当前位置：首页 > news >正文

怎样找做淘宝客的网站小学生关键词大全

news 2025/7/21 18:41:38

怎样找做淘宝客的网站,小学生关键词大全,网页设计网站建设招聘,给公司做网站🍨 本文为[🔗365天深度学习训练营学习记录博客 🍦 参考文章：365天深度学习训练营 🍖 原作者：[K同学啊] 🚀 文章来源：[K同学的学习圈子](https://www.yuque.com/mingtian-fkmxf/zxwb4…

🍨 本文为[🔗365天深度学习训练营学习记录博客
🍦 参考文章：365天深度学习训练营
🍖 原作者：[K同学啊]
🚀 文章来源：[K同学的学习圈子](https://www.yuque.com/mingtian-fkmxf/zxwb45)

改进前模型框架图：

改进后模型框架图：

改进前：

改进后：

# YOLOv5 v6.0 backbone
backbone:# [from, number, module, args][[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2[-1, 1, Conv, [128, 3, 2]],    # 1-P2/4[-1, 3, C3, [128]],            # 2[-1, 1, Conv, [256, 3, 2]],    # 3-P3/8[-1, 6, C2, [256]],            # 4-修改为C2*2[-1, 1, Conv, [512, 3, 2]],    # 5-P4/16[-1, 3, C3, [512]],            # 6-修改为C3*1
#   [-1, 1, Conv, [1024, 3, 2]],   # 7-删除P5/32
#   [-1, 3, C3, [1024]],           # 8-删除[-1, 1, SPPF, [512, 5]],      # 9-修改参数;层数变为7]

修改前：

修改后：

# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [512, 3, 2]], # 修改参数[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]],  # cat backbone P4[-1, 3, C3, [512, False]],  # 13->11[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 4], 1, Concat, [1]],  # cat backbone P3[-1, 3, C3, [256, False]],  # 17->15 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 12], 1, Concat, [1]],  # cat head P4 修改层数-2[-1, 3, C3, [512, False]],  # 20->18 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 8], 1, Concat, [1]],  # cat head P5 修改层数-2[-1, 3, C3, [1024, False]],  # 23->21 (P5/32-large)[[15, 18, 21], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5) 修改层数-2]

执行命令行：

python train.py --img 900 --batch 2 --epoch 100 --data D:/yolov5-master/data/ab.yaml --cfg D:/yolov5-master/models/yolov5s.yaml --weights yolov5s.pt

运行结果：

D:\yolov5-master>python train.py --img 900 --batch 2 --epoch 100 --data D:/yolov5-master/data/ab.yaml --cfg D:/yolov5-master/models/yolov5s.yaml --weights yolov5s.pt
train: weights=yolov5s.pt, cfg=D:/yolov5-master/models/yolov5s.yaml, data=D:/yolov5-master/data/ab.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=100, batch_size=2, imgsz=900, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5
YOLOv5  2023-10-15 Python-3.10.7 torch-2.0.1+cpu CPUhyperparameters: 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=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, mosaic=1.0, mixup=0.0, copy_paste=0.0
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5  runs in Comet
TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=4from  n    params  module                                  arguments0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]2                -1  1     18816  models.common.C3                        [64, 64, 1]3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]4                -1  2    115712  models.common.C2                        [128, 128, 2]5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]6                -1  3    625152  models.common.C3                        [256, 256, 3]7                -1  1    296192  models.common.SPPF                      [256, 512, 5]8                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]9                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']10           [-1, 6]  1         0  models.common.Concat                    [1]11                -1  1    361984  models.common.C3                        [512, 256, 1, False]12                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]13                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']14           [-1, 4]  1         0  models.common.Concat                    [1]15                -1  1     90880  models.common.C3                        [256, 128, 1, False]16                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]17          [-1, 12]  1         0  models.common.Concat                    [1]18                -1  1    296448  models.common.C3                        [256, 256, 1, False]19                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]20           [-1, 8]  1         0  models.common.Concat                    [1]21                -1  1   1182720  models.common.C3                        [512, 512, 1, False]22      [15, 18, 21]  1     24273  models.yolo.Detect                      [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Traceback (most recent call last):File "D:\yolov5-master\train.py", line 647, in <module>main(opt)File "D:\yolov5-master\train.py", line 536, in maintrain(opt.hyp, opt, device, callbacks)File "D:\yolov5-master\train.py", line 130, in trainmodel = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # createFile "D:\yolov5-master\models\yolo.py", line 195, in __init__m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))])  # forwardFile "D:\yolov5-master\models\yolo.py", line 194, in <lambda>forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)File "D:\yolov5-master\models\yolo.py", line 209, in forwardreturn self._forward_once(x, profile, visualize)  # single-scale inference, trainFile "D:\yolov5-master\models\yolo.py", line 121, in _forward_oncex = m(x)  # runFile "D:\Python\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_implreturn forward_call(*args, **kwargs)File "D:\yolov5-master\models\common.py", line 336, in forwardreturn torch.cat(x, self.d)
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 32 but got size 16 for tensor number 1 in the list.D:\yolov5-master>python train.py --img 900 --batch 2 --epoch 100 --data D:/yolov5-master/data/ab.yaml --cfg D:/yolov5-master/models/yolov5s.yaml --weights yolov5s.pt
train: weights=yolov5s.pt, cfg=D:/yolov5-master/models/yolov5s.yaml, data=D:/yolov5-master/data/ab.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=100, batch_size=2, imgsz=900, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5
YOLOv5  2023-10-15 Python-3.10.7 torch-2.0.1+cpu CPUhyperparameters: 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=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, mosaic=1.0, mixup=0.0, copy_paste=0.0
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5  runs in Comet
TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=4from  n    params  module                                  arguments0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]2                -1  1     18816  models.common.C3                        [64, 64, 1]3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]4                -1  2    115712  models.common.C2                        [128, 128, 2]5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]6                -1  1    296448  models.common.C3                        [256, 256, 1]7                -1  1    164608  models.common.SPPF                      [256, 256, 5]8                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]9                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']10           [-1, 6]  1         0  models.common.Concat                    [1]11                -1  1    361984  models.common.C3                        [512, 256, 1, False]12                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]13                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']14           [-1, 4]  1         0  models.common.Concat                    [1]15                -1  1     90880  models.common.C3                        [256, 128, 1, False]16                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]17          [-1, 12]  1         0  models.common.Concat                    [1]18                -1  1    296448  models.common.C3                        [256, 256, 1, False]19                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]20           [-1, 8]  1         0  models.common.Concat                    [1]21                -1  1   1182720  models.common.C3                        [512, 512, 1, False]22      [15, 18, 21]  1     24273  models.yolo.Detect                      [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
YOLOv5s summary: 179 layers, 4304785 parameters, 4304785 gradients, 13.4 GFLOPsTransferred 126/289 items from yolov5s.pt
WARNING  --img-size 900 must be multiple of max stride 32, updating to 928
optimizer: SGD(lr=0.01) with parameter groups 47 weight(decay=0.0), 50 weight(decay=0.0005), 50 bias
train: Scanning D:\yolov5-master\Y2\train... 1 images, 0 backgrounds, 159 corrupt: 100%|██████████| 160/160 [00:13<00:0
train: WARNING   D:\yolov5-master\Y2\images\fruit1.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit1.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit10.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit10.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit100.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit100.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit102.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit102.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit103.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit103.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit104.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit104.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit106.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit106.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit108.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit108.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit109.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit109.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit11.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit11.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit110.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit110.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit111.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit111.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit113.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit113.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit114.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit114.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit115.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit115.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit116.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit116.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit117.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit117.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit118.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit118.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit119.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit119.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit12.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit12.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit120.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit120.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit121.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit121.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit122.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit122.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit123.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit123.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit124.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit124.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit125.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit125.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit127.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit127.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit129.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit129.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit13.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit13.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit130.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit130.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit131.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit131.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit132.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit132.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit133.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit133.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit134.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit134.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit135.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit135.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit136.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit136.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit138.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit138.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit14.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit14.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit142.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit142.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit143.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit143.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit144.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit144.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit145.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit145.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit147.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit147.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit148.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit148.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit149.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit149.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit15.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit15.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit151.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit151.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit152.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit152.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit155.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit155.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit156.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit156.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit157.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit157.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit158.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit158.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit159.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit159.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit16.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit16.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit161.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit161.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit162.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit162.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit163.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit163.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit164.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit164.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit165.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit165.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit167.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit167.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit168.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit168.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit169.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit169.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit17.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit17.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit170.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit170.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit171.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit171.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit172.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit172.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit173.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit173.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit174.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit174.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit175.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit175.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit176.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit176.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit177.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit177.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit178.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit178.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit179.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit179.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit18.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit18.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit180.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit180.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit181.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit181.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit182.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit182.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit183.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit183.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit184.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit184.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit185.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit185.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit186.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit186.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit187.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit187.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit188.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit188.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit19.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit19.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit196.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit196.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit197.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit197.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit198.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit198.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit199.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit199.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit2.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit2.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit200.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit200.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit202.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit202.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit208.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit208.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit209.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit209.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit211.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit211.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit22.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit22.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit23.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit23.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit25.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit25.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit26.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit26.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit27.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit27.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit28.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit28.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit29.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit29.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit3.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit3.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit30.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit30.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit31.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit31.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit33.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit33.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit34.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit34.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit35.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit35.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit36.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit36.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit38.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit38.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit39.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit39.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit4.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit4.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit40.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit40.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit43.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit43.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit44.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit44.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit45.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit45.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit46.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit46.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit49.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit49.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit50.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit50.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit51.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit51.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit52.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit52.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit53.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit53.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit54.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit54.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit55.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit55.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit57.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit57.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit59.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit59.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit6.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit6.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit60.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit60.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit61.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit61.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit62.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit62.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit63.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit63.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit65.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit65.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit66.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit66.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit68.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit68.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit69.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit69.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit7.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit7.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit70.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit70.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit71.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit71.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit73.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit73.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit74.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit74.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit75.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit75.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit77.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit77.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit78.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit78.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit79.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit79.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit80.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit80.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit81.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit81.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit82.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit82.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit83.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit83.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit85.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit85.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit86.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit86.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit87.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit87.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit88.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit88.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit89.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit89.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit90.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit90.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit91.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit91.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit94.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit94.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit95.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit95.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit97.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit97.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit98.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit98.png'
train: WARNING   D:\yolov5-master\Y2\images\fruit99.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit99.png'
train: WARNING  Cache directory D:\yolov5-master\Y2 is not writeable: [WinError 183] : 'D:\\yolov5-master\\Y2\\train.cache.npy' -> 'D:\\yolov5-master\\Y2\\train.cache'
val: Scanning D:\yolov5-master\Y2\val.cache... 1 images, 0 backgrounds, 19 corrupt: 100%|██████████| 20/20 [00:00<?, ?i
val: WARNING   D:\yolov5-master\Y2\images\fruit107.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit107.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit112.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit112.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit139.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit139.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit140.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit140.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit141.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit141.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit146.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit146.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit20.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit20.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit210.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit210.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit24.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit24.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit32.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit32.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit41.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit41.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit47.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit47.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit48.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit48.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit5.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit5.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit64.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit64.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit8.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit8.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit84.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit84.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit92.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit92.png'
val: WARNING   D:\yolov5-master\Y2\images\fruit96.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit96.png'AutoAnchor: 4.33 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset
Plotting labels to runs\train\exp16\labels.jpg...
Image sizes 928 train, 928 val
Using 0 dataloader workers
Logging results to runs\train\exp16
Starting training for 100 epochs...Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size0/99         0G     0.1023    0.06894     0.0481          7        928:   0%|          | 0/1 [00:01<?, ?it/s]WARNING  TensorBoard graph visualization failure Sizes of tensors must match except in dimension 1. Expected size 58 but got size 57 for tensor number 1 in the list.0/99         0G     0.1023    0.06894     0.0481          7        928: 100%|██████████| 1/1 [00:02<00:00,  2.22Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size1/99         0G      0.116     0.0604    0.04635          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.19Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size2/99         0G     0.1082    0.05426    0.05132          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.25Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size3/99         0G    0.07671    0.04771     0.0333          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.14Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size4/99         0G    0.08278    0.04585     0.0296          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.10Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size5/99         0G      0.111    0.09066    0.04756         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.16Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size6/99         0G      0.116    0.06792    0.04824          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.25Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size7/99         0G    0.07378    0.05158    0.03187          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.23Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size8/99         0G     0.1157    0.05443    0.05176          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.27Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size9/99         0G     0.1144    0.05169    0.06036          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.20Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size10/99         0G     0.1124    0.09406    0.04572         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.24Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size11/99         0G     0.0766    0.04767    0.03086          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.19Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size12/99         0G     0.1054    0.08936    0.04594         10        928: 100%|██████████| 1/1 [00:01<00:00,  1.22Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size13/99         0G     0.1048    0.04886    0.05069          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.22Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size14/99         0G    0.07221    0.04882    0.02977          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.17Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size15/99         0G     0.1178    0.04965    0.05099          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.16Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size16/99         0G     0.1314    0.05189    0.05638          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.21Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3     0.0045      0.333    0.00582    0.00279Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size17/99         0G     0.0856    0.04364    0.02689          2        928: 100%|██████████| 1/1 [00:01<00:00,  1.14Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3     0.0045      0.333    0.00582    0.00279Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size18/99         0G     0.1042     0.0573    0.04667          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.16Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3     0.0045      0.333    0.00582    0.00279Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size19/99         0G     0.0898    0.05344    0.04976          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.14Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3     0.0045      0.333    0.00582    0.00279Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size20/99         0G     0.1115     0.0723    0.04465         10        928: 100%|██████████| 1/1 [00:01<00:00,  1.20Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3     0.0045      0.333    0.00582    0.00279Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size21/99         0G     0.0894    0.05696    0.04866          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.18Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3     0.0045      0.333    0.00582    0.00279Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size22/99         0G     0.1064    0.09512    0.04609         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.13Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3     0.0045      0.333    0.00582    0.00279Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size23/99         0G     0.1139    0.05224    0.04599          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.31Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3     0.0045      0.333    0.00582    0.00279Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size24/99         0G     0.1062    0.06945    0.04768          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.26Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00433      0.333     0.0051    0.00251Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size25/99         0G      0.116    0.09317    0.04589         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.28Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00433      0.333     0.0051    0.00251Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size26/99         0G     0.1084    0.07551    0.04941          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.14Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00433      0.333     0.0051    0.00251Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size27/99         0G    0.09334    0.05819    0.04563          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.13Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00433      0.333     0.0051    0.00251Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size28/99         0G    0.07549    0.05148     0.0278          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.36Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00433      0.333     0.0051    0.00251Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size29/99         0G     0.1213    0.05355    0.05749          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.21Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00433      0.333     0.0051    0.00251Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size30/99         0G    0.09584    0.07607     0.0448          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.22Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00433      0.333     0.0051    0.00251Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size31/99         0G    0.09792    0.06158    0.04772          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.14Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00433      0.333     0.0051    0.00251Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size32/99         0G    0.07992    0.04641    0.03711          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.16Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00433      0.333     0.0051    0.00251Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size33/99         0G     0.1093     0.1033    0.04237         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.27Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00433      0.333     0.0051    0.00251Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size34/99         0G     0.0973    0.05861    0.05034          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.16Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00433      0.333     0.0051    0.00251Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size35/99         0G     0.1088    0.07091    0.05135          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.15Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00433      0.333     0.0051    0.00251Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size36/99         0G      0.106    0.09896     0.0442         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.16Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size37/99         0G    0.09571    0.06897     0.0473          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.11Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size38/99         0G     0.0849    0.04579    0.03352          2        928: 100%|██████████| 1/1 [00:01<00:00,  1.11Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size39/99         0G    0.09164    0.07926    0.04676          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.21Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size40/99         0G     0.1012    0.06744    0.04635          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.31Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size41/99         0G    0.09285     0.0705    0.05205          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.37Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size42/99         0G    0.09498    0.05619    0.04658          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.26Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size43/99         0G    0.09886     0.0651    0.05261          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.24Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size44/99         0G     0.1023    0.09061    0.04475         10        928: 100%|██████████| 1/1 [00:01<00:00,  1.29Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size45/99         0G     0.1112    0.05952    0.04528          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.25Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size46/99         0G    0.06881    0.04866    0.03377          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.24Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size47/99         0G      0.107    0.09679     0.0465         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.27Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size48/99         0G     0.0966    0.06407    0.05717          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.22Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size49/99         0G     0.1058    0.05406    0.04795          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.23Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size50/99         0G     0.1129    0.09446    0.04697         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.25Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size51/99         0G     0.1094    0.05599    0.04734          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.33Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size52/99         0G     0.1011    0.08309     0.0515          9        928: 100%|██████████| 1/1 [00:01<00:00,  1.22Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00337      0.333    0.00436    0.00247Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size53/99         0G    0.07754    0.04801    0.03401          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.26Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size54/99         0G     0.1096    0.09382    0.04247         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.25Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size55/99         0G     0.1049    0.06054    0.04536          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.29Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size56/99         0G     0.1158    0.06923    0.04261          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.27Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size57/99         0G     0.1096    0.05218    0.05424          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.22Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size58/99         0G    0.07191    0.06456    0.03113          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.24Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size59/99         0G     0.1026    0.09789      0.045         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.23Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size60/99         0G    0.09762    0.05207     0.0531          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.22Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size61/99         0G    0.07858    0.05155    0.03045          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.27Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size62/99         0G     0.1158    0.05363    0.04873          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.24Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size63/99         0G     0.1099     0.1002    0.04338         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.23Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size64/99         0G     0.1129    0.05755    0.04531          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.21Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size65/99         0G     0.1013    0.07252    0.04573          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.26Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size66/99         0G    0.09242    0.05776    0.05089          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.25Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size67/99         0G     0.1057    0.06878    0.04572          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.22Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size68/99         0G     0.1167    0.05181    0.04856          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.28Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size69/99         0G    0.09379    0.07217     0.0474          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.32Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size70/99         0G     0.0967    0.06586    0.04948          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.29Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size71/99         0G     0.1116    0.09719    0.04578         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.33Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size72/99         0G    0.09084    0.05456    0.04548          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.37Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size73/99         0G      0.111    0.05615    0.04613          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.31Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size74/99         0G     0.1121    0.09089    0.04653         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.32Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size75/99         0G    0.07513    0.04933    0.02963          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.34Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size76/99         0G    0.07868    0.04755    0.02927          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.24Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size77/99         0G    0.09763    0.05113    0.04474          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.20Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00366      0.333    0.00448    0.00245Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size78/99         0G    0.09904    0.05832    0.04777          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.31Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size79/99         0G    0.07866    0.05068    0.03276          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.24Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size80/99         0G    0.07645    0.05212    0.02934          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.29Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size81/99         0G    0.08324    0.04543    0.03031          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.23Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size82/99         0G     0.1052    0.06037    0.04437          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.25Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size83/99         0G     0.1036    0.06233    0.05686          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.26Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size84/99         0G     0.1042    0.09739    0.04465         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.28Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size85/99         0G    0.08622    0.05649     0.0523          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.26Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size86/99         0G     0.1084     0.1003    0.04353         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.26Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size87/99         0G    0.08857    0.06209    0.04712          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.22Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size88/99         0G    0.09376    0.05289    0.05011          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.26Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size89/99         0G     0.1045    0.05565    0.04718          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.25Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size90/99         0G     0.1034    0.05799    0.04736          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.28Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size91/99         0G     0.1023    0.06441    0.04832          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.24Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size92/99         0G     0.1109    0.08371    0.04649         10        928: 100%|██████████| 1/1 [00:01<00:00,  1.29Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size93/99         0G     0.1058     0.0641    0.05235          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.27Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size94/99         0G     0.1209     0.0516    0.04735          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.24Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size95/99         0G       0.11    0.09829    0.04451         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.26Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size96/99         0G    0.09733    0.05244    0.05601          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.27Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size97/99         0G    0.09427    0.05871    0.05337          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.29Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size98/99         0G    0.09102     0.1003    0.04611         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.25Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size99/99         0G    0.07237     0.0536    0.03054          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.31Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00881    0.00254100 epochs completed in 0.053 hours.
Optimizer stripped from runs\train\exp16\weights\last.pt, 9.1MB
Optimizer stripped from runs\train\exp16\weights\best.pt, 9.1MBValidating runs\train\exp16\weights\best.pt...
Fusing layers...
YOLOv5s summary: 132 layers, 4298225 parameters, 0 gradients, 13.2 GFLOPsClass     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00726      0.667    0.00891    0.00258banana          1          1    0.00943          1    0.00985   0.000985snake fruit          1          1          0          0          0          0pineapple          1          1     0.0123          1     0.0169    0.00675
Results saved to runs\train\exp16

查看全文

http://www.mnyf.cn/news/51555.html