├── README.md
└── Copy_of_Licence_Plate_Detection_YOLO_V8.ipynb


/README.md:
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1 | # Licence_Plate_Detection
2 | Licence_Plate_Detection_YOLO_V8 using Collab
3 | 


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/Copy_of_Licence_Plate_Detection_YOLO_V8.ipynb:
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   1 | {
   2 |   "nbformat": 4,
   3 |   "nbformat_minor": 0,
   4 |   "metadata": {
   5 |     "colab": {
   6 |       "provenance": [],
   7 |       "gpuType": "T4",
   8 |       "toc_visible": true,
   9 |       "include_colab_link": true
  10 |     },
  11 |     "kernelspec": {
  12 |       "name": "python3",
  13 |       "display_name": "Python 3"
  14 |     },
  15 |     "language_info": {
  16 |       "name": "python"
  17 |     },
  18 |     "accelerator": "GPU"
  19 |   },
  20 |   "cells": [
  21 |     {
  22 |       "cell_type": "markdown",
  23 |       "metadata": {
  24 |         "id": "view-in-github",
  25 |         "colab_type": "text"
  26 |       },
  27 |       "source": [
  28 |         "<a href=\"https://colab.research.google.com/github/Naveenk069/Licence_Plate_Detection/blob/main/Copy_of_Licence_Plate_Detection_YOLO_V8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
  29 |       ]
  30 |     },
  31 |     {
  32 |       "cell_type": "code",
  33 |       "execution_count": null,
  34 |       "metadata": {
  35 |         "colab": {
  36 |           "base_uri": "https://localhost:8080/"
  37 |         },
  38 |         "id": "pmM1dop72HfK",
  39 |         "outputId": "599f0744-40c7-4e99-c301-81c558e04fd3"
  40 |       },
  41 |       "outputs": [
  42 |         {
  43 |           "output_type": "stream",
  44 |           "name": "stdout",
  45 |           "text": [
  46 |             "Cloning into 'Licence-Plate-Detection-using-YOLO-V8'...\n",
  47 |             "remote: Enumerating objects: 133, done.\u001b[K\n",
  48 |             "remote: Counting objects: 100% (16/16), done.\u001b[K\n",
  49 |             "remote: Compressing objects: 100% (13/13), done.\u001b[K\n",
  50 |             "remote: Total 133 (delta 4), reused 8 (delta 3), pack-reused 117\u001b[K\n",
  51 |             "Receiving objects: 100% (133/133), 14.73 MiB | 9.41 MiB/s, done.\n",
  52 |             "Resolving deltas: 100% (34/34), done.\n"
  53 |           ]
  54 |         }
  55 |       ],
  56 |       "source": [
  57 |         "!git clone https://github.com/Arijit1080/Licence-Plate-Detection-using-YOLO-V8.git"
  58 |       ]
  59 |     },
  60 |     {
  61 |       "cell_type": "code",
  62 |       "source": [
  63 |         "cd /content/Licence-Plate-Detection-using-YOLO-V8"
  64 |       ],
  65 |       "metadata": {
  66 |         "colab": {
  67 |           "base_uri": "https://localhost:8080/"
  68 |         },
  69 |         "id": "TotCpH174BPu",
  70 |         "outputId": "dab6532f-c57e-4a5e-92d8-cac1c39e8ffb"
  71 |       },
  72 |       "execution_count": null,
  73 |       "outputs": [
  74 |         {
  75 |           "output_type": "stream",
  76 |           "name": "stdout",
  77 |           "text": [
  78 |             "/content/Licence-Plate-Detection-using-YOLO-V8\n"
  79 |           ]
  80 |         }
  81 |       ]
  82 |     },
  83 |     {
  84 |       "cell_type": "code",
  85 |       "source": [
  86 |         "!pip install roboflow\n",
  87 |         "\n",
  88 |         "from roboflow import Roboflow\n",
  89 |         "rf = Roboflow(api_key=\"39kI1UOl1RSGuSuIi6UI\")\n",
  90 |         "project = rf.workspace(\"mochoye\").project(\"license-plate-detector-ogxxg\")\n",
  91 |         "version = project.version(2)\n",
  92 |         "dataset = version.download(\"yolov8\")\n"
  93 |       ],
  94 |       "metadata": {
  95 |         "colab": {
  96 |           "base_uri": "https://localhost:8080/"
  97 |         },
  98 |         "id": "i7nQY2yu4Ee3",
  99 |         "outputId": "eb3af606-b467-49a2-aff2-2e2c480dde71"
 100 |       },
 101 |       "execution_count": null,
 102 |       "outputs": [
 103 |         {
 104 |           "output_type": "stream",
 105 |           "name": "stdout",
 106 |           "text": [
 107 |             "Requirement already satisfied: roboflow in /usr/local/lib/python3.10/dist-packages (1.1.27)\n",
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 109 |             "Requirement already satisfied: chardet==4.0.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.0.0)\n",
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 112 |             "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.4.5)\n",
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 115 |             "Requirement already satisfied: opencv-python-headless==4.8.0.74 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.8.0.74)\n",
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 117 |             "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.8.2)\n",
 118 |             "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.0.1)\n",
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 125 |             "Requirement already satisfied: python-magic in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.4.27)\n",
 126 |             "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (1.2.1)\n",
 127 |             "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (4.51.0)\n",
 128 |             "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (24.0)\n",
 129 |             "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (3.1.2)\n",
 130 |             "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->roboflow) (3.3.2)\n",
 131 |             "loading Roboflow workspace...\n",
 132 |             "loading Roboflow project...\n",
 133 |             "Dependency ultralytics==8.0.196 is required but found version=8.0.3, to fix: `pip install ultralytics==8.0.196`\n"
 134 |           ]
 135 |         }
 136 |       ]
 137 |     },
 138 |     {
 139 |       "cell_type": "code",
 140 |       "source": [
 141 |         "!pip install -r requirements.txt"
 142 |       ],
 143 |       "metadata": {
 144 |         "colab": {
 145 |           "base_uri": "https://localhost:8080/",
 146 |           "height": 1000
 147 |         },
 148 |         "id": "qd0UgEnF5KH8",
 149 |         "outputId": "6247a34f-a0c4-4441-99a9-d9eb057ae8ee"
 150 |       },
 151 |       "execution_count": null,
 152 |       "outputs": [
 153 |         {
 154 |           "output_type": "stream",
 155 |           "name": "stdout",
 156 |           "text": [
 157 |             "Collecting ultralytics==8.0.3 (from -r requirements.txt (line 4))\n",
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 189 |             "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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 244 |             "Collecting jedi>=0.16 (from ipython->-r requirements.txt (line 26))\n",
 245 |             "  Downloading jedi-0.19.1-py2.py3-none-any.whl (1.6 MB)\n",
 246 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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 255 |             "Collecting gitdb<5,>=4.0.1 (from GitPython>=3.1.24->-r requirements.txt (line 31))\n",
 256 |             "  Downloading gitdb-4.0.11-py3-none-any.whl (62 kB)\n",
 257 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
 258 |             "\u001b[?25hCollecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->GitPython>=3.1.24->-r requirements.txt (line 31))\n",
 259 |             "  Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
 260 |             "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 18)) (5.3.3)\n",
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 270 |             "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard>=2.4.1->-r requirements.txt (line 18)) (3.2.2)\n",
 271 |             "Building wheels for collected packages: antlr4-python3-runtime\n",
 272 |             "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
 273 |             "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=bc49b033ae4e2ff83094d66d6202cb2c18906c67104211d796a6badb43bc49f6\n",
 274 |             "  Stored in directory: /root/.cache/pip/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n",
 275 |             "Successfully built antlr4-python3-runtime\n",
 276 |             "Installing collected packages: antlr4-python3-runtime, smmap, omegaconf, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, jedi, nvidia-cusparse-cu12, nvidia-cudnn-cu12, hydra-core, gitdb, nvidia-cusolver-cu12, GitPython, thop, ultralytics\n",
 277 |             "Successfully installed GitPython-3.1.43 antlr4-python3-runtime-4.9.3 gitdb-4.0.11 hydra-core-1.3.2 jedi-0.19.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 omegaconf-2.3.0 smmap-5.0.1 thop-0.1.1.post2209072238 ultralytics-8.0.3\n"
 278 |           ]
 279 |         },
 280 |         {
 281 |           "output_type": "display_data",
 282 |           "data": {
 283 |             "application/vnd.colab-display-data+json": {
 284 |               "pip_warning": {
 285 |                 "packages": [
 286 |                   "pydevd_plugins"
 287 |                 ]
 288 |               },
 289 |               "id": "b9fe8e3c39264c519e9728dfaca6ca1c"
 290 |             }
 291 |           },
 292 |           "metadata": {}
 293 |         }
 294 |       ]
 295 |     },
 296 |     {
 297 |       "cell_type": "code",
 298 |       "source": [
 299 |         "!python /content/Licence-Plate-Detection-using-YOLO-V8/ultralytics/yolo/v8/detect/train.py model=yolov8n.pt data=/content/Licence-Plate-Detection-using-YOLO-V8/License-Plate-Detector-2/data.yaml epochs=10"
 300 |       ],
 301 |       "metadata": {
 302 |         "colab": {
 303 |           "base_uri": "https://localhost:8080/"
 304 |         },
 305 |         "id": "dK5DpY9D5iiu",
 306 |         "outputId": "4a1de32c-b9b3-4748-eb42-74ea985341e1"
 307 |       },
 308 |       "execution_count": null,
 309 |       "outputs": [
 310 |         {
 311 |           "output_type": "stream",
 312 |           "name": "stdout",
 313 |           "text": [
 314 |             "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=/content/Licence-Plate-Detection-using-YOLO-V8/License-Plate-Detector-2/data.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=False, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, 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, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, retina_masks=False, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=17, 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, fl_gamma=0.0, 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, hydra={'output_subdir': None, 'run': {'dir': '.'}}, v5loader=False, save_dir=runs/detect/train2\n",
 315 |             "Ultralytics YOLOv8.0.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
 316 |             "2024-04-12 01:39:11.287102: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
 317 |             "2024-04-12 01:39:11.287150: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
 318 |             "2024-04-12 01:39:11.288375: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
 319 |             "2024-04-12 01:39:12.485944: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
 320 |             "Overriding model.yaml nc=80 with nc=1\n",
 321 |             "\n",
 322 |             "                   from  n    params  module                                       arguments                     \n",
 323 |             "  0                  -1  1       464  ultralytics.nn.modules.Conv                  [3, 16, 3, 2]                 \n",
 324 |             "  1                  -1  1      4672  ultralytics.nn.modules.Conv                  [16, 32, 3, 2]                \n",
 325 |             "  2                  -1  1      7360  ultralytics.nn.modules.C2f                   [32, 32, 1, True]             \n",
 326 |             "  3                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                \n",
 327 |             "  4                  -1  2     49664  ultralytics.nn.modules.C2f                   [64, 64, 2, True]             \n",
 328 |             "  5                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               \n",
 329 |             "  6                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           \n",
 330 |             "  7                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              \n",
 331 |             "  8                  -1  1    460288  ultralytics.nn.modules.C2f                   [256, 256, 1, True]           \n",
 332 |             "  9                  -1  1    164608  ultralytics.nn.modules.SPPF                  [256, 256, 5]                 \n",
 333 |             " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
 334 |             " 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
 335 |             " 12                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 \n",
 336 |             " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
 337 |             " 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
 338 |             " 15                  -1  1     37248  ultralytics.nn.modules.C2f                   [192, 64, 1]                  \n",
 339 |             " 16                  -1  1     36992  ultralytics.nn.modules.Conv                  [64, 64, 3, 2]                \n",
 340 |             " 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
 341 |             " 18                  -1  1    123648  ultralytics.nn.modules.C2f                   [192, 128, 1]                 \n",
 342 |             " 19                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              \n",
 343 |             " 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
 344 |             " 21                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 \n",
 345 |             " 22        [15, 18, 21]  1    751507  ultralytics.nn.modules.Detect                [1, [64, 128, 256]]           \n",
 346 |             "Model summary: 225 layers, 3011043 parameters, 3011027 gradients, 8.2 GFLOPs\n",
 347 |             "\n",
 348 |             "Transferred 319/355 items from pretrained weights\n",
 349 |             "\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",
 350 |             "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/Licence-Plate-Detection-using-YOLO-V8/License-Plate-Detector-2/train/labels.cache... 277 images, 0 backgrounds, 0 corrupt: 100% 277/277 [00:00<?, ?it/s]\n",
 351 |             "\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",
 352 |             "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/Licence-Plate-Detection-using-YOLO-V8/License-Plate-Detector-2/valid/labels.cache... 81 images, 0 backgrounds, 0 corrupt: 100% 81/81 [00:00<?, ?it/s]\n",
 353 |             "Image sizes 640 train, 640 val\n",
 354 |             "Using 2 dataloader workers\n",
 355 |             "Logging results to \u001b[1mruns/detect/train2\u001b[0m\n",
 356 |             "Starting training for 10 epochs...\n",
 357 |             "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 358 |             "  self.pid = os.fork()\n",
 359 |             "Closing dataloader mosaic\n",
 360 |             "\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",
 361 |             "\n",
 362 |             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
 363 |             "       1/10      2.44G      1.185      3.942      1.311          5        640: 100% 18/18 [00:10<00:00,  1.70it/s]\n",
 364 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):   0% 0/3 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 365 |             "  self.pid = os.fork()\n",
 366 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:02<00:00,  1.04it/s]\n",
 367 |             "                   all         81         81    0.00325      0.975      0.444      0.336\n",
 368 |             "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 369 |             "  self.pid = os.fork()\n",
 370 |             "\n",
 371 |             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
 372 |             "       2/10      2.73G     0.8558      2.451     0.9601          5        640: 100% 18/18 [00:06<00:00,  2.98it/s]\n",
 373 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):   0% 0/3 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 374 |             "  self.pid = os.fork()\n",
 375 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.06it/s]\n",
 376 |             "                   all         81         81          1      0.366      0.924      0.651\n",
 377 |             "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 378 |             "  self.pid = os.fork()\n",
 379 |             "\n",
 380 |             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
 381 |             "       3/10      2.74G     0.8546      1.567     0.9666          5        640: 100% 18/18 [00:07<00:00,  2.50it/s]\n",
 382 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):   0% 0/3 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 383 |             "  self.pid = os.fork()\n",
 384 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.13it/s]\n",
 385 |             "                   all         81         81          1      0.272      0.917      0.646\n",
 386 |             "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 387 |             "  self.pid = os.fork()\n",
 388 |             "\n",
 389 |             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
 390 |             "       4/10      2.74G     0.9083      1.277      1.032          5        640: 100% 18/18 [00:06<00:00,  2.61it/s]\n",
 391 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):   0% 0/3 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 392 |             "  self.pid = os.fork()\n",
 393 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.13it/s]\n",
 394 |             "                   all         81         81          1      0.501       0.97      0.718\n",
 395 |             "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 396 |             "  self.pid = os.fork()\n",
 397 |             "\n",
 398 |             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
 399 |             "       5/10      2.74G     0.8821      1.126     0.9786          5        640: 100% 18/18 [00:06<00:00,  2.71it/s]\n",
 400 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):   0% 0/3 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 401 |             "  self.pid = os.fork()\n",
 402 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.92it/s]\n",
 403 |             "                   all         81         81      0.939      0.778      0.934      0.689\n",
 404 |             "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 405 |             "  self.pid = os.fork()\n",
 406 |             "\n",
 407 |             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
 408 |             "       6/10      2.74G     0.8125      1.075     0.9483          5        640: 100% 18/18 [00:05<00:00,  3.09it/s]\n",
 409 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):   0% 0/3 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 410 |             "  self.pid = os.fork()\n",
 411 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  1.93it/s]\n",
 412 |             "                   all         81         81      0.902      0.906      0.956      0.668\n",
 413 |             "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 414 |             "  self.pid = os.fork()\n",
 415 |             "\n",
 416 |             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
 417 |             "       7/10      2.74G     0.8531      1.029     0.9736          5        640: 100% 18/18 [00:05<00:00,  3.07it/s]\n",
 418 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):   0% 0/3 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 419 |             "  self.pid = os.fork()\n",
 420 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.18it/s]\n",
 421 |             "                   all         81         81      0.939      0.947      0.983      0.784\n",
 422 |             "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 423 |             "  self.pid = os.fork()\n",
 424 |             "\n",
 425 |             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
 426 |             "       8/10      2.74G     0.8205      1.004     0.9976          5        640: 100% 18/18 [00:06<00:00,  2.72it/s]\n",
 427 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):   0% 0/3 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 428 |             "  self.pid = os.fork()\n",
 429 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.20it/s]\n",
 430 |             "                   all         81         81      0.918      0.926      0.976      0.768\n",
 431 |             "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 432 |             "  self.pid = os.fork()\n",
 433 |             "\n",
 434 |             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
 435 |             "       9/10      2.74G     0.7593     0.9332     0.9639          5        640: 100% 18/18 [00:07<00:00,  2.46it/s]\n",
 436 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):   0% 0/3 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 437 |             "  self.pid = os.fork()\n",
 438 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:01<00:00,  2.20it/s]\n",
 439 |             "                   all         81         81      0.907      0.958      0.972      0.803\n",
 440 |             "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 441 |             "  self.pid = os.fork()\n",
 442 |             "\n",
 443 |             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
 444 |             "      10/10      2.74G     0.7605     0.8775     0.9622          5        640: 100% 18/18 [00:06<00:00,  2.61it/s]\n",
 445 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):   0% 0/3 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 446 |             "  self.pid = os.fork()\n",
 447 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:02<00:00,  1.09it/s]\n",
 448 |             "                   all         81         81      0.975      0.958      0.985      0.801\n",
 449 |             "\n",
 450 |             "10 epochs completed in 0.032 hours.\n",
 451 |             "Optimizer stripped from runs/detect/train2/weights/last.pt, 6.3MB\n",
 452 |             "Optimizer stripped from runs/detect/train2/weights/best.pt, 6.3MB\n",
 453 |             "\n",
 454 |             "Validating runs/detect/train2/weights/best.pt...\n",
 455 |             "Ultralytics YOLOv8.0.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
 456 |             "Fusing layers... \n",
 457 |             "Model summary: 168 layers, 3005843 parameters, 0 gradients, 8.1 GFLOPs\n",
 458 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):   0% 0/3 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
 459 |             "  self.pid = os.fork()\n",
 460 |             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 3/3 [00:03<00:00,  1.14s/it]\n",
 461 |             "                   all         81         81      0.907      0.959      0.972      0.803\n",
 462 |             "Speed: 0.3ms pre-process, 3.1ms inference, 0.0ms loss, 3.6ms post-process per image\n",
 463 |             "Saving runs/detect/train2/predictions.json...\n",
 464 |             "Results saved to \u001b[1mruns/detect/train2\u001b[0m\n"
 465 |           ]
 466 |         }
 467 |       ]
 468 |     },
 469 |     {
 470 |       "cell_type": "code",
 471 |       "source": [
 472 |         "!python /content/Licence-Plate-Detection-using-YOLO-V8/ultralytics/yolo/v8/detect/predict.py model='/content/Licence-Plate-Detection-using-YOLO-V8/best.pt' source='/content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4'"
 473 |       ],
 474 |       "metadata": {
 475 |         "colab": {
 476 |           "base_uri": "https://localhost:8080/"
 477 |         },
 478 |         "id": "eh9d2hdFMO0l",
 479 |         "outputId": "069776d0-2579-44a9-b13f-2cb94636cebf"
 480 |       },
 481 |       "execution_count": null,
 482 |       "outputs": [
 483 |         {
 484 |           "output_type": "stream",
 485 |           "name": "stdout",
 486 |           "text": [
 487 |             "2024-04-12 01:42:03.728136: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
 488 |             "2024-04-12 01:42:03.728188: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
 489 |             "2024-04-12 01:42:03.729612: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
 490 |             "2024-04-12 01:42:04.974733: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
 491 |             "Ultralytics YOLOv8.0.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
 492 |             "Fusing layers... \n",
 493 |             "Model summary: 168 layers, 3005843 parameters, 0 gradients, 8.1 GFLOPs\n",
 494 |             "WARNING ⚠️ NMS time limit 0.550s exceeded\n",
 495 |             "video 1/1 (1/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 64.0ms\n",
 496 |             "video 1/1 (2/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.4ms\n",
 497 |             "video 1/1 (3/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.8ms\n",
 498 |             "video 1/1 (4/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.8ms\n",
 499 |             "video 1/1 (5/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.9ms\n",
 500 |             "video 1/1 (6/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 3 LicensePlates, 11.0ms\n",
 501 |             "video 1/1 (7/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 3 LicensePlates, 13.6ms\n",
 502 |             "video 1/1 (8/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 2 LicensePlates, 7.2ms\n",
 503 |             "video 1/1 (9/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 3 LicensePlates, 7.4ms\n",
 504 |             "video 1/1 (10/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 2 LicensePlates, 7.1ms\n",
 505 |             "video 1/1 (11/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 2 LicensePlates, 7.3ms\n",
 506 |             "video 1/1 (12/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 2 LicensePlates, 7.2ms\n",
 507 |             "video 1/1 (13/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 2 LicensePlates, 7.0ms\n",
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 509 |             "video 1/1 (15/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.9ms\n",
 510 |             "video 1/1 (16/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 2 LicensePlates, 6.8ms\n",
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1000 |             "video 1/1 (506/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.7ms\n",
1001 |             "video 1/1 (507/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.0ms\n",
1002 |             "video 1/1 (508/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.7ms\n",
1003 |             "video 1/1 (509/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.5ms\n",
1004 |             "video 1/1 (510/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.4ms\n",
1005 |             "video 1/1 (511/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 9.6ms\n",
1006 |             "video 1/1 (512/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.4ms\n",
1007 |             "video 1/1 (513/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.3ms\n",
1008 |             "video 1/1 (514/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.4ms\n",
1009 |             "video 1/1 (515/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.6ms\n",
1010 |             "video 1/1 (516/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.7ms\n",
1011 |             "video 1/1 (517/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.6ms\n",
1012 |             "video 1/1 (518/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.5ms\n",
1013 |             "video 1/1 (519/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.5ms\n",
1014 |             "video 1/1 (520/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.0ms\n",
1015 |             "video 1/1 (521/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 10.7ms\n",
1016 |             "video 1/1 (522/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.4ms\n",
1017 |             "video 1/1 (523/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.5ms\n",
1018 |             "video 1/1 (524/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 2 LicensePlates, 6.6ms\n",
1019 |             "video 1/1 (525/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.4ms\n",
1020 |             "video 1/1 (526/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.4ms\n",
1021 |             "video 1/1 (527/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.3ms\n",
1022 |             "video 1/1 (528/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.3ms\n",
1023 |             "video 1/1 (529/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.2ms\n",
1024 |             "video 1/1 (530/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.6ms\n",
1025 |             "video 1/1 (531/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.6ms\n",
1026 |             "video 1/1 (532/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.6ms\n",
1027 |             "video 1/1 (533/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.5ms\n",
1028 |             "video 1/1 (534/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 9.3ms\n",
1029 |             "video 1/1 (535/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.7ms\n",
1030 |             "video 1/1 (536/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 10.2ms\n",
1031 |             "video 1/1 (537/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.4ms\n",
1032 |             "video 1/1 (538/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.6ms\n",
1033 |             "video 1/1 (539/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.9ms\n",
1034 |             "video 1/1 (540/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.3ms\n",
1035 |             "video 1/1 (541/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.7ms\n",
1036 |             "video 1/1 (542/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.9ms\n",
1037 |             "video 1/1 (543/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.4ms\n",
1038 |             "video 1/1 (544/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.7ms\n",
1039 |             "video 1/1 (545/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.7ms\n",
1040 |             "video 1/1 (546/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.6ms\n",
1041 |             "video 1/1 (547/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.6ms\n",
1042 |             "video 1/1 (548/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.7ms\n",
1043 |             "video 1/1 (549/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.6ms\n",
1044 |             "video 1/1 (550/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.5ms\n",
1045 |             "video 1/1 (551/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 9.9ms\n",
1046 |             "video 1/1 (552/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 8.0ms\n",
1047 |             "video 1/1 (553/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.1ms\n",
1048 |             "video 1/1 (554/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 8.0ms\n",
1049 |             "video 1/1 (555/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.8ms\n",
1050 |             "video 1/1 (556/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.5ms\n",
1051 |             "video 1/1 (557/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.6ms\n",
1052 |             "video 1/1 (558/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.5ms\n",
1053 |             "video 1/1 (559/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.4ms\n",
1054 |             "video 1/1 (560/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.8ms\n",
1055 |             "video 1/1 (561/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.5ms\n",
1056 |             "video 1/1 (562/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.6ms\n",
1057 |             "video 1/1 (563/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.9ms\n",
1058 |             "video 1/1 (564/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.6ms\n",
1059 |             "video 1/1 (565/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.5ms\n",
1060 |             "video 1/1 (566/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 11.8ms\n",
1061 |             "video 1/1 (567/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 11.6ms\n",
1062 |             "video 1/1 (568/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.0ms\n",
1063 |             "video 1/1 (569/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.7ms\n",
1064 |             "video 1/1 (570/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.6ms\n",
1065 |             "video 1/1 (571/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 11.5ms\n",
1066 |             "video 1/1 (572/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.0ms\n",
1067 |             "video 1/1 (573/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.4ms\n",
1068 |             "video 1/1 (574/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.2ms\n",
1069 |             "video 1/1 (575/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.6ms\n",
1070 |             "video 1/1 (576/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.6ms\n",
1071 |             "video 1/1 (577/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.5ms\n",
1072 |             "video 1/1 (578/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.4ms\n",
1073 |             "video 1/1 (579/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 10.1ms\n",
1074 |             "video 1/1 (580/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.7ms\n",
1075 |             "video 1/1 (581/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.7ms\n",
1076 |             "video 1/1 (582/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.3ms\n",
1077 |             "video 1/1 (583/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.1ms\n",
1078 |             "video 1/1 (584/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.7ms\n",
1079 |             "video 1/1 (585/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.9ms\n",
1080 |             "video 1/1 (586/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.5ms\n",
1081 |             "video 1/1 (587/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.2ms\n",
1082 |             "video 1/1 (588/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.9ms\n",
1083 |             "video 1/1 (589/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 6.8ms\n",
1084 |             "video 1/1 (590/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.4ms\n",
1085 |             "video 1/1 (591/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 7.3ms\n",
1086 |             "video 1/1 (592/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 8.7ms\n",
1087 |             "video 1/1 (593/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 7.1ms\n",
1088 |             "video 1/1 (594/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 7.8ms\n",
1089 |             "video 1/1 (595/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 7.2ms\n",
1090 |             "video 1/1 (596/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 7.0ms\n",
1091 |             "video 1/1 (597/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 7.2ms\n",
1092 |             "video 1/1 (598/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 7.1ms\n",
1093 |             "video 1/1 (599/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 6.9ms\n",
1094 |             "video 1/1 (600/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 7.6ms\n",
1095 |             "video 1/1 (601/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 6.9ms\n",
1096 |             "video 1/1 (602/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 7.1ms\n",
1097 |             "video 1/1 (603/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 6.8ms\n",
1098 |             "video 1/1 (604/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 10.6ms\n",
1099 |             "video 1/1 (605/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 10.9ms\n",
1100 |             "video 1/1 (606/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 9.3ms\n",
1101 |             "video 1/1 (607/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 9.8ms\n",
1102 |             "video 1/1 (608/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 13.0ms\n",
1103 |             "video 1/1 (609/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 8.0ms\n",
1104 |             "video 1/1 (610/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 8.6ms\n",
1105 |             "video 1/1 (611/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 8.4ms\n",
1106 |             "video 1/1 (612/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 11.9ms\n",
1107 |             "video 1/1 (613/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 9.1ms\n",
1108 |             "video 1/1 (614/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 9.1ms\n",
1109 |             "video 1/1 (615/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 18.8ms\n",
1110 |             "video 1/1 (616/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 8.1ms\n",
1111 |             "video 1/1 (617/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 11.4ms\n",
1112 |             "video 1/1 (618/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 7.9ms\n",
1113 |             "video 1/1 (619/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 8.1ms\n",
1114 |             "video 1/1 (620/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 8.5ms\n",
1115 |             "video 1/1 (621/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 10.4ms\n",
1116 |             "video 1/1 (622/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 8.5ms\n",
1117 |             "video 1/1 (623/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 9.3ms\n",
1118 |             "video 1/1 (624/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 8.1ms\n",
1119 |             "video 1/1 (625/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 8.1ms\n",
1120 |             "video 1/1 (626/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 8.3ms\n",
1121 |             "video 1/1 (627/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 8.1ms\n",
1122 |             "video 1/1 (628/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 8.4ms\n",
1123 |             "video 1/1 (629/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 8.3ms\n",
1124 |             "video 1/1 (630/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 7.9ms\n",
1125 |             "video 1/1 (631/631) /content/Licence-Plate-Detection-using-YOLO-V8/demo.mp4: 416x640 1 LicensePlate, 10.0ms\n",
1126 |             "Speed: 0.4ms pre-process, 7.8ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 640)\n",
1127 |             "Results saved to \u001b[1mruns/detect/train3\u001b[0m\n"
1128 |           ]
1129 |         }
1130 |       ]
1131 |     },
1132 |     {
1133 |       "cell_type": "code",
1134 |       "source": [
1135 |         "!python /content/Licence-Plate-Detection-using-YOLO-V8/ultralytics/yolo/v8/detect/predict.py model='/content/Licence-Plate-Detection-using-YOLO-V8/runs/detect/train11/weights/best.pt' source='/content/Licence-Plate-Detection-using-YOLO-V8/nm.jpeg'"
1136 |       ],
1137 |       "metadata": {
1138 |         "id": "XFSS5xzGYszt",
1139 |         "outputId": "38208546-5296-40c5-cf14-2f9f87184f34",
1140 |         "colab": {
1141 |           "base_uri": "https://localhost:8080/"
1142 |         }
1143 |       },
1144 |       "execution_count": null,
1145 |       "outputs": [
1146 |         {
1147 |           "output_type": "stream",
1148 |           "name": "stdout",
1149 |           "text": [
1150 |             "2023-09-02 10:16:10.876094: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
1151 |             "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
1152 |             "2023-09-02 10:16:12.318694: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
1153 |             "Ultralytics YOLOv8.0.3 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
1154 |             "Fusing layers... \n",
1155 |             "Model summary: 168 layers, 3005843 parameters, 0 gradients, 8.1 GFLOPs\n",
1156 |             "image 1/1 /content/Licence-Plate-Detection-using-YOLO-V8/nm.jpeg: 512x640 1 LicensePlate, 60.3ms\n",
1157 |             "Speed: 0.5ms pre-process, 60.3ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 640)\n",
1158 |             "Results saved to \u001b[1mruns/detect/train13\u001b[0m\n"
1159 |           ]
1160 |         }
1161 |       ]
1162 |     }
1163 |   ]
1164 | }


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