/content
Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt to yolov8s-seg.pt...
100% 22.8M/22.8M [00:00<00:00, 150MB/s]
Ultralytics YOLOv8.0.11 ๐ 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=False, 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, 87.0MB/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
train: Scanning /content/datasets/TicketDetection-3/train/labels... 150 images, 0 backgrounds, 0 corrupt: 100% 150/150 [00:00<00:00, 1433.15it/s]
train: New cache created: /content/datasets/TicketDetection-3/train/labels.cache
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))
val: Scanning /content/datasets/TicketDetection-3/valid/labels... 14 images, 0 backgrounds, 0 corrupt: 100% 14/14 [00:00<00:00, 637.08it/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 [00:00<?, ?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 285, in _do_train
for i, batch in pbar:
File "/usr/local/lib/python3.8/dist-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 628, in __next__
data = self._next_data()
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 1333, in _next_data
return self._process_data(data)
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 1359, in _process_data
data.reraise()
File "/usr/local/lib/python3.8/dist-packages/torch/_utils.py", line 543, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/worker.py", line 302, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py", line 58, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py", line 58, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/data/base.py", line 179, in __getitem__
return self.transforms(self.get_label_info(index))
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/data/augment.py", line 48, in __call__
data = t(data)
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/data/augment.py", line 48, in __call__
data = t(data)
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/data/augment.py", line 361, in __call__
i = self.box_candidates(box1=instances.bboxes.T,
File "/usr/local/lib/python3.8/dist-packages/ultralytics/yolo/data/augment.py", line 375, in box_candidates
return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
ValueError: operands could not be broadcast together with shapes (3,) (4,)