ValueError: Caught ValueError in DataLoader worker process 0. about notebooks HOT 6 CLOSED

!yolo task=segment mode=train model=yolov8s-seg.pt data={dataset.location}/data.yaml epochs=15 imgsz=640

!yolo task=segment mode=train model=yolov8s-seg.pt data={dataset.location}/data.yaml epochs=15 imgsz=640 v5loader=True

/content
Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt to yolov8s-seg.pt...
100% 22.8M/22.8M [00:01<00:00, 17.2MB/s]

Ultralytics YOLOv8.0.11 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)
yolo/engine/trainer: task=segment, mode=train, model=yolov8s-seg.pt, data=/content/datasets/TicketDetection-3/data.yaml, epochs=15, patience=50, batch=16, imgsz=640, save=True, cache=False, device=, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=False, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, retina_masks=False, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=17, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, hydra={'output_subdir': None, 'run': {'dir': '.'}}, v5loader=True, save_dir=runs/segment/train
Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...
100% 755k/755k [00:00<00:00, 135MB/s]
Overriding model.yaml nc=80 with nc=1

                   from  n    params  module                                       arguments                     
  0                  -1  1       928  ultralytics.nn.modules.Conv                  [3, 32, 3, 2]                 
  1                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                
  2                  -1  1     29056  ultralytics.nn.modules.C2f                   [64, 64, 1, True]             
  3                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               
  4                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           
  5                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              
  6                  -1  2    788480  ultralytics.nn.modules.C2f                   [256, 256, 2, True]           
  7                  -1  1   1180672  ultralytics.nn.modules.Conv                  [256, 512, 3, 2]              
  8                  -1  1   1838080  ultralytics.nn.modules.C2f                   [512, 512, 1, True]           
  9                  -1  1    656896  ultralytics.nn.modules.SPPF                  [512, 512, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           
 12                  -1  1    591360  ultralytics.nn.modules.C2f                   [768, 256, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           
 15                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 
 16                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              
 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           
 18                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 
 19                  -1  1    590336  ultralytics.nn.modules.Conv                  [256, 256, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           
 21                  -1  1   1969152  ultralytics.nn.modules.C2f                   [768, 512, 1]                 
 22        [15, 18, 21]  1   2770931  ultralytics.nn.modules.Segment               [1, 32, 128, [128, 256, 512]] 
YOLOv8s-seg summary: 261 layers, 11790483 parameters, 11790467 gradients, 42.7 GFLOPs

Transferred 411/417 items from pretrained weights
optimizer: SGD(lr=0.01) with parameter groups 66 weight(decay=0.0), 77 weight(decay=0.0005), 76 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning /content/datasets/TicketDetection-3/train/labels... 150 images, 0 backgrounds, 0 corrupt: 100% 150/150 [00:00<00:00, 1414.79it/s]
train: New cache created: /content/datasets/TicketDetection-3/train/labels.cache
val: Scanning /content/datasets/TicketDetection-3/valid/labels... 14 images, 0 backgrounds, 0 corrupt: 100% 14/14 [00:00<00:00, 422.50it/s]
val: New cache created: /content/datasets/TicketDetection-3/valid/labels.cache
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to runs/segment/train
Starting training for 15 epochs...

      Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size
  0% 0/10 [00:07<?, ?it/s]
Traceback (most recent call last):
  File "/usr/local/bin/yolo", line 8, in <module>
    sys.exit(entrypoint())
  File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/cli.py", line 148, in entrypoint
    cli(cfg)
  File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/cli.py", line 84, in cli
    func(cfg)
  File "/usr/local/lib/python3.8/dist-packages/hydra/main.py", line 79, in decorated_main
    return task_function(cfg_passthrough)
  File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/v8/segment/train.py", line 153, in train
    model.train(**cfg)
  File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/engine/model.py", line 203, in train
    self.trainer.train()
  File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/engine/trainer.py", line 185, in train
    self._do_train(int(os.getenv("RANK", -1)), world_size)
  File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/engine/trainer.py", line 303, in _do_train
    self.loss, self.loss_items = self.criterion(preds, batch)
  File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/v8/segment/train.py", line 45, in criterion
    return self.compute_loss(preds, batch)
  File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/v8/segment/train.py", line 91, in __call__
    masks = batch["masks"].to(self.device).float()
KeyError: 'masks'
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