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Traceback (most recent call last): File "F:\ultralytics-8.3.89\train.py", line 1, in <module>
from ultralytics import YOLO
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\__init__.py", line 11, in <module>
from ultralytics.models import NAS, RTDETR, SAM, YOLO, FastSAM, YOLOWorld
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\models\__init__.py", line 3, in <module>
from .fastsam import FastSAM
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\models\fastsam\model.py", line 5, in <module>
from ultralytics.engine.model import Model
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\engine\model.py", line 12, in <module>
from ultralytics.engine.results import Results
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\engine\results.py", line 15, in <module>
from ultralytics.data.augment import LetterBox
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\data\__init__.py", line 3, in <module>
from .base import BaseDataset
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\data\base.py", line 17, in <module>
from ultralytics.data.utils import FORMATS_HELP_MSG, HELP_URL, IMG_FORMATS
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\data\utils.py", line 18, in <module>
from ultralytics.nn.autobackend import check_class_names
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\nn\autobackend.py", line 54, in <module>
class AutoBackend(nn.Module):
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\nn\autobackend.py", line 89, in AutoBackend
device=torch.device("cpu"),
F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\nn\autobackend.py:89: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at ..\torch\csrc\utils\tensor_numpy.cpp:84.)
device=torch.device("cpu"),
New https://pypi.org/project/ultralytics/8.3.165 available 😃 Update with 'pip install -U ultralytics'
Ultralytics 8.3.89 🚀 Python-3.11.13 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4080 Laptop GPU, 12282MiB)
engine\trainer: task=detect, mode=train, model=yolo11n.pt, data=data.yaml, epochs=200, time=None, patience=100, batch=20, imgsz=640, save=True, save_period=-1, cache=False, device=0, workers=8, project=None, name=train2, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, 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, pose=12.0, kobj=1.0, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs\detect\train2
Overriding model.yaml nc=80 with nc=1
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 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25]
3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25]
5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False]
14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False]
17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1]
19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False]
20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1]
22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True]
23 [16, 19, 22] 1 430867 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]]
YOLO11n summary: 181 layers, 2,590,035 parameters, 2,590,019 gradients, 6.4 GFLOPs
Transferred 448/499 items from pretrained weights
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
Traceback (most recent call last):
File "F:\ultralytics-8.3.89\train.py", line 20, in <module>
main()
File "F:\ultralytics-8.3.89\train.py", line 8, in main
results = model.train(
^^^^^^^^^^^^
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\engine\model.py", line 810, in train
self.trainer.train()
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\engine\trainer.py", line 208, in train
self._do_train(world_size)
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\engine\trainer.py", line 323, in _do_train
self._setup_train(world_size)
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\engine\trainer.py", line 265, in _setup_train
self.amp = torch.tensor(check_amp(self.model), device=self.device)
^^^^^^^^^^^^^^^^^^^^^
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\utils\checks.py", line 698, in check_amp
assert amp_allclose(YOLO("yolo11n.pt"), im)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\utils\checks.py", line 686, in amp_allclose
a = m(batch, imgsz=imgsz, device=device, verbose=False)[0].boxes.data # FP32 inference
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\engine\model.py", line 182, in __call__
return self.predict(source, stream, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\engine\model.py", line 560, in predict
return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\engine\predictor.py", line 175, in __call__
return list(self.stream_inference(source, model, *args, **kwargs)) # merge list of Result into one
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\anaconda\envs\model\Lib\site-packages\torch\utils\_contextlib.py", line 35, in generator_context
response = gen.send(None)
^^^^^^^^^^^^^^
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\engine\predictor.py", line 257, in stream_inference
im = self.preprocess(im0s)
^^^^^^^^^^^^^^^^^^^^^
File "F:\ultralytics-8.3.89\ultralytics-8.1.0\ultralytics\engine\predictor.py", line 130, in preprocess
im = torch.from_numpy(im)
^^^^^^^^^^^^^^^^^^^^
RuntimeError: Numpy is not available
本文介绍了JVM内存中方法区的设置及其在不同JDK版本下的变化，包括 PermGen 和 Metaspace 的区别。通过-XX:PermSize和-XX:MaxPermSize（JDK7及以前）或-XX:MetaspaceSize和-XX:MaxMetaspaceSize（JDK8及以上）调整内存大小。当内存溢出时，可能导致OutOfMemoryError。解决OOM异常需要分析内存转储，判断是内存泄漏还是内存溢出，并相应调整代码或内存参数。
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