{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":1176415,"sourceType":"datasetVersion","datasetId":667889}],"dockerImageVersionId":31040,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install -q ultralytics xmltodict\n\nimport os\nimport shutil\nimport xmltodict\nfrom glob import glob\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom ultralytics import YOLO\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-05T15:30:11.293647Z","iopub.execute_input":"2025-06-05T15:30:11.294216Z","iopub.status.idle":"2025-06-05T15:30:15.377209Z","shell.execute_reply.started":"2025-06-05T15:30:11.294138Z","shell.execute_reply":"2025-06-05T15:30:15.375860Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"# Define dataset base path and label mappings\nbase_path = \"/kaggle/input/face-mask-detection\"\nCLASS_MAP = {'with_mask': 0, 'mask_weared_incorrect': 1, 'without_mask': 2}\n\nprint(\"Sample annotations:\", os.listdir(f\"{base_path}/annotations\")[:3])\nprint(\"Sample images:\", os.listdir(f\"{base_path}/images\")[:3])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-05T15:30:27.832663Z","iopub.execute_input":"2025-06-05T15:30:27.833008Z","iopub.status.idle":"2025-06-05T15:30:27.869518Z","shell.execute_reply.started":"2025-06-05T15:30:27.832976Z","shell.execute_reply":"2025-06-05T15:30:27.868386Z"}},"outputs":[{"name":"stdout","text":"Sample annotations: ['maksssksksss737.xml', 'maksssksksss410.xml', 'maksssksksss537.xml']\nSample images: ['maksssksksss810.png', 'maksssksksss848.png', 'maksssksksss145.png']\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"os.makedirs(\"/kaggle/working/labels\", exist_ok=True)\n\ndef convert_bbox(size, box):\n dw, dh = 1. / size[0], 1. / size[1]\n x_center = (box[0] + box[1]) / 2.0 * dw\n y_center = (box[2] + box[3]) / 2.0 * dh\n width = (box[1] - box[0]) * dw\n height = (box[3] - box[2]) * dh\n return (x_center, y_center, width, height)\n\nfor xml_file in glob(os.path.join(base_path, \"annotations\", \"*.xml\")):\n with open(xml_file) as f:\n doc = xmltodict.parse(f.read())\n\n filename = doc['annotation']['filename']\n img_w = int(doc['annotation']['size']['width'])\n img_h = int(doc['annotation']['size']['height'])\n objects = doc['annotation'].get('object', [])\n if not isinstance(objects, list):\n objects = [objects]\n\n lines = []\n for obj in objects:\n cls = obj['name']\n if cls not in CLASS_MAP:\n continue\n bbox = obj['bndbox']\n box = [int(bbox['xmin']), int(bbox['xmax']), int(bbox['ymin']), int(bbox['ymax'])]\n yolo_box = convert_bbox((img_w, img_h), box)\n lines.append(f\"{CLASS_MAP[cls]} {' '.join(map(str, yolo_box))}\")\n\n with open(f\"/kaggle/working/labels/{filename.replace('.png', '.txt')}\", \"w\") as f:\n f.write(\"\\n\".join(lines))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-05T15:30:31.394230Z","iopub.execute_input":"2025-06-05T15:30:31.394583Z","iopub.status.idle":"2025-06-05T15:30:35.325559Z","shell.execute_reply.started":"2025-06-05T15:30:31.394558Z","shell.execute_reply":"2025-06-05T15:30:35.324190Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"all_imgs = glob(os.path.join(base_path, \"images\", \"*.png\"))\ntrain_imgs, val_imgs = train_test_split(all_imgs, test_size=0.2, random_state=42)\n\nfor split in ['train', 'val']:\n os.makedirs(f\"/kaggle/working/images/{split}\", exist_ok=True)\n os.makedirs(f\"/kaggle/working/labels/{split}\", exist_ok=True)\n\ndef move_files(images, split):\n skipped = 0\n for img_path in images:\n fname = os.path.basename(img_path)\n label = fname.replace(\".png\", \".txt\")\n label_src = f\"/kaggle/working/labels/{label}\"\n\n if not os.path.exists(label_src):\n skipped += 1\n continue\n\n shutil.copy(img_path, f\"/kaggle/working/images/{split}/{fname}\")\n shutil.copy(label_src, f\"/kaggle/working/labels/{split}/{label}\")\n\n print(f\"{split}: Skipped {skipped} files without labels\")\n\nmove_files(train_imgs, \"train\")\nmove_files(val_imgs, \"val\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-05T15:30:52.688657Z","iopub.execute_input":"2025-06-05T15:30:52.689041Z","iopub.status.idle":"2025-06-05T15:31:03.303241Z","shell.execute_reply.started":"2025-06-05T15:30:52.689008Z","shell.execute_reply":"2025-06-05T15:31:03.302010Z"}},"outputs":[{"name":"stdout","text":"train: Skipped 0 files without labels\nval: Skipped 0 files without labels\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"yaml_text = \"\"\"\npath: /kaggle/working\ntrain: images/train\nval: images/val\nnc: 3\nnames: ['with_mask', 'mask_weared_incorrect', 'without_mask']\n\"\"\"\n\nwith open(\"/kaggle/working/data.yaml\", \"w\") as f:\n f.write(yaml_text.strip())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-05T15:31:13.066828Z","iopub.execute_input":"2025-06-05T15:31:13.067201Z","iopub.status.idle":"2025-06-05T15:31:13.072941Z","shell.execute_reply.started":"2025-06-05T15:31:13.067174Z","shell.execute_reply":"2025-06-05T15:31:13.071997Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"model = YOLO(\"yolov8n.pt\") # Replace with 'yolov8s.pt' for better accuracy\n\nmodel.train(\n data=\"/kaggle/working/data.yaml\",\n epochs=25,\n imgsz=640,\n batch=16,\n project=\"/kaggle/working\",\n name=\"face-mask-yolov8\",\n exist_ok=True\n)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-05T15:31:16.443339Z","iopub.execute_input":"2025-06-05T15:31:16.444519Z","iopub.status.idle":"2025-06-05T18:08:21.146715Z","shell.execute_reply.started":"2025-06-05T15:31:16.444486Z","shell.execute_reply":"2025-06-05T18:08:21.144900Z"}},"outputs":[{"name":"stdout","text":"Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n.pt to 'yolov8n.pt'...\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 6.25M/6.25M [00:00<00:00, 174MB/s]","output_type":"stream"},{"name":"stdout","text":"Ultralytics 8.3.150 🚀 Python-3.11.11 torch-2.6.0+cu124 CPU (Intel Xeon 2.20GHz)\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, 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, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/kaggle/working/data.yaml, degrees=0.0, deterministic=True, device=cpu, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=25, erasing=0.4, exist_ok=True, 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=False, name=face-mask-yolov8, 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=/kaggle/working, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=/kaggle/working/face-mask-yolov8, 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=8, workspace=None\nDownloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 755k/755k [00:00<00:00, 41.6MB/s]\n","output_type":"stream"},{"name":"stdout","text":"Overriding model.yaml nc=80 with nc=3\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 751897 ultralytics.nn.modules.head.Detect [3, [64, 128, 256]] \nModel summary: 129 layers, 3,011,433 parameters, 3,011,417 gradients, 8.2 GFLOPs\n\nTransferred 319/355 items from pretrained weights\nFreezing layer 'model.22.dfl.conv.weight'\n\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 2572.0±591.4 MB/s, size: 406.2 KB)\n","output_type":"stream"},{"name":"stderr","text":"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/labels/train... 682 images, 0 backgrounds, 0 corrupt: 100%|██████████| 682/682 [00:01<00:00, 377.52it/s]","output_type":"stream"},{"name":"stdout","text":"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /kaggle/working/labels/train.cache\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\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, num_output_channels=3, method='weighted_average'), 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: 2980.0±628.1 MB/s, size: 406.2 KB)\n","output_type":"stream"},{"name":"stderr","text":"\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/labels/val... 171 images, 0 backgrounds, 0 corrupt: 100%|██████████| 171/171 [00:00<00:00, 368.46it/s]","output_type":"stream"},{"name":"stdout","text":"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /kaggle/working/labels/val.cache\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"Plotting labels to /kaggle/working/face-mask-yolov8/labels.jpg... \n\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001429, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\nImage sizes 640 train, 640 val\nUsing 0 dataloader workers\nLogging results to \u001b[1m/kaggle/working/face-mask-yolov8\u001b[0m\nStarting training for 25 epochs...\n\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 1/25 0G 1.692 2.632 1.362 66 640: 100%|██████████| 43/43 [05:58<00:00, 8.34s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:14<00:13, 4.61s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:28<00:00, 4.83s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.0119 0.427 0.214 0.14\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 2/25 0G 1.289 1.428 1.071 37 640: 100%|██████████| 43/43 [06:11<00:00, 8.64s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:13<00:13, 4.42s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:26<00:00, 4.48s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.425 0.045 0.173 0.109\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 3/25 0G 1.271 1.224 1.065 123 640: 100%|██████████| 43/43 [05:52<00:00, 8.19s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:13<00:13, 4.47s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:27<00:00, 4.64s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.826 0.408 0.458 0.273\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 4/25 0G 1.224 1.116 1.051 56 640: 100%|██████████| 43/43 [05:48<00:00, 8.10s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:13<00:13, 4.54s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:27<00:00, 4.52s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.6 0.552 0.563 0.337\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 5/25 0G 1.214 1.057 1.053 98 640: 100%|██████████| 43/43 [05:52<00:00, 8.20s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:13<00:13, 4.54s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:35<00:00, 5.88s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.74 0.522 0.571 0.346\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 6/25 0G 1.194 0.9709 1.04 78 640: 100%|██████████| 43/43 [05:51<00:00, 8.18s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:13<00:13, 4.44s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:28<00:00, 4.69s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.825 0.554 0.661 0.404\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 7/25 0G 1.2 0.956 1.024 136 640: 100%|██████████| 43/43 [05:46<00:00, 8.06s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:13<00:13, 4.44s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:28<00:00, 4.67s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.692 0.575 0.608 0.37\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 8/25 0G 1.188 0.8867 1.023 97 640: 100%|██████████| 43/43 [05:46<00:00, 8.07s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:12<00:13, 4.36s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:26<00:00, 4.47s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.621 0.596 0.634 0.397\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 9/25 0G 1.137 0.8426 1.013 68 640: 100%|██████████| 43/43 [05:45<00:00, 8.05s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:13<00:13, 4.48s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:27<00:00, 4.55s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.636 0.68 0.692 0.43\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 10/25 0G 1.135 0.8157 1.002 98 640: 100%|██████████| 43/43 [05:50<00:00, 8.16s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:12<00:12, 4.32s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:27<00:00, 4.51s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.714 0.645 0.68 0.416\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 11/25 0G 1.118 0.7841 1.002 66 640: 100%|██████████| 43/43 [05:52<00:00, 8.19s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:13<00:13, 4.40s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:27<00:00, 4.61s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.789 0.675 0.739 0.465\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 12/25 0G 1.104 0.7557 0.9952 76 640: 100%|██████████| 43/43 [05:52<00:00, 8.19s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:12<00:13, 4.35s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:27<00:00, 4.61s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.746 0.694 0.718 0.446\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 13/25 0G 1.114 0.738 0.9847 57 640: 100%|██████████| 43/43 [05:45<00:00, 8.03s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:13<00:13, 4.37s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:26<00:00, 4.48s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.72 0.693 0.745 0.481\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 14/25 0G 1.085 0.7199 0.9905 53 640: 100%|██████████| 43/43 [05:48<00:00, 8.11s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:27<00:00, 4.61s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.742 0.719 0.749 0.461\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 15/25 0G 1.054 0.69 0.9763 88 640: 100%|██████████| 43/43 [05:49<00:00, 8.14s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:12<00:13, 4.34s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:26<00:00, 4.42s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.785 0.69 0.76 0.49\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"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, num_output_channels=3, method='weighted_average'), 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","output_type":"stream"},{"name":"stderr","text":" 16/25 0G 1.04 0.721 0.9713 39 640: 100%|██████████| 43/43 [05:44<00:00, 8.00s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:12<00:13, 4.36s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:27<00:00, 4.61s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.878 0.62 0.752 0.474\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 17/25 0G 1.045 0.6688 0.9705 28 640: 100%|██████████| 43/43 [05:42<00:00, 7.97s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:13<00:13, 4.38s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:27<00:00, 4.57s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.722 0.7 0.737 0.466\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 18/25 0G 1.017 0.6493 0.9686 74 640: 100%|██████████| 43/43 [05:39<00:00, 7.88s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:12<00:12, 4.31s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:26<00:00, 4.40s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.823 0.714 0.776 0.5\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 19/25 0G 1.011 0.6276 0.9528 34 640: 100%|██████████| 43/43 [05:36<00:00, 7.82s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:12<00:13, 4.37s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:28<00:00, 4.74s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.763 0.722 0.77 0.497\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 20/25 0G 1.009 0.6112 0.9555 57 640: 100%|██████████| 43/43 [05:36<00:00, 7.83s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:12<00:12, 4.27s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:26<00:00, 4.36s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.819 0.666 0.756 0.487\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 21/25 0G 0.9931 0.5964 0.952 34 640: 100%|██████████| 43/43 [05:40<00:00, 7.92s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:12<00:12, 4.26s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:26<00:00, 4.38s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.805 0.731 0.777 0.49\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 22/25 0G 0.979 0.5912 0.9477 37 640: 100%|██████████| 43/43 [05:40<00:00, 7.91s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:12<00:12, 4.26s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:27<00:00, 4.51s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.83 0.709 0.78 0.504\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 23/25 0G 0.9671 0.5664 0.9389 28 640: 100%|██████████| 43/43 [05:43<00:00, 7.99s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:12<00:13, 4.34s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:27<00:00, 4.57s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.879 0.705 0.787 0.494\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 24/25 0G 0.951 0.5514 0.9333 37 640: 100%|██████████| 43/43 [05:36<00:00, 7.83s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:12<00:12, 4.30s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:26<00:00, 4.47s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.832 0.709 0.786 0.513\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n","output_type":"stream"},{"name":"stderr","text":" 25/25 0G 0.9392 0.5417 0.9273 34 640: 100%|██████████| 43/43 [05:45<00:00, 8.04s/it]\n Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:12<00:12, 4.33s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:26<00:00, 4.48s/it]","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.9 0.696 0.784 0.51\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"},{"name":"stdout","text":"\n25 epochs completed in 2.605 hours.\nOptimizer stripped from /kaggle/working/face-mask-yolov8/weights/last.pt, 6.2MB\nOptimizer stripped from /kaggle/working/face-mask-yolov8/weights/best.pt, 6.2MB\n\nValidating /kaggle/working/face-mask-yolov8/weights/best.pt...\nUltralytics 8.3.150 🚀 Python-3.11.11 torch-2.6.0+cu124 CPU (Intel Xeon 2.20GHz)\nModel summary (fused): 72 layers, 3,006,233 parameters, 0 gradients, 8.1 GFLOPs\n","output_type":"stream"},{"name":"stderr","text":" Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 3/6 [00:11<00:11, 3.94s/it]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited\n Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:24<00:00, 4.15s/it]\n/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less\n xa[xa < 0] = -1\n/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less\n xa[xa < 0] = -1\n","output_type":"stream"},{"name":"stdout","text":" all 171 883 0.833 0.709 0.786 0.513\n with_mask 156 686 0.846 0.911 0.925 0.636\n mask_weared_incorrect 24 35 0.87 0.382 0.585 0.39\n without_mask 57 162 0.783 0.833 0.848 0.511\nSpeed: 2.3ms preprocess, 116.7ms inference, 0.0ms loss, 0.9ms postprocess per image\nResults saved to \u001b[1m/kaggle/working/face-mask-yolov8\u001b[0m\n","output_type":"stream"},{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"ultralytics.utils.metrics.DetMetrics object with attributes:\n\nap_class_index: array([0, 1, 2])\nbox: ultralytics.utils.metrics.Metric object\nconfusion_matrix: \ncurves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']\ncurves_results: [[array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n 0.024024, 0.025025, 0.026026, 0.027027, 0.028028, 0.029029, 0.03003, 0.031031, 0.032032, 0.033033, 0.034034, 0.035035, 0.036036, 0.037037, 0.038038, 0.039039, 0.04004, 0.041041, 0.042042, 0.043043, 0.044044, 0.045045, 0.046046, 0.047047,\n 0.048048, 0.049049, 0.05005, 0.051051, 0.052052, 0.053053, 0.054054, 0.055055, 0.056056, 0.057057, 0.058058, 0.059059, 0.06006, 0.061061, 0.062062, 0.063063, 0.064064, 0.065065, 0.066066, 0.067067, 0.068068, 0.069069, 0.07007, 0.071071,\n 0.072072, 0.073073, 0.074074, 0.075075, 0.076076, 0.077077, 0.078078, 0.079079, 0.08008, 0.081081, 0.082082, 0.083083, 0.084084, 0.085085, 0.086086, 0.087087, 0.088088, 0.089089, 0.09009, 0.091091, 0.092092, 0.093093, 0.094094, 0.095095,\n 0.096096, 0.097097, 0.098098, 0.099099, 0.1001, 0.1011, 0.1021, 0.1031, 0.1041, 0.10511, 0.10611, 0.10711, 0.10811, 0.10911, 0.11011, 0.11111, 0.11211, 0.11311, 0.11411, 0.11512, 0.11612, 0.11712, 0.11812, 0.11912,\n 0.12012, 0.12112, 0.12212, 0.12312, 0.12412, 0.12513, 0.12613, 0.12713, 0.12813, 0.12913, 0.13013, 0.13113, 0.13213, 0.13313, 0.13413, 0.13514, 0.13614, 0.13714, 0.13814, 0.13914, 0.14014, 0.14114, 0.14214, 0.14314,\n 0.14414, 0.14515, 0.14615, 0.14715, 0.14815, 0.14915, 0.15015, 0.15115, 0.15215, 0.15315, 0.15415, 0.15516, 0.15616, 0.15716, 0.15816, 0.15916, 0.16016, 0.16116, 0.16216, 0.16316, 0.16416, 0.16517, 0.16617, 0.16717,\n 0.16817, 0.16917, 0.17017, 0.17117, 0.17217, 0.17317, 0.17417, 0.17518, 0.17618, 0.17718, 0.17818, 0.17918, 0.18018, 0.18118, 0.18218, 0.18318, 0.18418, 0.18519, 0.18619, 0.18719, 0.18819, 0.18919, 0.19019, 0.19119,\n 0.19219, 0.19319, 0.19419, 0.1952, 0.1962, 0.1972, 0.1982, 0.1992, 0.2002, 0.2012, 0.2022, 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0.31431, 0.31532, 0.31632, 0.31732, 0.31832, 0.31932, 0.32032, 0.32132, 0.32232, 0.32332, 0.32432, 0.32533, 0.32633, 0.32733, 0.32833, 0.32933, 0.33033, 0.33133, 0.33233, 0.33333, 0.33433, 0.33534,\n 0.33634, 0.33734, 0.33834, 0.33934, 0.34034, 0.34134, 0.34234, 0.34334, 0.34434, 0.34535, 0.34635, 0.34735, 0.34835, 0.34935, 0.35035, 0.35135, 0.35235, 0.35335, 0.35435, 0.35536, 0.35636, 0.35736, 0.35836, 0.35936,\n 0.36036, 0.36136, 0.36236, 0.36336, 0.36436, 0.36537, 0.36637, 0.36737, 0.36837, 0.36937, 0.37037, 0.37137, 0.37237, 0.37337, 0.37437, 0.37538, 0.37638, 0.37738, 0.37838, 0.37938, 0.38038, 0.38138, 0.38238, 0.38338,\n 0.38438, 0.38539, 0.38639, 0.38739, 0.38839, 0.38939, 0.39039, 0.39139, 0.39239, 0.39339, 0.39439, 0.3954, 0.3964, 0.3974, 0.3984, 0.3994, 0.4004, 0.4014, 0.4024, 0.4034, 0.4044, 0.40541, 0.40641, 0.40741,\n 0.40841, 0.40941, 0.41041, 0.41141, 0.41241, 0.41341, 0.41441, 0.41542, 0.41642, 0.41742, 0.41842, 0.41942, 0.42042, 0.42142, 0.42242, 0.42342, 0.42442, 0.42543, 0.42643, 0.42743, 0.42843, 0.42943, 0.43043, 0.43143,\n 0.43243, 0.43343, 0.43443, 0.43544, 0.43644, 0.43744, 0.43844, 0.43944, 0.44044, 0.44144, 0.44244, 0.44344, 0.44444, 0.44545, 0.44645, 0.44745, 0.44845, 0.44945, 0.45045, 0.45145, 0.45245, 0.45345, 0.45445, 0.45546,\n 0.45646, 0.45746, 0.45846, 0.45946, 0.46046, 0.46146, 0.46246, 0.46346, 0.46446, 0.46547, 0.46647, 0.46747, 0.46847, 0.46947, 0.47047, 0.47147, 0.47247, 0.47347, 0.47447, 0.47548, 0.47648, 0.47748, 0.47848, 0.47948,\n 0.48048, 0.48148, 0.48248, 0.48348, 0.48448, 0.48549, 0.48649, 0.48749, 0.48849, 0.48949, 0.49049, 0.49149, 0.49249, 0.49349, 0.49449, 0.4955, 0.4965, 0.4975, 0.4985, 0.4995, 0.5005, 0.5015, 0.5025, 0.5035,\n 0.5045, 0.50551, 0.50651, 0.50751, 0.50851, 0.50951, 0.51051, 0.51151, 0.51251, 0.51351, 0.51451, 0.51552, 0.51652, 0.51752, 0.51852, 0.51952, 0.52052, 0.52152, 0.52252, 0.52352, 0.52452, 0.52553, 0.52653, 0.52753,\n 0.52853, 0.52953, 0.53053, 0.53153, 0.53253, 0.53353, 0.53453, 0.53554, 0.53654, 0.53754, 0.53854, 0.53954, 0.54054, 0.54154, 0.54254, 0.54354, 0.54454, 0.54555, 0.54655, 0.54755, 0.54855, 0.54955, 0.55055, 0.55155,\n 0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.56156, 0.56256, 0.56356, 0.56456, 0.56557, 0.56657, 0.56757, 0.56857, 0.56957, 0.57057, 0.57157, 0.57257, 0.57357, 0.57457, 0.57558,\n 0.57658, 0.57758, 0.57858, 0.57958, 0.58058, 0.58158, 0.58258, 0.58358, 0.58458, 0.58559, 0.58659, 0.58759, 0.58859, 0.58959, 0.59059, 0.59159, 0.59259, 0.59359, 0.59459, 0.5956, 0.5966, 0.5976, 0.5986, 0.5996,\n 0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.60961, 0.61061, 0.61161, 0.61261, 0.61361, 0.61461, 0.61562, 0.61662, 0.61762, 0.61862, 0.61962, 0.62062, 0.62162, 0.62262, 0.62362,\n 0.62462, 0.62563, 0.62663, 0.62763, 0.62863, 0.62963, 0.63063, 0.63163, 0.63263, 0.63363, 0.63463, 0.63564, 0.63664, 0.63764, 0.63864, 0.63964, 0.64064, 0.64164, 0.64264, 0.64364, 0.64464, 0.64565, 0.64665, 0.64765,\n 0.64865, 0.64965, 0.65065, 0.65165, 0.65265, 0.65365, 0.65465, 0.65566, 0.65666, 0.65766, 0.65866, 0.65966, 0.66066, 0.66166, 0.66266, 0.66366, 0.66466, 0.66567, 0.66667, 0.66767, 0.66867, 0.66967, 0.67067, 0.67167,\n 0.67267, 0.67367, 0.67467, 0.67568, 0.67668, 0.67768, 0.67868, 0.67968, 0.68068, 0.68168, 0.68268, 0.68368, 0.68468, 0.68569, 0.68669, 0.68769, 0.68869, 0.68969, 0.69069, 0.69169, 0.69269, 0.69369, 0.69469, 0.6957,\n 0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.70571, 0.70671, 0.70771, 0.70871, 0.70971, 0.71071, 0.71171, 0.71271, 0.71371, 0.71471, 0.71572, 0.71672, 0.71772, 0.71872, 0.71972,\n 0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.72973, 0.73073, 0.73173, 0.73273, 0.73373, 0.73473, 0.73574, 0.73674, 0.73774, 0.73874, 0.73974, 0.74074, 0.74174, 0.74274, 0.74374,\n 0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.75375, 0.75475, 0.75576, 0.75676, 0.75776, 0.75876, 0.75976, 0.76076, 0.76176, 0.76276, 0.76376, 0.76476, 0.76577, 0.76677, 0.76777,\n 0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 1, 1, 1, ..., 0.011032, 0.0055161, 0],\n [ 1, 1, 1, ..., 0.001982, 0.00099101, 0],\n [ 1, 1, 1, ..., 0.0027621, 0.0013811, 0]]), 'Recall', 'Precision'], [array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n 0.024024, 0.025025, 0.026026, 0.027027, 0.028028, 0.029029, 0.03003, 0.031031, 0.032032, 0.033033, 0.034034, 0.035035, 0.036036, 0.037037, 0.038038, 0.039039, 0.04004, 0.041041, 0.042042, 0.043043, 0.044044, 0.045045, 0.046046, 0.047047,\n 0.048048, 0.049049, 0.05005, 0.051051, 0.052052, 0.053053, 0.054054, 0.055055, 0.056056, 0.057057, 0.058058, 0.059059, 0.06006, 0.061061, 0.062062, 0.063063, 0.064064, 0.065065, 0.066066, 0.067067, 0.068068, 0.069069, 0.07007, 0.071071,\n 0.072072, 0.073073, 0.074074, 0.075075, 0.076076, 0.077077, 0.078078, 0.079079, 0.08008, 0.081081, 0.082082, 0.083083, 0.084084, 0.085085, 0.086086, 0.087087, 0.088088, 0.089089, 0.09009, 0.091091, 0.092092, 0.093093, 0.094094, 0.095095,\n 0.096096, 0.097097, 0.098098, 0.099099, 0.1001, 0.1011, 0.1021, 0.1031, 0.1041, 0.10511, 0.10611, 0.10711, 0.10811, 0.10911, 0.11011, 0.11111, 0.11211, 0.11311, 0.11411, 0.11512, 0.11612, 0.11712, 0.11812, 0.11912,\n 0.12012, 0.12112, 0.12212, 0.12312, 0.12412, 0.12513, 0.12613, 0.12713, 0.12813, 0.12913, 0.13013, 0.13113, 0.13213, 0.13313, 0.13413, 0.13514, 0.13614, 0.13714, 0.13814, 0.13914, 0.14014, 0.14114, 0.14214, 0.14314,\n 0.14414, 0.14515, 0.14615, 0.14715, 0.14815, 0.14915, 0.15015, 0.15115, 0.15215, 0.15315, 0.15415, 0.15516, 0.15616, 0.15716, 0.15816, 0.15916, 0.16016, 0.16116, 0.16216, 0.16316, 0.16416, 0.16517, 0.16617, 0.16717,\n 0.16817, 0.16917, 0.17017, 0.17117, 0.17217, 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0.28428, 0.28529, 0.28629, 0.28729,\n 0.28829, 0.28929, 0.29029, 0.29129, 0.29229, 0.29329, 0.29429, 0.2953, 0.2963, 0.2973, 0.2983, 0.2993, 0.3003, 0.3013, 0.3023, 0.3033, 0.3043, 0.30531, 0.30631, 0.30731, 0.30831, 0.30931, 0.31031, 0.31131,\n 0.31231, 0.31331, 0.31431, 0.31532, 0.31632, 0.31732, 0.31832, 0.31932, 0.32032, 0.32132, 0.32232, 0.32332, 0.32432, 0.32533, 0.32633, 0.32733, 0.32833, 0.32933, 0.33033, 0.33133, 0.33233, 0.33333, 0.33433, 0.33534,\n 0.33634, 0.33734, 0.33834, 0.33934, 0.34034, 0.34134, 0.34234, 0.34334, 0.34434, 0.34535, 0.34635, 0.34735, 0.34835, 0.34935, 0.35035, 0.35135, 0.35235, 0.35335, 0.35435, 0.35536, 0.35636, 0.35736, 0.35836, 0.35936,\n 0.36036, 0.36136, 0.36236, 0.36336, 0.36436, 0.36537, 0.36637, 0.36737, 0.36837, 0.36937, 0.37037, 0.37137, 0.37237, 0.37337, 0.37437, 0.37538, 0.37638, 0.37738, 0.37838, 0.37938, 0.38038, 0.38138, 0.38238, 0.38338,\n 0.38438, 0.38539, 0.38639, 0.38739, 0.38839, 0.38939, 0.39039, 0.39139, 0.39239, 0.39339, 0.39439, 0.3954, 0.3964, 0.3974, 0.3984, 0.3994, 0.4004, 0.4014, 0.4024, 0.4034, 0.4044, 0.40541, 0.40641, 0.40741,\n 0.40841, 0.40941, 0.41041, 0.41141, 0.41241, 0.41341, 0.41441, 0.41542, 0.41642, 0.41742, 0.41842, 0.41942, 0.42042, 0.42142, 0.42242, 0.42342, 0.42442, 0.42543, 0.42643, 0.42743, 0.42843, 0.42943, 0.43043, 0.43143,\n 0.43243, 0.43343, 0.43443, 0.43544, 0.43644, 0.43744, 0.43844, 0.43944, 0.44044, 0.44144, 0.44244, 0.44344, 0.44444, 0.44545, 0.44645, 0.44745, 0.44845, 0.44945, 0.45045, 0.45145, 0.45245, 0.45345, 0.45445, 0.45546,\n 0.45646, 0.45746, 0.45846, 0.45946, 0.46046, 0.46146, 0.46246, 0.46346, 0.46446, 0.46547, 0.46647, 0.46747, 0.46847, 0.46947, 0.47047, 0.47147, 0.47247, 0.47347, 0.47447, 0.47548, 0.47648, 0.47748, 0.47848, 0.47948,\n 0.48048, 0.48148, 0.48248, 0.48348, 0.48448, 0.48549, 0.48649, 0.48749, 0.48849, 0.48949, 0.49049, 0.49149, 0.49249, 0.49349, 0.49449, 0.4955, 0.4965, 0.4975, 0.4985, 0.4995, 0.5005, 0.5015, 0.5025, 0.5035,\n 0.5045, 0.50551, 0.50651, 0.50751, 0.50851, 0.50951, 0.51051, 0.51151, 0.51251, 0.51351, 0.51451, 0.51552, 0.51652, 0.51752, 0.51852, 0.51952, 0.52052, 0.52152, 0.52252, 0.52352, 0.52452, 0.52553, 0.52653, 0.52753,\n 0.52853, 0.52953, 0.53053, 0.53153, 0.53253, 0.53353, 0.53453, 0.53554, 0.53654, 0.53754, 0.53854, 0.53954, 0.54054, 0.54154, 0.54254, 0.54354, 0.54454, 0.54555, 0.54655, 0.54755, 0.54855, 0.54955, 0.55055, 0.55155,\n 0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.56156, 0.56256, 0.56356, 0.56456, 0.56557, 0.56657, 0.56757, 0.56857, 0.56957, 0.57057, 0.57157, 0.57257, 0.57357, 0.57457, 0.57558,\n 0.57658, 0.57758, 0.57858, 0.57958, 0.58058, 0.58158, 0.58258, 0.58358, 0.58458, 0.58559, 0.58659, 0.58759, 0.58859, 0.58959, 0.59059, 0.59159, 0.59259, 0.59359, 0.59459, 0.5956, 0.5966, 0.5976, 0.5986, 0.5996,\n 0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.60961, 0.61061, 0.61161, 0.61261, 0.61361, 0.61461, 0.61562, 0.61662, 0.61762, 0.61862, 0.61962, 0.62062, 0.62162, 0.62262, 0.62362,\n 0.62462, 0.62563, 0.62663, 0.62763, 0.62863, 0.62963, 0.63063, 0.63163, 0.63263, 0.63363, 0.63463, 0.63564, 0.63664, 0.63764, 0.63864, 0.63964, 0.64064, 0.64164, 0.64264, 0.64364, 0.64464, 0.64565, 0.64665, 0.64765,\n 0.64865, 0.64965, 0.65065, 0.65165, 0.65265, 0.65365, 0.65465, 0.65566, 0.65666, 0.65766, 0.65866, 0.65966, 0.66066, 0.66166, 0.66266, 0.66366, 0.66466, 0.66567, 0.66667, 0.66767, 0.66867, 0.66967, 0.67067, 0.67167,\n 0.67267, 0.67367, 0.67467, 0.67568, 0.67668, 0.67768, 0.67868, 0.67968, 0.68068, 0.68168, 0.68268, 0.68368, 0.68468, 0.68569, 0.68669, 0.68769, 0.68869, 0.68969, 0.69069, 0.69169, 0.69269, 0.69369, 0.69469, 0.6957,\n 0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.70571, 0.70671, 0.70771, 0.70871, 0.70971, 0.71071, 0.71171, 0.71271, 0.71371, 0.71471, 0.71572, 0.71672, 0.71772, 0.71872, 0.71972,\n 0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.72973, 0.73073, 0.73173, 0.73273, 0.73373, 0.73473, 0.73574, 0.73674, 0.73774, 0.73874, 0.73974, 0.74074, 0.74174, 0.74274, 0.74374,\n 0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.75375, 0.75475, 0.75576, 0.75676, 0.75776, 0.75876, 0.75976, 0.76076, 0.76176, 0.76276, 0.76376, 0.76476, 0.76577, 0.76677, 0.76777,\n 0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.23957, 0.23967, 0.33478, ..., 0, 0, 0],\n [ 0.054972, 0.054972, 0.084567, ..., 0, 0, 0],\n [ 0.11224, 0.11228, 0.1755, ..., 0, 0, 0]]), 'Confidence', 'F1'], [array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n 0.024024, 0.025025, 0.026026, 0.027027, 0.028028, 0.029029, 0.03003, 0.031031, 0.032032, 0.033033, 0.034034, 0.035035, 0.036036, 0.037037, 0.038038, 0.039039, 0.04004, 0.041041, 0.042042, 0.043043, 0.044044, 0.045045, 0.046046, 0.047047,\n 0.048048, 0.049049, 0.05005, 0.051051, 0.052052, 0.053053, 0.054054, 0.055055, 0.056056, 0.057057, 0.058058, 0.059059, 0.06006, 0.061061, 0.062062, 0.063063, 0.064064, 0.065065, 0.066066, 0.067067, 0.068068, 0.069069, 0.07007, 0.071071,\n 0.072072, 0.073073, 0.074074, 0.075075, 0.076076, 0.077077, 0.078078, 0.079079, 0.08008, 0.081081, 0.082082, 0.083083, 0.084084, 0.085085, 0.086086, 0.087087, 0.088088, 0.089089, 0.09009, 0.091091, 0.092092, 0.093093, 0.094094, 0.095095,\n 0.096096, 0.097097, 0.098098, 0.099099, 0.1001, 0.1011, 0.1021, 0.1031, 0.1041, 0.10511, 0.10611, 0.10711, 0.10811, 0.10911, 0.11011, 0.11111, 0.11211, 0.11311, 0.11411, 0.11512, 0.11612, 0.11712, 0.11812, 0.11912,\n 0.12012, 0.12112, 0.12212, 0.12312, 0.12412, 0.12513, 0.12613, 0.12713, 0.12813, 0.12913, 0.13013, 0.13113, 0.13213, 0.13313, 0.13413, 0.13514, 0.13614, 0.13714, 0.13814, 0.13914, 0.14014, 0.14114, 0.14214, 0.14314,\n 0.14414, 0.14515, 0.14615, 0.14715, 0.14815, 0.14915, 0.15015, 0.15115, 0.15215, 0.15315, 0.15415, 0.15516, 0.15616, 0.15716, 0.15816, 0.15916, 0.16016, 0.16116, 0.16216, 0.16316, 0.16416, 0.16517, 0.16617, 0.16717,\n 0.16817, 0.16917, 0.17017, 0.17117, 0.17217, 0.17317, 0.17417, 0.17518, 0.17618, 0.17718, 0.17818, 0.17918, 0.18018, 0.18118, 0.18218, 0.18318, 0.18418, 0.18519, 0.18619, 0.18719, 0.18819, 0.18919, 0.19019, 0.19119,\n 0.19219, 0.19319, 0.19419, 0.1952, 0.1962, 0.1972, 0.1982, 0.1992, 0.2002, 0.2012, 0.2022, 0.2032, 0.2042, 0.20521, 0.20621, 0.20721, 0.20821, 0.20921, 0.21021, 0.21121, 0.21221, 0.21321, 0.21421, 0.21522,\n 0.21622, 0.21722, 0.21822, 0.21922, 0.22022, 0.22122, 0.22222, 0.22322, 0.22422, 0.22523, 0.22623, 0.22723, 0.22823, 0.22923, 0.23023, 0.23123, 0.23223, 0.23323, 0.23423, 0.23524, 0.23624, 0.23724, 0.23824, 0.23924,\n 0.24024, 0.24124, 0.24224, 0.24324, 0.24424, 0.24525, 0.24625, 0.24725, 0.24825, 0.24925, 0.25025, 0.25125, 0.25225, 0.25325, 0.25425, 0.25526, 0.25626, 0.25726, 0.25826, 0.25926, 0.26026, 0.26126, 0.26226, 0.26326,\n 0.26426, 0.26527, 0.26627, 0.26727, 0.26827, 0.26927, 0.27027, 0.27127, 0.27227, 0.27327, 0.27427, 0.27528, 0.27628, 0.27728, 0.27828, 0.27928, 0.28028, 0.28128, 0.28228, 0.28328, 0.28428, 0.28529, 0.28629, 0.28729,\n 0.28829, 0.28929, 0.29029, 0.29129, 0.29229, 0.29329, 0.29429, 0.2953, 0.2963, 0.2973, 0.2983, 0.2993, 0.3003, 0.3013, 0.3023, 0.3033, 0.3043, 0.30531, 0.30631, 0.30731, 0.30831, 0.30931, 0.31031, 0.31131,\n 0.31231, 0.31331, 0.31431, 0.31532, 0.31632, 0.31732, 0.31832, 0.31932, 0.32032, 0.32132, 0.32232, 0.32332, 0.32432, 0.32533, 0.32633, 0.32733, 0.32833, 0.32933, 0.33033, 0.33133, 0.33233, 0.33333, 0.33433, 0.33534,\n 0.33634, 0.33734, 0.33834, 0.33934, 0.34034, 0.34134, 0.34234, 0.34334, 0.34434, 0.34535, 0.34635, 0.34735, 0.34835, 0.34935, 0.35035, 0.35135, 0.35235, 0.35335, 0.35435, 0.35536, 0.35636, 0.35736, 0.35836, 0.35936,\n 0.36036, 0.36136, 0.36236, 0.36336, 0.36436, 0.36537, 0.36637, 0.36737, 0.36837, 0.36937, 0.37037, 0.37137, 0.37237, 0.37337, 0.37437, 0.37538, 0.37638, 0.37738, 0.37838, 0.37938, 0.38038, 0.38138, 0.38238, 0.38338,\n 0.38438, 0.38539, 0.38639, 0.38739, 0.38839, 0.38939, 0.39039, 0.39139, 0.39239, 0.39339, 0.39439, 0.3954, 0.3964, 0.3974, 0.3984, 0.3994, 0.4004, 0.4014, 0.4024, 0.4034, 0.4044, 0.40541, 0.40641, 0.40741,\n 0.40841, 0.40941, 0.41041, 0.41141, 0.41241, 0.41341, 0.41441, 0.41542, 0.41642, 0.41742, 0.41842, 0.41942, 0.42042, 0.42142, 0.42242, 0.42342, 0.42442, 0.42543, 0.42643, 0.42743, 0.42843, 0.42943, 0.43043, 0.43143,\n 0.43243, 0.43343, 0.43443, 0.43544, 0.43644, 0.43744, 0.43844, 0.43944, 0.44044, 0.44144, 0.44244, 0.44344, 0.44444, 0.44545, 0.44645, 0.44745, 0.44845, 0.44945, 0.45045, 0.45145, 0.45245, 0.45345, 0.45445, 0.45546,\n 0.45646, 0.45746, 0.45846, 0.45946, 0.46046, 0.46146, 0.46246, 0.46346, 0.46446, 0.46547, 0.46647, 0.46747, 0.46847, 0.46947, 0.47047, 0.47147, 0.47247, 0.47347, 0.47447, 0.47548, 0.47648, 0.47748, 0.47848, 0.47948,\n 0.48048, 0.48148, 0.48248, 0.48348, 0.48448, 0.48549, 0.48649, 0.48749, 0.48849, 0.48949, 0.49049, 0.49149, 0.49249, 0.49349, 0.49449, 0.4955, 0.4965, 0.4975, 0.4985, 0.4995, 0.5005, 0.5015, 0.5025, 0.5035,\n 0.5045, 0.50551, 0.50651, 0.50751, 0.50851, 0.50951, 0.51051, 0.51151, 0.51251, 0.51351, 0.51451, 0.51552, 0.51652, 0.51752, 0.51852, 0.51952, 0.52052, 0.52152, 0.52252, 0.52352, 0.52452, 0.52553, 0.52653, 0.52753,\n 0.52853, 0.52953, 0.53053, 0.53153, 0.53253, 0.53353, 0.53453, 0.53554, 0.53654, 0.53754, 0.53854, 0.53954, 0.54054, 0.54154, 0.54254, 0.54354, 0.54454, 0.54555, 0.54655, 0.54755, 0.54855, 0.54955, 0.55055, 0.55155,\n 0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.56156, 0.56256, 0.56356, 0.56456, 0.56557, 0.56657, 0.56757, 0.56857, 0.56957, 0.57057, 0.57157, 0.57257, 0.57357, 0.57457, 0.57558,\n 0.57658, 0.57758, 0.57858, 0.57958, 0.58058, 0.58158, 0.58258, 0.58358, 0.58458, 0.58559, 0.58659, 0.58759, 0.58859, 0.58959, 0.59059, 0.59159, 0.59259, 0.59359, 0.59459, 0.5956, 0.5966, 0.5976, 0.5986, 0.5996,\n 0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.60961, 0.61061, 0.61161, 0.61261, 0.61361, 0.61461, 0.61562, 0.61662, 0.61762, 0.61862, 0.61962, 0.62062, 0.62162, 0.62262, 0.62362,\n 0.62462, 0.62563, 0.62663, 0.62763, 0.62863, 0.62963, 0.63063, 0.63163, 0.63263, 0.63363, 0.63463, 0.63564, 0.63664, 0.63764, 0.63864, 0.63964, 0.64064, 0.64164, 0.64264, 0.64364, 0.64464, 0.64565, 0.64665, 0.64765,\n 0.64865, 0.64965, 0.65065, 0.65165, 0.65265, 0.65365, 0.65465, 0.65566, 0.65666, 0.65766, 0.65866, 0.65966, 0.66066, 0.66166, 0.66266, 0.66366, 0.66466, 0.66567, 0.66667, 0.66767, 0.66867, 0.66967, 0.67067, 0.67167,\n 0.67267, 0.67367, 0.67467, 0.67568, 0.67668, 0.67768, 0.67868, 0.67968, 0.68068, 0.68168, 0.68268, 0.68368, 0.68468, 0.68569, 0.68669, 0.68769, 0.68869, 0.68969, 0.69069, 0.69169, 0.69269, 0.69369, 0.69469, 0.6957,\n 0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.70571, 0.70671, 0.70771, 0.70871, 0.70971, 0.71071, 0.71171, 0.71271, 0.71371, 0.71471, 0.71572, 0.71672, 0.71772, 0.71872, 0.71972,\n 0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.72973, 0.73073, 0.73173, 0.73273, 0.73373, 0.73473, 0.73574, 0.73674, 0.73774, 0.73874, 0.73974, 0.74074, 0.74174, 0.74274, 0.74374,\n 0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.75375, 0.75475, 0.75576, 0.75676, 0.75776, 0.75876, 0.75976, 0.76076, 0.76176, 0.76276, 0.76376, 0.76476, 0.76577, 0.76677, 0.76777,\n 0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.13656, 0.13662, 0.20233, ..., 1, 1, 1],\n [ 0.028286, 0.028286, 0.044208, ..., 1, 1, 1],\n [ 0.059615, 0.059637, 0.096612, ..., 1, 1, 1]]), 'Confidence', 'Precision'], [array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n 0.024024, 0.025025, 0.026026, 0.027027, 0.028028, 0.029029, 0.03003, 0.031031, 0.032032, 0.033033, 0.034034, 0.035035, 0.036036, 0.037037, 0.038038, 0.039039, 0.04004, 0.041041, 0.042042, 0.043043, 0.044044, 0.045045, 0.046046, 0.047047,\n 0.048048, 0.049049, 0.05005, 0.051051, 0.052052, 0.053053, 0.054054, 0.055055, 0.056056, 0.057057, 0.058058, 0.059059, 0.06006, 0.061061, 0.062062, 0.063063, 0.064064, 0.065065, 0.066066, 0.067067, 0.068068, 0.069069, 0.07007, 0.071071,\n 0.072072, 0.073073, 0.074074, 0.075075, 0.076076, 0.077077, 0.078078, 0.079079, 0.08008, 0.081081, 0.082082, 0.083083, 0.084084, 0.085085, 0.086086, 0.087087, 0.088088, 0.089089, 0.09009, 0.091091, 0.092092, 0.093093, 0.094094, 0.095095,\n 0.096096, 0.097097, 0.098098, 0.099099, 0.1001, 0.1011, 0.1021, 0.1031, 0.1041, 0.10511, 0.10611, 0.10711, 0.10811, 0.10911, 0.11011, 0.11111, 0.11211, 0.11311, 0.11411, 0.11512, 0.11612, 0.11712, 0.11812, 0.11912,\n 0.12012, 0.12112, 0.12212, 0.12312, 0.12412, 0.12513, 0.12613, 0.12713, 0.12813, 0.12913, 0.13013, 0.13113, 0.13213, 0.13313, 0.13413, 0.13514, 0.13614, 0.13714, 0.13814, 0.13914, 0.14014, 0.14114, 0.14214, 0.14314,\n 0.14414, 0.14515, 0.14615, 0.14715, 0.14815, 0.14915, 0.15015, 0.15115, 0.15215, 0.15315, 0.15415, 0.15516, 0.15616, 0.15716, 0.15816, 0.15916, 0.16016, 0.16116, 0.16216, 0.16316, 0.16416, 0.16517, 0.16617, 0.16717,\n 0.16817, 0.16917, 0.17017, 0.17117, 0.17217, 0.17317, 0.17417, 0.17518, 0.17618, 0.17718, 0.17818, 0.17918, 0.18018, 0.18118, 0.18218, 0.18318, 0.18418, 0.18519, 0.18619, 0.18719, 0.18819, 0.18919, 0.19019, 0.19119,\n 0.19219, 0.19319, 0.19419, 0.1952, 0.1962, 0.1972, 0.1982, 0.1992, 0.2002, 0.2012, 0.2022, 0.2032, 0.2042, 0.20521, 0.20621, 0.20721, 0.20821, 0.20921, 0.21021, 0.21121, 0.21221, 0.21321, 0.21421, 0.21522,\n 0.21622, 0.21722, 0.21822, 0.21922, 0.22022, 0.22122, 0.22222, 0.22322, 0.22422, 0.22523, 0.22623, 0.22723, 0.22823, 0.22923, 0.23023, 0.23123, 0.23223, 0.23323, 0.23423, 0.23524, 0.23624, 0.23724, 0.23824, 0.23924,\n 0.24024, 0.24124, 0.24224, 0.24324, 0.24424, 0.24525, 0.24625, 0.24725, 0.24825, 0.24925, 0.25025, 0.25125, 0.25225, 0.25325, 0.25425, 0.25526, 0.25626, 0.25726, 0.25826, 0.25926, 0.26026, 0.26126, 0.26226, 0.26326,\n 0.26426, 0.26527, 0.26627, 0.26727, 0.26827, 0.26927, 0.27027, 0.27127, 0.27227, 0.27327, 0.27427, 0.27528, 0.27628, 0.27728, 0.27828, 0.27928, 0.28028, 0.28128, 0.28228, 0.28328, 0.28428, 0.28529, 0.28629, 0.28729,\n 0.28829, 0.28929, 0.29029, 0.29129, 0.29229, 0.29329, 0.29429, 0.2953, 0.2963, 0.2973, 0.2983, 0.2993, 0.3003, 0.3013, 0.3023, 0.3033, 0.3043, 0.30531, 0.30631, 0.30731, 0.30831, 0.30931, 0.31031, 0.31131,\n 0.31231, 0.31331, 0.31431, 0.31532, 0.31632, 0.31732, 0.31832, 0.31932, 0.32032, 0.32132, 0.32232, 0.32332, 0.32432, 0.32533, 0.32633, 0.32733, 0.32833, 0.32933, 0.33033, 0.33133, 0.33233, 0.33333, 0.33433, 0.33534,\n 0.33634, 0.33734, 0.33834, 0.33934, 0.34034, 0.34134, 0.34234, 0.34334, 0.34434, 0.34535, 0.34635, 0.34735, 0.34835, 0.34935, 0.35035, 0.35135, 0.35235, 0.35335, 0.35435, 0.35536, 0.35636, 0.35736, 0.35836, 0.35936,\n 0.36036, 0.36136, 0.36236, 0.36336, 0.36436, 0.36537, 0.36637, 0.36737, 0.36837, 0.36937, 0.37037, 0.37137, 0.37237, 0.37337, 0.37437, 0.37538, 0.37638, 0.37738, 0.37838, 0.37938, 0.38038, 0.38138, 0.38238, 0.38338,\n 0.38438, 0.38539, 0.38639, 0.38739, 0.38839, 0.38939, 0.39039, 0.39139, 0.39239, 0.39339, 0.39439, 0.3954, 0.3964, 0.3974, 0.3984, 0.3994, 0.4004, 0.4014, 0.4024, 0.4034, 0.4044, 0.40541, 0.40641, 0.40741,\n 0.40841, 0.40941, 0.41041, 0.41141, 0.41241, 0.41341, 0.41441, 0.41542, 0.41642, 0.41742, 0.41842, 0.41942, 0.42042, 0.42142, 0.42242, 0.42342, 0.42442, 0.42543, 0.42643, 0.42743, 0.42843, 0.42943, 0.43043, 0.43143,\n 0.43243, 0.43343, 0.43443, 0.43544, 0.43644, 0.43744, 0.43844, 0.43944, 0.44044, 0.44144, 0.44244, 0.44344, 0.44444, 0.44545, 0.44645, 0.44745, 0.44845, 0.44945, 0.45045, 0.45145, 0.45245, 0.45345, 0.45445, 0.45546,\n 0.45646, 0.45746, 0.45846, 0.45946, 0.46046, 0.46146, 0.46246, 0.46346, 0.46446, 0.46547, 0.46647, 0.46747, 0.46847, 0.46947, 0.47047, 0.47147, 0.47247, 0.47347, 0.47447, 0.47548, 0.47648, 0.47748, 0.47848, 0.47948,\n 0.48048, 0.48148, 0.48248, 0.48348, 0.48448, 0.48549, 0.48649, 0.48749, 0.48849, 0.48949, 0.49049, 0.49149, 0.49249, 0.49349, 0.49449, 0.4955, 0.4965, 0.4975, 0.4985, 0.4995, 0.5005, 0.5015, 0.5025, 0.5035,\n 0.5045, 0.50551, 0.50651, 0.50751, 0.50851, 0.50951, 0.51051, 0.51151, 0.51251, 0.51351, 0.51451, 0.51552, 0.51652, 0.51752, 0.51852, 0.51952, 0.52052, 0.52152, 0.52252, 0.52352, 0.52452, 0.52553, 0.52653, 0.52753,\n 0.52853, 0.52953, 0.53053, 0.53153, 0.53253, 0.53353, 0.53453, 0.53554, 0.53654, 0.53754, 0.53854, 0.53954, 0.54054, 0.54154, 0.54254, 0.54354, 0.54454, 0.54555, 0.54655, 0.54755, 0.54855, 0.54955, 0.55055, 0.55155,\n 0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.56156, 0.56256, 0.56356, 0.56456, 0.56557, 0.56657, 0.56757, 0.56857, 0.56957, 0.57057, 0.57157, 0.57257, 0.57357, 0.57457, 0.57558,\n 0.57658, 0.57758, 0.57858, 0.57958, 0.58058, 0.58158, 0.58258, 0.58358, 0.58458, 0.58559, 0.58659, 0.58759, 0.58859, 0.58959, 0.59059, 0.59159, 0.59259, 0.59359, 0.59459, 0.5956, 0.5966, 0.5976, 0.5986, 0.5996,\n 0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.60961, 0.61061, 0.61161, 0.61261, 0.61361, 0.61461, 0.61562, 0.61662, 0.61762, 0.61862, 0.61962, 0.62062, 0.62162, 0.62262, 0.62362,\n 0.62462, 0.62563, 0.62663, 0.62763, 0.62863, 0.62963, 0.63063, 0.63163, 0.63263, 0.63363, 0.63463, 0.63564, 0.63664, 0.63764, 0.63864, 0.63964, 0.64064, 0.64164, 0.64264, 0.64364, 0.64464, 0.64565, 0.64665, 0.64765,\n 0.64865, 0.64965, 0.65065, 0.65165, 0.65265, 0.65365, 0.65465, 0.65566, 0.65666, 0.65766, 0.65866, 0.65966, 0.66066, 0.66166, 0.66266, 0.66366, 0.66466, 0.66567, 0.66667, 0.66767, 0.66867, 0.66967, 0.67067, 0.67167,\n 0.67267, 0.67367, 0.67467, 0.67568, 0.67668, 0.67768, 0.67868, 0.67968, 0.68068, 0.68168, 0.68268, 0.68368, 0.68468, 0.68569, 0.68669, 0.68769, 0.68869, 0.68969, 0.69069, 0.69169, 0.69269, 0.69369, 0.69469, 0.6957,\n 0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.70571, 0.70671, 0.70771, 0.70871, 0.70971, 0.71071, 0.71171, 0.71271, 0.71371, 0.71471, 0.71572, 0.71672, 0.71772, 0.71872, 0.71972,\n 0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.72973, 0.73073, 0.73173, 0.73273, 0.73373, 0.73473, 0.73574, 0.73674, 0.73774, 0.73874, 0.73974, 0.74074, 0.74174, 0.74274, 0.74374,\n 0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.75375, 0.75475, 0.75576, 0.75676, 0.75776, 0.75876, 0.75976, 0.76076, 0.76176, 0.76276, 0.76376, 0.76476, 0.76577, 0.76677, 0.76777,\n 0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.97522, 0.97522, 0.96939, ..., 0, 0, 0],\n [ 0.97143, 0.97143, 0.97143, ..., 0, 0, 0],\n [ 0.95679, 0.95679, 0.95679, ..., 0, 0, 0]]), 'Confidence', 'Recall']]\nfitness: 0.5399205302084829\nkeys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\nmaps: array([ 0.63644, 0.38974, 0.51147])\nnames: {0: 'with_mask', 1: 'mask_weared_incorrect', 2: 'without_mask'}\nplot: True\nresults_dict: {'metrics/precision(B)': 0.8330161947912919, 'metrics/recall(B)': 0.7086991423055139, 'metrics/mAP50(B)': 0.7862281092858673, 'metrics/mAP50-95(B)': 0.5125530214221068, 'fitness': 0.5399205302084829}\nsave_dir: PosixPath('/kaggle/working/face-mask-yolov8')\nspeed: {'preprocess': 2.2714711579001285, 'inference': 116.69545542104832, 'loss': 5.778362363514256e-05, 'postprocess': 0.857955210523752}\ntask: 'detect'"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"metrics = model.val()\n\nresults = model.predict(\n source=\"/kaggle/working/images/val\",\n save=True,\n conf=0.4\n)\n\nmodel.save(\"yolov8n-mask-detection.pt\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-05T18:08:42.943074Z","iopub.execute_input":"2025-06-05T18:08:42.943388Z","iopub.status.idle":"2025-06-05T18:09:33.847395Z","shell.execute_reply.started":"2025-06-05T18:08:42.943367Z","shell.execute_reply":"2025-06-05T18:09:33.846361Z"}},"outputs":[{"name":"stdout","text":"Ultralytics 8.3.150 🚀 Python-3.11.11 torch-2.6.0+cu124 CPU (Intel Xeon 2.20GHz)\nModel summary (fused): 72 layers, 3,006,233 parameters, 0 gradients, 8.1 GFLOPs\n\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 2042.9±555.3 MB/s, size: 526.2 KB)\n","output_type":"stream"},{"name":"stderr","text":"\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/labels/val.cache... 171 images, 0 backgrounds, 0 corrupt: 100%|██████████| 171/171 [00:00 1:\n training_results[columns_existing].plot(\n x='epoch',\n figsize=(12, 6),\n title=\"Training Metrics Over Epochs\"\n )\n plt.grid()\n plt.show()\nelse:\n print(\"Some expected columns are missing. Cannot plot.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-05T18:12:32.112867Z","iopub.execute_input":"2025-06-05T18:12:32.113204Z","iopub.status.idle":"2025-06-05T18:12:32.427549Z","shell.execute_reply.started":"2025-06-05T18:12:32.113177Z","shell.execute_reply":"2025-06-05T18:12:32.426379Z"}},"outputs":[{"name":"stdout","text":"Available columns: ['epoch', 'time', 'train/box_loss', 'train/cls_loss', 'train/dfl_loss', 'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)', 'val/box_loss', 'val/cls_loss', 'val/dfl_loss', 'lr/pg0', 'lr/pg1', 'lr/pg2']\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":13}]}