcyGO3kYHkKTUVuqxwIi9AMUy4mjnsZnicOuGGR6jg/wAqy9ft9NuLGFNVaRYFfeBGrHJCk84BwMZ9KnqUtjZL4NKJBXLaMnh2JTeWGn/Z9rEbjEQQRkdOT0JrcN/a8jzcY45Rh/SpehSsWPN+ZvanRSg5571VF9aE/wCuTrjmlF5a44nj/wC+hSuPQu7x60Bx61QTULV5REjszEZ+WNiMfXGKQajBt3ETqCcDdA4749Kdwsi67jeAPSlR8sc8VTF/akA+YAD0ypH9KBqNscYcnPojf4UXFoX8imySpGhZyQPpmqY1CDIG5+mf9W319Ky/EF+WsFWGOZlMg3EKVPtjOCDnB/Cqjq7EtpK5tTur2jOpyu3cCPzqa3lWUhlYH5QDg9DVOGUXGm+YsbxKUOEkXBGOKNI0+OwabymYiZhIQTnBI5oW4MXW7hbPTr25ZXYJA2Qilj09BWbpumecXuLuX7WhYNCk0SfuTz0IGehA59K0fEU4ttFvZmVyqwNkIhY9PQA1LYDFlGeSWGeR61LWpaehk3/hlLq6jmheOBUXGwR55z16isXU9Om01IcSB3E5Rc/Koz91ieeOcdD3ruqzNW0/7bGvCsq5DowyGBBH9T+FaRk46IxnFPU8xmSeyuoEl0+a1LISUaffEcnGQOcHIPYVsq8n2WSO48uIlCpLM2MHgche/QVp3mnzeZHbRssabdxmZZJUPOABtIOev0/Gli0ee4YJvWZQ2cuhG78+cAd+Kv2kjJxuU7e8DQyAoCWGF2tkHgDg4xVCZ2jvd8RCOCpUxseDxjBrtotHhtocRQrG6DCyAck/hXI6sjLq8yT4DfLkxkf3RznivQy6s1Ocu0W/yPHzuhz0qUHpecV99zXj8aa3bwhJUic9A8sRBP5ECsjUr3UtUm869Mr7RwNmFUew7Vnh/wBzd7p3eI5TJPyrwOQPUZFdCp+7uyRjBA78f/Wq4Y5U3zRppNmdXKalaHJUryaMBkdQCysAeORXRxw61b6LLpKWsbQTOGZtrbhyOcnAxwO2aw53aS1iLr8wkdS20rnG3HUn/Jr0pWOPUfrW2Lx0/ZU5pLW/4PQ5suyml9ZrUnJ+6ku2kk7nFWOj3VleR3Eo+Rc5CqxPII9PetwTg/8ALOT8Y2H9K3AQehpa82rjalaXNM+gwmX0sJBwpbN3OVuLtortUCx+UQ3mMz4K46cY78+lbVzPJBZQEQvIXZUYJjK5HXnsK0MD0qC6JFtJt644rlqT5kdkI8rKWhoQJWJJJkwfwAFblcvp8012sptn8qJZZBvxyxDkcflWtZ3M8VwttcsH3gmOQd8dRURfQHc0qKKWrEFJSO6xoXcgKBkk1STVrSSQIJOvQnoaTaQGXJGf7fiY4wpwAfXaTUt3d3Ed08cbKFAH8Oe31qK7t0k163nJIaKbjBOCGRhz+lM1rTTL5t4cuIgsiomdxZemAOvNKna+pUr8qsSreXZQHzU/79//AF6zPEdxcv4fu1eRCpUZATHce9aFhZ3U1hbyS7UkaNSyt1BxzVLxLZSR+HrxyykBR0z6iuh+zsZLnPMq0vD3/Iy6V/1+Q/8AoYrNrS8Pf8jLpX/X5D/6GKyND3on5jTTmkY7ZCccGlz3rJvcVwpJF3RtjrjinUUgZGG3KD6jNGaRV2gj3OKUgmoY4sXOa4v/AIQH/qJ/+QP/ALKuzApwrGrh6da3tFex04fF1sPf2Ttf0/U4r/hAT/0Ev/IH/wBlXQadpx0qwhtDL5nl5+fbjOST0/GtWmSpuX6VNPCUqT5oKz+ZdfHV8RHkqyuvRfoVsd6evTHelVM1IsYU+9apHJdCLGTzineXg57VJ2qK4l8qPOM7mCj6k4q7JDWgqrxkCuRvrC6ku7oC1nKPI3IjPIz2rswu1cVDdRNcWk0KOUaRCocdiR1ram3Td0a066hdNXTMCPVtSjCwR2SDy1ACCJsgfTNRE395qttPPaSJsdASI2AADZzzXF+GdA8WabrsptrdoYU3RNPNGirJg9cFtxB7HmvS9JstQtY5TqGoNdvI2QuwBYx6DA5/GqcrbI0hi4L4YJM0Se1KBnk0hXHOOacOlZJHJfogxilooqxCUoopaACiiimAUUUUAFFFFACUUUUAFLSUtABUb/66P8akqNv9dH+NMCSiiikAUUlFADW6VThH+mSn1q41U4c/bH9MVhPdFlrFO7U0kikDGrHYkxRTaKdxWEfHFMwPWnPjFMqGWthkibxiofIIqz0pM+1S4plqTWxU8lge9SbHA4qfNHNSoornbKjBwe+KUM2KsGm7c9hSsPmIlc5+8alVzzyfxFAjX+6KcFWmkxNokXlRmlC0wHHelBJq0ZjseppVHzD603dj0NOVvmHHeqQtTC1T/kJS/h/IVTq5qv8AyEpfw/kKp1sYhTWX0p1FICAr3H5UVMVz9ajZefek0Ib9KKTNL24pAGefpXNa5pcioZ7domcuNiyDGOcnBFdLVK7tmuZ4sthFbkY7dauErMUldFfR3v5Y5mvkZMvmINjO3HtWmBmgdacOKl6s0SshCmeKaYlNS0dKVguVWtVJyOvtUM1gssbIWO1hzWhSZHtUuCHzs5PU9GvApNufMTqR0NVNPtL2e7Uuu62iG0nOeR0ArtC4ph5HCgD2p6JWM+S8rmcdx5I2t6YpfOIB6MfSr5XNNEKkHKZFZcj6HSqi2ZyXiTTZLiQXcS53qsUqF8Aj3qz4ZWSLS5rWRY0WKYqiochVwCB+ta+rW4XT5HWN2IIJVAWJGfSszQLST7RfylJ0jkZCglQpzjnAP4Vu7ulZmCsqumxrDHAzxXK3ur6xPrT6dZIkQL7CWhLFV/vg9D9K61oHAyKjAYckEVlFqO6Knd7C2MCQHaozjlmxyx9a0AAR0FV4FIjye/NTbsHBqkRZdBcDNJ7ClwSfSnqoHNOwWQix4pSvPFPoFOwrDMEUZ4Ip9HXtTsKxGoy341YpqLjJp1CVhpWEYcVWk4lX3q1VeUYkT60SKQ+g0neikWNdioyBUXnjvxUkv3KrZ92qWFyQzDIA/lUwyRmqmTuX7x5q2hyooQXAg0mKfminYdxmKMEU/ApMCiwXG4pMU7HFJg+lIZHIOB7VH5YDBl49amePNCoduDU2KvoCin0AY4pcH0qyRKKXBowaBCVBLIyHgE+wqxiq06gv2/GkwuRGZyOEc/lTI5Hab5gQOnWpQFB/5Z0+Ffm7fhU2FzMlQZWrNkuL+3/66r/MVHjip7NcX1v/ANdF/nVpDbMDxYQPE158oP3P/QFrF3D+6K2fFv8AyM95/wAA/wDQFrFqzEXcP7oo3D+6KSikAu4f3RRuH90UlFMBdw/uijcP7opKKQC7h/dFG4f3RSUUALuH90Ubh/dFJRTAXcP7ooyP7opKKQDgQf4RVZtQsk37riABGCuS/Ck9AfepjypGByMc1iHwrpptbm1EQWG4kEjopIGR+PHSgDrdG2TPdKuCNqdPxqjd6ZDNqwlZGZlGACeBU/hyG307zoxsij2qFA4AxngVtiSzEpk82Pcf9qs6kJN3i7f5ClGMlaauUrXSCHEskjbs5/yO1a20Yx2qL7Xb/wDPaP8A76o+12//AD2T/vqnCnGOxEKagrIhubFXBeIBXx+Bqpplq6TyvJkEnlSOB9K0ftdv/wA94/8Avqj7Xb/89k/76qXRg5c1iuWzuivq6KdMmJHIHB/GvmnVf+Qvef8AXZ/5mvpPU7mF9NnVJUZivAB968Kv/Buu3Go3M0VmrRvIzKfNQZBP1r6bh7EUqE5urJRuluRUTexy1FdF/wAIP4g/58V/7/J/jR/wg/iH/nxX/v8AJ/jX1P8AaWD/AOfsfvMuSXY59WKnPUdwehppOa6L/hB/EP8Az4r/AN/k/wAaP+EG8Q/8+K/9/k/xo/tLB/8AP2P3hyS7HO0V0X/CDeIf+fJf+/yf40f8IN4h/wCfJf8Av8n+NH9pYP8A5+x+8OSXY54feH1r0+8/4+m+i/yFckvgfxBuH+hL1/57J/jXcXWl3r3LMluWXA5Dr6D1NfF8Y1YYqFJUHzWbvbU4Mwo1J0koxb1M2irn9k6h/wA+rf8Afa/40f2VqH/Pq3/fa/418H9Wrfys8f6niP5H9xU3HPJJpS5J44+hq1/ZN/8A8+rf99r/AI0f2VqH/Pq3/fa/40fVq38rD6niP5H9xUJJ6nNJVz+ytQ/59W/77X/Gj+ytQ/59W/77X/Gj6tW/lf3B9TxH8j+4p1sQ/wDHrbf9cU/9BFU/7K1D/n1b/vtf8a0ltLmOGFPIZisSq2GXqBj1pTw1Z02lF7rp6n0vDMJUK85Vlyq3UZRUn2e5/wCfZ/8Avpf8aPs9z/z7P/30v+Ncv1PEfyP7j7T61R/mX3jMmlLZ6cfSnfZ7n/n2f/vpf8aPs9z/AM+z/wDfS/40fU8R/I/uD6zR/mX3jKKf9nuf+fZ/++l/xo+z3P8Az7P/AN9L/jR9TxH8j+4PrVH+ZfeMrRspY0tIgxXOO59zVH7Pc/8APs//AH0v+NT/ANlwXNqiXcTEhcFRIR+eDzXqZVQq06knOLWh5+Y1qc4JRd9TQaaNVLNsAAyST0pVkR1DKFZSMgg8GqkemWcSyKkW0SZ3YdsnP41YjjSKNY0XaigKB6CvdPHJMj+6KMj+6KSigBdw/uijcP7opKKYC7h/dFG4f3RSUUgF3D+6KMj+6KSimAu4f3RV3RyP7bsPlH/HzH/6EKo1d0f/AJDlh/18x/8AoQoA9Muf+Phvw/lUDj9230qxc/8AHw34fyqBhlCPamUc14fuPOnlHlumIIj8wHI+YZGD04qh4sle51C3soSRtQvKQR06859Bz/nNXPD8U8E+2Zw261TGFxjDNx1561T1qYWXiISS48udNmTyAGUDJ9srj8amO4T2KOlPbSXqzw3IjZo1KRqcAJwSc5IYY9u/at2FrfzUkD+fJjcnPyjOOfQfWs2S3tJHW1S3RyiAbTHhQvXC8Y9OnrzTorHZbTWyyXEEb4HlxnP4fMDgewqHK71EovYzI0nlctBeQhW2tkRbg3ztyDnpVCeW70LXnlt5US6iOd8cY2gsvOARj+I9qs2MSWkjwQS+WIwuA0fJ/eNy3b8sVFq1peX2vXKQQSXMoClhDETxtAzgdq9nJHF15KW3K/zRz1NvmJpug6r4hklntoxIDJ+8ldwoDHnnv+Qqglzs06a128ySxybvTaHGP/H/ANKIb28s8pDczwYbJVHK4P0HetjS9NWfwnrV1NCR5ZhaCUrgZBYMAe/Xn8K+lnJwu525bq1vUxSvsZp1i/OkjS/P/wBCDbvKCKOc564yefeuj8K6a1zqy6g0ojgsP3kh5JOQcAAfQ1zkuj38Olxam8GLOU7UkDqcnnsDkdDV/TtWvNJ1K3ktJGAdwJIx0kGehH4n864sZFTlGNG13zffbX5nm4h2x+H5v735DdW1GPVfEl5exKVjkOFyMEgADP44zXYwsv2TS0PU2+R/37rI8b2UFr4jSWFQhng3yAd23Yz+NaCljbaf5ZXzlsHaMOSAWCKOcdvmrw8xnGcYOKsrL8jowitmdW/8q/Mrm8ju4rXSo3BlubmZ5AOojSVgf1wPzrr1AAwBgCuP8F6PLG1zq18E+1zSSRoqHKonmMeMjPJJP0IrsK86Ox7L3FqtfzyW2nXU8MTSyxxM6RqMlmAJAA75NWKKYGJptlJp/hqC0lOZo4AJD6vjLH8yaedUsikkErsCqfONhPHIP6gj8K0Lr/j3k/3TUMdvCybmijJ55KipVr6j1toZHhm4FzYyFcGNZCAQD+PPf1/HFbeFHpQqhQAoAA6ADFGaJO7uOKsrBtFISB2pc+9IScmpGIGyKXPFNyQOcUB8Dmlcqw4YJ9DTse9Rgk9MClwc5JouFiRRlutPwKYnLVJVIhkU4zBIPVSKW1OVQ/7Aom/1Lj2pLM5Rf90ULcT2I9eONAvjjOYWGPqMU+0BFpCD12DP5UurQPc6TdQxjLtGdo9T6VBpdyLrToJQwJKDdxjnHNN7gti52pAc0uahuLiCztpbid9kUSl3Y84A6mgY5reFm3tEhY/xFRmlSFI1CxqFUdABgUyW6ghMXmSBTMwSMH+JiM4/SpaQCbW9a4bWFD+KplZQ33eCP9gV3Wa5/UvDA1HUJbr7Z5fmY+Xys4wAOufau7A1KcJTVR2Ti133seVm1GtVp03RjzOMlK10tr9znv7Hh+3y3Y24ZUzEq4BZScH9R9cCm37MzW8UKtvSQSkYOMDjB+u7+tbH/CF/9RD/AMg//ZUf8IV/1EP/ACD/APZVqqWEW9X/AMlZz+3zD/oG/wDJ4nOzwSQWVuJZGeRmdnJfdhsjIGemPTtXof2qETGNnAb0rnj4KOOL8f8Afn/7Ktue1cmQBQY5PvA9QemRU4ypSdOnCnK9r9Gt2Xl1LExr1q1aHLzctldPZNdC1GJMuXZSCRtx6Y/xzUy5xyc1nafaNbDaJJWXp+8b39K0q889lO6FFc/p91GkNxDvnSWNuUu5gzDPIxycCug7c1wF34S8P6019qk0ralcOAu8zFQrKMAYXA9OtKRUSSwtdQ1D4fGy069W11LaFMo4Mb7gzZ9CQT+ddQiSK2nwyvvmDAswGM4U5P41S0vwlb6THu0pzamRQJFJLhsZwee/JrastOFs7TSymadhguRjA9AO1JJibLtLSUtaEGZqmZZ7W2P+rcl2HrtxgfrWTBrNjf69qGgLC3m2kaNIdvy4YAgD35FbWpQuwiuIlLvCSdo/iUjkfyrEu9WsbaOeezjjl1CQBREFw7sOgPfis5blxES5K4aQ7vJlRWbGSQHZf8K2rO7hvYBNAWKEkZZCp4OOhGa83u/EmtWNncn/AIR122kPJJ57ZGXJJxs44J78e9d34fmhn0iGWC4eaN8tueTzCCTkjd3x0oSaHpbQ1axvFn/Ir33+4P8A0IVs1jeLP+RXvv8AcH/oQqyTyOtLw9/yMulf9fkP/oYrNrS8Pf8AIy6V/wBfkP8A6GKoR7y44NNUkjB4qQ1GRg1lJa3ExaBS9qSkAUnanUGiSBDMUtLRUlXCij8aKYCEUU4UdKLBcQD1qtOwe9t4v7uZCPYcD+dWjUSxYuGlJySoUe1PyEmTUUlFUIKWkpaAOS8b+IJtIjtrSzukgvLrd5eQCWI7DPFWLS61EJb/AGy7lhkfaHEiKAGI5AOOfwroZIYZGV5IkZkOVLKCV+lOKhjz0rVTilblIcW3e4kZLZG4Ng4yKkpAABxS1miwooopgFFFFABRRRQAUUUUAJRS0lABSikooAWo3/10f41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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "image/jpeg": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%cd {HOME}\n",
    "Image(filename=f'{HOME}/runs/detect/train/train_batch0.jpg', width=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e1f07298",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/SageMaker\n",
      "Ultralytics YOLOv8.0.196 🚀 Python-3.10.14 torch-2.1.0 CUDA:0 (Tesla T4, 15102MiB)\n",
      "Model summary (fused): 218 layers, 25864078 parameters, 0 gradients, 78.8 GFLOPs\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /home/ec2-user/SageMaker/aws_data/aws_dataset/val.cache... 546 ima\u001b[0m\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
      "                   all        546       3040      0.948      0.918       0.95      0.853\n",
      "                Hammer        546        129          1      0.977      0.995      0.951\n",
      "                   SSD        546         91      0.964      0.876      0.924      0.811\n",
      "               Alcohol        546          6          1      0.919      0.995      0.878\n",
      "               Spanner        546        136      0.967      0.993      0.995      0.963\n",
      "                   Axe        546         81      0.978      0.975      0.993      0.916\n",
      "                   Awl        546         32      0.883      0.944      0.919      0.725\n",
      "        Throwing Knife        546          8       0.96          1      0.995       0.86\n",
      "           Firecracker        546         44      0.823      0.727      0.845      0.628\n",
      "               Thinner        546          4       0.87       0.75      0.775      0.698\n",
      "                 Plier        546         31      0.967          1      0.995      0.941\n",
      "                 Match        546          2          1          0      0.512      0.307\n",
      "           Smart Phone        546        271      0.991      0.985      0.993      0.963\n",
      "              Scissors        546        294      0.986      0.969      0.992       0.93\n",
      "             Tablet PC        546         49      0.992      0.918      0.957       0.92\n",
      "            Solid Fuel        546          4        0.9          1      0.995      0.845\n",
      "                   Bat        546         16          1      0.991      0.995      0.934\n",
      "          Portable Gas        546         15      0.935      0.967      0.987      0.952\n",
      "         Nail Clippers        546         19      0.935          1      0.995      0.919\n",
      "                 Knife        546        296      0.983      0.983      0.986      0.911\n",
      "            Metal Pipe        546         28          1      0.979      0.995      0.935\n",
      "Electronic Cigarettes(Liquid)        546         10      0.877          1      0.977      0.908\n",
      " Supplymentary Battery        546        117       0.98          1      0.995      0.947\n",
      "                Bullet        546         34      0.985      0.941      0.978      0.882\n",
      "             Gun Parts        546         52      0.969      0.981      0.987        0.9\n",
      "                   USB        546        242      0.908      0.858      0.922      0.676\n",
      "                Liquid        546         37      0.985          1      0.995      0.927\n",
      "               Aerosol        546         22      0.993          1      0.995      0.983\n",
      "           Screwdriver        546         71      0.948      0.901      0.975      0.829\n",
      "                Chisel        546         50      0.953       0.94       0.95      0.882\n",
      "             Handcuffs        546          6      0.947          1      0.995      0.905\n",
      "               Lighter        546        234      0.987      0.991      0.995      0.961\n",
      "                   HDD        546        165       0.98          1      0.988      0.934\n",
      " Electronic Cigarettes        546         14          1      0.987      0.995      0.902\n",
      "               Battery        546         33      0.973      0.909      0.967      0.858\n",
      "                   Gun        546         56      0.966          1      0.995      0.983\n",
      "                Laptop        546         27      0.951          1      0.982      0.973\n",
      "                   Saw        546        106      0.961      0.934      0.961       0.85\n",
      "             Zippo Oil        546          6      0.935      0.833      0.922      0.889\n",
      "                Camera        546         64      0.953      0.957       0.98      0.799\n",
      "             Camcorder        546         22      0.745      0.955      0.892      0.697\n",
      "               SD Card        546        116      0.729      0.511      0.653      0.312\n",
      "Speed: 0.4ms preprocess, 13.0ms inference, 0.0ms loss, 2.6ms postprocess per image\n",
      "Results saved to \u001b[1mruns/detect/val3\u001b[0m\n",
      "💡 Learn more at https://docs.ultralytics.com/modes/val\n"
     ]
    }
   ],
   "source": [
    "%cd {HOME}\n",
    "\n",
    "!yolo task=detect mode=val model={HOME}/runs/detect/train/weights/best.pt data=./dataset_aws.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d908d826",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/SageMaker\n",
      "Ultralytics YOLOv8.0.196 🚀 Python-3.10.14 torch-2.1.0 CUDA:0 (Tesla T4, 15102MiB)\n",
      "Model summary (fused): 218 layers, 25864078 parameters, 0 gradients, 78.8 GFLOPs\n",
      "\n",
      "image 1/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/2024032609093515_aug2.png: 384x640 4 SSDs, 7 USBs, 4 Cameras, 2 Camcorders, 75.5ms\n",
      "image 2/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/2024032609122018_aug16.png: 384x640 5 SSDs, 8 USBs, 5 Cameras, 3 Camcorders, 2 SD Cards, 11.1ms\n",
      "image 3/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/2024032609163462_aug17.png: 384x640 4 SSDs, 3 USBs, 4 Cameras, 11.0ms\n",
      "image 4/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_090445_140005_0_A_aug2.jpg: 640x384 1 SSD, 1 HDD, 1 Laptop, 73.2ms\n",
      "image 5/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_090445_140005_0_A_aug7.jpg: 640x384 1 HDD, 1 Laptop, 10.9ms\n",
      "image 6/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_090822_140005_0_A_aug12.jpg: 640x480 1 SSD, 4 USBs, 2 HDDs, 73.4ms\n",
      "image 7/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_090822_140005_0_A_aug4.jpg: 640x480 1 SSD, 4 USBs, 2 HDDs, 12.6ms\n",
      "image 8/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_091536_140005_0_A_aug15.jpg: 640x480 1 HDD, 1 Laptop, 1 Camera, 12.8ms\n",
      "image 9/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_091602_140005_0_A_aug11.jpg: 640x544 1 HDD, 1 Laptop, 1 Camera, 77.9ms\n",
      "image 10/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_091602_140005_0_A_aug14.jpg: 640x544 1 HDD, 1 Laptop, 1 Camera, 15.7ms\n",
      "image 11/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_094434_140005_0_A_aug13.jpg: 640x544 1 Camera, 16.0ms\n",
      "image 12/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_094434_140005_0_A_aug16.jpg: 640x544 1 Camera, 15.9ms\n",
      "image 13/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_094636_140005_0_A_aug9.jpg: 640x544 1 Camera, 17.2ms\n",
      "image 14/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_094802_140005_0_A_aug6.jpg: 640x480 1 Camera, 2 SD Cards, 13.7ms\n",
      "image 15/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_094957_140005_0_A_aug0.jpg: 640x384 1 Camera, 1 SD Card, 12.0ms\n",
      "image 16/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_094957_140005_0_A_aug1.jpg: 640x384 1 Camera, 1 SD Card, 11.4ms\n",
      "image 17/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_094957_140005_0_A_aug4.jpg: 640x384 1 Camera, 1 SD Card, 11.2ms\n",
      "image 18/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_094957_140005_0_A_aug7.jpg: 640x384 1 Camera, 1 SD Card, 11.1ms\n",
      "image 19/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_094957_140005_0_A_aug8.jpg: 640x384 1 Camera, 1 SD Card, 11.1ms\n",
      "image 20/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_100844_140005_SUPERVISOR_A_aug1.jpg: 640x544 1 Tablet PC, 16.7ms\n",
      "image 21/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_104753_140005_SUPERVISOR_A_aug10.jpg: 640x544 1 Smart Phone, 1 Tablet PC, 16.1ms\n",
      "image 22/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_104803_140005_SUPERVISOR_A_aug11.jpg: 640x544 1 Smart Phone, 1 Tablet PC, 16.1ms\n",
      "image 23/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_105217_140005_SUPERVISOR_A.jpg: 640x480 1 Smart Phone, 1 Tablet PC, 1 HDD, 1 Camera, 3 SD Cards, 13.6ms\n",
      "image 24/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_105217_140005_SUPERVISOR_A_aug12.jpg: 640x480 1 Smart Phone, 1 Tablet PC, 1 HDD, 1 Camera, 2 SD Cards, 12.9ms\n",
      "image 25/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_105217_140005_SUPERVISOR_A_aug13.jpg: 640x480 1 Smart Phone, 1 Tablet PC, 1 HDD, 1 Camera, 2 SD Cards, 12.8ms\n",
      "image 26/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_105217_140005_SUPERVISOR_A_aug16.jpg: 640x480 1 Smart Phone, 1 Tablet PC, 1 HDD, 1 Camera, 2 SD Cards, 12.8ms\n",
      "image 27/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_105217_140005_SUPERVISOR_A_aug4.jpg: 640x480 1 Smart Phone, 1 Tablet PC, 1 HDD, 1 Camera, 2 SD Cards, 13.1ms\n",
      "image 28/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_105217_140005_SUPERVISOR_A_aug8.jpg: 640x480 1 Smart Phone, 1 Tablet PC, 1 HDD, 1 Camera, 3 SD Cards, 12.9ms\n",
      "image 29/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_135111_140005_SUPERVISOR_A_aug5.jpg: 640x544 1 Camcorder, 17.0ms\n",
      "image 30/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_135155_140005_SUPERVISOR_A_aug17.jpg: 640x320 1 Camcorder, 73.8ms\n",
      "image 31/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240221_135155_140005_SUPERVISOR_A_aug9.jpg: 640x320 1 Camcorder, 10.4ms\n",
      "image 32/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240222_101056_140005_SUPERVISOR_A_aug1.jpg: 640x384 1 Camera, 11.6ms\n",
      "image 33/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240222_102137_140005_SUPERVISOR_A_aug11.jpg: 640x480 1 SSD, 1 HDD, 3 Cameras, 1 Camcorder, 13.8ms\n",
      "image 34/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240222_102137_140005_SUPERVISOR_A_aug9.jpg: 640x480 1 SSD, 1 HDD, 3 Cameras, 1 Camcorder, 12.9ms\n",
      "image 35/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240223_133808_140005_SUPERVISOR_A_aug15.jpg: 640x384 1 USB, 1 Laptop, 11.7ms\n",
      "image 36/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240223_133808_140005_SUPERVISOR_A_aug17.jpg: 640x384 1 USB, 1 Laptop, 11.4ms\n",
      "image 37/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240223_133808_140005_SUPERVISOR_A_aug4.jpg: 640x384 2 USBs, 1 Laptop, 11.2ms\n",
      "image 38/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240223_133808_140005_SUPERVISOR_A_aug6.jpg: 640x384 1 Laptop, 11.1ms\n",
      "image 39/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240223_135423_140005_SUPERVISOR_A_aug4.jpg: 640x384 1 Laptop, 1 Camcorder, 11.1ms\n",
      "image 40/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240226_095841_140005_SUPERVISOR_A_aug14.jpg: 640x480 4 USBs, 5 SD Cards, 13.3ms\n",
      "image 41/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240226_095841_140005_SUPERVISOR_A_aug8.jpg: 640x480 3 USBs, 4 SD Cards, 13.3ms\n",
      "image 42/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240226_101300_140005_SUPERVISOR_A_aug17.jpg: 640x384 2 SD Cards, 11.7ms\n",
      "image 43/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240226_102212_140005_SUPERVISOR_A_aug11.jpg: 640x384 1 SSD, 2 SD Cards, 11.1ms\n",
      "image 44/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240226_102212_140005_SUPERVISOR_A_aug5.jpg: 640x384 1 SSD, 3 SD Cards, 11.1ms\n",
      "image 45/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240226_102212_140005_SUPERVISOR_A_aug7.jpg: 640x384 1 SSD, 3 SD Cards, 11.5ms\n",
      "image 46/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240227_081100_140005_SUPERVISOR_A_aug11.jpg: 640x384 2 SSDs, 1 Tablet PC, 4 USBs, 4 SD Cards, 11.2ms\n",
      "image 47/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240227_081100_140005_SUPERVISOR_A_aug13.jpg: 640x384 2 SSDs, 1 Tablet PC, 4 USBs, 4 SD Cards, 11.1ms\n",
      "image 48/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240227_082549_140005_0_A.jpg: 640x544 2 SSDs, 1 Tablet PC, 3 USBs, 4 SD Cards, 16.5ms\n",
      "image 49/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240227_082549_140005_0_A_aug16.jpg: 640x544 2 SSDs, 1 Tablet PC, 4 USBs, 6 SD Cards, 15.8ms\n",
      "image 50/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240228_084614_140005_SUPERVISOR_A_aug15.jpg: 640x512 2 SSDs, 1 Tablet PC, 1 USB, 1 HDD, 2 Cameras, 73.8ms\n",
      "image 51/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240228_084614_140005_SUPERVISOR_A_aug5.jpg: 640x512 2 SSDs, 1 Tablet PC, 1 USB, 1 HDD, 2 Cameras, 1 SD Card, 12.7ms\n",
      "image 52/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240229_155414_140005_SUPERVISOR_A_aug16.jpg: 640x384 2 SSDs, 1 Smart Phone, 1 Tablet PC, 2 USBs, 1 HDD, 2 Cameras, 2 Camcorders, 11.6ms\n",
      "image 53/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240229_155414_140005_SUPERVISOR_A_aug7.jpg: 640x384 1 SSD, 1 Smart Phone, 1 Tablet PC, 4 USBs, 1 HDD, 2 Cameras, 1 Camcorder, 1 SD Card, 11.4ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 54/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240229_155426_140005_SUPERVISOR_A_aug16.jpg: 640x384 1 SSD, 1 Smart Phone, 1 Tablet PC, 4 USBs, 1 HDD, 2 Cameras, 1 Camcorder, 1 SD Card, 11.2ms\n",
      "image 55/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240229_155647_140005_SUPERVISOR_A_aug15.jpg: 640x480 1 SSD, 1 Tablet PC, 4 USBs, 1 HDD, 3 Cameras, 1 Camcorder, 2 SD Cards, 13.3ms\n",
      "image 56/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240229_160531_140005_0_A_aug11.jpg: 640x384 1 SSD, 1 Smart Phone, 1 Tablet PC, 2 USBs, 1 HDD, 1 Camera, 1 Camcorder, 2 SD Cards, 11.6ms\n",
      "image 57/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240306_104646_140005_SUPERVISOR_A_aug10.jpg: 640x480 2 SSDs, 3 USBs, 3 SD Cards, 13.3ms\n",
      "image 58/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240306_104646_140005_SUPERVISOR_A_aug12.jpg: 640x480 2 SSDs, 3 USBs, 5 SD Cards, 12.7ms\n",
      "image 59/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240306_105522_140005_SUPERVISOR_A_aug5.jpg: 640x544 1 Tablet PC, 1 HDD, 2 Cameras, 1 Camcorder, 16.5ms\n",
      "image 60/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240306_105552_140005_SUPERVISOR_A_aug14.jpg: 640x480 1 Tablet PC, 1 HDD, 2 Cameras, 1 Camcorder, 13.3ms\n",
      "image 61/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240306_105552_140005_SUPERVISOR_A_aug19.jpg: 640x480 1 Tablet PC, 1 USB, 1 HDD, 2 Cameras, 1 Camcorder, 13.3ms\n",
      "image 62/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240307_092053_140005_SUPERVISOR_A_aug1.jpg: 640x224 1 Tablet PC, 72.5ms\n",
      "image 63/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240307_092053_140005_SUPERVISOR_A_aug16.jpg: 640x224 1 Tablet PC, 7.8ms\n",
      "image 64/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240307_092053_140005_SUPERVISOR_A_aug19.jpg: 640x224 1 Tablet PC, 7.8ms\n",
      "image 65/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240307_092053_140005_SUPERVISOR_A_aug2.jpg: 640x224 1 Tablet PC, 7.7ms\n",
      "image 66/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240307_092449_140005_SUPERVISOR_A_aug14.jpg: 640x416 1 SSD, 3 SD Cards, 73.7ms\n",
      "image 67/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/BAGGAGE_20240307_093721_140005_SUPERVISOR_A_aug4.jpg: 640x384 1 Spanner, 1 Plier, 5 Knifes, 1 Screwdriver, 11.5ms\n",
      "image 68/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_2707.99-1000_01_235.png: 416x640 1 Hammer, 1 Knife, 2 Zippo Oils, 71.7ms\n",
      "image 69/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_2707.99-1000_01_254.png: 416x640 1 Portable Gas, 1 Lighter, 2 Zippo Oils, 11.8ms\n",
      "image 70/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_2707.99-1000_03_290.png: 416x640 1 Supplymentary Battery, 1 Screwdriver, 2 Zippo Oils, 11.8ms\n",
      "image 71/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_2905.12-2090_05_267.png: 416x640 2 Alcohols, 1 Knife, 1 Gun, 11.7ms\n",
      "image 72/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_3605.00-9000_02_208.png: 416x640 1 Spanner, 2 Matchs, 1 Knife, 11.7ms\n",
      "image 73/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_3605.00-9000_03_228.png: 416x640 2 Matchs, 1 Scissors, 1 Supplymentary Battery, 11.8ms\n",
      "image 74/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_3605.00-9000_04_030.png: 416x640 1 Axe, 1 Match, 11.9ms\n",
      "image 75/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_3605.00-9000_06_292.png: 416x640 1 Match, 1 Saw, 11.9ms\n",
      "image 76/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_3605.00-9000_06_298.png: 416x640 2 Matchs, 1 Saw, 11.8ms\n",
      "image 77/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_3605.00-9000_08_297.png: 416x640 1 Match, 1 Saw, 11.8ms\n",
      "image 78/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_3814.00-1090_01_219.png: 416x640 2 Thinners, 1 Scissors, 1 Portable Gas, 1 Knife, 1 Chisel, 11.8ms\n",
      "image 79/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_3814.00-1090_05_216.png: 416x640 3 Thinners, 1 Scissors, 1 Knife, 1 Chisel, 11.9ms\n",
      "image 80/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_3814.00-1090_05_298.png: 416x640 1 Portable Gas, 1 Chisel, 11.9ms\n",
      "image 81/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_7306.90-9000_03_305.png: 416x640 1 Hammer, 1 Bat, 1 Knife, 2 Metal Pipes, 11.9ms\n",
      "image 82/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_7306.90-9000_04_287.png: 416x640 1 Hammer, 1 Spanner, 2 Metal Pipes, 11.8ms\n",
      "image 83/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_7306.90-9000_09_252.png: 416x640 1 Hammer, 1 Knife, 4 Metal Pipes, 11.9ms\n",
      "image 84/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_7306.90-9000_14_259.png: 416x640 1 Plier, 1 Scissors, 2 Metal Pipes, 11.8ms\n",
      "image 85/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_7306.90-9000_14_276.png: 416x640 1 Spanner, 3 Metal Pipes, 1 Chisel, 11.9ms\n",
      "image 86/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_7306.90-9000_16_297.png: 416x640 1 Knife, 3 Metal Pipes, 1 Screwdriver, 11.9ms\n",
      "image 87/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_7306.90-9000_19_295.png: 416x640 1 Hammer, 1 Spanner, 2 Metal Pipes, 11.9ms\n",
      "image 88/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_7306.90-9000_21_009.png: 416x640 1 Spanner, 2 Metal Pipes, 1 Chisel, 11.9ms\n",
      "image 89/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_7306.90-9000_21_034.png: 416x640 1 Axe, 1 Knife, 3 Metal Pipes, 1 Screwdriver, 11.8ms\n",
      "image 90/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_7306.90-9000_23_293.png: 416x640 1 Hammer, 2 Metal Pipes, 1 Screwdriver, 11.8ms\n",
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      "image 96/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_7321.89-0000_01_318.png: 416x640 1 Spanner, 2 Solid Fuels, 1 Electronic Cigarettes(Liquid), 1 Lighter, 11.7ms\n",
      "image 97/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_7321.89-0000_04_299.png: 416x640 1 Scissors, 2 Solid Fuels, 1 Knife, 11.8ms\n",
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      "image 102/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_7326.90-9000_01_252.png: 416x640 1 Spanner, 1 Supplymentary Battery, 2 Handcuffss, 11.8ms\n",
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     ]
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      "image 109/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8201.40-0000_06_426.png: 416x640 5 Axes, 3 Smart Phones, 2 Scissorss, 3 Lighters, 11.9ms\n",
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      "image 159/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8204.20-0000_04_310.png: 416x640 6 Spanners, 3 Smart Phones, 2 Scissorss, 2 Lighters, 11.9ms\n",
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     ]
    },
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     "name": "stdout",
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     "text": [
      "image 168/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8204.20-0000_12_306.png: 416x640 2 Spanners, 1 Smart Phone, 1 Scissors, 1 Knife, 11.8ms\n",
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     ]
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      "image 225/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8205.40-0000_14_334.png: 416x640 1 Spanner, 1 Knife, 2 Screwdrivers, 11.8ms\n",
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     ]
    },
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     "text": [
      "image 283/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8213.00-1000_13_561.png: 416x640 4 Scissorss, 2 Lighters, 2 HDDs, 11.8ms\n",
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      "image 321/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8424.20-1000_20_399.png: 416x640 1 Axe, 2 Guns, 11.9ms\n",
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      "image 328/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8424.90-2000_06_436.png: 416x640 1 Spanner, 1 Axe, 2 Gun Partss, 11.9ms\n",
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      "image 331/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8424.90-2000_09_392.png: 416x640 1 Axe, 1 Supplymentary Battery, 2 Gun Partss, 1 Gun, 11.8ms\n",
      "image 332/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8424.90-2000_09_441.png: 416x640 1 Spanner, 1 Scissors, 2 Gun Partss, 11.8ms\n",
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      "image 334/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8424.90-2000_11_393.png: 416x640 1 Axe, 1 Supplymentary Battery, 2 Gun Partss, 11.8ms\n",
      "image 335/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8424.90-2000_12_421.png: 416x640 1 Axe, 1 Supplymentary Battery, 3 Gun Partss, 11.8ms\n",
      "image 336/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8424.90-2000_12_472.png: 416x640 1 Spanner, 2 Gun Partss, 1 Screwdriver, 11.8ms\n",
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      "image 340/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8424.90-2000_15_456.png: 416x640 1 Spanner, 1 Scissors, 2 Gun Partss, 11.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 341/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8424.90-2000_16_377.png: 416x640 1 Hammer, 2 Gun Partss, 1 Screwdriver, 11.8ms\n",
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      "image 344/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8424.90-2000_19_408.png: 416x640 1 Hammer, 1 Scissors, 2 Gun Partss, 11.7ms\n",
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      "image 348/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8424.90-2000_21_384.png: 416x640 1 Axe, 1 Supplymentary Battery, 2 Gun Partss, 11.9ms\n",
      "image 349/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8424.90-2000_21_483.png: 416x640 2 Gun Partss, 1 Chisel, 1 Saw, 11.8ms\n",
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      "image 354/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8424.90-2000_24_453.png: 416x640 1 Hammer, 2 Gun Partss, 1 Chisel, 11.9ms\n",
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      "image 356/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8481.80-1090_03_415.png: 416x640 1 Smart Phone, 1 Scissors, 2 Aerosols, 1 Lighter, 11.8ms\n",
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      "image 361/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8507.60-1000_04_349.png: 416x640 2 Knifes, 4 Supplymentary Batterys, 2 Lighters, 11.7ms\n",
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      "image 363/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8507.60-1000_08_375.png: 416x640 3 Knifes, 4 Supplymentary Batterys, 2 USBs, 3 Lighters, 11.8ms\n",
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      "image 371/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8507.60-1000_24_194.png: 416x640 1 Knife, 2 Supplymentary Batterys, 1 Screwdriver, 1 Lighter, 11.8ms\n",
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      "image 377/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8543.70-4000_01_175.png: 416x640 1 Supplymentary Battery, 1 Chisel, 1 Lighter, 1 Electronic Cigarettes, 11.9ms\n",
      "image 378/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8543.70-4000_02_172.png: 416x640 1 Chisel, 1 Lighter, 2 Electronic Cigarettess, 11.8ms\n",
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      "image 381/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8543.70-4000_06_205.png: 416x640 1 Supplymentary Battery, 1 Chisel, 1 Lighter, 2 Electronic Cigarettess, 11.8ms\n",
      "image 382/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8543.70-4000_07_192.png: 416x640 1 Spanner, 1 Supplymentary Battery, 2 Electronic Cigarettess, 11.9ms\n",
      "image 383/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8543.70-4000_07_298.png: 416x640 1 Scissors, 2 Electronic Cigarettess, 11.9ms\n",
      "image 384/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8543.70-4010_04_285.png: 416x640 2 Electronic Cigarettes(Liquid)s, 1 Supplymentary Battery, 1 Gun, 11.9ms\n",
      "image 385/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8543.70-4010_04_330.png: 416x640 2 Spanners, 1 Scissors, 1 Electronic Cigarettes(Liquid), 11.9ms\n",
      "image 386/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8543.70-4010_05_321.png: 416x640 1 Knife, 2 Electronic Cigarettes(Liquid)s, 1 Screwdriver, 2 Lighters, 11.9ms\n",
      "image 387/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8543.70-4010_06_297.png: 416x640 1 Spanner, 1 Scissors, 1 Electronic Cigarettes(Liquid), 1 Lighter, 11.8ms\n",
      "image 388/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8543.70-4010_08_265.png: 416x640 2 Electronic Cigarettes(Liquid)s, 1 Supplymentary Battery, 1 Gun, 11.8ms\n",
      "image 389/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_8543.70-4010_10_285.png: 416x640 1 Axe, 2 Electronic Cigarettes(Liquid)s, 1 Screwdriver, 11.8ms\n",
      "image 390/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9306.30-9000_02_425.png: 416x640 1 Axe, 2 Bullets, 1 Saw, 11.7ms\n",
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      "image 392/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9306.30-9000_07_479.png: 416x640 1 Spanner, 1 Axe, 2 Bullets, 1 Gun Parts, 11.7ms\n",
      "image 393/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9306.30-9000_08_163.png: 416x640 1 Hammer, 1 Scissors, 2 Bullets, 11.7ms\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 395/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9306.30-9000_10_158.png: 416x640 1 Hammer, 1 Scissors, 2 Bullets, 11.8ms\n",
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      "image 397/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9306.30-9000_12_442.png: 416x640 1 Axe, 1 Supplymentary Battery, 2 Bullets, 11.8ms\n",
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      "image 402/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_93307.90-9000_03_444.png: 416x640 3 Smart Phones, 2 Scissorss, 4 Liquids, 2 Lighters, 11.8ms\n",
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      "image 422/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_02_439.png: 416x640 1 Firecracker, 1 Scissors, 1 Supplymentary Battery, 1 Lighter, 11.8ms\n",
      "image 423/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_06_408.png: 416x640 1 Spanner, 3 Firecrackers, 1 Supplymentary Battery, 11.8ms\n",
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      "image 428/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_10_480.png: 416x640 2 Firecrackers, 1 Knife, 1 Supplymentary Battery, 1 Chisel, 11.8ms\n",
      "image 429/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_11_294.png: 416x640 1 Axe, 2 Firecrackers, 1 Screwdriver, 11.8ms\n",
      "image 430/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_11_341.png: 416x640 1 Spanner, 2 Firecrackers, 1 Supplymentary Battery, 11.8ms\n",
      "image 431/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_11_434.png: 416x640 1 Axe, 1 Firecracker, 1 Screwdriver, 11.9ms\n",
      "image 432/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_12_442.png: 416x640 1 Hammer, 2 Firecrackers, 1 Scissors, 11.8ms\n",
      "image 433/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_12_457.png: 416x640 2 Firecrackers, 1 Screwdriver, 1 Saw, 11.9ms\n",
      "image 434/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_14_435.png: 416x640 1 Hammer, 2 Firecrackers, 1 Scissors, 11.9ms\n",
      "image 435/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_15_312.png: 416x640 1 Firecracker, 1 Screwdriver, 1 Saw, 11.8ms\n",
      "image 436/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_16_479.png: 416x640 1 Hammer, 2 Firecrackers, 1 Chisel, 11.8ms\n",
      "image 437/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_17_319.png: 416x640 1 Spanner, 4 Firecrackers, 1 Scissors, 11.9ms\n",
      "image 438/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_17_329.png: 416x640 1 Hammer, 3 Firecrackers, 1 Scissors, 11.9ms\n",
      "image 439/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_17_458.png: 416x640 1 Spanner, 3 Firecrackers, 1 Scissors, 11.9ms\n",
      "image 440/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_17_481.png: 416x640 1 Spanner, 2 Firecrackers, 1 Supplymentary Battery, 11.8ms\n",
      "image 441/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_18_344.png: 416x640 2 Axes, 2 Firecrackers, 1 Screwdriver, 11.8ms\n",
      "image 442/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_19_325.png: 416x640 1 Hammer, 2 Firecrackers, 1 Scissors, 11.8ms\n",
      "image 443/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_19_445.png: 416x640 1 Hammer, 2 Firecrackers, 1 Chisel, 11.8ms\n",
      "image 444/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_20_320.png: 416x640 2 Firecrackers, 1 Screwdriver, 1 Saw, 11.8ms\n",
      "image 445/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_23_312.png: 416x640 1 Hammer, 2 Firecrackers, 1 Scissors, 11.8ms\n",
      "image 446/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_23_320.png: 416x640 1 Firecracker, 1 Scissors, 1 Chisel, 11.8ms\n",
      "image 447/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_23_466.png: 416x640 1 Hammer, 3 Firecrackers, 1 Scissors, 11.8ms\n",
      "image 448/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_24_454.png: 416x640 1 Spanner, 2 Firecrackers, 1 Screwdriver, 11.7ms\n",
      "image 449/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9405.40-9000_25_299.png: 416x640 1 Hammer, 1 Firecracker, 1 Scissors, 11.9ms\n",
      "image 450/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9506.91-0000_05_254.png: 416x640 1 Hammer, 2 Bats, 1 Gun, 11.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 451/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9506.91-0000_06_243.png: 416x640 2 Bats, 1 Screwdriver, 1 Gun, 11.8ms\n",
      "image 452/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9506.91-0000_06_303.png: 416x640 2 Bats, 1 Gun, 1 Saw, 11.7ms\n",
      "image 453/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9506.91-0000_08_259.png: 416x640 2 Hammers, 2 Bats, 1 Gun, 11.8ms\n",
      "image 454/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_01_606.png: 416x640 2 Smart Phones, 2 Scissorss, 3 Lighters, 11.8ms\n",
      "image 455/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_02_589.png: 416x640 2 Smart Phones, 2 Scissorss, 3 Lighters, 12.0ms\n",
      "image 456/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_04_369.png: 416x640 2 Smart Phones, 2 Scissorss, 4 Lighters, 11.9ms\n",
      "image 457/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_04_607.png: 416x640 2 Knifes, 3 USBs, 5 Lighters, 3 HDDs, 11.9ms\n",
      "image 458/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_06_612.png: 416x640 2 Smart Phones, 2 Knifes, 4 Lighters, 11.9ms\n",
      "image 459/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_07_452.png: 416x640 2 Smart Phones, 2 Scissorss, 3 Lighters, 11.8ms\n",
      "image 460/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_07_455.png: 416x640 2 Smart Phones, 2 Scissorss, 4 Lighters, 11.9ms\n",
      "image 461/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_07_483.png: 416x640 2 Smart Phones, 2 Scissorss, 4 Lighters, 11.8ms\n",
      "image 462/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_07_614.png: 416x640 2 Smart Phones, 2 Knifes, 4 Lighters, 11.8ms\n",
      "image 463/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_09_489.png: 416x640 2 Smart Phones, 2 Scissorss, 4 Lighters, 11.8ms\n",
      "image 464/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_09_492.png: 416x640 3 Knifes, 2 USBs, 4 Lighters, 3 HDDs, 11.8ms\n",
      "image 465/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_12_650.png: 416x640 2 Smart Phones, 4 Lighters, 11.8ms\n",
      "image 466/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_15_484.png: 416x640 2 Smart Phones, 2 Scissorss, 4 Lighters, 11.8ms\n",
      "image 467/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_19_400.png: 416x640 1 Supplymentary Battery, 3 Lighters, 11.8ms\n",
      "image 468/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_19_402.png: 416x640 1 Supplymentary Battery, 2 Lighters, 11.7ms\n",
      "image 469/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_20_471.png: 416x640 1 Spanner, 1 Handcuffs, 3 Lighters, 11.8ms\n",
      "image 470/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_22_430.png: 416x640 1 Portable Gas, 1 Electronic Cigarettes(Liquid), 2 Gun Partss, 2 Lighters, 11.8ms\n",
      "image 471/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_24_387.png: 416x640 1 Electronic Cigarettes(Liquid), 2 Lighters, 1 Saw, 1 Zippo Oil, 11.8ms\n",
      "image 472/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_25_421.png: 416x640 1 Portable Gas, 1 Electronic Cigarettes(Liquid), 4 Lighters, 11.8ms\n",
      "image 473/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/H_9613.80-0000_25_453.png: 416x640 1 Plier, 1 Electronic Cigarettes(Liquid), 1 Screwdriver, 2 Lighters, 11.8ms\n",
      "image 474/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_01_417.png: 416x640 2 Smart Phones, 2 Laptops, 11.8ms\n",
      "image 475/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_02_383.png: 416x640 2 Smart Phones, 2 Laptops, 11.8ms\n",
      "image 476/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_03_412.png: 416x640 2 Smart Phones, 1 Knife, 2 Laptops, 11.8ms\n",
      "image 477/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_04_384.png: 416x640 2 Smart Phones, 2 Knifes, 2 Laptops, 11.8ms\n",
      "image 478/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_06_385.png: 416x640 2 Smart Phones, 1 Knife, 2 Laptops, 11.8ms\n",
      "image 479/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_08_387.png: 416x640 2 Smart Phones, 2 Knifes, 2 Laptops, 11.7ms\n",
      "image 480/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_14_319.png: 416x640 1 Knife, 2 HDDs, 2 Laptops, 11.8ms\n",
      "image 481/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_15_322.png: 416x640 2 HDDs, 2 Laptops, 11.8ms\n",
      "image 482/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_15_367.png: 416x640 2 Smart Phones, 2 Scissorss, 2 Laptops, 11.8ms\n",
      "image 483/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_16_314.png: 416x640 2 HDDs, 2 Laptops, 11.8ms\n",
      "image 484/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_18_372.png: 416x640 1 Smart Phone, 2 Scissorss, 2 Laptops, 11.8ms\n",
      "image 485/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_21_287.png: 416x640 2 Smart Phones, 2 Scissorss, 2 Tablet PCs, 11.8ms\n",
      "image 486/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_24_244.png: 416x640 2 Smart Phones, 2 Tablet PCs, 1 Knife, 11.8ms\n",
      "image 487/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_25_248.png: 416x640 2 Smart Phones, 2 Tablet PCs, 1 Knife, 11.9ms\n",
      "image 488/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_26_252.png: 416x640 2 Smart Phones, 2 Tablet PCs, 2 Knifes, 2 USBs, 11.9ms\n",
      "image 489/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_26_285.png: 416x640 2 Smart Phones, 2 Scissorss, 2 Tablet PCs, 3 USBs, 11.8ms\n",
      "image 490/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_26_289.png: 416x640 3 Smart Phones, 2 Scissorss, 2 Tablet PCs, 1 USB, 11.8ms\n",
      "image 491/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_28_270.png: 416x640 2 Smart Phones, 2 Tablet PCs, 2 Knifes, 11.8ms\n",
      "image 492/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_29_271.png: 416x640 2 Smart Phones, 2 Tablet PCs, 2 Knifes, 11.9ms\n",
      "image 493/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.30-0000_30_301.png: 416x640 2 Smart Phones, 3 Scissorss, 2 Tablet PCs, 11.9ms\n",
      "image 494/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.70-2020_02_183.png: 416x640 1 Scissors, 4 Knifes, 4 HDDs, 2 Batterys, 11.8ms\n",
      "image 495/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.70-2020_02_188.png: 416x640 2 Scissorss, 2 Knifes, 4 HDDs, 2 Batterys, 11.8ms\n",
      "image 496/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.70-2020_03_178.png: 416x640 2 Scissorss, 2 Knifes, 4 HDDs, 2 Batterys, 11.7ms\n",
      "image 497/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.70-2020_04_170.png: 416x640 3 Knifes, 5 HDDs, 3 Batterys, 11.8ms\n",
      "image 498/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.70-2020_04_193.png: 416x640 2 Scissorss, 2 Knifes, 5 HDDs, 11.8ms\n",
      "image 499/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.70-2020_05_192.png: 416x640 3 Scissorss, 6 HDDs, 3 Batterys, 11.8ms\n",
      "image 500/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.70-2020_10_249.png: 416x640 2 Scissorss, 2 Knifes, 1 Lighter, 4 HDDs, 11.8ms\n",
      "image 501/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8471.70-2020_10_288.png: 416x640 2 Scissorss, 2 Lighters, 4 HDDs, 11.7ms\n",
      "image 502/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8517.70-1029_07_481.png: 416x640 5 Smart Phones, 2 Scissorss, 1 Lighter, 2 Batterys, 11.8ms\n",
      "image 503/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8517.70-1029_08_419.png: 416x640 4 Smart Phones, 1 Scissors, 3 Knifes, 11.8ms\n",
      "image 504/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8517.70-1029_08_464.png: 416x640 4 Smart Phones, 2 Scissorss, 2 Batterys, 11.8ms\n",
      "image 505/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8517.70-1029_12_450.png: 416x640 4 Smart Phones, 2 Scissorss, 2 Knifes, 2 Batterys, 11.8ms\n",
      "image 506/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8517.70-1029_13_439.png: 416x640 3 Spanners, 6 Smart Phones, 2 Lighters, 11.8ms\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 507/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8517.70-1029_14_411.png: 416x640 4 Smart Phones, 4 Scissorss, 11.8ms\n",
      "image 508/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8517.70-1029_14_467.png: 416x640 4 Smart Phones, 2 Knifes, 2 Batterys, 11.8ms\n",
      "image 509/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8523.51-1000_04_528.png: 416x640 6 SSDs, 2 Smart Phones, 3 Knifes, 11.8ms\n",
      "image 510/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8523.51-1000_06_502.png: 416x640 4 SSDs, 4 Smart Phones, 2 Knifes, 11.8ms\n",
      "image 511/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8523.51-1000_06_504.png: 416x640 4 SSDs, 4 Smart Phones, 2 Knifes, 11.9ms\n",
      "image 512/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8523.51-1000_09_488.png: 416x640 4 SSDs, 2 Smart Phones, 3 Knifes, 11.9ms\n",
      "image 513/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8523.51-1000_10_489.png: 416x640 4 SSDs, 4 Smart Phones, 11.8ms\n",
      "image 514/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_01_437.png: 416x640 2 Knifes, 4 USBs, 2 Lighters, 4 Batterys, 11.8ms\n",
      "image 515/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_02_461.png: 416x640 2 Scissorss, 2 Knifes, 6 USBs, 11.9ms\n",
      "image 516/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_04_455.png: 416x640 3 Scissorss, 9 USBs, 3 Lighters, 11.8ms\n",
      "image 517/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_07_462.png: 416x640 2 Knifes, 5 USBs, 1 Lighter, 2 Batterys, 11.9ms\n",
      "image 518/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_07_473.png: 416x640 2 Knifes, 4 USBs, 3 Lighters, 5 Batterys, 11.9ms\n",
      "image 519/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_08_483.png: 416x640 3 Knifes, 5 USBs, 3 Lighters, 6 Batterys, 11.9ms\n",
      "image 520/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_08_486.png: 416x640 3 Scissorss, 6 USBs, 5 Batterys, 11.9ms\n",
      "image 521/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_10_452.png: 416x640 2 Scissorss, 6 USBs, 2 Lighters, 11.8ms\n",
      "image 522/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_10_470.png: 416x640 3 Scissorss, 5 USBs, 3 Lighters, 3 Batterys, 11.7ms\n",
      "image 523/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_10_471.png: 416x640 3 Scissorss, 4 USBs, 2 Lighters, 2 Batterys, 11.7ms\n",
      "image 524/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_13_439.png: 416x640 2 Scissorss, 2 Supplymentary Batterys, 7 USBs, 11.7ms\n",
      "image 525/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_13_468.png: 416x640 4 Knifes, 8 USBs, 2 Lighters, 11.7ms\n",
      "image 526/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_14_453.png: 416x640 6 USBs, 4 Lighters, 11.8ms\n",
      "image 527/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_16_442.png: 416x640 2 Knifes, 2 Supplymentary Batterys, 4 USBs, 11.8ms\n",
      "image 528/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_17_433.png: 416x640 2 Spanners, 2 Scissorss, 3 USBs, 11.8ms\n",
      "image 529/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_19_438.png: 416x640 2 Knifes, 5 USBs, 3 Batterys, 11.8ms\n",
      "image 530/530 /home/ec2-user/SageMaker/aws_data/aws_dataset/test/S_8544.42-2090_19_466.png: 416x640 2 Knifes, 2 Supplymentary Batterys, 8 USBs, 1 Battery, 11.7ms\n",
      "Speed: 2.0ms preprocess, 13.0ms inference, 2.0ms postprocess per image at shape (1, 3, 416, 640)\n",
      "Results saved to \u001b[1mruns/detect/predict\u001b[0m\n",
      "530 labels saved to runs/detect/predict/labels\n",
      "💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
     ]
    }
   ],
   "source": [
    "%cd {HOME}\n",
    "!yolo task=detect mode=predict model={HOME}/runs/detect/train/weights/best.pt conf=0.25 source=/home/ec2-user/SageMaker/aws_data/aws_dataset/test/ save=True save_txt=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "db999772",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unterminated string literal (detected at line 5) (3352847861.py, line 5)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[24], line 5\u001b[0;36m\u001b[0m\n\u001b[0;31m    predict_results_path = \"/home/ec2-user/SageMaker/runs/detect/predict2\u001b[0m\n\u001b[0m                           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unterminated string literal (detected at line 5)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from ultralytics import YOLO\n",
    "\n",
    "test_images_path = \"/home/ec2-user/SageMaker/aws_data/aws_dataset/test\"\n",
    "predict_results_path = \"/home/ec2-user/SageMaker/runs/detect/predict2\n",
    "model = YOLO(\"/home/ec2-user/SageMaker/runs/detect/train/weights/best.pt\")\n",
    "\n",
    "test_image_files = [file for file in os.listdir(test_images_path) if file.endswith(\".png\")]\n",
    "\n",
    "total_match_count = 0\n",
    "total_image_count = len(test_image_files)\n",
    "class_match_counts = {}\n",
    "\n",
    "# class ID와 class 명의 매핑 정보\n",
    "class_names = ['Hammer','SSD','Alcohol','Spanner','Axe','Awl','Throwing Knife','Firecracker','Thinner','Plier','Match',\n",
    "               'Smart Phone','Scissors','Tablet PC','Solid Fuel','Bat','Portable Gas','Nail Clippers','Knife','Metal Pipe',\n",
    "               'Electronic Cigarettes(Liquid)','Supplymentary Battery','Bullet','Gun Parts','USB','Liquid','Aerosol','Screwdriver',\n",
    "               'Chisel','Handcuffs','Lighter','HDD','Electronic Cigarettes','Battery','Gun','Laptop','Saw','Zippo Oil','Stun Gun',\n",
    "               'Camera','Camcorder','SD Card']\n",
    "\n",
    "for image_file in test_image_files:\n",
    "    image_path = os.path.join(test_images_path, image_file)\n",
    "    label_path = os.path.join(test_images_path, image_file[:-4] + \".txt\")\n",
    "    \n",
    "    with open(label_path, \"r\") as f:\n",
    "        actual_labels = f.read().splitlines()\n",
    "    \n",
    "    actual_class_ids = [int(float(label.split()[0])) for label in actual_labels]\n",
    "    \n",
    "    results = model.predict(source=image_path, save=False, save_txt=False)\n",
    "    \n",
    "    predict_result_path = os.path.join(predict_results_path, \"labels\", image_file[:-4] + \".txt\")\n",
    "\n",
    "    with open(predict_result_path, \"r\") as f:\n",
    "        predicted_labels = f.read().splitlines()\n",
    "    \n",
    "    predicted_class_ids = [int(float(label.split()[0])) for label in predicted_labels]\n",
    "\n",
    "    print(f\"Image: {image_file}\")\n",
    "    print(\"Actual Class IDs:\")\n",
    "    print([f\"{class_id} ({class_names[class_id]})\" for class_id in actual_class_ids])\n",
    "    print(\"Predicted Class IDs:\")\n",
    "    print([f\"{class_id} ({class_names[class_id]})\" for class_id in predicted_class_ids])\n",
    "\n",
    "    match_count = len(set(actual_class_ids) & set(predicted_class_ids))\n",
    "    total_count = len(set(actual_class_ids) | set(predicted_class_ids))\n",
    "    match_percentage = match_count / total_count * 100\n",
    "\n",
    "    print(f\"Match Percentage: {match_percentage:.2f}%\")\n",
    "\n",
    "    if set(actual_class_ids) == set(predicted_class_ids):\n",
    "        print(\"Class IDs match!\")\n",
    "        total_match_count += 1\n",
    "    else:\n",
    "        print(\"Class IDs do not match.\")\n",
    "\n",
    "    for class_id in set(actual_class_ids + predicted_class_ids):\n",
    "        if class_id not in class_match_counts:\n",
    "            class_match_counts[class_id] = [0, 0]  # [match_count, total_count]\n",
    "        \n",
    "        if class_id in actual_class_ids and class_id in predicted_class_ids:\n",
    "            class_match_counts[class_id][0] += 1\n",
    "        \n",
    "        class_match_counts[class_id][1] += 1\n",
    "    \n",
    "    print(\"---\")\n",
    "\n",
    "total_match_percentage = total_match_count / total_image_count * 100\n",
    "print(f\"Total Match Percentage: {total_match_percentage:.2f}%\")\n",
    "\n",
    "class_match_percentages = []\n",
    "for class_id, (match_count, total_count) in class_match_counts.items():\n",
    "    match_percentage = match_count / total_count * 100\n",
    "    class_match_percentages.append((class_id, match_percentage))\n",
    "\n",
    "class_match_percentages.sort(key=lambda x: x[1])\n",
    "\n",
    "print(\"Class IDs with the lowest match percentages:\")\n",
    "for class_id, match_percentage in class_match_percentages[:10]:\n",
    "    class_name = class_names[class_id]\n",
    "    print(f\"Class ID: {class_id} ({class_name}), Match Percentage: {match_percentage:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "204cebcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class\t\t\tTest Images\tTest Instances\tPredict Images\tPredict Instances\tTest Instances/Predict Instances\n",
      "Hammer         \t       119\t          119\t          121\t             121\t0.98\n",
      "SSD            \t        34\t           34\t           34\t              34\t1.00\n",
      "Alcohol        \t         2\t            2\t            4\t               4\t0.50\n",
      "Spanner        \t       121\t          121\t          141\t             141\t0.86\n",
      "Axe            \t        77\t           77\t           88\t              88\t0.88\n",
      "Awl            \t        32\t           32\t           33\t              33\t0.97\n",
      "Throwing Knife \t         4\t            4\t            4\t               4\t1.00\n",
      "Firecracker    \t        56\t           56\t           68\t              68\t0.82\n",
      "Thinner        \t         6\t            6\t            9\t               9\t0.67\n",
      "Plier          \t        27\t           27\t           32\t              32\t0.84\n",
      "Match          \t        12\t           12\t           11\t              11\t1.09\n",
      "Smart Phone    \t       269\t          269\t          280\t             280\t0.96\n",
      "Scissors       \t       334\t          334\t          347\t             347\t0.96\n",
      "Tablet PC      \t        16\t           16\t           18\t              18\t0.89\n",
      "Solid Fuel     \t        12\t           12\t           12\t              12\t1.00\n",
      "Bat            \t         8\t            8\t            8\t               8\t1.00\n",
      "Portable Gas   \t        11\t           11\t           12\t              12\t0.92\n",
      "Nail Clippers  \t        18\t           18\t           17\t              17\t1.06\n",
      "Knife          \t       306\t          306\t          320\t             320\t0.96\n",
      "Metal Pipe     \t        22\t           22\t           31\t              31\t0.71\n",
      "Electronic Ciga\t        16\t           16\t           15\t              15\t1.07\n",
      "Supplymentary B\t       107\t          107\t          109\t             109\t0.98\n",
      "Bullet         \t        22\t           22\t           22\t              22\t1.00\n",
      "Gun Parts      \t        65\t           65\t           72\t              72\t0.90\n",
      "USB            \t       162\t          162\t          170\t             170\t0.95\n",
      "Liquid         \t        85\t           85\t           91\t              91\t0.93\n",
      "Aerosol        \t        10\t           10\t           13\t              13\t0.77\n",
      "Screwdriver    \t        63\t           63\t           71\t              71\t0.89\n",
      "Chisel         \t        47\t           47\t           58\t              58\t0.81\n",
      "Handcuffs      \t        13\t           13\t           21\t              21\t0.62\n",
      "Lighter        \t       278\t          278\t          296\t             296\t0.94\n",
      "HDD            \t       121\t          121\t          122\t             122\t0.99\n",
      "Electronic Ciga\t        14\t           14\t            8\t               8\t1.75\n",
      "Battery        \t        53\t           53\t           48\t              48\t1.10\n",
      "Gun            \t        46\t           46\t           47\t              47\t0.98\n",
      "Laptop         \t        21\t           21\t           22\t              22\t0.95\n",
      "Saw            \t        70\t           70\t           80\t              80\t0.88\n",
      "Zippo Oil      \t         7\t            7\t            8\t               8\t0.88\n",
      "Stun Gun       \t         0\t            0\t            0\t               0\tinf\n",
      "Camera         \t        11\t           11\t           16\t              16\t0.69\n",
      "Camcorder      \t         7\t            7\t            6\t               6\t1.17\n",
      "SD Card        \t         6\t            6\t            0\t               0\tinf\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "\n",
    "test_path = \"/home/ec2-user/SageMaker/aws_data/aws_dataset/test\"\n",
    "predict_path = \"/home/ec2-user/SageMaker/runs/detect/predict2\"\n",
    "class_names = ['Hammer','SSD','Alcohol','Spanner','Axe','Awl','Throwing Knife','Firecracker','Thinner','Plier','Match',\n",
    "               'Smart Phone','Scissors','Tablet PC','Solid Fuel','Bat','Portable Gas','Nail Clippers','Knife','Metal Pipe',\n",
    "               'Electronic Cigarettes(Liquid)','Supplymentary Battery','Bullet','Gun Parts','USB','Liquid','Aerosol','Screwdriver',\n",
    "               'Chisel','Handcuffs','Lighter','HDD','Electronic Cigarettes','Battery','Gun','Laptop','Saw','Zippo Oil','Stun Gun',\n",
    "               'Camera','Camcorder','SD Card']\n",
    "\n",
    "# 클래스별 통계 정보 초기화\n",
    "test_class_stats = {class_name: {\"Images\": 0, \"Instances\": 0} for class_name in class_names}\n",
    "predict_class_stats = {class_name: {\"Images\": 0, \"Instances\": 0} for class_name in class_names}\n",
    "\n",
    "# 테스트 이미지 파일 목록 가져오기\n",
    "test_image_files = glob.glob(os.path.join(test_path, \"*.png\"))\n",
    "predict_image_files = glob.glob(os.path.join(predict_path, \"*.png\"))\n",
    "\n",
    "# 테스트 데이터셋 통계 계산\n",
    "for image_file in test_image_files:\n",
    "    label_file = image_file[:-4] + \".txt\"\n",
    "    \n",
    "    with open(label_file, \"r\") as f:\n",
    "        labels = f.read().splitlines()\n",
    "    \n",
    "    for label in labels:\n",
    "        class_id, _, _, _, _ = label.split()\n",
    "        class_id = int(float(class_id))\n",
    "        class_name = class_names[class_id]\n",
    "        \n",
    "        test_class_stats[class_name][\"Images\"] += 1\n",
    "        test_class_stats[class_name][\"Instances\"] += 1\n",
    "\n",
    "# 예측 결과 데이터셋 통계 계산\n",
    "for image_file in predict_image_files:\n",
    "    label_file = os.path.join(predict_path, \"labels\", os.path.basename(image_file)[:-4] + \".txt\")\n",
    "    \n",
    "    with open(label_file, \"r\") as f:\n",
    "        labels = f.read().splitlines()\n",
    "    \n",
    "    for label in labels:\n",
    "        class_id, _, _, _, _ = label.split()\n",
    "        class_id = int(float(class_id))\n",
    "        class_name = class_names[class_id]\n",
    "        \n",
    "        predict_class_stats[class_name][\"Images\"] += 1\n",
    "        predict_class_stats[class_name][\"Instances\"] += 1\n",
    "\n",
    "# 결과 출력\n",
    "print(\"Class\\t\\t\\tTest Images\\tTest Instances\\tPredict Images\\tPredict Instances\\tTest Instances/Predict Instances\")\n",
    "for class_name in class_names:\n",
    "    test_images = test_class_stats[class_name][\"Images\"]\n",
    "    test_instances = test_class_stats[class_name][\"Instances\"]\n",
    "    predict_images = predict_class_stats[class_name][\"Images\"]\n",
    "    predict_instances = predict_class_stats[class_name][\"Instances\"]\n",
    "    \n",
    "    # Test Instances/Predict Instances 비율 계산\n",
    "    if predict_instances > 0:\n",
    "        instance_ratio = test_instances / predict_instances\n",
    "    else:\n",
    "        instance_ratio = float('inf')\n",
    "    \n",
    "    print(f\"{class_name[:15]:<15}\\t{test_images:>10}\\t{test_instances:>13}\\t{predict_images:>13}\\t{predict_instances:>16}\\t{instance_ratio:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a83e7018",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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