Jupyter Notebooks/YOLOv8/YOLOv8 Object Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dcb9bf4d-fc5b-4f6b-bce2-7475fe1bfdc5",
   "metadata": {},
   "source": [
    "## GPU Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e15d2a19-f4d0-4e09-85f8-cde118902bf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thu Feb 15 05:03:46 2024       \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 535.129.03             Driver Version: 535.129.03   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 V100-PCIE-16GB           On  | 00000001:00:00.0 Off |                  Off |\n",
      "| N/A   31C    P0              24W / 250W |      0MiB / 16384MiB |      0%      Default |\n",
      "|                                         |                      |                  N/A |\n",
      "+-----------------------------------------+----------------------+----------------------+\n",
      "|   1  Tesla V100-PCIE-16GB           On  | 00000002:00:00.0 Off |                  Off |\n",
      "| N/A   32C    P0              24W / 250W |      0MiB / 16384MiB |      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",
      "|  No running processes found                                                           |\n",
      "+---------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6efe1be5-506c-4676-9f44-0a5b0120d710",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Installation of Ultralytics Module for YOLO Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "71d5e816-771a-4d0e-97d9-924621dde401",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mnt/batch/tasks/shared/LS_root/mounts/clusters/myinstance1/code\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "HOME = os.getcwd()\n",
    "print(HOME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "817f6e86-bec6-425d-a940-9f48fdcf1883",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -q ultralytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ebb89aeb-2bbb-4b2c-b90c-eb6e7db17a2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n"
     ]
    }
   ],
   "source": [
    "from ultralytics import YOLO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9e710a2-3651-4898-89bd-9c1a6167ef81",
   "metadata": {},
   "source": [
    "## Inferencing on single image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4a8c686-8cb2-4eba-80c3-d56b50c40aea",
   "metadata": {},
   "source": [
    "### 1. Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "329284f6-d295-45d5-b0c6-e2aaa4b26928",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mnt/batch/tasks/shared/LS_root/mounts/clusters/myinstance1/code\n",
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt to 'yolov8n.pt'...\n",
      "100%|██████████████████████████████████████| 6.23M/6.23M [00:00<00:00, 19.1MB/s]\n",
      "Ultralytics YOLOv8.1.11 🚀 Python-3.8.5 torch-2.2.0+cu121 CUDA:0 (Tesla V100-PCIE-16GB, 16151MiB)\n",
      "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
      "\n",
      "image 1/1 /mnt/batch/tasks/shared/LS_root/mounts/clusters/myinstance1/code/images/8808.jpg: 480x640 1 bear, 109.4ms\n",
      "Speed: 29.0ms preprocess, 109.4ms inference, 1031.3ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns/detect/predict\u001b[0m\n",
      "💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
     ]
    }
   ],
   "source": [
    "%cd {HOME}\n",
    "!yolo task=detect mode=predict model=yolov8n.pt conf=0.25 source = \"images/8808.jpg\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0deb7a09-35bc-4fad-9576-eb2e06a43a1f",
   "metadata": {},
   "source": [
    "### 2. Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1612b53e-2f6a-413e-b467-d5d04063cbd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n-seg.pt to 'yolov8n-seg.pt'...\n",
      "100%|██████████████████████████████████████| 6.73M/6.73M [00:00<00:00, 19.3MB/s]\n",
      "Ultralytics YOLOv8.1.11 🚀 Python-3.8.5 torch-2.2.0+cu121 CUDA:0 (Tesla V100-PCIE-16GB, 16151MiB)\n",
      "YOLOv8n-seg summary (fused): 195 layers, 3404320 parameters, 0 gradients, 12.6 GFLOPs\n",
      "\n",
      "image 1/1 /mnt/batch/tasks/shared/LS_root/mounts/clusters/myinstance1/code/images/8808.jpg: 480x640 1 bear, 139.3ms\n",
      "Speed: 6.0ms preprocess, 139.3ms inference, 669.3ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns/segment/predict\u001b[0m\n",
      "💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
     ]
    }
   ],
   "source": [
    "!yolo task=segment mode=predict model=yolov8n-seg.pt source = \"images/8808.jpg\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95840961-99c1-4d4f-8b62-d9bf9dcef7ec",
   "metadata": {},
   "source": [
    "### 3. Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4c8d9daa-4d97-420e-b619-4d15626a7367",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n-cls.pt to 'yolov8n-cls.pt'...\n",
      "100%|██████████████████████████████████████| 5.30M/5.30M [00:00<00:00, 16.4MB/s]\n",
      "Ultralytics YOLOv8.1.11 🚀 Python-3.8.5 torch-2.2.0+cu121 CUDA:0 (Tesla V100-PCIE-16GB, 16151MiB)\n",
      "YOLOv8n-cls summary (fused): 73 layers, 2715880 parameters, 0 gradients, 4.3 GFLOPs\n",
      "\n",
      "image 1/1 /mnt/batch/tasks/shared/LS_root/mounts/clusters/myinstance1/code/images/8808.jpg: 224x224 wild_boar 0.36, hog 0.08, sloth_bear 0.08, American_black_bear 0.05, wallaby 0.05, 5.1ms\n",
      "Speed: 104.0ms preprocess, 5.1ms inference, 0.1ms postprocess per image at shape (1, 3, 224, 224)\n",
      "Results saved to \u001b[1mruns/classify/predict\u001b[0m\n",
      "💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
     ]
    }
   ],
   "source": [
    "!yolo task=classify mode=predict model=yolov8n-cls.pt source = \"images/8808.jpg\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "217f0307-b098-487f-bc85-9d8e07d6494b",
   "metadata": {},
   "source": [
    "## Custom Training of YOLOv8 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4174c2f5-69e9-4263-ae09-b85447954fc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New https://pypi.org/project/ultralytics/8.1.14 available 😃 Update with 'pip install -U ultralytics'\n",
      "Ultralytics YOLOv8.1.11 🚀 Python-3.8.5 torch-2.2.0+cu121 CUDA:0 (Tesla V100-PCIE-16GB, 16151MiB)\n",
      "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=data.yaml, epochs=50, time=None, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, 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, multi_scale=False, 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, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, 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, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
      "Overriding model.yaml nc=80 with nc=23\n",
      "\n",
      "                   from  n    params  module                                       arguments                     \n",
      "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
      "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
      "  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n",
      "  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
      "  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n",
      "  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
      "  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
      "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
      "  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n",
      "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 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  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \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  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  \n",
      " 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
      " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 \n",
      " 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
      " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n",
      " 22        [15, 18, 21]  1    755797  ultralytics.nn.modules.head.Detect           [23, [64, 128, 256]]          \n",
      "Model summary: 225 layers, 3015333 parameters, 3015317 gradients, 8.2 GFLOPs\n",
      "\n",
      "Transferred 319/355 items from pretrained weights\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /mnt/batch/tasks/shared/LS_root/mounts/clusters/myinstance1/code/Split Dataset/train/labels.cache... 6152 images, 0 backgrounds, 0 corrupt: 100%|██████████| 6152/6152 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /mnt/batch/tasks/shared/LS_root/mounts/clusters/myinstance1/code/Split Dataset/val/labels.cache... 879 images, 0 backgrounds, 0 corrupt: 100%|██████████| 879/879 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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 AdamW(lr=0.00037, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/02/16 19:19:43 INFO mlflow.tracking.fluent: Experiment with name '/Shared/YOLOv8' does not exist. Creating a new experiment.\n",
      "2024/02/16 19:19:44 INFO mlflow.tracking.fluent: Autologging successfully enabled for tensorflow.\n",
      "2024/02/16 19:19:44 WARNING mlflow.utils.autologging_utils: You are using an unsupported version of transformers. If you encounter errors during autologging, try upgrading / downgrading transformers to a supported version, or try upgrading MLflow.\n",
      "2024/02/16 19:19:45 WARNING mlflow.tracking.fluent: Exception raised while enabling autologging for sklearn: module 'numpy' has no attribute 'float'.\n",
      "`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:\n",
      "    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "2024/02/16 19:19:45 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/anaconda/envs/azureml_py38/lib/python3.8/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\"\n",
      "2024/02/16 19:19:46 INFO mlflow.tracking.fluent: Autologging successfully enabled for transformers.\n",
      "2024/02/16 19:19:46 WARNING mlflow.utils.autologging_utils: You are using an unsupported version of statsmodels. If you encounter errors during autologging, try upgrading / downgrading statsmodels to a supported version, or try upgrading MLflow.\n",
      "2024/02/16 19:19:46 WARNING mlflow.tracking.fluent: Exception raised while enabling autologging for statsmodels: module 'numpy' has no attribute 'long'\n",
      "2024/02/16 19:19:46 INFO mlflow.tracking.fluent: Autologging successfully enabled for pyspark.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mMLflow: \u001b[0mlogging run_id(2598df96-1d5f-493a-b7eb-f4f13fb85c67) to azureml://centralindia.api.azureml.ms/mlflow/v1.0/subscriptions/1c59fae0-1d58-41f8-b33a-0dd64015c9aa/resourceGroups/sameer-rg/providers/Microsoft.MachineLearningServices/workspaces/sameer-workspace\n",
      "\u001b[34m\u001b[1mMLflow: \u001b[0mdisable with 'yolo settings mlflow=False'\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
      "Image sizes 640 train, 640 val\n",
      "Using 6 dataloader workers\n",
      "Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       1/50       2.5G      1.724      4.461      1.626         23        640: 100%|██████████| 385/385 [01:53<00:00,  3.39it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:12<00:00,  2.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.447      0.301      0.242       0.17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       2/50      2.51G      1.598      3.233      1.501         19        640: 100%|██████████| 385/385 [01:46<00:00,  3.61it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:11<00:00,  2.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.464      0.386      0.397      0.256\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       3/50      2.52G      1.581      2.738      1.487         15        640: 100%|██████████| 385/385 [01:40<00:00,  3.84it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.467      0.502      0.486      0.308\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       4/50      2.52G      1.523      2.358      1.442         14        640: 100%|██████████| 385/385 [01:46<00:00,  3.61it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.64it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.506      0.454      0.487      0.315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       5/50      2.51G      1.483      2.095       1.41         12        640: 100%|██████████| 385/385 [01:42<00:00,  3.74it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.574      0.535      0.578      0.376\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       6/50       2.5G      1.455      1.873      1.391         19        640: 100%|██████████| 385/385 [01:46<00:00,  3.61it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.75it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967       0.63      0.599      0.619      0.404\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       7/50      2.51G      1.436      1.713      1.366         19        640: 100%|██████████| 385/385 [01:46<00:00,  3.62it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.646      0.617      0.641      0.418\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       8/50      2.51G      1.399      1.597      1.332         18        640: 100%|██████████| 385/385 [01:44<00:00,  3.67it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:11<00:00,  2.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.691      0.615       0.69      0.456\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "       9/50      2.51G      1.372      1.511      1.318         18        640: 100%|██████████| 385/385 [01:50<00:00,  3.49it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.64it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.774      0.656      0.722      0.481\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      10/50       2.5G      1.358       1.43      1.303         18        640: 100%|██████████| 385/385 [01:46<00:00,  3.61it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.62it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.734      0.683      0.732      0.494\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      11/50      2.51G      1.327      1.339      1.281         14        640: 100%|██████████| 385/385 [01:49<00:00,  3.53it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.58it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.777      0.701      0.759       0.52\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      12/50      2.51G      1.301      1.298      1.266         27        640: 100%|██████████| 385/385 [01:50<00:00,  3.48it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.805      0.678       0.76      0.513\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      13/50      2.51G      1.282      1.254      1.254         16        640: 100%|██████████| 385/385 [01:48<00:00,  3.53it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:11<00:00,  2.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.774      0.703      0.765      0.528\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      14/50       2.5G      1.277      1.214       1.25         19        640: 100%|██████████| 385/385 [01:49<00:00,  3.50it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:11<00:00,  2.40it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967        0.8      0.725      0.777      0.538\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      15/50      2.51G      1.256      1.169      1.241         16        640: 100%|██████████| 385/385 [01:50<00:00,  3.48it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.65it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.844      0.709      0.808       0.55\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      16/50      2.51G       1.26      1.146      1.239         20        640: 100%|██████████| 385/385 [01:45<00:00,  3.65it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.853      0.747      0.811       0.57\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      17/50      2.51G      1.244      1.109      1.229         14        640: 100%|██████████| 385/385 [01:45<00:00,  3.65it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967       0.82      0.764      0.813      0.562\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      18/50       2.5G      1.218       1.06      1.207         19        640: 100%|██████████| 385/385 [01:45<00:00,  3.64it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:11<00:00,  2.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.795      0.766      0.817      0.572\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      19/50      2.51G      1.212      1.045      1.205         16        640: 100%|██████████| 385/385 [01:45<00:00,  3.64it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:11<00:00,  2.52it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.891      0.725      0.826      0.584\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      20/50      2.51G      1.202      1.017      1.201         25        640: 100%|██████████| 385/385 [01:44<00:00,  3.67it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.859      0.754      0.833      0.582\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      21/50      2.51G       1.17     0.9857      1.187         16        640: 100%|██████████| 385/385 [01:44<00:00,  3.68it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.71it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967       0.82      0.785      0.833      0.587\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      22/50       2.5G      1.172     0.9747      1.186         13        640: 100%|██████████| 385/385 [01:47<00:00,  3.59it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:10<00:00,  2.60it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.901      0.767       0.85      0.599\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      23/50      2.51G       1.16     0.9425      1.178         10        640: 100%|██████████| 385/385 [01:50<00:00,  3.48it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 28/28 [00:11<00:00,  2.37it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        879        967      0.889      0.762      0.845      0.599\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "      24/50       2.5G      1.122     0.9156      1.148         29        640:  10%|▉         | 37/385 [00:09<02:24,  2.41it/s]"
     ]
    }
   ],
   "source": [
    "model = YOLO()\n",
    "model.train(data=\"data.yaml\", epochs=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c28f01-414d-4e29-9985-3bf7778b5ef0",
   "metadata": {},
   "source": [
    "## Validation of Trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5fa1cfb5-abc0-4254-a82d-6b92d5c556dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "Ultralytics YOLOv8.1.11 🚀 Python-3.8.5 torch-2.2.0+cu121 CUDA:0 (Tesla V100-PCIE-16GB, 16151MiB)\n",
      "Model summary (fused): 168 layers, 3010133 parameters, 0 gradients, 8.1 GFLOPs\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /mnt/batch/tasks/shared/LS_root/mounts/clusters/myinstance1/code/S\u001b[0m\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  m\n",
      "                   all        879        967      0.929      0.863      0.915      0.681\n",
      "                  Bird        879         25      0.905       0.68      0.817      0.506\n",
      " Eastern Gray Squirrel        879         20      0.992       0.85      0.889      0.511\n",
      "      Eastern Chipmunk        879         36       0.87      0.583      0.694      0.416\n",
      "             Woodchuck        879         17      0.945          1      0.995      0.856\n",
      "           Wild Turkey        879         48      0.907      0.896      0.945      0.642\n",
      "     White_Tailed_Deer        879         48      0.965      0.917      0.962      0.801\n",
      "      Virginia Opossum        879         66      0.995          1      0.995      0.786\n",
      "    Eastern Cottontail        879         35      0.983      0.886      0.925      0.705\n",
      "               Vehicle        879         15      0.734        0.8      0.814      0.756\n",
      "         Striped Skunk        879         32          1      0.918      0.991      0.782\n",
      "               Red Fox        879         37      0.889      0.865      0.922      0.731\n",
      "  Eastern Fox Squirrel        879         35      0.979      0.971      0.977      0.732\n",
      "      Northern Raccoon        879         29      0.912      0.718      0.795      0.485\n",
      "              Grey Fox        879         48      0.868      0.688       0.81       0.56\n",
      "                 Horse        879          6      0.911          1      0.995      0.897\n",
      "                   Dog        879         79      0.986      0.924      0.961      0.702\n",
      "         American Crow        879        128      0.975      0.913      0.965      0.643\n",
      "               Chicken        879         56      0.937      0.796      0.903      0.587\n",
      "          Domestic Cat        879         45      0.946      0.889      0.929      0.629\n",
      "                Coyote        879         32       0.93          1       0.99      0.775\n",
      "                Bobcat        879         32      0.887      0.719      0.862      0.653\n",
      "   American Black Bear        879         98      0.924      0.969       0.99      0.829\n",
      "Speed: 0.2ms preprocess, 1.3ms inference, 0.0ms loss, 1.3ms postprocess per image\n",
      "Results saved to \u001b[1mruns/detect/val\u001b[0m\n",
      "💡 Learn more at https://docs.ultralytics.com/modes/val\n"
     ]
    }
   ],
   "source": [
    "!yolo task=detect mode=val model={HOME}/runs/detect/train/weights/best.pt data=data.yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed8e94b2-0925-4a7b-bc27-63c90fdc2b1b",
   "metadata": {},
   "source": [
    "## Test the Trained Model on Test Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "959437d8-cb43-47fc-8eb6-a08efc1cb94f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/azureml_py38/lib/python3.8/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "Ultralytics YOLOv8.1.11 🚀 Python-3.8.5 torch-2.2.0+cu121 CUDA:0 (Tesla V100-PCIE-16GB, 16151MiB)\n",
      "Model summary (fused): 168 layers, 3010133 parameters, 0 gradients, 8.1 GFLOPs\n",
      "\n",
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      "image 1756/1758 /mnt/batch/tasks/shared/LS_root/mounts/clusters/myinstance1/code/Split Dataset/test/images/987.jpg: 480x640 1 Eastern Chipmunk, 8.6ms\n",
      "image 1757/1758 /mnt/batch/tasks/shared/LS_root/mounts/clusters/myinstance1/code/Split Dataset/test/images/99.jpg: 384x640 1 Eastern Gray Squirrel, 9.6ms\n",
      "image 1758/1758 /mnt/batch/tasks/shared/LS_root/mounts/clusters/myinstance1/code/Split Dataset/test/images/994.jpg: 480x640 1 Eastern Chipmunk, 9.4ms\n",
      "Speed: 2.3ms preprocess, 9.3ms inference, 1.8ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns/detect/predict2\u001b[0m\n",
      "💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
     ]
    }
   ],
   "source": [
    "!yolo task=detect mode=predict model={HOME}/runs/detect/train/weights/best.pt conf=0.25 source = '{HOME}/Split Dataset/test/images'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1576d009-ad01-4c38-b6df-e48e2a984312",
   "metadata": {},
   "source": [
    "## Check the Model Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7f07015-8a75-45ce-82bf-8b5c929fb8b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rwxrwxrwx 1 root root 6246873 Feb 16 20:57 /mnt/batch/tasks/shared/LS_root/mounts/clusters/myinstance1/code/runs/detect/train/weights/best.pt\n",
      "\n",
      "File Size: 6.246873 MB\n"
     ]
    }
   ],
   "source": [
    "!ls -l {HOME}/runs/detect/train/weights/best.pt\n",
    "print(\"\\nFile Size:\", 6246873 / 1000000, \"MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "86c3dd2d-b724-4802-891e-9c7189fd8ae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of the model: 0.0006103515625 MB\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import sys\n",
    "\n",
    "# Load the model\n",
    "model = torch.load('runs/detect/train/weights/best.pt', map_location=torch.device('cpu'))\n",
    "\n",
    "# Get the size of the model in bytes\n",
    "model_size_bytes = sys.getsizeof(model)\n",
    "\n",
    "# Convert bytes to megabytes\n",
    "model_size_megabytes = model_size_bytes /  (1024*1024)\n",
    "\n",
    "print(f\"Size of the model: {model_size_megabytes} MB\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53ccd8b9-a223-46e8-b8b5-c7efd4ed272a",
   "metadata": {},
   "source": []
  }
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