object_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from ultralytics import YOLO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=YOLO('yolov8n.pt') #Importing YOLO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "New https://pypi.org/project/ultralytics/8.0.104 available  Update with 'pip install -U ultralytics'\n",
      "Ultralytics YOLOv8.0.103  Python-3.10.11 torch-1.12.1+cpu CPU\n",
      "WARNING  Upgrade to torch>=2.0.0 for deterministic training.\n",
      "\u001b[34m\u001b[1myolo\\engine\\trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=data.yaml, epochs=20, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=0, project=None, name=None, exist_ok=False, pretrained=False, optimizer=Adam, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=0, resume=False, amp=True, 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, 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, v5loader=False, tracker=botsort.yaml, save_dir=runs\\detect\\train5\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    752677  ultralytics.nn.modules.head.Detect           [7, [64, 128, 256]]           \n",
      "Model summary: 225 layers, 3012213 parameters, 3012197 gradients, 8.2 GFLOPs\n",
      "\n",
      "Transferred 355/355 items from pretrained weights\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs\\detect\\train5', view at http://localhost:6006/\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m Adam(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\train\\labels.cache... 448 images, 1 backgrounds, 0 corrupt: 100%|██████████| 448/448 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\valid\\labels.cache... 127 images, 0 backgrounds, 0 corrupt: 100%|██████████| 127/127 [00:00<?, ?it/s]\n",
      "Plotting labels to runs\\detect\\train5\\labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 0 dataloader workers\n",
      "Logging results to \u001b[1mruns\\detect\\train5\u001b[0m\n",
      "Starting training for 20 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "  0%|          | 0/28 [00:00<?, ?it/s]C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\torch\\amp\\autocast_mode.py:198: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
      "  warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n",
      "       1/20         0G      1.896      2.403      1.501        186        640: 100%|██████████| 28/28 [03:25<00:00,  7.33s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:19<00:00,  4.87s/it]\n",
      "                   all        127        909     0.0462      0.104     0.0553     0.0319\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       2/20         0G      1.835      2.209      1.448        173        640: 100%|██████████| 28/28 [03:31<00:00,  7.56s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:19<00:00,  4.76s/it]\n",
      "                   all        127        909      0.347      0.102     0.0584     0.0288\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       3/20         0G       1.81      2.212      1.461        158        640: 100%|██████████| 28/28 [03:12<00:00,  6.88s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:21<00:00,  5.37s/it]\n",
      "                   all        127        909      0.169      0.222      0.156     0.0714\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       4/20         0G      1.807      2.035      1.467        233        640: 100%|██████████| 28/28 [03:13<00:00,  6.89s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:20<00:00,  5.07s/it]\n",
      "                   all        127        909      0.262      0.358      0.248      0.124\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       5/20         0G      1.832      2.031      1.445        247        640: 100%|██████████| 28/28 [03:08<00:00,  6.75s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:20<00:00,  5.12s/it]\n",
      "                   all        127        909      0.406      0.314      0.288      0.127\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       6/20         0G      1.801       1.95      1.426        297        640: 100%|██████████| 28/28 [03:09<00:00,  6.76s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:20<00:00,  5.17s/it]\n",
      "                   all        127        909      0.437        0.3      0.314      0.149\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       7/20         0G      1.737      1.813      1.394        212        640: 100%|██████████| 28/28 [03:09<00:00,  6.76s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:19<00:00,  4.91s/it]\n",
      "                   all        127        909      0.502      0.432      0.438      0.213\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       8/20         0G      1.696      1.699      1.362        304        640: 100%|██████████| 28/28 [03:16<00:00,  7.03s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:24<00:00,  6.14s/it]\n",
      "                   all        127        909      0.449      0.433       0.41      0.199\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       9/20         0G      1.677      1.679      1.364        206        640: 100%|██████████| 28/28 [03:29<00:00,  7.46s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:22<00:00,  5.50s/it]\n",
      "                   all        127        909      0.487      0.434      0.413      0.208\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      10/20         0G      1.644      1.642      1.367        274        640: 100%|██████████| 28/28 [03:23<00:00,  7.28s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:21<00:00,  5.46s/it]\n",
      "                   all        127        909        0.5      0.456       0.48      0.255\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      11/20         0G      1.618      1.579      1.351        198        640: 100%|██████████| 28/28 [03:32<00:00,  7.60s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:21<00:00,  5.36s/it]\n",
      "                   all        127        909      0.593       0.54       0.56      0.304\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      12/20         0G       1.57      1.553      1.306        185        640: 100%|██████████| 28/28 [03:19<00:00,  7.13s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:19<00:00,  4.97s/it]\n",
      "                   all        127        909      0.548      0.489      0.491      0.258\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      13/20         0G      1.572      1.522       1.33        183        640: 100%|██████████| 28/28 [03:25<00:00,  7.34s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:24<00:00,  6.01s/it]\n",
      "                   all        127        909      0.642      0.514       0.56      0.303\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      14/20         0G      1.572      1.479      1.331        194        640: 100%|██████████| 28/28 [03:25<00:00,  7.35s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:26<00:00,  6.54s/it]\n",
      "                   all        127        909      0.544      0.535      0.562      0.323\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      15/20         0G      1.521      1.472      1.299        174        640: 100%|██████████| 28/28 [03:14<00:00,  6.95s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:19<00:00,  4.83s/it]\n",
      "                   all        127        909      0.647      0.551      0.587       0.32\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      16/20         0G      1.514      1.395      1.297        201        640: 100%|██████████| 28/28 [03:32<00:00,  7.61s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:19<00:00,  4.93s/it]\n",
      "                   all        127        909      0.584      0.587      0.597       0.34\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      17/20         0G      1.498       1.36      1.272        184        640: 100%|██████████| 28/28 [03:35<00:00,  7.69s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:19<00:00,  4.83s/it]\n",
      "                   all        127        909      0.681      0.597      0.622      0.356\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      18/20         0G      1.414        1.3      1.234        219        640: 100%|██████████| 28/28 [03:44<00:00,  8.01s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:25<00:00,  6.34s/it]\n",
      "                   all        127        909      0.688      0.591      0.636      0.358\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      19/20         0G      1.465      1.293      1.252        171        640: 100%|██████████| 28/28 [03:49<00:00,  8.20s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:24<00:00,  6.19s/it]\n",
      "                   all        127        909      0.679      0.593      0.652      0.368\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      20/20         0G      1.434      1.221      1.219        183        640: 100%|██████████| 28/28 [03:46<00:00,  8.10s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:23<00:00,  5.77s/it]\n",
      "                   all        127        909      0.709      0.596      0.659      0.379\n",
      "\n",
      "20 epochs completed in 1.264 hours.\n",
      "Optimizer stripped from runs\\detect\\train5\\weights\\last.pt, 6.2MB\n",
      "Optimizer stripped from runs\\detect\\train5\\weights\\best.pt, 6.2MB\n",
      "\n",
      "Validating runs\\detect\\train5\\weights\\best.pt...\n",
      "Ultralytics YOLOv8.0.103  Python-3.10.11 torch-1.12.1+cpu CPU\n",
      "Model summary (fused): 168 layers, 3007013 parameters, 0 gradients, 8.1 GFLOPs\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:16<00:00,  4.24s/it]\n",
      "                   all        127        909      0.709      0.596      0.659      0.378\n",
      "                  fish        127        459      0.797      0.623      0.743      0.423\n",
      "             jellyfish        127        155      0.731      0.877      0.872      0.469\n",
      "               penguin        127        104      0.669      0.596      0.588      0.256\n",
      "                puffin        127         74      0.775      0.279      0.428      0.192\n",
      "                 shark        127         57      0.683      0.561       0.58      0.349\n",
      "              starfish        127         27      0.659      0.667      0.686      0.469\n",
      "              stingray        127         33      0.652      0.569      0.717      0.489\n",
      "Speed: 2.2ms preprocess, 108.6ms inference, 0.0ms loss, 1.8ms postprocess per image\n",
      "Results saved to \u001b[1mruns\\detect\\train5\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "model.train(data='data.yaml', epochs=20, optimizer='Adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>train/box_loss</th>\n",
       "      <th>train/cls_loss</th>\n",
       "      <th>train/dfl_loss</th>\n",
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       "      <th>val/cls_loss</th>\n",
       "      <th>val/dfl_loss</th>\n",
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       "      <th>lr/pg2</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.8956</td>\n",
       "      <td>2.4034</td>\n",
       "      <td>1.5012</td>\n",
       "      <td>0.04624</td>\n",
       "      <td>0.10358</td>\n",
       "      <td>0.05534</td>\n",
       "      <td>0.03189</td>\n",
       "      <td>3.2461</td>\n",
       "      <td>4.7676</td>\n",
       "      <td>2.4886</td>\n",
       "      <td>0.075700</td>\n",
       "      <td>0.002700</td>\n",
       "      <td>0.002700</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.8353</td>\n",
       "      <td>2.2090</td>\n",
       "      <td>1.4481</td>\n",
       "      <td>0.34667</td>\n",
       "      <td>0.10196</td>\n",
       "      <td>0.05837</td>\n",
       "      <td>0.02877</td>\n",
       "      <td>2.3799</td>\n",
       "      <td>5.1016</td>\n",
       "      <td>1.9425</td>\n",
       "      <td>0.050228</td>\n",
       "      <td>0.005228</td>\n",
       "      <td>0.005228</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.8100</td>\n",
       "      <td>2.2118</td>\n",
       "      <td>1.4610</td>\n",
       "      <td>0.16891</td>\n",
       "      <td>0.22179</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>0.07143</td>\n",
       "      <td>2.0186</td>\n",
       "      <td>3.7559</td>\n",
       "      <td>1.6201</td>\n",
       "      <td>0.024478</td>\n",
       "      <td>0.007478</td>\n",
       "      <td>0.007478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.8073</td>\n",
       "      <td>2.0346</td>\n",
       "      <td>1.4674</td>\n",
       "      <td>0.26218</td>\n",
       "      <td>0.35846</td>\n",
       "      <td>0.24757</td>\n",
       "      <td>0.12395</td>\n",
       "      <td>1.9018</td>\n",
       "      <td>3.0407</td>\n",
       "      <td>1.5488</td>\n",
       "      <td>0.008515</td>\n",
       "      <td>0.008515</td>\n",
       "      <td>0.008515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.8318</td>\n",
       "      <td>2.0307</td>\n",
       "      <td>1.4451</td>\n",
       "      <td>0.40623</td>\n",
       "      <td>0.31411</td>\n",
       "      <td>0.28752</td>\n",
       "      <td>0.12738</td>\n",
       "      <td>1.8974</td>\n",
       "      <td>2.5947</td>\n",
       "      <td>1.5624</td>\n",
       "      <td>0.008515</td>\n",
       "      <td>0.008515</td>\n",
       "      <td>0.008515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1.8010</td>\n",
       "      <td>1.9496</td>\n",
       "      <td>1.4256</td>\n",
       "      <td>0.43656</td>\n",
       "      <td>0.30025</td>\n",
       "      <td>0.31426</td>\n",
       "      <td>0.14902</td>\n",
       "      <td>1.9014</td>\n",
       "      <td>2.7516</td>\n",
       "      <td>1.5139</td>\n",
       "      <td>0.008020</td>\n",
       "      <td>0.008020</td>\n",
       "      <td>0.008020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1.7367</td>\n",
       "      <td>1.8131</td>\n",
       "      <td>1.3942</td>\n",
       "      <td>0.50225</td>\n",
       "      <td>0.43214</td>\n",
       "      <td>0.43821</td>\n",
       "      <td>0.21345</td>\n",
       "      <td>1.7450</td>\n",
       "      <td>1.9157</td>\n",
       "      <td>1.4537</td>\n",
       "      <td>0.007525</td>\n",
       "      <td>0.007525</td>\n",
       "      <td>0.007525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1.6960</td>\n",
       "      <td>1.6992</td>\n",
       "      <td>1.3617</td>\n",
       "      <td>0.44924</td>\n",
       "      <td>0.43281</td>\n",
       "      <td>0.41018</td>\n",
       "      <td>0.19855</td>\n",
       "      <td>1.7836</td>\n",
       "      <td>2.1626</td>\n",
       "      <td>1.4822</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.007030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1.6770</td>\n",
       "      <td>1.6794</td>\n",
       "      <td>1.3641</td>\n",
       "      <td>0.48680</td>\n",
       "      <td>0.43373</td>\n",
       "      <td>0.41276</td>\n",
       "      <td>0.20774</td>\n",
       "      <td>1.7446</td>\n",
       "      <td>1.9237</td>\n",
       "      <td>1.4532</td>\n",
       "      <td>0.006535</td>\n",
       "      <td>0.006535</td>\n",
       "      <td>0.006535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1.6438</td>\n",
       "      <td>1.6419</td>\n",
       "      <td>1.3668</td>\n",
       "      <td>0.50025</td>\n",
       "      <td>0.45579</td>\n",
       "      <td>0.48028</td>\n",
       "      <td>0.25515</td>\n",
       "      <td>1.7277</td>\n",
       "      <td>1.7673</td>\n",
       "      <td>1.4719</td>\n",
       "      <td>0.006040</td>\n",
       "      <td>0.006040</td>\n",
       "      <td>0.006040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1.6176</td>\n",
       "      <td>1.5793</td>\n",
       "      <td>1.3511</td>\n",
       "      <td>0.59290</td>\n",
       "      <td>0.53955</td>\n",
       "      <td>0.55972</td>\n",
       "      <td>0.30442</td>\n",
       "      <td>1.6929</td>\n",
       "      <td>1.5811</td>\n",
       "      <td>1.4329</td>\n",
       "      <td>0.005545</td>\n",
       "      <td>0.005545</td>\n",
       "      <td>0.005545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1.5701</td>\n",
       "      <td>1.5533</td>\n",
       "      <td>1.3064</td>\n",
       "      <td>0.54811</td>\n",
       "      <td>0.48875</td>\n",
       "      <td>0.49136</td>\n",
       "      <td>0.25795</td>\n",
       "      <td>1.6947</td>\n",
       "      <td>1.7156</td>\n",
       "      <td>1.4177</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>0.005050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>1.5715</td>\n",
       "      <td>1.5221</td>\n",
       "      <td>1.3304</td>\n",
       "      <td>0.64157</td>\n",
       "      <td>0.51449</td>\n",
       "      <td>0.55984</td>\n",
       "      <td>0.30337</td>\n",
       "      <td>1.6746</td>\n",
       "      <td>1.5625</td>\n",
       "      <td>1.4145</td>\n",
       "      <td>0.004555</td>\n",
       "      <td>0.004555</td>\n",
       "      <td>0.004555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>1.5722</td>\n",
       "      <td>1.4789</td>\n",
       "      <td>1.3314</td>\n",
       "      <td>0.54407</td>\n",
       "      <td>0.53474</td>\n",
       "      <td>0.56176</td>\n",
       "      <td>0.32269</td>\n",
       "      <td>1.5939</td>\n",
       "      <td>1.5381</td>\n",
       "      <td>1.3677</td>\n",
       "      <td>0.004060</td>\n",
       "      <td>0.004060</td>\n",
       "      <td>0.004060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>1.5213</td>\n",
       "      <td>1.4719</td>\n",
       "      <td>1.2991</td>\n",
       "      <td>0.64733</td>\n",
       "      <td>0.55088</td>\n",
       "      <td>0.58659</td>\n",
       "      <td>0.32042</td>\n",
       "      <td>1.6175</td>\n",
       "      <td>1.4746</td>\n",
       "      <td>1.3965</td>\n",
       "      <td>0.003565</td>\n",
       "      <td>0.003565</td>\n",
       "      <td>0.003565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1.5138</td>\n",
       "      <td>1.3951</td>\n",
       "      <td>1.2966</td>\n",
       "      <td>0.58371</td>\n",
       "      <td>0.58745</td>\n",
       "      <td>0.59733</td>\n",
       "      <td>0.33972</td>\n",
       "      <td>1.5906</td>\n",
       "      <td>1.4092</td>\n",
       "      <td>1.3675</td>\n",
       "      <td>0.003070</td>\n",
       "      <td>0.003070</td>\n",
       "      <td>0.003070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>1.4982</td>\n",
       "      <td>1.3604</td>\n",
       "      <td>1.2715</td>\n",
       "      <td>0.68073</td>\n",
       "      <td>0.59705</td>\n",
       "      <td>0.62152</td>\n",
       "      <td>0.35583</td>\n",
       "      <td>1.5688</td>\n",
       "      <td>1.3916</td>\n",
       "      <td>1.3439</td>\n",
       "      <td>0.002575</td>\n",
       "      <td>0.002575</td>\n",
       "      <td>0.002575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>1.4145</td>\n",
       "      <td>1.3004</td>\n",
       "      <td>1.2344</td>\n",
       "      <td>0.68785</td>\n",
       "      <td>0.59131</td>\n",
       "      <td>0.63601</td>\n",
       "      <td>0.35795</td>\n",
       "      <td>1.5538</td>\n",
       "      <td>1.3793</td>\n",
       "      <td>1.3554</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.002080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>1.4651</td>\n",
       "      <td>1.2935</td>\n",
       "      <td>1.2519</td>\n",
       "      <td>0.67873</td>\n",
       "      <td>0.59297</td>\n",
       "      <td>0.65208</td>\n",
       "      <td>0.36782</td>\n",
       "      <td>1.5596</td>\n",
       "      <td>1.3714</td>\n",
       "      <td>1.3315</td>\n",
       "      <td>0.001585</td>\n",
       "      <td>0.001585</td>\n",
       "      <td>0.001585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>1.4343</td>\n",
       "      <td>1.2210</td>\n",
       "      <td>1.2188</td>\n",
       "      <td>0.70917</td>\n",
       "      <td>0.59587</td>\n",
       "      <td>0.65915</td>\n",
       "      <td>0.37859</td>\n",
       "      <td>1.5190</td>\n",
       "      <td>1.2897</td>\n",
       "      <td>1.3108</td>\n",
       "      <td>0.001090</td>\n",
       "      <td>0.001090</td>\n",
       "      <td>0.001090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      epoch           train/box_loss           train/cls_loss  \\\n",
       "0                         0                   1.8956                   2.4034   \n",
       "1                         1                   1.8353                   2.2090   \n",
       "2                         2                   1.8100                   2.2118   \n",
       "3                         3                   1.8073                   2.0346   \n",
       "4                         4                   1.8318                   2.0307   \n",
       "5                         5                   1.8010                   1.9496   \n",
       "6                         6                   1.7367                   1.8131   \n",
       "7                         7                   1.6960                   1.6992   \n",
       "8                         8                   1.6770                   1.6794   \n",
       "9                         9                   1.6438                   1.6419   \n",
       "10                       10                   1.6176                   1.5793   \n",
       "11                       11                   1.5701                   1.5533   \n",
       "12                       12                   1.5715                   1.5221   \n",
       "13                       13                   1.5722                   1.4789   \n",
       "14                       14                   1.5213                   1.4719   \n",
       "15                       15                   1.5138                   1.3951   \n",
       "16                       16                   1.4982                   1.3604   \n",
       "17                       17                   1.4145                   1.3004   \n",
       "18                       18                   1.4651                   1.2935   \n",
       "19                       19                   1.4343                   1.2210   \n",
       "\n",
       "             train/dfl_loss     metrics/precision(B)        metrics/recall(B)  \\\n",
       "0                    1.5012                  0.04624                  0.10358   \n",
       "1                    1.4481                  0.34667                  0.10196   \n",
       "2                    1.4610                  0.16891                  0.22179   \n",
       "3                    1.4674                  0.26218                  0.35846   \n",
       "4                    1.4451                  0.40623                  0.31411   \n",
       "5                    1.4256                  0.43656                  0.30025   \n",
       "6                    1.3942                  0.50225                  0.43214   \n",
       "7                    1.3617                  0.44924                  0.43281   \n",
       "8                    1.3641                  0.48680                  0.43373   \n",
       "9                    1.3668                  0.50025                  0.45579   \n",
       "10                   1.3511                  0.59290                  0.53955   \n",
       "11                   1.3064                  0.54811                  0.48875   \n",
       "12                   1.3304                  0.64157                  0.51449   \n",
       "13                   1.3314                  0.54407                  0.53474   \n",
       "14                   1.2991                  0.64733                  0.55088   \n",
       "15                   1.2966                  0.58371                  0.58745   \n",
       "16                   1.2715                  0.68073                  0.59705   \n",
       "17                   1.2344                  0.68785                  0.59131   \n",
       "18                   1.2519                  0.67873                  0.59297   \n",
       "19                   1.2188                  0.70917                  0.59587   \n",
       "\n",
       "           metrics/mAP50(B)      metrics/mAP50-95(B)             val/box_loss  \\\n",
       "0                   0.05534                  0.03189                   3.2461   \n",
       "1                   0.05837                  0.02877                   2.3799   \n",
       "2                   0.15625                  0.07143                   2.0186   \n",
       "3                   0.24757                  0.12395                   1.9018   \n",
       "4                   0.28752                  0.12738                   1.8974   \n",
       "5                   0.31426                  0.14902                   1.9014   \n",
       "6                   0.43821                  0.21345                   1.7450   \n",
       "7                   0.41018                  0.19855                   1.7836   \n",
       "8                   0.41276                  0.20774                   1.7446   \n",
       "9                   0.48028                  0.25515                   1.7277   \n",
       "10                  0.55972                  0.30442                   1.6929   \n",
       "11                  0.49136                  0.25795                   1.6947   \n",
       "12                  0.55984                  0.30337                   1.6746   \n",
       "13                  0.56176                  0.32269                   1.5939   \n",
       "14                  0.58659                  0.32042                   1.6175   \n",
       "15                  0.59733                  0.33972                   1.5906   \n",
       "16                  0.62152                  0.35583                   1.5688   \n",
       "17                  0.63601                  0.35795                   1.5538   \n",
       "18                  0.65208                  0.36782                   1.5596   \n",
       "19                  0.65915                  0.37859                   1.5190   \n",
       "\n",
       "               val/cls_loss             val/dfl_loss                   lr/pg0  \\\n",
       "0                    4.7676                   2.4886                 0.075700   \n",
       "1                    5.1016                   1.9425                 0.050228   \n",
       "2                    3.7559                   1.6201                 0.024478   \n",
       "3                    3.0407                   1.5488                 0.008515   \n",
       "4                    2.5947                   1.5624                 0.008515   \n",
       "5                    2.7516                   1.5139                 0.008020   \n",
       "6                    1.9157                   1.4537                 0.007525   \n",
       "7                    2.1626                   1.4822                 0.007030   \n",
       "8                    1.9237                   1.4532                 0.006535   \n",
       "9                    1.7673                   1.4719                 0.006040   \n",
       "10                   1.5811                   1.4329                 0.005545   \n",
       "11                   1.7156                   1.4177                 0.005050   \n",
       "12                   1.5625                   1.4145                 0.004555   \n",
       "13                   1.5381                   1.3677                 0.004060   \n",
       "14                   1.4746                   1.3965                 0.003565   \n",
       "15                   1.4092                   1.3675                 0.003070   \n",
       "16                   1.3916                   1.3439                 0.002575   \n",
       "17                   1.3793                   1.3554                 0.002080   \n",
       "18                   1.3714                   1.3315                 0.001585   \n",
       "19                   1.2897                   1.3108                 0.001090   \n",
       "\n",
       "                     lr/pg1                   lr/pg2  \n",
       "0                  0.002700                 0.002700  \n",
       "1                  0.005228                 0.005228  \n",
       "2                  0.007478                 0.007478  \n",
       "3                  0.008515                 0.008515  \n",
       "4                  0.008515                 0.008515  \n",
       "5                  0.008020                 0.008020  \n",
       "6                  0.007525                 0.007525  \n",
       "7                  0.007030                 0.007030  \n",
       "8                  0.006535                 0.006535  \n",
       "9                  0.006040                 0.006040  \n",
       "10                 0.005545                 0.005545  \n",
       "11                 0.005050                 0.005050  \n",
       "12                 0.004555                 0.004555  \n",
       "13                 0.004060                 0.004060  \n",
       "14                 0.003565                 0.003565  \n",
       "15                 0.003070                 0.003070  \n",
       "16                 0.002575                 0.002575  \n",
       "17                 0.002080                 0.002080  \n",
       "18                 0.001585                 0.001585  \n",
       "19                 0.001090                 0.001090  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "performance1=pd.read_csv('results.csv')\n",
    "performance1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unexpected exception formatting exception. Falling back to standard exception\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 3442, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Temp\\ipykernel_25940\\927757553.py\", line 1, in <module>\n",
      "    model=YOLO('train5/weights/best.pt') #Importing YOLO\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ultralytics\\yolo\\engine\\model.py\", line 107, in __init__\n",
      "    self._load(model, task)\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ultralytics\\yolo\\engine\\model.py\", line 156, in _load\n",
      "    self.model, self.ckpt = attempt_load_one_weight(weights)\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ultralytics\\nn\\tasks.py\", line 437, in attempt_load_one_weight\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\ultralytics\\nn\\tasks.py\", line 377, in torch_safe_load\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\torch\\serialization.py\", line 699, in load\n",
      "    with _open_file_like(f, 'rb') as opened_file:\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\torch\\serialization.py\", line 230, in _open_file_like\n",
      "    return _open_file(name_or_buffer, mode)\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\torch\\serialization.py\", line 211, in __init__\n",
      "    super(_open_file, self).__init__(open(name, mode))\n",
      "FileNotFoundError: [Errno 2] No such file or directory: 'train5\\\\weights\\\\best.pt'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\interactiveshell.py\", line 2057, in showtraceback\n",
      "    stb = self.InteractiveTB.structured_traceback(\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\ultratb.py\", line 1118, in structured_traceback\n",
      "    return FormattedTB.structured_traceback(\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\ultratb.py\", line 1012, in structured_traceback\n",
      "    return VerboseTB.structured_traceback(\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\ultratb.py\", line 865, in structured_traceback\n",
      "    formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\ultratb.py\", line 818, in format_exception_as_a_whole\n",
      "    frames.append(self.format_record(r))\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\IPython\\core\\ultratb.py\", line 736, in format_record\n",
      "    result += ''.join(_format_traceback_lines(frame_info.lines, Colors, self.has_colors, lvals))\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\stack_data\\utils.py\", line 144, in cached_property_wrapper\n",
      "    value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\stack_data\\core.py\", line 734, in lines\n",
      "    pieces = self.included_pieces\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\stack_data\\utils.py\", line 144, in cached_property_wrapper\n",
      "    value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\stack_data\\core.py\", line 681, in included_pieces\n",
      "    pos = scope_pieces.index(self.executing_piece)\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\stack_data\\utils.py\", line 144, in cached_property_wrapper\n",
      "    value = obj.__dict__[self.func.__name__] = self.func(obj)\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\stack_data\\core.py\", line 660, in executing_piece\n",
      "    return only(\n",
      "  File \"C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\executing\\executing.py\", line 190, in only\n",
      "    raise NotOneValueFound('Expected one value, found 0')\n",
      "executing.executing.NotOneValueFound: Expected one value, found 0\n"
     ]
    }
   ],
   "source": [
    "model=YOLO('train5/weights/best.pt') #Loading the current model state "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "New https://pypi.org/project/ultralytics/8.0.104 available  Update with 'pip install -U ultralytics'\n",
      "Ultralytics YOLOv8.0.103  Python-3.10.11 torch-1.12.1+cpu CPU\n",
      "WARNING  Upgrade to torch>=2.0.0 for deterministic training.\n",
      "\u001b[34m\u001b[1myolo\\engine\\trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=data.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=0, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=0, resume=False, amp=True, 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, 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=1, 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, v5loader=False, tracker=botsort.yaml, save_dir=runs\\detect\\train7\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    752677  ultralytics.nn.modules.head.Detect           [7, [64, 128, 256]]           \n",
      "Model summary: 225 layers, 3012213 parameters, 3012197 gradients, 8.2 GFLOPs\n",
      "\n",
      "Transferred 355/355 items from pretrained weights\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs\\detect\\train7', view at http://localhost:6006/\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\train\\labels.cache... 448 images, 1 backgrounds, 0 corrupt: 100%|██████████| 448/448 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\valid\\labels.cache... 127 images, 0 backgrounds, 0 corrupt: 100%|██████████| 127/127 [00:00<?, ?it/s]\n",
      "Plotting labels to runs\\detect\\train7\\labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 0 dataloader workers\n",
      "Logging results to \u001b[1mruns\\detect\\train7\u001b[0m\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "  0%|          | 0/28 [00:00<?, ?it/s]C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\torch\\amp\\autocast_mode.py:198: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
      "  warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n",
      "       1/10         0G      1.367       1.21      1.215        186        640: 100%|██████████| 28/28 [03:28<00:00,  7.45s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:25<00:00,  6.36s/it]\n",
      "                   all        127        909      0.717      0.612      0.657      0.376\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       2/10         0G      1.373      1.172      1.198        173        640: 100%|██████████| 28/28 [03:49<00:00,  8.21s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:25<00:00,  6.32s/it]\n",
      "                   all        127        909      0.709      0.636       0.68      0.389\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       3/10         0G      1.358      1.186      1.203        158        640: 100%|██████████| 28/28 [03:58<00:00,  8.53s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:24<00:00,  6.24s/it]\n",
      "                   all        127        909      0.721      0.607       0.66      0.378\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       4/10         0G      1.379      1.177      1.212        233        640: 100%|██████████| 28/28 [03:54<00:00,  8.39s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:25<00:00,  6.42s/it]\n",
      "                   all        127        909      0.735      0.613      0.673      0.386\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       5/10         0G      1.405      1.225      1.207        247        640: 100%|██████████| 28/28 [03:54<00:00,  8.38s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:24<00:00,  6.11s/it]\n",
      "                   all        127        909      0.719       0.57      0.643       0.37\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       6/10         0G      1.414      1.207      1.197        297        640: 100%|██████████| 28/28 [03:57<00:00,  8.49s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:25<00:00,  6.28s/it]\n",
      "                   all        127        909      0.729      0.605      0.666      0.393\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       7/10         0G      1.397      1.179        1.2        212        640: 100%|██████████| 28/28 [04:01<00:00,  8.64s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:25<00:00,  6.29s/it]\n",
      "                   all        127        909      0.694      0.582      0.646      0.377\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       8/10         0G      1.399      1.148      1.195        304        640: 100%|██████████| 28/28 [04:00<00:00,  8.60s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:24<00:00,  6.22s/it]\n",
      "                   all        127        909      0.633       0.62      0.648      0.381\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       9/10         0G      1.383      1.156      1.182        206        640: 100%|██████████| 28/28 [03:28<00:00,  7.44s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:19<00:00,  4.80s/it]\n",
      "                   all        127        909      0.676      0.611      0.662       0.38\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      10/10         0G      1.353      1.133       1.19        274        640: 100%|██████████| 28/28 [03:31<00:00,  7.56s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:21<00:00,  5.31s/it]\n",
      "                   all        127        909      0.717      0.636      0.665      0.384\n",
      "\n",
      "10 epochs completed in 0.704 hours.\n",
      "Optimizer stripped from runs\\detect\\train7\\weights\\last.pt, 6.2MB\n",
      "Optimizer stripped from runs\\detect\\train7\\weights\\best.pt, 6.2MB\n",
      "\n",
      "Validating runs\\detect\\train7\\weights\\best.pt...\n",
      "Ultralytics YOLOv8.0.103  Python-3.10.11 torch-1.12.1+cpu CPU\n",
      "Model summary (fused): 168 layers, 3007013 parameters, 0 gradients, 8.1 GFLOPs\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:16<00:00,  4.15s/it]\n",
      "                   all        127        909      0.729      0.605      0.666      0.393\n",
      "                  fish        127        459      0.767      0.647      0.729      0.407\n",
      "             jellyfish        127        155      0.812      0.865      0.896      0.516\n",
      "               penguin        127        104      0.588      0.673      0.577      0.259\n",
      "                puffin        127         74      0.651      0.324      0.463      0.234\n",
      "                 shark        127         57      0.725      0.416      0.522      0.291\n",
      "              starfish        127         27       0.64      0.704      0.708      0.498\n",
      "              stingray        127         33      0.919      0.606       0.77      0.544\n",
      "Speed: 2.2ms preprocess, 106.8ms inference, 0.0ms loss, 1.6ms postprocess per image\n",
      "Results saved to \u001b[1mruns\\detect\\train7\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "model.train(data='data.yaml', epochs=10, optimizer='SGD', momentum=1) #further improving the model using the current model state "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>train/box_loss</th>\n",
       "      <th>train/cls_loss</th>\n",
       "      <th>train/dfl_loss</th>\n",
       "      <th>metrics/precision(B)</th>\n",
       "      <th>metrics/recall(B)</th>\n",
       "      <th>metrics/mAP50(B)</th>\n",
       "      <th>metrics/mAP50-95(B)</th>\n",
       "      <th>val/box_loss</th>\n",
       "      <th>val/cls_loss</th>\n",
       "      <th>val/dfl_loss</th>\n",
       "      <th>lr/pg0</th>\n",
       "      <th>lr/pg1</th>\n",
       "      <th>lr/pg2</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.3666</td>\n",
       "      <td>1.2103</td>\n",
       "      <td>1.2149</td>\n",
       "      <td>0.71721</td>\n",
       "      <td>0.61240</td>\n",
       "      <td>0.65659</td>\n",
       "      <td>0.37551</td>\n",
       "      <td>1.5175</td>\n",
       "      <td>1.2911</td>\n",
       "      <td>1.3051</td>\n",
       "      <td>0.075700</td>\n",
       "      <td>0.002700</td>\n",
       "      <td>0.002700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.3726</td>\n",
       "      <td>1.1723</td>\n",
       "      <td>1.1976</td>\n",
       "      <td>0.70854</td>\n",
       "      <td>0.63572</td>\n",
       "      <td>0.68001</td>\n",
       "      <td>0.38893</td>\n",
       "      <td>1.5354</td>\n",
       "      <td>1.2757</td>\n",
       "      <td>1.3172</td>\n",
       "      <td>0.049955</td>\n",
       "      <td>0.004955</td>\n",
       "      <td>0.004955</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.3577</td>\n",
       "      <td>1.1863</td>\n",
       "      <td>1.2034</td>\n",
       "      <td>0.72077</td>\n",
       "      <td>0.60671</td>\n",
       "      <td>0.66027</td>\n",
       "      <td>0.37848</td>\n",
       "      <td>1.5255</td>\n",
       "      <td>1.2992</td>\n",
       "      <td>1.3173</td>\n",
       "      <td>0.023657</td>\n",
       "      <td>0.006657</td>\n",
       "      <td>0.006657</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.3785</td>\n",
       "      <td>1.1773</td>\n",
       "      <td>1.2119</td>\n",
       "      <td>0.73520</td>\n",
       "      <td>0.61255</td>\n",
       "      <td>0.67306</td>\n",
       "      <td>0.38587</td>\n",
       "      <td>1.5279</td>\n",
       "      <td>1.3076</td>\n",
       "      <td>1.3277</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.007030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.4052</td>\n",
       "      <td>1.2251</td>\n",
       "      <td>1.2066</td>\n",
       "      <td>0.71895</td>\n",
       "      <td>0.56986</td>\n",
       "      <td>0.64291</td>\n",
       "      <td>0.36972</td>\n",
       "      <td>1.5772</td>\n",
       "      <td>1.3747</td>\n",
       "      <td>1.3527</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.007030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1.4142</td>\n",
       "      <td>1.2066</td>\n",
       "      <td>1.1968</td>\n",
       "      <td>0.72897</td>\n",
       "      <td>0.60502</td>\n",
       "      <td>0.66633</td>\n",
       "      <td>0.39266</td>\n",
       "      <td>1.5365</td>\n",
       "      <td>1.3027</td>\n",
       "      <td>1.3301</td>\n",
       "      <td>0.006040</td>\n",
       "      <td>0.006040</td>\n",
       "      <td>0.006040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1.3974</td>\n",
       "      <td>1.1789</td>\n",
       "      <td>1.2001</td>\n",
       "      <td>0.69409</td>\n",
       "      <td>0.58250</td>\n",
       "      <td>0.64640</td>\n",
       "      <td>0.37666</td>\n",
       "      <td>1.5574</td>\n",
       "      <td>1.3232</td>\n",
       "      <td>1.3538</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>0.005050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1.3987</td>\n",
       "      <td>1.1481</td>\n",
       "      <td>1.1954</td>\n",
       "      <td>0.63278</td>\n",
       "      <td>0.61977</td>\n",
       "      <td>0.64847</td>\n",
       "      <td>0.38071</td>\n",
       "      <td>1.5567</td>\n",
       "      <td>1.3184</td>\n",
       "      <td>1.3407</td>\n",
       "      <td>0.004060</td>\n",
       "      <td>0.004060</td>\n",
       "      <td>0.004060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1.3827</td>\n",
       "      <td>1.1557</td>\n",
       "      <td>1.1818</td>\n",
       "      <td>0.67648</td>\n",
       "      <td>0.61099</td>\n",
       "      <td>0.66174</td>\n",
       "      <td>0.37997</td>\n",
       "      <td>1.5582</td>\n",
       "      <td>1.3043</td>\n",
       "      <td>1.3387</td>\n",
       "      <td>0.003070</td>\n",
       "      <td>0.003070</td>\n",
       "      <td>0.003070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1.3530</td>\n",
       "      <td>1.1330</td>\n",
       "      <td>1.1903</td>\n",
       "      <td>0.71699</td>\n",
       "      <td>0.63608</td>\n",
       "      <td>0.66461</td>\n",
       "      <td>0.38439</td>\n",
       "      <td>1.5584</td>\n",
       "      <td>1.2728</td>\n",
       "      <td>1.3378</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.002080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     epoch           train/box_loss           train/cls_loss  \\\n",
       "0                        0                   1.3666                   1.2103   \n",
       "1                        1                   1.3726                   1.1723   \n",
       "2                        2                   1.3577                   1.1863   \n",
       "3                        3                   1.3785                   1.1773   \n",
       "4                        4                   1.4052                   1.2251   \n",
       "5                        5                   1.4142                   1.2066   \n",
       "6                        6                   1.3974                   1.1789   \n",
       "7                        7                   1.3987                   1.1481   \n",
       "8                        8                   1.3827                   1.1557   \n",
       "9                        9                   1.3530                   1.1330   \n",
       "\n",
       "            train/dfl_loss     metrics/precision(B)        metrics/recall(B)  \\\n",
       "0                   1.2149                  0.71721                  0.61240   \n",
       "1                   1.1976                  0.70854                  0.63572   \n",
       "2                   1.2034                  0.72077                  0.60671   \n",
       "3                   1.2119                  0.73520                  0.61255   \n",
       "4                   1.2066                  0.71895                  0.56986   \n",
       "5                   1.1968                  0.72897                  0.60502   \n",
       "6                   1.2001                  0.69409                  0.58250   \n",
       "7                   1.1954                  0.63278                  0.61977   \n",
       "8                   1.1818                  0.67648                  0.61099   \n",
       "9                   1.1903                  0.71699                  0.63608   \n",
       "\n",
       "          metrics/mAP50(B)      metrics/mAP50-95(B)             val/box_loss  \\\n",
       "0                  0.65659                  0.37551                   1.5175   \n",
       "1                  0.68001                  0.38893                   1.5354   \n",
       "2                  0.66027                  0.37848                   1.5255   \n",
       "3                  0.67306                  0.38587                   1.5279   \n",
       "4                  0.64291                  0.36972                   1.5772   \n",
       "5                  0.66633                  0.39266                   1.5365   \n",
       "6                  0.64640                  0.37666                   1.5574   \n",
       "7                  0.64847                  0.38071                   1.5567   \n",
       "8                  0.66174                  0.37997                   1.5582   \n",
       "9                  0.66461                  0.38439                   1.5584   \n",
       "\n",
       "              val/cls_loss             val/dfl_loss                   lr/pg0  \\\n",
       "0                   1.2911                   1.3051                 0.075700   \n",
       "1                   1.2757                   1.3172                 0.049955   \n",
       "2                   1.2992                   1.3173                 0.023657   \n",
       "3                   1.3076                   1.3277                 0.007030   \n",
       "4                   1.3747                   1.3527                 0.007030   \n",
       "5                   1.3027                   1.3301                 0.006040   \n",
       "6                   1.3232                   1.3538                 0.005050   \n",
       "7                   1.3184                   1.3407                 0.004060   \n",
       "8                   1.3043                   1.3387                 0.003070   \n",
       "9                   1.2728                   1.3378                 0.002080   \n",
       "\n",
       "                    lr/pg1                   lr/pg2  \n",
       "0                 0.002700                 0.002700  \n",
       "1                 0.004955                 0.004955  \n",
       "2                 0.006657                 0.006657  \n",
       "3                 0.007030                 0.007030  \n",
       "4                 0.007030                 0.007030  \n",
       "5                 0.006040                 0.006040  \n",
       "6                 0.005050                 0.005050  \n",
       "7                 0.004060                 0.004060  \n",
       "8                 0.003070                 0.003070  \n",
       "9                 0.002080                 0.002080  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "performance2=pd.read_csv('results copy.csv')\n",
    "performance2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>train/box_loss</th>\n",
       "      <th>train/cls_loss</th>\n",
       "      <th>train/dfl_loss</th>\n",
       "      <th>metrics/precision(B)</th>\n",
       "      <th>metrics/recall(B)</th>\n",
       "      <th>metrics/mAP50(B)</th>\n",
       "      <th>metrics/mAP50-95(B)</th>\n",
       "      <th>val/box_loss</th>\n",
       "      <th>val/cls_loss</th>\n",
       "      <th>val/dfl_loss</th>\n",
       "      <th>lr/pg0</th>\n",
       "      <th>lr/pg1</th>\n",
       "      <th>lr/pg2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.8956</td>\n",
       "      <td>2.4034</td>\n",
       "      <td>1.5012</td>\n",
       "      <td>0.04624</td>\n",
       "      <td>0.10358</td>\n",
       "      <td>0.05534</td>\n",
       "      <td>0.03189</td>\n",
       "      <td>3.2461</td>\n",
       "      <td>4.7676</td>\n",
       "      <td>2.4886</td>\n",
       "      <td>0.075700</td>\n",
       "      <td>0.002700</td>\n",
       "      <td>0.002700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.8353</td>\n",
       "      <td>2.2090</td>\n",
       "      <td>1.4481</td>\n",
       "      <td>0.34667</td>\n",
       "      <td>0.10196</td>\n",
       "      <td>0.05837</td>\n",
       "      <td>0.02877</td>\n",
       "      <td>2.3799</td>\n",
       "      <td>5.1016</td>\n",
       "      <td>1.9425</td>\n",
       "      <td>0.050228</td>\n",
       "      <td>0.005228</td>\n",
       "      <td>0.005228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.8100</td>\n",
       "      <td>2.2118</td>\n",
       "      <td>1.4610</td>\n",
       "      <td>0.16891</td>\n",
       "      <td>0.22179</td>\n",
       "      <td>0.15625</td>\n",
       "      <td>0.07143</td>\n",
       "      <td>2.0186</td>\n",
       "      <td>3.7559</td>\n",
       "      <td>1.6201</td>\n",
       "      <td>0.024478</td>\n",
       "      <td>0.007478</td>\n",
       "      <td>0.007478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.8073</td>\n",
       "      <td>2.0346</td>\n",
       "      <td>1.4674</td>\n",
       "      <td>0.26218</td>\n",
       "      <td>0.35846</td>\n",
       "      <td>0.24757</td>\n",
       "      <td>0.12395</td>\n",
       "      <td>1.9018</td>\n",
       "      <td>3.0407</td>\n",
       "      <td>1.5488</td>\n",
       "      <td>0.008515</td>\n",
       "      <td>0.008515</td>\n",
       "      <td>0.008515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.8318</td>\n",
       "      <td>2.0307</td>\n",
       "      <td>1.4451</td>\n",
       "      <td>0.40623</td>\n",
       "      <td>0.31411</td>\n",
       "      <td>0.28752</td>\n",
       "      <td>0.12738</td>\n",
       "      <td>1.8974</td>\n",
       "      <td>2.5947</td>\n",
       "      <td>1.5624</td>\n",
       "      <td>0.008515</td>\n",
       "      <td>0.008515</td>\n",
       "      <td>0.008515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1.8010</td>\n",
       "      <td>1.9496</td>\n",
       "      <td>1.4256</td>\n",
       "      <td>0.43656</td>\n",
       "      <td>0.30025</td>\n",
       "      <td>0.31426</td>\n",
       "      <td>0.14902</td>\n",
       "      <td>1.9014</td>\n",
       "      <td>2.7516</td>\n",
       "      <td>1.5139</td>\n",
       "      <td>0.008020</td>\n",
       "      <td>0.008020</td>\n",
       "      <td>0.008020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1.7367</td>\n",
       "      <td>1.8131</td>\n",
       "      <td>1.3942</td>\n",
       "      <td>0.50225</td>\n",
       "      <td>0.43214</td>\n",
       "      <td>0.43821</td>\n",
       "      <td>0.21345</td>\n",
       "      <td>1.7450</td>\n",
       "      <td>1.9157</td>\n",
       "      <td>1.4537</td>\n",
       "      <td>0.007525</td>\n",
       "      <td>0.007525</td>\n",
       "      <td>0.007525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1.6960</td>\n",
       "      <td>1.6992</td>\n",
       "      <td>1.3617</td>\n",
       "      <td>0.44924</td>\n",
       "      <td>0.43281</td>\n",
       "      <td>0.41018</td>\n",
       "      <td>0.19855</td>\n",
       "      <td>1.7836</td>\n",
       "      <td>2.1626</td>\n",
       "      <td>1.4822</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.007030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1.6770</td>\n",
       "      <td>1.6794</td>\n",
       "      <td>1.3641</td>\n",
       "      <td>0.48680</td>\n",
       "      <td>0.43373</td>\n",
       "      <td>0.41276</td>\n",
       "      <td>0.20774</td>\n",
       "      <td>1.7446</td>\n",
       "      <td>1.9237</td>\n",
       "      <td>1.4532</td>\n",
       "      <td>0.006535</td>\n",
       "      <td>0.006535</td>\n",
       "      <td>0.006535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1.6438</td>\n",
       "      <td>1.6419</td>\n",
       "      <td>1.3668</td>\n",
       "      <td>0.50025</td>\n",
       "      <td>0.45579</td>\n",
       "      <td>0.48028</td>\n",
       "      <td>0.25515</td>\n",
       "      <td>1.7277</td>\n",
       "      <td>1.7673</td>\n",
       "      <td>1.4719</td>\n",
       "      <td>0.006040</td>\n",
       "      <td>0.006040</td>\n",
       "      <td>0.006040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1.6176</td>\n",
       "      <td>1.5793</td>\n",
       "      <td>1.3511</td>\n",
       "      <td>0.59290</td>\n",
       "      <td>0.53955</td>\n",
       "      <td>0.55972</td>\n",
       "      <td>0.30442</td>\n",
       "      <td>1.6929</td>\n",
       "      <td>1.5811</td>\n",
       "      <td>1.4329</td>\n",
       "      <td>0.005545</td>\n",
       "      <td>0.005545</td>\n",
       "      <td>0.005545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1.5701</td>\n",
       "      <td>1.5533</td>\n",
       "      <td>1.3064</td>\n",
       "      <td>0.54811</td>\n",
       "      <td>0.48875</td>\n",
       "      <td>0.49136</td>\n",
       "      <td>0.25795</td>\n",
       "      <td>1.6947</td>\n",
       "      <td>1.7156</td>\n",
       "      <td>1.4177</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>0.005050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>1.5715</td>\n",
       "      <td>1.5221</td>\n",
       "      <td>1.3304</td>\n",
       "      <td>0.64157</td>\n",
       "      <td>0.51449</td>\n",
       "      <td>0.55984</td>\n",
       "      <td>0.30337</td>\n",
       "      <td>1.6746</td>\n",
       "      <td>1.5625</td>\n",
       "      <td>1.4145</td>\n",
       "      <td>0.004555</td>\n",
       "      <td>0.004555</td>\n",
       "      <td>0.004555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>1.5722</td>\n",
       "      <td>1.4789</td>\n",
       "      <td>1.3314</td>\n",
       "      <td>0.54407</td>\n",
       "      <td>0.53474</td>\n",
       "      <td>0.56176</td>\n",
       "      <td>0.32269</td>\n",
       "      <td>1.5939</td>\n",
       "      <td>1.5381</td>\n",
       "      <td>1.3677</td>\n",
       "      <td>0.004060</td>\n",
       "      <td>0.004060</td>\n",
       "      <td>0.004060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>1.5213</td>\n",
       "      <td>1.4719</td>\n",
       "      <td>1.2991</td>\n",
       "      <td>0.64733</td>\n",
       "      <td>0.55088</td>\n",
       "      <td>0.58659</td>\n",
       "      <td>0.32042</td>\n",
       "      <td>1.6175</td>\n",
       "      <td>1.4746</td>\n",
       "      <td>1.3965</td>\n",
       "      <td>0.003565</td>\n",
       "      <td>0.003565</td>\n",
       "      <td>0.003565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1.5138</td>\n",
       "      <td>1.3951</td>\n",
       "      <td>1.2966</td>\n",
       "      <td>0.58371</td>\n",
       "      <td>0.58745</td>\n",
       "      <td>0.59733</td>\n",
       "      <td>0.33972</td>\n",
       "      <td>1.5906</td>\n",
       "      <td>1.4092</td>\n",
       "      <td>1.3675</td>\n",
       "      <td>0.003070</td>\n",
       "      <td>0.003070</td>\n",
       "      <td>0.003070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>1.4982</td>\n",
       "      <td>1.3604</td>\n",
       "      <td>1.2715</td>\n",
       "      <td>0.68073</td>\n",
       "      <td>0.59705</td>\n",
       "      <td>0.62152</td>\n",
       "      <td>0.35583</td>\n",
       "      <td>1.5688</td>\n",
       "      <td>1.3916</td>\n",
       "      <td>1.3439</td>\n",
       "      <td>0.002575</td>\n",
       "      <td>0.002575</td>\n",
       "      <td>0.002575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>1.4145</td>\n",
       "      <td>1.3004</td>\n",
       "      <td>1.2344</td>\n",
       "      <td>0.68785</td>\n",
       "      <td>0.59131</td>\n",
       "      <td>0.63601</td>\n",
       "      <td>0.35795</td>\n",
       "      <td>1.5538</td>\n",
       "      <td>1.3793</td>\n",
       "      <td>1.3554</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.002080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>1.4651</td>\n",
       "      <td>1.2935</td>\n",
       "      <td>1.2519</td>\n",
       "      <td>0.67873</td>\n",
       "      <td>0.59297</td>\n",
       "      <td>0.65208</td>\n",
       "      <td>0.36782</td>\n",
       "      <td>1.5596</td>\n",
       "      <td>1.3714</td>\n",
       "      <td>1.3315</td>\n",
       "      <td>0.001585</td>\n",
       "      <td>0.001585</td>\n",
       "      <td>0.001585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>1.4343</td>\n",
       "      <td>1.2210</td>\n",
       "      <td>1.2188</td>\n",
       "      <td>0.70917</td>\n",
       "      <td>0.59587</td>\n",
       "      <td>0.65915</td>\n",
       "      <td>0.37859</td>\n",
       "      <td>1.5190</td>\n",
       "      <td>1.2897</td>\n",
       "      <td>1.3108</td>\n",
       "      <td>0.001090</td>\n",
       "      <td>0.001090</td>\n",
       "      <td>0.001090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>1.3666</td>\n",
       "      <td>1.2103</td>\n",
       "      <td>1.2149</td>\n",
       "      <td>0.71721</td>\n",
       "      <td>0.61240</td>\n",
       "      <td>0.65659</td>\n",
       "      <td>0.37551</td>\n",
       "      <td>1.5175</td>\n",
       "      <td>1.2911</td>\n",
       "      <td>1.3051</td>\n",
       "      <td>0.075700</td>\n",
       "      <td>0.002700</td>\n",
       "      <td>0.002700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>1.3726</td>\n",
       "      <td>1.1723</td>\n",
       "      <td>1.1976</td>\n",
       "      <td>0.70854</td>\n",
       "      <td>0.63572</td>\n",
       "      <td>0.68001</td>\n",
       "      <td>0.38893</td>\n",
       "      <td>1.5354</td>\n",
       "      <td>1.2757</td>\n",
       "      <td>1.3172</td>\n",
       "      <td>0.049955</td>\n",
       "      <td>0.004955</td>\n",
       "      <td>0.004955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>1.3577</td>\n",
       "      <td>1.1863</td>\n",
       "      <td>1.2034</td>\n",
       "      <td>0.72077</td>\n",
       "      <td>0.60671</td>\n",
       "      <td>0.66027</td>\n",
       "      <td>0.37848</td>\n",
       "      <td>1.5255</td>\n",
       "      <td>1.2992</td>\n",
       "      <td>1.3173</td>\n",
       "      <td>0.023657</td>\n",
       "      <td>0.006657</td>\n",
       "      <td>0.006657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>1.3785</td>\n",
       "      <td>1.1773</td>\n",
       "      <td>1.2119</td>\n",
       "      <td>0.73520</td>\n",
       "      <td>0.61255</td>\n",
       "      <td>0.67306</td>\n",
       "      <td>0.38587</td>\n",
       "      <td>1.5279</td>\n",
       "      <td>1.3076</td>\n",
       "      <td>1.3277</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.007030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4</td>\n",
       "      <td>1.4052</td>\n",
       "      <td>1.2251</td>\n",
       "      <td>1.2066</td>\n",
       "      <td>0.71895</td>\n",
       "      <td>0.56986</td>\n",
       "      <td>0.64291</td>\n",
       "      <td>0.36972</td>\n",
       "      <td>1.5772</td>\n",
       "      <td>1.3747</td>\n",
       "      <td>1.3527</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.007030</td>\n",
       "      <td>0.007030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5</td>\n",
       "      <td>1.4142</td>\n",
       "      <td>1.2066</td>\n",
       "      <td>1.1968</td>\n",
       "      <td>0.72897</td>\n",
       "      <td>0.60502</td>\n",
       "      <td>0.66633</td>\n",
       "      <td>0.39266</td>\n",
       "      <td>1.5365</td>\n",
       "      <td>1.3027</td>\n",
       "      <td>1.3301</td>\n",
       "      <td>0.006040</td>\n",
       "      <td>0.006040</td>\n",
       "      <td>0.006040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6</td>\n",
       "      <td>1.3974</td>\n",
       "      <td>1.1789</td>\n",
       "      <td>1.2001</td>\n",
       "      <td>0.69409</td>\n",
       "      <td>0.58250</td>\n",
       "      <td>0.64640</td>\n",
       "      <td>0.37666</td>\n",
       "      <td>1.5574</td>\n",
       "      <td>1.3232</td>\n",
       "      <td>1.3538</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>0.005050</td>\n",
       "      <td>0.005050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7</td>\n",
       "      <td>1.3987</td>\n",
       "      <td>1.1481</td>\n",
       "      <td>1.1954</td>\n",
       "      <td>0.63278</td>\n",
       "      <td>0.61977</td>\n",
       "      <td>0.64847</td>\n",
       "      <td>0.38071</td>\n",
       "      <td>1.5567</td>\n",
       "      <td>1.3184</td>\n",
       "      <td>1.3407</td>\n",
       "      <td>0.004060</td>\n",
       "      <td>0.004060</td>\n",
       "      <td>0.004060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8</td>\n",
       "      <td>1.3827</td>\n",
       "      <td>1.1557</td>\n",
       "      <td>1.1818</td>\n",
       "      <td>0.67648</td>\n",
       "      <td>0.61099</td>\n",
       "      <td>0.66174</td>\n",
       "      <td>0.37997</td>\n",
       "      <td>1.5582</td>\n",
       "      <td>1.3043</td>\n",
       "      <td>1.3387</td>\n",
       "      <td>0.003070</td>\n",
       "      <td>0.003070</td>\n",
       "      <td>0.003070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9</td>\n",
       "      <td>1.3530</td>\n",
       "      <td>1.1330</td>\n",
       "      <td>1.1903</td>\n",
       "      <td>0.71699</td>\n",
       "      <td>0.63608</td>\n",
       "      <td>0.66461</td>\n",
       "      <td>0.38439</td>\n",
       "      <td>1.5584</td>\n",
       "      <td>1.2728</td>\n",
       "      <td>1.3378</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.002080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      epoch           train/box_loss           train/cls_loss  \\\n",
       "0                         0                   1.8956                   2.4034   \n",
       "1                         1                   1.8353                   2.2090   \n",
       "2                         2                   1.8100                   2.2118   \n",
       "3                         3                   1.8073                   2.0346   \n",
       "4                         4                   1.8318                   2.0307   \n",
       "5                         5                   1.8010                   1.9496   \n",
       "6                         6                   1.7367                   1.8131   \n",
       "7                         7                   1.6960                   1.6992   \n",
       "8                         8                   1.6770                   1.6794   \n",
       "9                         9                   1.6438                   1.6419   \n",
       "10                       10                   1.6176                   1.5793   \n",
       "11                       11                   1.5701                   1.5533   \n",
       "12                       12                   1.5715                   1.5221   \n",
       "13                       13                   1.5722                   1.4789   \n",
       "14                       14                   1.5213                   1.4719   \n",
       "15                       15                   1.5138                   1.3951   \n",
       "16                       16                   1.4982                   1.3604   \n",
       "17                       17                   1.4145                   1.3004   \n",
       "18                       18                   1.4651                   1.2935   \n",
       "19                       19                   1.4343                   1.2210   \n",
       "20                        0                   1.3666                   1.2103   \n",
       "21                        1                   1.3726                   1.1723   \n",
       "22                        2                   1.3577                   1.1863   \n",
       "23                        3                   1.3785                   1.1773   \n",
       "24                        4                   1.4052                   1.2251   \n",
       "25                        5                   1.4142                   1.2066   \n",
       "26                        6                   1.3974                   1.1789   \n",
       "27                        7                   1.3987                   1.1481   \n",
       "28                        8                   1.3827                   1.1557   \n",
       "29                        9                   1.3530                   1.1330   \n",
       "\n",
       "             train/dfl_loss     metrics/precision(B)        metrics/recall(B)  \\\n",
       "0                    1.5012                  0.04624                  0.10358   \n",
       "1                    1.4481                  0.34667                  0.10196   \n",
       "2                    1.4610                  0.16891                  0.22179   \n",
       "3                    1.4674                  0.26218                  0.35846   \n",
       "4                    1.4451                  0.40623                  0.31411   \n",
       "5                    1.4256                  0.43656                  0.30025   \n",
       "6                    1.3942                  0.50225                  0.43214   \n",
       "7                    1.3617                  0.44924                  0.43281   \n",
       "8                    1.3641                  0.48680                  0.43373   \n",
       "9                    1.3668                  0.50025                  0.45579   \n",
       "10                   1.3511                  0.59290                  0.53955   \n",
       "11                   1.3064                  0.54811                  0.48875   \n",
       "12                   1.3304                  0.64157                  0.51449   \n",
       "13                   1.3314                  0.54407                  0.53474   \n",
       "14                   1.2991                  0.64733                  0.55088   \n",
       "15                   1.2966                  0.58371                  0.58745   \n",
       "16                   1.2715                  0.68073                  0.59705   \n",
       "17                   1.2344                  0.68785                  0.59131   \n",
       "18                   1.2519                  0.67873                  0.59297   \n",
       "19                   1.2188                  0.70917                  0.59587   \n",
       "20                   1.2149                  0.71721                  0.61240   \n",
       "21                   1.1976                  0.70854                  0.63572   \n",
       "22                   1.2034                  0.72077                  0.60671   \n",
       "23                   1.2119                  0.73520                  0.61255   \n",
       "24                   1.2066                  0.71895                  0.56986   \n",
       "25                   1.1968                  0.72897                  0.60502   \n",
       "26                   1.2001                  0.69409                  0.58250   \n",
       "27                   1.1954                  0.63278                  0.61977   \n",
       "28                   1.1818                  0.67648                  0.61099   \n",
       "29                   1.1903                  0.71699                  0.63608   \n",
       "\n",
       "           metrics/mAP50(B)      metrics/mAP50-95(B)             val/box_loss  \\\n",
       "0                   0.05534                  0.03189                   3.2461   \n",
       "1                   0.05837                  0.02877                   2.3799   \n",
       "2                   0.15625                  0.07143                   2.0186   \n",
       "3                   0.24757                  0.12395                   1.9018   \n",
       "4                   0.28752                  0.12738                   1.8974   \n",
       "5                   0.31426                  0.14902                   1.9014   \n",
       "6                   0.43821                  0.21345                   1.7450   \n",
       "7                   0.41018                  0.19855                   1.7836   \n",
       "8                   0.41276                  0.20774                   1.7446   \n",
       "9                   0.48028                  0.25515                   1.7277   \n",
       "10                  0.55972                  0.30442                   1.6929   \n",
       "11                  0.49136                  0.25795                   1.6947   \n",
       "12                  0.55984                  0.30337                   1.6746   \n",
       "13                  0.56176                  0.32269                   1.5939   \n",
       "14                  0.58659                  0.32042                   1.6175   \n",
       "15                  0.59733                  0.33972                   1.5906   \n",
       "16                  0.62152                  0.35583                   1.5688   \n",
       "17                  0.63601                  0.35795                   1.5538   \n",
       "18                  0.65208                  0.36782                   1.5596   \n",
       "19                  0.65915                  0.37859                   1.5190   \n",
       "20                  0.65659                  0.37551                   1.5175   \n",
       "21                  0.68001                  0.38893                   1.5354   \n",
       "22                  0.66027                  0.37848                   1.5255   \n",
       "23                  0.67306                  0.38587                   1.5279   \n",
       "24                  0.64291                  0.36972                   1.5772   \n",
       "25                  0.66633                  0.39266                   1.5365   \n",
       "26                  0.64640                  0.37666                   1.5574   \n",
       "27                  0.64847                  0.38071                   1.5567   \n",
       "28                  0.66174                  0.37997                   1.5582   \n",
       "29                  0.66461                  0.38439                   1.5584   \n",
       "\n",
       "               val/cls_loss             val/dfl_loss                   lr/pg0  \\\n",
       "0                    4.7676                   2.4886                 0.075700   \n",
       "1                    5.1016                   1.9425                 0.050228   \n",
       "2                    3.7559                   1.6201                 0.024478   \n",
       "3                    3.0407                   1.5488                 0.008515   \n",
       "4                    2.5947                   1.5624                 0.008515   \n",
       "5                    2.7516                   1.5139                 0.008020   \n",
       "6                    1.9157                   1.4537                 0.007525   \n",
       "7                    2.1626                   1.4822                 0.007030   \n",
       "8                    1.9237                   1.4532                 0.006535   \n",
       "9                    1.7673                   1.4719                 0.006040   \n",
       "10                   1.5811                   1.4329                 0.005545   \n",
       "11                   1.7156                   1.4177                 0.005050   \n",
       "12                   1.5625                   1.4145                 0.004555   \n",
       "13                   1.5381                   1.3677                 0.004060   \n",
       "14                   1.4746                   1.3965                 0.003565   \n",
       "15                   1.4092                   1.3675                 0.003070   \n",
       "16                   1.3916                   1.3439                 0.002575   \n",
       "17                   1.3793                   1.3554                 0.002080   \n",
       "18                   1.3714                   1.3315                 0.001585   \n",
       "19                   1.2897                   1.3108                 0.001090   \n",
       "20                   1.2911                   1.3051                 0.075700   \n",
       "21                   1.2757                   1.3172                 0.049955   \n",
       "22                   1.2992                   1.3173                 0.023657   \n",
       "23                   1.3076                   1.3277                 0.007030   \n",
       "24                   1.3747                   1.3527                 0.007030   \n",
       "25                   1.3027                   1.3301                 0.006040   \n",
       "26                   1.3232                   1.3538                 0.005050   \n",
       "27                   1.3184                   1.3407                 0.004060   \n",
       "28                   1.3043                   1.3387                 0.003070   \n",
       "29                   1.2728                   1.3378                 0.002080   \n",
       "\n",
       "                     lr/pg1                   lr/pg2  \n",
       "0                  0.002700                 0.002700  \n",
       "1                  0.005228                 0.005228  \n",
       "2                  0.007478                 0.007478  \n",
       "3                  0.008515                 0.008515  \n",
       "4                  0.008515                 0.008515  \n",
       "5                  0.008020                 0.008020  \n",
       "6                  0.007525                 0.007525  \n",
       "7                  0.007030                 0.007030  \n",
       "8                  0.006535                 0.006535  \n",
       "9                  0.006040                 0.006040  \n",
       "10                 0.005545                 0.005545  \n",
       "11                 0.005050                 0.005050  \n",
       "12                 0.004555                 0.004555  \n",
       "13                 0.004060                 0.004060  \n",
       "14                 0.003565                 0.003565  \n",
       "15                 0.003070                 0.003070  \n",
       "16                 0.002575                 0.002575  \n",
       "17                 0.002080                 0.002080  \n",
       "18                 0.001585                 0.001585  \n",
       "19                 0.001090                 0.001090  \n",
       "20                 0.002700                 0.002700  \n",
       "21                 0.004955                 0.004955  \n",
       "22                 0.006657                 0.006657  \n",
       "23                 0.007030                 0.007030  \n",
       "24                 0.007030                 0.007030  \n",
       "25                 0.006040                 0.006040  \n",
       "26                 0.005050                 0.005050  \n",
       "27                 0.004060                 0.004060  \n",
       "28                 0.003070                 0.003070  \n",
       "29                 0.002080                 0.002080  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_performance=pd.concat([performance1,performance2],ignore_index=True)\n",
    "final_performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     1.8956,      1.8353,        1.81,      1.8073,      1.8318,       1.801,      1.7367,       1.696,       1.677,      1.6438,      1.6176,      1.5701,      1.5715,      1.5722,      1.5213,      1.5138,      1.4982,      1.4145,      1.4651,      1.4343,      1.3666,      1.3726,      1.3577,      1.3785,\n",
       "            1.4052,      1.4142,      1.3974,      1.3987,      1.3827,       1.353])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box_loss=final_performance['         train/box_loss'].to_numpy()\n",
    "class_loss=final_performance['         train/cls_loss'].to_numpy()\n",
    "epochs = range(1,30+1)\n",
    "box_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.plot(epochs, box_loss, 'b', label='Box Loss')\n",
    "plt.title('Box Loss vs. Epoch')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Box Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.plot(epochs, class_loss, 'b', label='Class Loss')\n",
    "plt.title('Class Loss vs. Epoch')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Class Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2289_jpeg_jpg.rf.fe2a7a149e7b11f2313f5a7b30386e85.jpg: 640x480 (no detections), 109.4ms\n",
      "image 2/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2301_jpeg_jpg.rf.2c19ae5efbd1f8611b5578125f001695.jpg: 640x480 1 fish, 8 penguins, 79.7ms\n",
      "image 3/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2319_jpeg_jpg.rf.6e20bf97d17b74a8948aa48776c40454.jpg: 640x480 3 penguins, 1 starfish, 74.0ms\n",
      "image 4/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2347_jpeg_jpg.rf.7c71ac4b9301eb358cd4a832844dedcb.jpg: 640x480 (no detections), 75.7ms\n",
      "image 5/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2354_jpeg_jpg.rf.396e872c7fb0a95e911806986995ee7a.jpg: 640x480 (no detections), 83.6ms\n",
      "image 6/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2371_jpeg_jpg.rf.54505f60b6706da151c164188c305849.jpg: 640x480 1 fish, 87.1ms\n",
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      "image 9/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2387_jpeg_jpg.rf.09b38bacfab0922a3a6b66480f01b719.jpg: 640x480 1 fish, 1 starfish, 78.6ms\n",
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      "image 19/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2470_jpeg_jpg.rf.75b359c8baa6866bfecf07a0e4e8c33d.jpg: 640x480 13 jellyfishs, 88.5ms\n",
      "image 20/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2473_jpeg_jpg.rf.6284677f9c781b0cfeec54981a17d573.jpg: 640x480 3 jellyfishs, 101.0ms\n",
      "image 21/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2477_jpeg_jpg.rf.7b2692f142d53c16ad477065f1f8ae6d.jpg: 640x480 6 jellyfishs, 97.6ms\n",
      "image 22/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2496_jpeg_jpg.rf.3f91e7f18502074c89fa720a11926fab.jpg: 640x480 5 fishs, 1 shark, 104.6ms\n",
      "image 23/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2499_jpeg_jpg.rf.6cbab3719b9063388b5ab3ab826d7bd3.jpg: 640x480 3 fishs, 71.7ms\n",
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      "image 26/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2532_jpeg_jpg.rf.2afeb76e5d9372dbbd6fbc53d5b75675.jpg: 640x480 1 fish, 1 starfish, 71.2ms\n",
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      "image 30/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2574_jpeg_jpg.rf.ca0c3ad32384309a61e92d9a8bef87b9.jpg: 640x480 6 jellyfishs, 76.6ms\n",
      "image 31/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2582_jpeg_jpg.rf.14f175066ce74b470bf31fa0c7a096cd.jpg: 640x480 2 fishs, 1 shark, 83.3ms\n",
      "image 32/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2588_jpeg_jpg.rf.cb9cea8f05891cfd55a3e93f2908201f.jpg: 640x480 1 fish, 106.6ms\n",
      "image 33/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2630_jpeg_jpg.rf.310f0c986a72be46b80ce31c2d00e46d.jpg: 640x480 (no detections), 108.0ms\n",
      "image 34/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2632_jpeg_jpg.rf.f44037edca490b16cbf06427e28ea946.jpg: 640x480 (no detections), 108.3ms\n",
      "image 35/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_2651_jpeg_jpg.rf.84b3930aa80b610cc97bf1c176763940.jpg: 640x480 1 shark, 94.4ms\n",
      "image 36/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_3129_jpeg_jpg.rf.90c472dcdf9b6713ec767cc97560ceca.jpg: 640x480 (no detections), 79.0ms\n",
      "image 37/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_3134_jpeg_jpg.rf.50750ca778773042a3c46a1d3e480132.jpg: 640x480 (no detections), 77.3ms\n",
      "image 38/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_3136_jpeg_jpg.rf.0d8fef73d4cc5e1c35ce424444d9e44b.jpg: 640x480 (no detections), 82.3ms\n",
      "image 39/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_3144_jpeg_jpg.rf.f29a36360174dc83ecef93275ed8f02e.jpg: 640x480 (no detections), 80.7ms\n",
      "image 40/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_3154_jpeg_jpg.rf.5f429a366c02d38bc9e2217f4508c3e0.jpg: 640x480 (no detections), 83.9ms\n",
      "image 41/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_3164_jpeg_jpg.rf.06637eee0b72df791aa729807ca45c4d.jpg: 640x480 2 fishs, 72.6ms\n",
      "image 42/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_3173_jpeg_jpg.rf.6f05acaa0b22d410a5df3ea3286e227d.jpg: 480x640 (no detections), 90.5ms\n",
      "image 43/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_3175_jpeg_jpg.rf.686c7d36e049eea974a363e99bf0bee0.jpg: 480x640 2 penguins, 94.0ms\n",
      "image 44/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8331_jpg.rf.ec024bdf1e9de02b020b5e6505c1c58b.jpg: 480x640 (no detections), 107.3ms\n",
      "image 45/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8343_jpg.rf.2d88000497d74d72aedc118b125a0c07.jpg: 480x640 2 fishs, 115.7ms\n",
      "image 46/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8395_jpg.rf.3bebece033961c9f665571644a14261f.jpg: 480x640 6 fishs, 92.8ms\n",
      "image 47/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8396_jpg.rf.106a6ced5c649ea81f0de8ecaa4ff3b8.jpg: 480x640 10 fishs, 1 shark, 85.6ms\n",
      "image 48/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8404_jpg.rf.265b89e862a375f6b89f781ea60ed480.jpg: 480x640 6 fishs, 77.8ms\n",
      "image 49/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8420_jpg.rf.31f1d5f1440e48ccf1dee988b565911b.jpg: 480x640 14 fishs, 82.6ms\n",
      "image 50/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8452_jpg.rf.6bbff701ab93e29553b3a70137fd4e66.jpg: 480x640 (no detections), 91.7ms\n",
      "image 51/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8490_jpg.rf.1836542cf054c6d303a2dd05d4194d7f.jpg: 480x640 7 jellyfishs, 74.6ms\n",
      "image 52/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8497_MOV-0_jpg.rf.5c59bd1bf7d8fd7a20999d51a79a12c0.jpg: 640x384 3 fishs, 2 jellyfishs, 106.0ms\n",
      "image 53/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8497_MOV-3_jpg.rf.fd813e14681c8b41e709a500748ce46a.jpg: 640x384 1 jellyfish, 68.4ms\n",
      "image 54/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8497_MOV-5_jpg.rf.3deffb208d656b7845661c5e33dd1afb.jpg: 640x384 1 fish, 2 jellyfishs, 66.8ms\n",
      "image 55/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8513_MOV-0_jpg.rf.2a2f77e3f73630b60aaf6ad3ca4ed130.jpg: 640x384 (no detections), 68.8ms\n",
      "image 56/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8515_jpg.rf.98a9daca7c5a5bad9872bd7fb2d4f198.jpg: 480x640 1 fish, 119.1ms\n",
      "image 57/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8582_MOV-0_jpg.rf.aa8304d7a5112d63c8841d96160d42cd.jpg: 640x384 2 fishs, 134.8ms\n",
      "image 58/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8582_MOV-3_jpg.rf.c7dde0639837077f76428d70223368a4.jpg: 640x384 (no detections), 97.5ms\n",
      "image 59/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8582_MOV-5_jpg.rf.9d7a26fbf145ce39ab0831b4e6bc1f1e.jpg: 640x384 (no detections), 87.1ms\n",
      "image 60/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8590_MOV-2_jpg.rf.2136fdb5dcbcd58a1dc456bb3e5bf476.jpg: 640x384 1 fish, 2 jellyfishs, 64.2ms\n",
      "image 61/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8590_MOV-5_jpg.rf.074e6d8acdd3fcad16d866c341b43769.jpg: 640x384 (no detections), 54.8ms\n",
      "image 62/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8595_MOV-0_jpg.rf.312ab0b8b9fca18134aee88044f45a06.jpg: 640x384 14 jellyfishs, 66.0ms\n",
      "image 63/63 C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\test\\images\\IMG_8599_MOV-3_jpg.rf.412ebb16ea80e964b4464c50e757df0e.jpg: 640x384 (no detections), 67.0ms\n",
      "Speed: 2.7ms preprocess, 87.0ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns\\detect\\predict2\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
       " speed: {'preprocess': 4.014015197753906, 'inference': 109.44056510925293, 'postprocess': 1.4247894287109375},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
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       " probs: None\n",
       " speed: {'preprocess': 3.0028820037841797, 'inference': 179.5339584350586, 'postprocess': 1.0182857513427734},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
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       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
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       " masks: None\n",
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       " probs: None\n",
       " speed: {'preprocess': 2.011537551879883, 'inference': 79.81204986572266, 'postprocess': 1.9915103912353516},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
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       " keys: ['boxes']\n",
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       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
       " speed: {'preprocess': 2.000093460083008, 'inference': 70.25814056396484, 'postprocess': 1.0008811950683594},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
       " keypoints: None\n",
       " keys: ['boxes']\n",
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       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
       " speed: {'preprocess': 1.9998550415039062, 'inference': 72.32904434204102, 'postprocess': 1.0025501251220703},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
       " keypoints: None\n",
       " keys: ['boxes']\n",
       " masks: None\n",
       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
       " speed: {'preprocess': 1.9979476928710938, 'inference': 82.25464820861816, 'postprocess': 0.9934902191162109},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
       " keypoints: None\n",
       " keys: ['boxes']\n",
       " masks: None\n",
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       " probs: None\n",
       " speed: {'preprocess': 2.010345458984375, 'inference': 88.50479125976562, 'postprocess': 2.0210742950439453},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
       " keypoints: None\n",
       " keys: ['boxes']\n",
       " masks: None\n",
       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       "         [ 83,  22,   2]],\n",
       " \n",
       "        [[ 80,  28,  11],\n",
       "         [ 80,  28,  11],\n",
       "         [ 80,  28,  11],\n",
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       "         [ 83,  22,   2]]], dtype=uint8)\n",
       " orig_shape: (1024, 768)\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
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       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
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       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
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       " masks: None\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
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       " keys: ['boxes']\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
       " keypoints: None\n",
       " keys: ['boxes']\n",
       " masks: None\n",
       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
       " speed: {'preprocess': 1.4965534210205078, 'inference': 66.82729721069336, 'postprocess': 0.0},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
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       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
       " speed: {'preprocess': 2.0046234130859375, 'inference': 68.79878044128418, 'postprocess': 0.0},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
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       " keys: ['boxes']\n",
       " masks: None\n",
       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
       " speed: {'preprocess': 3.0019283294677734, 'inference': 119.09103393554688, 'postprocess': 1.020193099975586},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
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       " keys: ['boxes']\n",
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       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
       " speed: {'preprocess': 4.0111541748046875, 'inference': 134.7525119781494, 'postprocess': 1.3856887817382812},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
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       " keys: ['boxes']\n",
       " masks: None\n",
       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
       " speed: {'preprocess': 3.1244754791259766, 'inference': 97.49937057495117, 'postprocess': 1.0242462158203125},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
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       " keys: ['boxes']\n",
       " masks: None\n",
       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
       " speed: {'preprocess': 1.9910335540771484, 'inference': 87.10074424743652, 'postprocess': 1.0013580322265625},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
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       " keys: ['boxes']\n",
       " masks: None\n",
       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
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       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
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       " probs: None\n",
       " speed: {'preprocess': 2.248048782348633, 'inference': 54.78978157043457, 'postprocess': 0.934600830078125},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
       " boxes: ultralytics.yolo.engine.results.Boxes object\n",
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       " keys: ['boxes']\n",
       " masks: None\n",
       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       " probs: None\n",
       " speed: {'preprocess': 1.5094280242919922, 'inference': 65.9942626953125, 'postprocess': 0.9984970092773438},\n",
       " ultralytics.yolo.engine.results.Results object with attributes:\n",
       " \n",
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       " keys: ['boxes']\n",
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       " names: {0: 'fish', 1: 'jellyfish', 2: 'penguin', 3: 'puffin', 4: 'shark', 5: 'starfish', 6: 'stingray'}\n",
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       "         [197, 101,   1],\n",
       "         [197, 101,   1],\n",
       "         ...,\n",
       "         [185,  89,   0],\n",
       "         [185,  89,   0],\n",
       "         [185,  89,   0]],\n",
       " \n",
       "        [[197, 101,   1],\n",
       "         [197, 101,   1],\n",
       "         [197, 101,   1],\n",
       "         ...,\n",
       "         [185,  89,   0],\n",
       "         [185,  89,   0],\n",
       "         [185,  89,   0]],\n",
       " \n",
       "        [[197, 101,   1],\n",
       "         [197, 101,   1],\n",
       "         [197, 101,   1],\n",
       "         ...,\n",
       "         [185,  89,   0],\n",
       "         [185,  89,   0],\n",
       "         [185,  89,   0]]], dtype=uint8)\n",
       " orig_shape: (1024, 576)\n",
       " path: 'C:\\\\Users\\\\KIIT\\\\OneDrive\\\\Desktop\\\\Celebal_Internship\\\\Object_Detection\\\\test\\\\images\\\\IMG_8599_MOV-3_jpg.rf.412ebb16ea80e964b4464c50e757df0e.jpg'\n",
       " probs: None\n",
       " speed: {'preprocess': 2.000093460083008, 'inference': 66.97821617126465, 'postprocess': 0.0}]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(\"test/images\", save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting git+https://github.com/albumentations-team/albumentations\n",
      "  Cloning https://github.com/albumentations-team/albumentations to c:\\users\\kiit\\appdata\\local\\temp\\pip-req-build-i0kmeh_3\n",
      "  Resolved https://github.com/albumentations-team/albumentations to commit b773a1aa69f9c823c7f593205614d05d32c039cb\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Requirement already satisfied: numpy>=1.11.1 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from albumentations==1.3.0) (1.23.1)\n",
      "Requirement already satisfied: scipy>=1.1.0 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from albumentations==1.3.0) (1.9.3)\n",
      "Requirement already satisfied: scikit-image>=0.16.1 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from albumentations==1.3.0) (0.19.3)\n",
      "Requirement already satisfied: PyYAML in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from albumentations==1.3.0) (6.0)\n",
      "Collecting qudida>=0.0.4\n",
      "  Using cached qudida-0.0.4-py3-none-any.whl (3.5 kB)\n",
      "Requirement already satisfied: opencv-python>=4.1.1 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from albumentations==1.3.0) (4.7.0.68)\n",
      "Requirement already satisfied: typing-extensions in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from qudida>=0.0.4->albumentations==1.3.0) (4.3.0)\n",
      "Collecting opencv-python-headless>=4.0.1\n",
      "  Using cached opencv_python_headless-4.7.0.72-cp37-abi3-win_amd64.whl (38.1 MB)\n",
      "Requirement already satisfied: scikit-learn>=0.19.1 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from qudida>=0.0.4->albumentations==1.3.0) (1.1.3)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-image>=0.16.1->albumentations==1.3.0) (21.3)\n",
      "Requirement already satisfied: networkx>=2.2 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-image>=0.16.1->albumentations==1.3.0) (2.8.8)\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-image>=0.16.1->albumentations==1.3.0) (1.4.1)\n",
      "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-image>=0.16.1->albumentations==1.3.0) (9.2.0)\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-image>=0.16.1->albumentations==1.3.0) (2022.10.10)\n",
      "Requirement already satisfied: imageio>=2.4.1 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-image>=0.16.1->albumentations==1.3.0) (2.22.3)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from packaging>=20.0->scikit-image>=0.16.1->albumentations==1.3.0) (3.0.9)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations==1.3.0) (3.1.0)\n",
      "Requirement already satisfied: joblib>=1.0.0 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations==1.3.0) (1.2.0)\n",
      "Building wheels for collected packages: albumentations\n",
      "  Building wheel for albumentations (setup.py): started\n",
      "  Building wheel for albumentations (setup.py): finished with status 'done'\n",
      "  Created wheel for albumentations: filename=albumentations-1.3.0-py3-none-any.whl size=126643 sha256=29b0de91943c2a8f7ad7262d69d698a107b832e91ae6f473074a17c8ad0b0117\n",
      "  Stored in directory: C:\\Users\\KIIT\\AppData\\Local\\Temp\\pip-ephem-wheel-cache-meo52rhb\\wheels\\51\\4d\\ab\\5aafa8b980086fbc362946de7da4aa3df33aacb3da0da29b93\n",
      "Successfully built albumentations\n",
      "Installing collected packages: opencv-python-headless, qudida, albumentations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  Running command git clone --filter=blob:none --quiet https://github.com/albumentations-team/albumentations 'C:\\Users\\KIIT\\AppData\\Local\\Temp\\pip-req-build-i0kmeh_3'\n",
      "ERROR: Could not install packages due to an OSError: [WinError 5] Access is denied: 'C:\\\\Users\\\\KIIT\\\\AppData\\\\Local\\\\Packages\\\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\\\LocalCache\\\\local-packages\\\\Python310\\\\site-packages\\\\cv2\\\\cv2.pyd'\n",
      "Check the permissions.\n",
      "\n",
      "\n",
      "[notice] A new release of pip is available: 23.0 -> 23.1.2\n",
      "[notice] To update, run: C:\\Users\\KIIT\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install -U git+https://github.com/albumentations-team/albumentations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting albumentations\n",
      "  Downloading albumentations-1.3.0.tar.gz (173 kB)\n",
      "     ---------------------------------------- 0.0/173.0 kB ? eta -:--:--\n",
      "     ---------------------------------------- 0.0/173.0 kB ? eta -:--:--\n",
      "     ------ -------------------------------- 30.7/173.0 kB 1.4 MB/s eta 0:00:01\n",
      "     -------- ---------------------------- 41.0/173.0 kB 667.8 kB/s eta 0:00:01\n",
      "     --------------- --------------------- 71.7/173.0 kB 660.6 kB/s eta 0:00:01\n",
      "     ------------------- ----------------- 92.2/173.0 kB 751.6 kB/s eta 0:00:01\n",
      "     ----------------------------- ------ 143.4/173.0 kB 778.5 kB/s eta 0:00:01\n",
      "     ------------------------------------ 173.0/173.0 kB 746.8 kB/s eta 0:00:00\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Requirement already satisfied: numpy>=1.11.1 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from albumentations) (1.23.1)\n",
      "Requirement already satisfied: scipy in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from albumentations) (1.9.3)\n",
      "Requirement already satisfied: scikit-image>=0.16.1 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from albumentations) (0.19.3)\n",
      "Requirement already satisfied: PyYAML in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from albumentations) (6.0)\n",
      "Collecting qudida>=0.0.4\n",
      "  Downloading qudida-0.0.4.tar.gz (3.1 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Requirement already satisfied: opencv-python>=4.1.1 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from albumentations) (4.7.0.68)\n",
      "Requirement already satisfied: scikit-learn>=0.19.1 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from qudida>=0.0.4->albumentations) (1.1.3)\n",
      "Requirement already satisfied: typing-extensions in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from qudida>=0.0.4->albumentations) (4.3.0)\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-image>=0.16.1->albumentations) (2022.10.10)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-image>=0.16.1->albumentations) (21.3)\n",
      "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-image>=0.16.1->albumentations) (9.2.0)\n",
      "Requirement already satisfied: networkx>=2.2 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-image>=0.16.1->albumentations) (2.8.8)\n",
      "Requirement already satisfied: imageio>=2.4.1 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-image>=0.16.1->albumentations) (2.22.3)\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-image>=0.16.1->albumentations) (1.4.1)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from packaging>=20.0->scikit-image>=0.16.1->albumentations) (3.0.9)\n",
      "Requirement already satisfied: joblib>=1.0.0 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\kiit\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations) (3.1.0)\n",
      "Installing collected packages: qudida, albumentations\n",
      "  Running setup.py install for qudida: started\n",
      "  Running setup.py install for qudida: finished with status 'done'\n",
      "  Running setup.py install for albumentations: started\n",
      "  Running setup.py install for albumentations: finished with status 'done'\n",
      "Successfully installed albumentations-1.3.0 qudida-0.0.4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEPRECATION: --no-binary currently disables reading from the cache of locally built wheels. In the future --no-binary will not influence the wheel cache. pip 23.1 will enforce this behaviour change. A possible replacement is to use the --no-cache-dir option. You can use the flag --use-feature=no-binary-enable-wheel-cache to test the upcoming behaviour. Discussion can be found at https://github.com/pypa/pip/issues/11453\n",
      "  DEPRECATION: qudida is being installed using the legacy 'setup.py install' method, because the '--no-binary' option was enabled for it and this currently disables local wheel building for projects that don't have a 'pyproject.toml' file. pip 23.1 will enforce this behaviour change. A possible replacement is to enable the '--use-pep517' option. Discussion can be found at https://github.com/pypa/pip/issues/11451\n",
      "  DEPRECATION: albumentations is being installed using the legacy 'setup.py install' method, because the '--no-binary' option was enabled for it and this currently disables local wheel building for projects that don't have a 'pyproject.toml' file. pip 23.1 will enforce this behaviour change. A possible replacement is to enable the '--use-pep517' option. Discussion can be found at https://github.com/pypa/pip/issues/11451\n",
      "\n",
      "[notice] A new release of pip is available: 23.0 -> 23.1.2\n",
      "[notice] To update, run: C:\\Users\\KIIT\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "!pip install -U albumentations --no-binary qudida,albumentations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import albumentations as A\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = A.Compose([\n",
    "    A.RandomCrop(width=450, height=450),\n",
    "    A.HorizontalFlip(p=0.5),\n",
    "    A.RandomBrightnessContrast(p=0.2),\n",
    "], bbox_params=A.BboxParams(format='yolo', min_area=1024, min_visibility=0.1, label_fields=[\"class_labels\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread(\"train/images/IMG_2275_jpeg_jpg.rf.66355520a49ba7fb7082052f7ca6fee0.jpg\")\n",
    "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"train/labels/IMG_2275_jpeg_jpg.rf.66355520a49ba7fb7082052f7ca6fee0.txt\", \"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.0, 0.150390625, 0.75927734375, 0.30078125, 0.1494140625], [0.0, 0.3658854166666667, 0.51953125, 0.5807291666666666, 0.146484375], [0.0, 0.8776041666666666, 0.0693359375, 0.24348958333333334, 0.1376953125], [0.0, 0.8359375, 0.376953125, 0.3268229166666667, 0.12109375], [0.0, 0.8541666666666666, 0.4990234375, 0.2890625, 0.1162109375], [0.0, 0.08072916666666667, 0.634765625, 0.16145833333333334, 0.111328125], [0.0, 0.037109375, 0.04833984375, 0.07421875, 0.0966796875], [0.0, 0.7565104166666666, 0.9365234375, 0.4856770833333333, 0.125]]\n"
     ]
    }
   ],
   "source": [
    "bboxes=[]\n",
    "for bbox in f:\n",
    "    l=bbox.split(\" \")\n",
    "    l=\n",
    "    l[-1]=l[-1].replace('\\n','')\n",
    "    toint = [float(i) for i in l]\n",
    "    bboxes.append(toint)\n",
    "print(bboxes)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# After performing augmentation with roboflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=YOLO('runs/detect/train7/weights/best.pt') #Loading the current model state "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "New https://pypi.org/project/ultralytics/8.0.105 available  Update with 'pip install -U ultralytics'\n",
      "Ultralytics YOLOv8.0.104  Python-3.10.11 torch-1.12.1+cpu CPU\n",
      "WARNING  Upgrade to torch>=2.0.0 for deterministic training.\n",
      "\u001b[34m\u001b[1myolo\\engine\\trainer: \u001b[0mtask=detect, mode=train, model=runs/detect/train7/weights/best.pt, data=data.yaml, epochs=10, 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=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=0, resume=False, amp=True, 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, 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=1, 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, v5loader=False, tracker=botsort.yaml, save_dir=runs\\detect\\train8\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    752677  ultralytics.nn.modules.head.Detect           [7, [64, 128, 256]]           \n",
      "Model summary: 225 layers, 3012213 parameters, 3012197 gradients, 8.2 GFLOPs\n",
      "\n",
      "Transferred 355/355 items from pretrained weights\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs\\detect\\train8', view at http://localhost:6006/\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\train\\labels... 1789 images, 1 backgrounds, 0 corrupt: 100%|██████████| 1789/1789 [00:01<00:00, 1218.20it/s]\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\train\\labels.cache\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning C:\\Users\\KIIT\\OneDrive\\Desktop\\Celebal_Internship\\Object_Detection\\valid\\labels.cache... 127 images, 0 backgrounds, 0 corrupt: 100%|██████████| 127/127 [00:00<?, ?it/s]\n",
      "Plotting labels to runs\\detect\\train8\\labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 0 dataloader workers\n",
      "Logging results to \u001b[1mruns\\detect\\train8\u001b[0m\n",
      "Starting training for 10 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "  0%|          | 0/112 [00:00<?, ?it/s]C:\\Users\\KIIT\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\torch\\amp\\autocast_mode.py:198: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
      "  warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n",
      "       1/10         0G      1.612      1.585      1.377        105        640: 100%|██████████| 112/112 [15:35<00:00,  8.35s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:31<00:00,  7.93s/it]\n",
      "                   all        127        909       0.72      0.597      0.664      0.381\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       2/10         0G      1.601      1.541      1.366        171        640: 100%|██████████| 112/112 [17:22<00:00,  9.31s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:25<00:00,  6.41s/it]\n",
      "                   all        127        909      0.708      0.636       0.67      0.385\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       3/10         0G      1.607       1.55      1.368        165        640: 100%|██████████| 112/112 [17:36<00:00,  9.44s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:31<00:00,  7.83s/it]\n",
      "                   all        127        909       0.64      0.536      0.566       0.31\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       4/10         0G      1.625      1.571      1.377        162        640: 100%|██████████| 112/112 [17:11<00:00,  9.21s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:26<00:00,  6.59s/it]\n",
      "                   all        127        909       0.69      0.565      0.612       0.33\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       5/10         0G      1.644       1.61      1.378        144        640: 100%|██████████| 112/112 [13:12<00:00,  7.07s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:21<00:00,  5.39s/it]\n",
      "                   all        127        909      0.607       0.52      0.561      0.302\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       6/10         0G      1.668      1.597      1.394        154        640: 100%|██████████| 112/112 [13:29<00:00,  7.23s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:28<00:00,  7.07s/it]\n",
      "                   all        127        909      0.696       0.51      0.576       0.32\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       7/10         0G      1.647      1.575      1.383        121        640: 100%|██████████| 112/112 [13:23<00:00,  7.17s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:22<00:00,  5.61s/it]\n",
      "                   all        127        909      0.629      0.539      0.579      0.308\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       8/10         0G       1.64      1.563      1.373        122        640: 100%|██████████| 112/112 [13:01<00:00,  6.98s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:20<00:00,  5.24s/it]\n",
      "                   all        127        909      0.601      0.538      0.564      0.302\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "       9/10         0G      1.664      1.545      1.385        172        640: 100%|██████████| 112/112 [13:07<00:00,  7.03s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:21<00:00,  5.36s/it]\n",
      "                   all        127        909      0.665       0.59      0.623      0.342\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
      "      10/10         0G      1.622      1.513      1.361        123        640: 100%|██████████| 112/112 [12:55<00:00,  6.92s/it]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:24<00:00,  6.10s/it]\n",
      "                   all        127        909      0.656      0.602      0.616      0.332\n",
      "\n",
      "10 epochs completed in 2.521 hours.\n",
      "Optimizer stripped from runs\\detect\\train8\\weights\\last.pt, 6.2MB\n",
      "Optimizer stripped from runs\\detect\\train8\\weights\\best.pt, 6.2MB\n",
      "\n",
      "Validating runs\\detect\\train8\\weights\\best.pt...\n",
      "Ultralytics YOLOv8.0.104  Python-3.10.11 torch-1.12.1+cpu CPU\n",
      "Model summary (fused): 168 layers, 3007013 parameters, 0 gradients, 8.1 GFLOPs\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:18<00:00,  4.68s/it]\n",
      "                   all        127        909      0.708      0.636       0.67      0.385\n",
      "                     0        127        459      0.842      0.612      0.743      0.411\n",
      "                     1        127        155      0.825      0.881        0.9      0.472\n",
      "                     2        127        104      0.581      0.683      0.594      0.258\n",
      "                     3        127         74      0.607      0.324      0.402      0.197\n",
      "                     4        127         57      0.651      0.579       0.64      0.387\n",
      "                     5        127         27      0.689      0.704       0.69      0.465\n",
      "                     6        127         33      0.759       0.67      0.723      0.507\n",
      "Speed: 2.3ms preprocess, 119.9ms inference, 0.0ms loss, 3.2ms postprocess per image\n",
      "Results saved to \u001b[1mruns\\detect\\train8\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "model.train(data='data.yaml', epochs=10, optimizer='SGD', momentum=1) #further improving the model using the current model state "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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