本文主要是介绍模型训练时报错Failed to allocate 12192768 bytes in function ‘cv::OutOfMemoryError‘，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
目录
报错信息：
查找网上解决方法：
改进思路：
改进方法：
报错信息：
D:\Programs\miniconda3\envs\python311\python.exe D:\python\project\VisDrone2019-DET-MOT\train.py
Ultralytics YOLOv8.1.9 🚀 Python-3.11.7 torch-2.2.0 CUDA:0 (NVIDIA GeForce GTX 1650, 4096MiB)
engine\trainer: task=detect, mode=train, model=yolov8n.pt, data=D:\python\project\VisDrone2019-DET-MOT\class.yaml, epochs=100, time=None, patience=50, batch=2, imgsz=900, save=True, save_period=-1, cache=False, device=0, workers=8, project=None, name=train4, 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=False, opset=None, 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, pose=12.0, kobj=1.0, 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, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs\detect\train4
Overriding model.yaml nc=80 with nc=12from 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 753652 ultralytics.nn.modules.head.Detect [12, [64, 128, 256]]
Model summary: 225 layers, 3013188 parameters, 3013172 gradients, 8.2 GFLOPsTransferred 319/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
WARNING ⚠️ imgsz=[900] must be multiple of max stride 32, updating to [928]
train: Scanning E:\DeepLearning\AI\VisDrone2019\VisDrone2019-DET-MOT\train\labels... 30669 images, 0 backgrounds, 0 corrupt: 100%|██████████| 30669/30669 [00:35<00:00, 875.04it/s]
train: WARNING ⚠️ E:\DeepLearning\AI\VisDrone2019\VisDrone2019-DET-MOT\train\images\0000137_02220_d_0000163.jpg: 1 duplicate labels removed
train: WARNING ⚠️ E:\DeepLearning\AI\VisDrone2019\VisDrone2019-DET-MOT\train\images\0000140_00118_d_0000002.jpg: 1 duplicate labels removed
train: WARNING ⚠️ E:\DeepLearning\AI\VisDrone2019\VisDrone2019-DET-MOT\train\images\9999945_00000_d_0000114.jpg: 1 duplicate labels removed
train: WARNING ⚠️ E:\DeepLearning\AI\VisDrone2019\VisDrone2019-DET-MOT\train\images\9999987_00000_d_0000049.jpg: 1 duplicate labels removed
train: WARNING ⚠️ E:\DeepLearning\AI\VisDrone2019\VisDrone2019-DET-MOT\train\images\9999998_00219_d_0000175.jpg: 1 duplicate labels removed
train: New cache created: E:\DeepLearning\AI\VisDrone2019\VisDrone2019-DET-MOT\train\labels.cache
val: Scanning E:\DeepLearning\AI\VisDrone2019\VisDrone2019-DET-MOT\val\labels... 3394 images, 1 backgrounds, 0 corrupt: 100%|██████████| 3394/3394 [00:02<00:00, 1257.53it/s]
val: New cache created: E:\DeepLearning\AI\VisDrone2019\VisDrone2019-DET-MOT\val\labels.cache
Plotting labels to runs\detect\train4\labels.jpg... 0%| | 0/15335 [00:00<?, ?it/s]optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically...
optimizer: SGD(lr=0.01, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
Image sizes 928 train, 928 val
Using 8 dataloader workers
Logging results to runs\detect\train4
Starting training for 100 epochs...Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size1/100 2.56G nan nan nan 77 928: 100%|██████████| 15335/15335 [1:25:43<00:00, 2.98it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.67it/s]all 3394 158168 0.00413 1.32e-05 0.00208 0.000415Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size2/100 3.19G nan nan nan 44 928: 100%|██████████| 15335/15335 [1:25:33<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.69it/s]all 3394 158168 0.00401 1.32e-05 0.00203 0.000406Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size3/100 2.1G nan nan nan 134 928: 100%|██████████| 15335/15335 [1:25:26<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.70it/s]all 3394 158168 0.00154 1.14e-05 0.000779 7.79e-05Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size4/100 3.26G nan nan nan 99 928: 100%|██████████| 15335/15335 [1:26:01<00:00, 2.97it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:52<00:00, 7.55it/s]all 3394 158168 0.00409 1.32e-05 0.00206 0.000412Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size5/100 3.16G nan nan nan 90 928: 100%|██████████| 15335/15335 [1:25:27<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.69it/s]all 3394 158168 0.00412 1.32e-05 0.00207 0.000330%| | 0/15335 [00:00<?, ?it/s]Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size6/100 2.25G nan nan nan 85 928: 100%|██████████| 15335/15335 [1:25:42<00:00, 2.98it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:52<00:00, 7.54it/s]all 3394 158168 0.0043 1.32e-05 0.00216 0.000343Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size7/100 3.53G nan nan nan 39 928: 100%|██████████| 15335/15335 [1:25:29<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.68it/s]all 3394 158168 0.00401 1.32e-05 0.00202 0.000322Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size8/100 2.61G nan nan nan 28 928: 100%|██████████| 15335/15335 [1:25:25<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.70it/s]all 3394 158168 0.0016 1.14e-05 0.00081 0.000162Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size9/100 3.4G nan nan nan 109 928: 100%|██████████| 15335/15335 [1:25:51<00:00, 2.98it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:52<00:00, 7.54it/s]all 3394 158168 0.00419 1.32e-05 0.00211 0.000338Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size10/100 2.79G nan nan nan 35 928: 100%|██████████| 15335/15335 [1:25:25<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.70it/s]all 3394 158168 0.0016 1.14e-05 0.00081 8.1e-050%| | 0/15335 [00:00<?, ?it/s]Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size11/100 3.68G nan nan nan 193 928: 100%|██████████| 15335/15335 [1:25:28<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.65it/s]all 3394 158168 0.00163 1.14e-05 0.000826 8.26e-050%| | 0/15335 [00:00<?, ?it/s]Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size12/100 3.38G nan nan nan 41 928: 100%|██████████| 15335/15335 [1:25:48<00:00, 2.98it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:52<00:00, 7.55it/s]all 3394 158168 0.00419 1.32e-05 0.00211 0.0003380%| | 0/15335 [00:00<?, ?it/s]Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size13/100 3.09G nan nan nan 72 928: 100%|██████████| 15335/15335 [1:25:25<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.71it/s]all 3394 158168 0.00427 1.32e-05 0.00215 0.000346Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size14/100 2.11G nan nan nan 135 928: 100%|██████████| 15335/15335 [1:25:26<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.69it/s]all 3394 158168 0.00412 1.32e-05 0.00207 0.000330%| | 0/15335 [00:00<?, ?it/s]Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size15/100 3.5G nan nan nan 35 928: 100%|██████████| 15335/15335 [1:26:21<00:00, 2.96it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:52<00:00, 7.54it/s]all 3394 158168 0.00412 1.32e-05 0.00207 0.00033Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size16/100 3.1G nan nan nan 29 928: 100%|██████████| 15335/15335 [1:25:27<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.69it/s]all 3394 158168 0.00405 1.32e-05 0.00204 0.000323Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size17/100 2.52G nan nan nan 9 928: 100%|██████████| 15335/15335 [1:25:25<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.71it/s]all 3394 158168 0.00405 1.32e-05 0.00204 0.0003230%| | 0/15335 [00:00<?, ?it/s]Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size18/100 3.05G nan nan nan 46 928: 100%|██████████| 15335/15335 [1:25:26<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.70it/s]all 3394 158168 0.00412 1.32e-05 0.00207 0.000330%| | 0/15335 [00:00<?, ?it/s]Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size19/100 2.82G nan nan nan 74 928: 100%|██████████| 15335/15335 [1:25:25<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.70it/s]all 3394 158168 0.00427 1.32e-05 0.00215 0.000346Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size20/100 2.88G nan nan nan 108 928: 100%|██████████| 15335/15335 [1:25:24<00:00, 2.99it/s]Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 849/849 [01:50<00:00, 7.70it/s]all 3394 158168 0.00423 1.32e-05 0.00213 0.0003390%| | 0/15335 [00:00<?, ?it/s]Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size21/100 3.52G nan nan nan 102 928: 84%|████████▎ | 12806/15335 [1:11:20<14:05, 2.99it/s]
Traceback (most recent call last):File "D:\python\project\VisDrone2019-DET-MOT\train.py", line 20, in <module>results = model.train(data=r"D:\python\project\VisDrone2019-DET-MOT\class.yaml", imgsz=900, epochs=100, batch=2,^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\ultralytics\engine\model.py", line 601, in trainself.trainer.train()File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\ultralytics\engine\trainer.py", line 208, in trainself._do_train(world_size)File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\ultralytics\engine\trainer.py", line 358, in _do_trainfor i, batch in pbar:File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\tqdm\std.py", line 1182, in __iter__for obj in iterable:File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\ultralytics\data\build.py", line 49, in __iter__yield next(self.iterator)^^^^^^^^^^^^^^^^^^^File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\torch\utils\data\dataloader.py", line 631, in __next__data = self._next_data()^^^^^^^^^^^^^^^^^File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\torch\utils\data\dataloader.py", line 1346, in _next_datareturn self._process_data(data)^^^^^^^^^^^^^^^^^^^^^^^^File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\torch\utils\data\dataloader.py", line 1372, in _process_datadata.reraise()File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\torch\_utils.py", line 722, in reraiseraise exception
cv2.error: Caught error in DataLoader worker process 2.
Original Traceback (most recent call last):File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\torch\utils\data\_utils\worker.py", line 308, in _worker_loopdata = fetcher.fetch(index)^^^^^^^^^^^^^^^^^^^^File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\torch\utils\data\_utils\fetch.py", line 51, in fetchdata = [self.dataset[idx] for idx in possibly_batched_index]^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\torch\utils\data\_utils\fetch.py", line 51, in <listcomp>data = [self.dataset[idx] for idx in possibly_batched_index]~~~~~~~~~~~~^^^^^File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\ultralytics\data\base.py", line 251, in __getitem__return self.transforms(self.get_image_and_label(index))^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\ultralytics\data\base.py", line 257, in get_image_and_labellabel["img"], label["ori_shape"], label["resized_shape"] = self.load_image(index)^^^^^^^^^^^^^^^^^^^^^^File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\ultralytics\data\base.py", line 157, in load_imageim = cv2.imread(f) # BGR^^^^^^^^^^^^^File "D:\Programs\miniconda3\envs\python311\Lib\site-packages\ultralytics\utils\patches.py", line 26, in imreadreturn cv2.imdecode(np.fromfile(filename, np.uint8), flags)^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
cv2.error: OpenCV(4.9.0) D:\a\opencv-python\opencv-python\opencv\modules\core\src\alloc.cpp:73: error: (-4:Insufficient memory) Failed to allocate 12192768 bytes in function 'cv::OutOfMemoryError'Process finished with exit code 1
查找网上解决方法：
改进思路：
报错OutOfMemoryError是说内存不足，上述方法本质上就是将图片缩小
改进方法：
模型训练时把epochs改小，这里下调到20，将图片大小imgsz改小
if __name__ == '__main__':os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"# 加载模型# model = YOLO("yolov8n.yaml") # 从头开始构建新模型model = YOLO("yolov8n.pt") # 加载预训练模型（推荐用于训练）# Use the modelresults = model.train(data="class.yaml", imgsz=1120, epochs=20, batch=4, device=0) # 训练模型这篇关于模型训练时报错Failed to allocate 12192768 bytes in function ‘cv::OutOfMemoryError‘的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
