{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyNjPEFYWXwD9jwHNyN97Pwa"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cXU-5dl-IHWH","executionInfo":{"status":"ok","timestamp":1777784061698,"user_tz":-330,"elapsed":26465,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"6322878d-ef73-4253-cfac-b3b71d6ebfd1"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":["!pip install roboflow"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"3QYVE44JKgf4","executionInfo":{"status":"ok","timestamp":1777784164185,"user_tz":-330,"elapsed":12597,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"23ae87ae-8a45-4c56-edd3-61b8b68ba58d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting roboflow\n"," Downloading roboflow-1.3.7-py3-none-any.whl.metadata (11 kB)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from roboflow) (2026.4.22)\n","Collecting idna==3.7 (from roboflow)\n"," Downloading idna-3.7-py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: cycler in /usr/local/lib/python3.12/dist-packages (from roboflow) (0.12.1)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from roboflow) (1.5.0)\n","Requirement already satisfied: matplotlib in 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project.version(1)\n","dataset = version.download(\"yolov8\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"LUNo-ydTIYdK","executionInfo":{"status":"ok","timestamp":1777784236697,"user_tz":-330,"elapsed":47000,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"40144908-a236-4c6f-e715-a143d12041ca"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["loading Roboflow workspace...\n","loading Roboflow project...\n"]},{"output_type":"stream","name":"stderr","text":["Downloading Dataset Version Zip in License-Plate-Recognition-1 to yolov8:: 100%|██████████| 527622/527622 [00:09<00:00, 55485.72it/s]"]},{"output_type":"stream","name":"stdout","text":["\n"]},{"output_type":"stream","name":"stderr","text":["\n","Extracting Dataset Version Zip to License-Plate-Recognition-1 in yolov8:: 100%|██████████| 20264/20264 [00:02<00:00, 6912.29it/s]\n"]}]},{"cell_type":"code","source":["!pip install ultralytics"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"Ms6uvhlxK4v0","executionInfo":{"status":"ok","timestamp":1777784277555,"user_tz":-330,"elapsed":6345,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"bbe28333-b0cc-4645-8025-eeb53fdab59e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ultralytics\n"," Downloading ultralytics-8.4.46-py3-none-any.whl.metadata (39 kB)\n","Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (2.0.2)\n","Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (3.10.0)\n","Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (4.13.0.92)\n","Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.12/dist-packages (from ultralytics) 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kB)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.3)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.62.1)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.5.0)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (26.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.3.2)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) 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torch>=1.8.0->ultralytics) (11.7.3.90)\n","Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.5.8.93)\n","Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (0.7.1)\n","Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (2.27.5)\n","Requirement already satisfied: nvidia-nvshmem-cu12==3.4.5 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (3.4.5)\n","Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.8.90)\n","Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.8.93)\n","Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in 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\u001b[31m34.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading ultralytics_thop-2.0.19-py3-none-any.whl (28 kB)\n","Installing collected packages: ultralytics-thop, ultralytics\n","Successfully installed ultralytics-8.4.46 ultralytics-thop-2.0.19\n"]}]},{"cell_type":"code","source":["from ultralytics import YOLO\n","import shutil, os"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"Ug7dS0X2MzAk","executionInfo":{"status":"ok","timestamp":1777784315244,"user_tz":-330,"elapsed":10954,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"8397fdf5-e503-496f-c407-dac6d24579f7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Creating new Ultralytics Settings v0.0.6 file ✅ \n","View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n","Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n"]}]},{"cell_type":"code","source":["# Load pretrained YOLOv8 nano model\n","model = YOLO(\"yolov8n.pt\")\n","\n","# Train\n","results = model.train(\n"," data=dataset.location + \"/data.yaml\",\n"," epochs=50,\n"," imgsz=640,\n"," batch=16,\n"," workers=2,\n"," device=0\n",")\n","print(\"Model trained\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SBnFMPKPM8Mb","executionInfo":{"status":"ok","timestamp":1777791861990,"user_tz":-330,"elapsed":2008731,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"6bfdb16d-9666-45a8-bd54-661453b452a4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8n.pt to 'yolov8n.pt': 100% ━━━━━━━━━━━━ 6.2MB 119.2MB/s 0.1s\n","Ultralytics 8.4.46 🚀 Python-3.12.13 torch-2.10.0+cu128 CUDA:0 (Tesla T4, 14913MiB)\n","\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, angle=1.0, augment=False, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, cls_pw=0.0, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/content/License-Plate-Recognition-1/data.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=50, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8n.pt, momentum=0.937, mosaic=1.0, multi_scale=0.0, name=train, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/content/runs/detect/train, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=2, workspace=None\n","\u001b[KDownloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf': 100% ━━━━━━━━━━━━ 755.1KB 21.7MB/s 0.0s\n","Overriding model.yaml nc=80 with nc=1\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 751507 ultralytics.nn.modules.head.Detect [1, 16, None, [64, 128, 256]] \n","Model summary: 130 layers, 3,011,043 parameters, 3,011,027 gradients, 8.2 GFLOPs\n","\n","Transferred 319/355 items from pretrained weights\n","Freezing layer 'model.22.dfl.conv.weight'\n","\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n","\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt to 'yolo26n.pt': 100% ━━━━━━━━━━━━ 5.3MB 122.4MB/s 0.0s\n","\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n","\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 758.7±492.7 MB/s, size: 288.1 KB)\n","\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/License-Plate-Recognition-1/train/labels... 7058 images, 5 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 7058/7058 2.0Kit/s 3.5s\n","\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/License-Plate-Recognition-1/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, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n","\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 306.0±205.0 MB/s, size: 21.9 KB)\n","\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/License-Plate-Recognition-1/valid/labels... 2048 images, 3 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 2048/2048 1.1Kit/s 1.9s\n","\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/License-Plate-Recognition-1/valid/labels.cache\n","\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n","\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n","Plotting labels to /content/runs/detect/train/labels.jpg... \n","Image sizes 640 train, 640 val\n","Using 2 dataloader workers\n","Logging results to \u001b[1m/content/runs/detect/train\u001b[0m\n","Starting training for 50 epochs...\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 1/50 2.03G 1.248 1.428 1.137 5 640: 100% ━━━━━━━━━━━━ 442/442 3.3it/s 2:15\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 3.8it/s 16.7s\n"," all 2048 2134 0.889 0.871 0.904 0.585\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 2/50 2.89G 1.273 0.8566 1.157 3 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:12\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.1it/s 15.8s\n"," all 2048 2134 0.936 0.885 0.941 0.613\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 3/50 2.89G 1.267 0.7819 1.15 7 640: 100% ━━━━━━━━━━━━ 442/442 3.3it/s 2:15\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.0it/s 16.0s\n"," all 2048 2134 0.935 0.896 0.934 0.605\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 4/50 2.89G 1.252 0.7423 1.145 2 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:11\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.0it/s 16.0s\n"," all 2048 2134 0.933 0.917 0.939 0.597\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 5/50 2.89G 1.214 0.6984 1.126 5 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:10\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.1it/s 15.4s\n"," all 2048 2134 0.951 0.919 0.955 0.627\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 6/50 2.89G 1.204 0.6655 1.114 4 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:11\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.2it/s 15.4s\n"," all 2048 2134 0.975 0.918 0.956 0.631\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 7/50 2.89G 1.188 0.6416 1.11 3 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:11\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.2it/s 15.3s\n"," all 2048 2134 0.97 0.928 0.962 0.649\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 8/50 2.89G 1.176 0.6242 1.102 4 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:08\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.2it/s 15.1s\n"," all 2048 2134 0.969 0.937 0.963 0.639\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 9/50 2.89G 1.173 0.6164 1.102 6 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:10\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 14.9s\n"," all 2048 2134 0.971 0.938 0.969 0.657\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 10/50 2.89G 1.162 0.6001 1.094 6 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:10\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.2it/s 15.1s\n"," all 2048 2134 0.962 0.944 0.967 0.663\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 11/50 2.89G 1.154 0.5872 1.087 3 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:10\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.2it/s 15.3s\n"," all 2048 2134 0.972 0.939 0.97 0.666\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 12/50 2.89G 1.131 0.5663 1.079 4 640: 100% ━━━━━━━━━━━━ 442/442 3.3it/s 2:14\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.0it/s 15.8s\n"," all 2048 2134 0.967 0.94 0.969 0.662\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 13/50 2.89G 1.141 0.5646 1.082 4 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:11\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.2it/s 15.4s\n"," all 2048 2134 0.969 0.943 0.972 0.66\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 14/50 2.89G 1.139 0.5629 1.081 3 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:10\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 15.0s\n"," all 2048 2134 0.977 0.94 0.972 0.663\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 15/50 2.89G 1.136 0.5614 1.073 3 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:09\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 15.1s\n"," all 2048 2134 0.967 0.953 0.978 0.664\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 16/50 2.89G 1.123 0.5499 1.074 3 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:11\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.2it/s 15.1s\n"," all 2048 2134 0.976 0.953 0.976 0.673\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 17/50 2.89G 1.12 0.5392 1.072 2 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:10\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.0it/s 15.8s\n"," all 2048 2134 0.979 0.94 0.975 0.677\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 18/50 2.89G 1.113 0.5369 1.072 7 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:11\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 15.0s\n"," all 2048 2134 0.978 0.948 0.976 0.676\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 19/50 2.89G 1.109 0.5282 1.065 1 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:11\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.1it/s 15.5s\n"," all 2048 2134 0.976 0.948 0.976 0.673\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 20/50 2.89G 1.099 0.5259 1.064 4 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:10\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.2it/s 15.1s\n"," all 2048 2134 0.962 0.949 0.973 0.677\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 21/50 2.89G 1.099 0.5212 1.063 6 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:10\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 15.0s\n"," all 2048 2134 0.976 0.94 0.976 0.681\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 22/50 2.89G 1.098 0.5141 1.059 10 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:11\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 15.0s\n"," all 2048 2134 0.98 0.952 0.978 0.682\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 23/50 2.89G 1.088 0.5054 1.058 2 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:07\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 15.1s\n"," all 2048 2134 0.977 0.958 0.98 0.685\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 24/50 2.89G 1.085 0.5027 1.049 2 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:11\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 14.9s\n"," all 2048 2134 0.966 0.955 0.975 0.691\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 25/50 2.89G 1.081 0.5017 1.051 3 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:10\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 15.0s\n"," all 2048 2134 0.973 0.955 0.98 0.688\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 26/50 2.89G 1.08 0.4942 1.046 5 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:07\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.2it/s 15.4s\n"," all 2048 2134 0.982 0.953 0.98 0.685\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 27/50 2.89G 1.071 0.4899 1.05 3 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:11\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.2it/s 15.1s\n"," all 2048 2134 0.98 0.948 0.978 0.687\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 28/50 2.89G 1.075 0.4919 1.05 2 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:10\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.1it/s 15.6s\n"," all 2048 2134 0.978 0.953 0.981 0.688\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 29/50 2.89G 1.061 0.4828 1.045 3 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:08\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.4it/s 14.4s\n"," all 2048 2134 0.975 0.955 0.98 0.688\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 30/50 2.89G 1.057 0.4733 1.04 6 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:08\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 14.9s\n"," all 2048 2134 0.979 0.956 0.981 0.693\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 31/50 2.89G 1.06 0.478 1.045 5 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:07\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.5it/s 14.3s\n"," all 2048 2134 0.979 0.958 0.979 0.69\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 32/50 2.89G 1.041 0.4645 1.037 2 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:06\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 15.1s\n"," all 2048 2134 0.977 0.96 0.98 0.695\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 33/50 2.89G 1.045 0.4656 1.036 5 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:07\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.4it/s 14.5s\n"," all 2048 2134 0.974 0.965 0.981 0.698\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 34/50 2.89G 1.041 0.4608 1.035 5 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:06\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 14.8s\n"," all 2048 2134 0.981 0.961 0.98 0.696\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 35/50 2.89G 1.038 0.4622 1.03 5 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:05\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 15.0s\n"," all 2048 2134 0.979 0.957 0.981 0.698\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 36/50 2.89G 1.025 0.4522 1.027 4 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:05\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 15.0s\n"," all 2048 2134 0.979 0.962 0.98 0.698\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 37/50 2.89G 1.022 0.4474 1.025 5 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:06\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.4it/s 14.7s\n"," all 2048 2134 0.977 0.965 0.981 0.7\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 38/50 2.89G 1.027 0.4434 1.027 5 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:07\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 14.8s\n"," all 2048 2134 0.98 0.963 0.981 0.701\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 39/50 2.89G 1.021 0.4412 1.024 2 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:05\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.4it/s 14.6s\n"," all 2048 2134 0.974 0.966 0.981 0.701\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 40/50 2.89G 1.016 0.4357 1.025 3 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:07\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 14.9s\n"," all 2048 2134 0.982 0.959 0.98 0.7\n","Closing dataloader mosaic\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, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 41/50 2.89G 1.005 0.3998 1.029 2 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:08\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 14.9s\n"," all 2048 2134 0.979 0.966 0.982 0.697\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 42/50 2.89G 0.997 0.3875 1.027 2 640: 100% ━━━━━━━━━━━━ 442/442 3.6it/s 2:04\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.2it/s 15.2s\n"," all 2048 2134 0.982 0.959 0.982 0.702\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 43/50 2.89G 0.988 0.3824 1.019 2 640: 100% ━━━━━━━━━━━━ 442/442 3.4it/s 2:09\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 14.9s\n"," all 2048 2134 0.98 0.965 0.982 0.702\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 44/50 2.89G 0.9797 0.3764 1.015 2 640: 100% ━━━━━━━━━━━━ 442/442 3.6it/s 2:02\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.2it/s 15.3s\n"," all 2048 2134 0.985 0.96 0.982 0.704\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 45/50 2.89G 0.9749 0.3764 1.018 2 640: 100% ━━━━━━━━━━━━ 442/442 3.6it/s 2:04\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.1it/s 15.6s\n"," all 2048 2134 0.975 0.963 0.982 0.704\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 46/50 2.89G 0.9697 0.371 1.017 2 640: 100% ━━━━━━━━━━━━ 442/442 3.6it/s 2:03\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 14.8s\n"," all 2048 2134 0.973 0.969 0.982 0.703\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 47/50 2.89G 0.9672 0.3678 1.012 2 640: 100% ━━━━━━━━━━━━ 442/442 3.6it/s 2:04\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.4it/s 14.6s\n"," all 2048 2134 0.98 0.966 0.982 0.706\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 48/50 2.89G 0.9592 0.3626 1.011 2 640: 100% ━━━━━━━━━━━━ 442/442 3.6it/s 2:02\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 14.8s\n"," all 2048 2134 0.979 0.968 0.982 0.705\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 49/50 2.89G 0.9538 0.3583 1.014 2 640: 100% ━━━━━━━━━━━━ 442/442 3.6it/s 2:03\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.2it/s 15.1s\n"," all 2048 2134 0.984 0.966 0.983 0.708\n","\n"," Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","\u001b[K 50/50 2.89G 0.9466 0.3532 1.006 2 640: 100% ━━━━━━━━━━━━ 442/442 3.5it/s 2:05\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 4.3it/s 14.9s\n"," all 2048 2134 0.982 0.966 0.982 0.706\n","\n","50 epochs completed in 2.003 hours.\n","Optimizer stripped from /content/runs/detect/train/weights/last.pt, 6.2MB\n","Optimizer stripped from /content/runs/detect/train/weights/best.pt, 6.2MB\n","\n","Validating /content/runs/detect/train/weights/best.pt...\n","Ultralytics 8.4.46 🚀 Python-3.12.13 torch-2.10.0+cu128 CUDA:0 (Tesla T4, 14913MiB)\n","Model summary (fused): 73 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs\n","\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 64/64 3.9it/s 16.4s\n"," all 2048 2134 0.984 0.966 0.983 0.708\n","Speed: 0.2ms preprocess, 1.7ms inference, 0.0ms loss, 1.6ms postprocess per image\n","Results saved to \u001b[1m/content/runs/detect/train\u001b[0m\n","Model trained\n"]}]},{"cell_type":"code","source":["# Create folder to save weights\n","os.makedirs(\"saved_models\", exist_ok=True)\n","\n","# Copy trained weights\n","shutil.copy(\"runs/detect/train/weights/best.pt\", \"saved_models/license_plate_best.pt\")\n","shutil.copy(\"runs/detect/train/weights/last.pt\", \"saved_models/license_plate_last.pt\")\n","\n","print(\"Weights saved in saved_models/\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gcF1eDUtbPGI","executionInfo":{"status":"ok","timestamp":1777791872142,"user_tz":-330,"elapsed":24,"user":{"displayName":"Krishna Verma","userId":"18291293214161594665"}},"outputId":"98d4ab92-3947-42e7-8dd1-c660dd3c53c7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Weights saved in saved_models/\n"]}]}]}