(yolo) PS F:\YOLO\ultralytics-main> yolo detect train data=datasets/sign/sign.yaml model=yolov8n.yaml pretrained=ultralytics/yolov8n.pt epochs=200 batch=8 lr0=0.01 resume=True device=0
Transferred 355/355 items from pretrained weights
Ultralytics 8.3.162 Python-3.12.11 torch-2.7.1+cu118 CUDA:0 (NVIDIA GeForce GTX 1050, 2048MiB)
engine\trainer: agnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=8, 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=datasets/sign/sign.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dn
n=False, dropout=0.0, dynamic=False, embed=None, epochs=200, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=Fa
lse, 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.yaml, momentum=0.937, mosaic=1.0, multi_scale=False, name=train19, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_ma
sk=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=ultralytics/yolov8n.pt, profile=False, project=None, rect=False, resume=None, retina_masks=Fal
se, save=True, save_conf=False, save_crop=False, save_dir=runs\detect\train19, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, sh
ear=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
Overriding model.yaml nc=80 with nc=2
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
22 [15, 18, 21] 1 751702 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]]
YOLOv8n summary: 129 layers, 3,011,238 parameters, 3,011,222 gradients, 8.2 GFLOPs
Transferred 319/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.35M/5.35M [00:01<00:00, 4.45MB/s]
AMP: checks passed
train: Fast image access (ping: 3.06.0 ms, read: 90.38.8 MB/s, size: 7468.0 KB)
train: Scanning F:\YOLO\ultralytics-main\datasets\sign\labels\train2025.cache... 20 images, 4 backgrounds, 0 corrupt: 100%|██████████| 24/24 [00:00<?, ?it/s]
val: Fast image access (ping: 0.10.0 ms, read: 416.7707.1 MB/s, size: 7252.3 KB)
val: Scanning F:\YOLO\ultralytics-main\datasets\sign\labels\train2025.cache... 20 images, 4 backgrounds, 0 corrupt: 100%|██████████| 24/24 [00:00<?, ?it/s]
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrec
t results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any
library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrec
t results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any
library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrec
t results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any
library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrec
t results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any
library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
Plotting labels to runs\detect\train19\labels.jpg...
OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrec
t results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any
library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
(yolo) PS F:\YOLO\ultralytics-main> Traceback (most recent call last):
File "<string>", line 1, in <module>
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\spawn.py", line 122, in spawn_main
exitcode = _main(fd, parent_sentinel)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\spawn.py", line 132, in _main
self = reduction.pickle.load(from_parent)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\connection.py", line 1167, in rebuild_pipe_connection
handle = dh.detach()
^^^^^^^^^^^
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\reduction.py", line 131, in detach
return _winapi.DuplicateHandle(
^^^^^^^^^^^^^^^^^^^^^^^^
PermissionError: [WinError 5] 拒绝访问。
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\spawn.py", line 122, in spawn_main
exitcode = _main(fd, parent_sentinel)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\spawn.py", line 132, in _main
self = reduction.pickle.load(from_parent)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\connection.py", line 1167, in rebuild_pipe_connection
handle = dh.detach()
^^^^^^^^^^^
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\reduction.py", line 131, in detach
return _winapi.DuplicateHandle(
^^^^^^^^^^^^^^^^^^^^^^^^
PermissionError: [WinError 5] 拒绝访问。
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\spawn.py", line 122, in spawn_main
exitcode = _main(fd, parent_sentinel)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\spawn.py", line 132, in _main
self = reduction.pickle.load(from_parent)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\connection.py", line 1167, in rebuild_pipe_connection
handle = dh.detach()
^^^^^^^^^^^
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\reduction.py", line 131, in detach
return _winapi.DuplicateHandle(
^^^^^^^^^^^^^^^^^^^^^^^^
PermissionError: [WinError 5] 拒绝访问。
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\spawn.py", line 122, in spawn_main
exitcode = _main(fd, parent_sentinel)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\spawn.py", line 132, in _main
self = reduction.pickle.load(from_parent)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\connection.py", line 1167, in rebuild_pipe_connection
handle = dh.detach()
^^^^^^^^^^^
File "D:\miniconda3\envs\yolo\Lib\multiprocessing\reduction.py", line 131, in detach
return _winapi.DuplicateHandle(
^^^^^^^^^^^^^^^^^^^^^^^^
PermissionError: [WinError 5] 拒绝访问。
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