(venv) E:\yolov5-master>python train.py --img 640 --batch 4 --epoch 300 --data ./data/A.yaml --cfg ./models/yolov5m.yaml --weights weights/yolov5m.pt --workers 0
?[34m?[1mtrain: ?[0mweights=weights/yolov5m.pt, cfg=./models/yolov5m.yaml, data=./data/A.yaml, hyp=data/hyp.scratch.yaml, epochs=300, batch_size=4, img_size=[640], rect=False, resume=F
alse, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket=, cache_images=False, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=
False, workers=0, project=runs/train, entity=None, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, a
rtifact_alias=latest, local_rank=-1
?[34m?[1mgithub: ?[0mskipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5
YOLOv5 2021-6-20 torch 1.9.0+cpu CPU
?[34m?[1mhyperparameters: ?[0mlr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0
, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, 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, mosai
c=1.0, mixup=0.0
?[34m?[1mtensorboard: ?[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/
?[34m?[1mwandb: ?[0mInstall Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)
from n params module arguments
0 -1 1 5280 models.common.Focus [3, 48, 3]
1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
2 -1 1 65280 models.common.C3 [96, 96, 2]
3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
4 -1 1 629760 models.common.C3 [192, 192, 6]
5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
6 -1 1 2512896 models.common.C3 [384, 384, 6]
7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
8 -1 1 1476864 models.common.SPP [768, 768, [5, 9, 13]]
9 -1 1 4134912 models.common.C3 [768, 768, 2, False]
10 -1 1 295680 models.common.Conv [768, 384, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 1182720 models.common.C3 [768, 384, 2, False]
14 -1 1 74112 models.common.Conv [384, 192, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 296448 models.common.C3 [384, 192, 2, False]
18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 1035264 models.common.C3 [384, 384, 2, False]
21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 4134912 models.common.C3 [768, 768, 2, False]
24 [17, 20, 23] 1 56574 models.yolo.Detect [9, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
E:\yolov5-master\venv\lib\site-packages\torch\nn\functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please d
o not use them for anything important until they are released as stable. (Triggered internally at ..\c10/core/TensorImpl.h:1156.)
return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
Model Summary: 391 layers, 21088734 parameters, 21088734 gradients, 50.5 GFLOPs
Transferred 498/506 items from weights\yolov5m.pt
Scaled weight_decay = 0.0005
Optimizer groups: 86 .bias, 86 conv.weight, 83 other
?[34m?[1mtrain: ?[0mScanning 'yolo_A\train' images and labels...: 0%| | 0/22 [00:
?[34m?[1mtrain: ?[0mScanning 'yolo_A\train' images and labels...0 found, 1 missing, 0 empty, 0 corrupted: 5%|██▍ | 1/22 [00:02<00:58
?[34m?[1mtrain: ?[0mScanning 'yolo_A\train' images and labels...0 found, 21 missing, 0 empty, 0 corrupted: 95%|█████████████████████████████████████████████████▋ | 21/22 [00:02<00:00
?[34m?[1mtrain: ?[0mScanning 'yolo_A\train' images and labels...0 found, 22 missing, 0 empty, 0 corrupted: 100%|████████████████████████████████████████████████████| 22/22 [00:02<00:00
, 7.58it/s]?[0m
?[34m?[1mtrain: ?[0mWARNING: No labels found in yolo_A\train.cache. See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
?[34m?[1mtrain: ?[0mNew cache created: yolo_A\train.cache
Traceback (most recent call last):
File "train.py", line 647, in <module>
main(opt)
File "train.py", line 548, in main
train(opt.hyp, opt, device)
File "train.py", line 212, in train
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
File "E:\yolov5-master\utils\datasets.py", line 70, in create_dataloader
dataset = LoadImagesAndLabels(path, imgsz, batch_size,
File "E:\yolov5-master\utils\datasets.py", line 405, in __init__
assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}'
AssertionError: ?[34m?[1mtrain: ?[0mNo labels in yolo_A\train.cache. Can not train without labels. See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
