• YOLOv5 配置C2模块构造新模型

    🍨 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)

    目标：在YOLOv5s网络模型中，修改common.py、yolo.py、yolov5s.yaml文件，将C2模块插入第2层与第3层之间，且跑通YOLOv5s。

    操作步骤：

    1.在common.py文件中插入C2模块

    1. class C2(nn.Module):
    2.     # CSP Bottleneck with 3 convolutions
    3.     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
    4.         super().__init__()
    5.         c_ = int(c2 * e)  # hidden channels
    6.         self.cv1 = Conv(c1, c_, 1, 1)
    7.         self.cv2 = Conv(c1, c_, 1, 1)
    8.         self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
    9.         self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
    10.  
    11.     def forward(self, x):
    12.         return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))

     

    2.修改yolo.py文件，改动模型框架

    1. def parse_model(d, ch):  # model_dict, input_channels(3)
    2.     # Parse a YOLOv5 model.yaml dictionary
    3.     LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
    4.     anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
    5.     if act:
    6.         Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
    7.         LOGGER.info(f"{colorstr('activation:')} {act}")  # print
    8.     na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
    9.     no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
    10.  
    11.     layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    12.     for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
    13.         m = eval(m) if isinstance(m, str) else m  # eval strings
    14.         for j, a in enumerate(args):
    15.             with contextlib.suppress(NameError):
    16.                 args[j] = eval(a) if isinstance(a, str) else a  # eval strings
    17.  
    18.         n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
    19.         if m in {
    20.                 Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
    21.                 BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
    22.             c1, c2 = ch[f], args[0]
    23.             if c2 != no:  # if not output
    24.                 c2 = make_divisible(c2 * gw, 8)
    25.  
    26.             args = [c1, c2, *args[1:]]
    27.             if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:
    28.                 args.insert(2, n)  # number of repeats
    29.                 n = 1
    30.         elif m is nn.BatchNorm2d:
    31.             args = [ch[f]]
    32.         elif m is Concat:
    33.             c2 = sum(ch[x] for x in f)
    34.         # TODO: channel, gw, gd
    35.         elif m in {Detect, Segment}:
    36.             args.append([ch[x] for x in f])
    37.             if isinstance(args[1], int):  # number of anchors
    38.                 args[1] = [list(range(args[1] * 2))] * len(f)
    39.             if m is Segment:
    40.                 args[3] = make_divisible(args[3] * gw, 8)
    41.         elif m is Contract:
    42.             c2 = ch[f] * args[0] ** 2
    43.         elif m is Expand:
    44.             c2 = ch[f] // args[0] ** 2
    45.         else:
    46.             c2 = ch[f]
    47.  
    48.         m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
    49.         t = str(m)[8:-2].replace('__main__.', '')  # module type
    50.         np = sum(x.numel() for x in m_.parameters())  # number params
    51.         m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
    52.         LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}')  # print
    53.         save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
    54.         layers.append(m_)
    55.         if i == 0:
    56.             ch = []
    57.         ch.append(c2)
    58.     return nn.Sequential(*layers), sorted(save)

    函数用于将模型的模块拼接起来，搭建完成的网络模型。后续如果需要动模型框架的话，需要对这个函数做相应的改动。

    修改前：

    修改后：

     3.yolov5s.yaml文件中加入C2层

    4.命令窗运行

    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： 

    1. 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
    2. 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
    3. github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5
    4. YOLOv5  2023-10-15 Python-3.10.7 torch-2.0.1+cpu CPU
    5.  
    6. hyperparameters: 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
    7. Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5  runs in Comet
    8. TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
    9. Overriding model.yaml nc=80 with nc=4
    10.  
    11.                  from  n    params  module                                  arguments
    12. Traceback (most recent call last):
    13.   File "D:\yolov5-master\train.py", line 647, in <module>
    14.     main(opt)
    15.   File "D:\yolov5-master\train.py", line 536, in main
    16.     train(opt.hyp, opt, device, callbacks)
    17.   File "D:\yolov5-master\train.py", line 130, in train
    18.     model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
    19.   File "D:\yolov5-master\models\yolo.py", line 185, in __init__
    20.     self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
    21.   File "D:\yolov5-master\models\yolo.py", line 319, in parse_model
    22.     BottleneckCSP, C2, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
    23. NameError: name 'C2' is not defined. Did you mean: 'c2'?
    24.  
    25. 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
    26. 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
    27. github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5
    28. YOLOv5  2023-10-15 Python-3.10.7 torch-2.0.1+cpu CPU
    29.  
    30. hyperparameters: 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
    31. Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5  runs in Comet
    32. TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
    33. Overriding model.yaml nc=80 with nc=4
    34.  
    35.                  from  n    params  module                                  arguments
    36.   0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]
    37.   1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]
    38.   2                -1  1     18816  models.common.C3                        [64, 64, 1]
    39.   3                -1  1     18816  models.common.C2                        [64, 64, 1]
    40.   4                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]
    41.   5                -1  2    115712  models.common.C3                        [128, 128, 2]
    42.   6                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]
    43.   7                -1  3    625152  models.common.C3                        [256, 256, 3]
    44.   8                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]
    45.   9                -1  1   1182720  models.common.C3                        [512, 512, 1]
    46.  10                -1  1    656896  models.common.SPPF                      [512, 512, 5]
    47.  11                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]
    48.  12                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
    49.  13           [-1, 6]  1         0  models.common.Concat                    [1]
    50.  14                -1  1    361984  models.common.C3                        [512, 256, 1, False]
    51.  15                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]
    52.  16                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
    53.  17           [-1, 4]  1         0  models.common.Concat                    [1]
    54.  18                -1  1     90880  models.common.C3                        [256, 128, 1, False]
    55.  19                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]
    56.  20          [-1, 14]  1         0  models.common.Concat                    [1]
    57.  21                -1  1    329216  models.common.C3                        [384, 256, 1, False]
    58.  22                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]
    59.  23          [-1, 10]  1         0  models.common.Concat                    [1]
    60.  24                -1  1   1313792  models.common.C3                        [768, 512, 1, False]
    61.  25      [17, 20, 23]  1     38097  models.yolo.Detect                      [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [256, 384, 768]]
    62. YOLOv5s summary: 232 layers, 7226897 parameters, 7226897 gradients, 17.2 GFLOPs
    63.  
    64. Transferred 49/379 items from yolov5s.pt
    65. WARNING  --img-size 900 must be multiple of max stride 32, updating to 928
    66. optimizer: SGD(lr=0.01) with parameter groups 62 weight(decay=0.0), 65 weight(decay=0.0005), 65 bias
    67. train: Scanning D:\yolov5-master\Y2\train... 1 images, 0 backgrounds, 159 corrupt: 100%|██████████| 160/160 [00:13<00:0
    68. train: WARNING   D:\yolov5-master\Y2\images\fruit1.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit1.png'
    69. train: WARNING   D:\yolov5-master\Y2\images\fruit10.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit10.png'
    70. train: WARNING   D:\yolov5-master\Y2\images\fruit100.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit100.png'
    71. train: WARNING   D:\yolov5-master\Y2\images\fruit102.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit102.png'
    72. train: WARNING   D:\yolov5-master\Y2\images\fruit103.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit103.png'
    73. train: WARNING   D:\yolov5-master\Y2\images\fruit104.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit104.png'
    74. train: WARNING   D:\yolov5-master\Y2\images\fruit106.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit106.png'
    75. train: WARNING   D:\yolov5-master\Y2\images\fruit108.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit108.png'
    76. train: WARNING   D:\yolov5-master\Y2\images\fruit109.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit109.png'
    77. train: WARNING   D:\yolov5-master\Y2\images\fruit11.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit11.png'
    78. train: WARNING   D:\yolov5-master\Y2\images\fruit110.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit110.png'
    79. train: WARNING   D:\yolov5-master\Y2\images\fruit111.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit111.png'
    80. train: WARNING   D:\yolov5-master\Y2\images\fruit113.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit113.png'
    81. train: WARNING   D:\yolov5-master\Y2\images\fruit114.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit114.png'
    82. train: WARNING   D:\yolov5-master\Y2\images\fruit115.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit115.png'
    83. train: WARNING   D:\yolov5-master\Y2\images\fruit116.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit116.png'
    84. train: WARNING   D:\yolov5-master\Y2\images\fruit117.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit117.png'
    85. train: WARNING   D:\yolov5-master\Y2\images\fruit118.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit118.png'
    86. train: WARNING   D:\yolov5-master\Y2\images\fruit119.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit119.png'
    87. train: WARNING   D:\yolov5-master\Y2\images\fruit12.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit12.png'
    88. train: WARNING   D:\yolov5-master\Y2\images\fruit120.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit120.png'
    89. train: WARNING   D:\yolov5-master\Y2\images\fruit121.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit121.png'
    90. train: WARNING   D:\yolov5-master\Y2\images\fruit122.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit122.png'
    91. train: WARNING   D:\yolov5-master\Y2\images\fruit123.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit123.png'
    92. train: WARNING   D:\yolov5-master\Y2\images\fruit124.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit124.png'
    93. train: WARNING   D:\yolov5-master\Y2\images\fruit125.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit125.png'
    94. train: WARNING   D:\yolov5-master\Y2\images\fruit127.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit127.png'
    95. train: WARNING   D:\yolov5-master\Y2\images\fruit129.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit129.png'
    96. train: WARNING   D:\yolov5-master\Y2\images\fruit13.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit13.png'
    97. train: WARNING   D:\yolov5-master\Y2\images\fruit130.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit130.png'
    98. train: WARNING   D:\yolov5-master\Y2\images\fruit131.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit131.png'
    99. train: WARNING   D:\yolov5-master\Y2\images\fruit132.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit132.png'
    100. train: WARNING   D:\yolov5-master\Y2\images\fruit133.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit133.png'
    101. train: WARNING   D:\yolov5-master\Y2\images\fruit134.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit134.png'
    102. train: WARNING   D:\yolov5-master\Y2\images\fruit135.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit135.png'
    103. train: WARNING   D:\yolov5-master\Y2\images\fruit136.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit136.png'
    104. train: WARNING   D:\yolov5-master\Y2\images\fruit138.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit138.png'
    105. train: WARNING   D:\yolov5-master\Y2\images\fruit14.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit14.png'
    106. train: WARNING   D:\yolov5-master\Y2\images\fruit142.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit142.png'
    107. train: WARNING   D:\yolov5-master\Y2\images\fruit143.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit143.png'
    108. train: WARNING   D:\yolov5-master\Y2\images\fruit144.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit144.png'
    109. train: WARNING   D:\yolov5-master\Y2\images\fruit145.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit145.png'
    110. train: WARNING   D:\yolov5-master\Y2\images\fruit147.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit147.png'
    111. train: WARNING   D:\yolov5-master\Y2\images\fruit148.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit148.png'
    112. train: WARNING   D:\yolov5-master\Y2\images\fruit149.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit149.png'
    113. train: WARNING   D:\yolov5-master\Y2\images\fruit15.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit15.png'
    114. train: WARNING   D:\yolov5-master\Y2\images\fruit151.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit151.png'
    115. train: WARNING   D:\yolov5-master\Y2\images\fruit152.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit152.png'
    116. train: WARNING   D:\yolov5-master\Y2\images\fruit155.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit155.png'
    117. train: WARNING   D:\yolov5-master\Y2\images\fruit156.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit156.png'
    118. train: WARNING   D:\yolov5-master\Y2\images\fruit157.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit157.png'
    119. train: WARNING   D:\yolov5-master\Y2\images\fruit158.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit158.png'
    120. train: WARNING   D:\yolov5-master\Y2\images\fruit159.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit159.png'
    121. train: WARNING   D:\yolov5-master\Y2\images\fruit16.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit16.png'
    122. train: WARNING   D:\yolov5-master\Y2\images\fruit161.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit161.png'
    123. train: WARNING   D:\yolov5-master\Y2\images\fruit162.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit162.png'
    124. train: WARNING   D:\yolov5-master\Y2\images\fruit163.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit163.png'
    125. train: WARNING   D:\yolov5-master\Y2\images\fruit164.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit164.png'
    126. train: WARNING   D:\yolov5-master\Y2\images\fruit165.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit165.png'
    127. train: WARNING   D:\yolov5-master\Y2\images\fruit167.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit167.png'
    128. train: WARNING   D:\yolov5-master\Y2\images\fruit168.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit168.png'
    129. train: WARNING   D:\yolov5-master\Y2\images\fruit169.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit169.png'
    130. train: WARNING   D:\yolov5-master\Y2\images\fruit17.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit17.png'
    131. train: WARNING   D:\yolov5-master\Y2\images\fruit170.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit170.png'
    132. train: WARNING   D:\yolov5-master\Y2\images\fruit171.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit171.png'
    133. train: WARNING   D:\yolov5-master\Y2\images\fruit172.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit172.png'
    134. train: WARNING   D:\yolov5-master\Y2\images\fruit173.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit173.png'
    135. train: WARNING   D:\yolov5-master\Y2\images\fruit174.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit174.png'
    136. train: WARNING   D:\yolov5-master\Y2\images\fruit175.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit175.png'
    137. train: WARNING   D:\yolov5-master\Y2\images\fruit176.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit176.png'
    138. train: WARNING   D:\yolov5-master\Y2\images\fruit177.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit177.png'
    139. train: WARNING   D:\yolov5-master\Y2\images\fruit178.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit178.png'
    140. train: WARNING   D:\yolov5-master\Y2\images\fruit179.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit179.png'
    141. train: WARNING   D:\yolov5-master\Y2\images\fruit18.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit18.png'
    142. train: WARNING   D:\yolov5-master\Y2\images\fruit180.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit180.png'
    143. train: WARNING   D:\yolov5-master\Y2\images\fruit181.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit181.png'
    144. train: WARNING   D:\yolov5-master\Y2\images\fruit182.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit182.png'
    145. train: WARNING   D:\yolov5-master\Y2\images\fruit183.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit183.png'
    146. train: WARNING   D:\yolov5-master\Y2\images\fruit184.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit184.png'
    147. train: WARNING   D:\yolov5-master\Y2\images\fruit185.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit185.png'
    148. train: WARNING   D:\yolov5-master\Y2\images\fruit186.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit186.png'
    149. train: WARNING   D:\yolov5-master\Y2\images\fruit187.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit187.png'
    150. train: WARNING   D:\yolov5-master\Y2\images\fruit188.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit188.png'
    151. train: WARNING   D:\yolov5-master\Y2\images\fruit19.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit19.png'
    152. train: WARNING   D:\yolov5-master\Y2\images\fruit196.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit196.png'
    153. train: WARNING   D:\yolov5-master\Y2\images\fruit197.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit197.png'
    154. train: WARNING   D:\yolov5-master\Y2\images\fruit198.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit198.png'
    155. train: WARNING   D:\yolov5-master\Y2\images\fruit199.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit199.png'
    156. train: WARNING   D:\yolov5-master\Y2\images\fruit2.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit2.png'
    157. train: WARNING   D:\yolov5-master\Y2\images\fruit200.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit200.png'
    158. train: WARNING   D:\yolov5-master\Y2\images\fruit202.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit202.png'
    159. train: WARNING   D:\yolov5-master\Y2\images\fruit208.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit208.png'
    160. train: WARNING   D:\yolov5-master\Y2\images\fruit209.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit209.png'
    161. train: WARNING   D:\yolov5-master\Y2\images\fruit211.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit211.png'
    162. train: WARNING   D:\yolov5-master\Y2\images\fruit22.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit22.png'
    163. train: WARNING   D:\yolov5-master\Y2\images\fruit23.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit23.png'
    164. train: WARNING   D:\yolov5-master\Y2\images\fruit25.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit25.png'
    165. train: WARNING   D:\yolov5-master\Y2\images\fruit26.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit26.png'
    166. train: WARNING   D:\yolov5-master\Y2\images\fruit27.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit27.png'
    167. train: WARNING   D:\yolov5-master\Y2\images\fruit28.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit28.png'
    168. train: WARNING   D:\yolov5-master\Y2\images\fruit29.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit29.png'
    169. train: WARNING   D:\yolov5-master\Y2\images\fruit3.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit3.png'
    170. train: WARNING   D:\yolov5-master\Y2\images\fruit30.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit30.png'
    171. train: WARNING   D:\yolov5-master\Y2\images\fruit31.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit31.png'
    172. train: WARNING   D:\yolov5-master\Y2\images\fruit33.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit33.png'
    173. train: WARNING   D:\yolov5-master\Y2\images\fruit34.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit34.png'
    174. train: WARNING   D:\yolov5-master\Y2\images\fruit35.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit35.png'
    175. train: WARNING   D:\yolov5-master\Y2\images\fruit36.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit36.png'
    176. train: WARNING   D:\yolov5-master\Y2\images\fruit38.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit38.png'
    177. train: WARNING   D:\yolov5-master\Y2\images\fruit39.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit39.png'
    178. train: WARNING   D:\yolov5-master\Y2\images\fruit4.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit4.png'
    179. train: WARNING   D:\yolov5-master\Y2\images\fruit40.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit40.png'
    180. train: WARNING   D:\yolov5-master\Y2\images\fruit43.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit43.png'
    181. train: WARNING   D:\yolov5-master\Y2\images\fruit44.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit44.png'
    182. train: WARNING   D:\yolov5-master\Y2\images\fruit45.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit45.png'
    183. train: WARNING   D:\yolov5-master\Y2\images\fruit46.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit46.png'
    184. train: WARNING   D:\yolov5-master\Y2\images\fruit49.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit49.png'
    185. train: WARNING   D:\yolov5-master\Y2\images\fruit50.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit50.png'
    186. train: WARNING   D:\yolov5-master\Y2\images\fruit51.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit51.png'
    187. train: WARNING   D:\yolov5-master\Y2\images\fruit52.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit52.png'
    188. train: WARNING   D:\yolov5-master\Y2\images\fruit53.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit53.png'
    189. train: WARNING   D:\yolov5-master\Y2\images\fruit54.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit54.png'
    190. train: WARNING   D:\yolov5-master\Y2\images\fruit55.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit55.png'
    191. train: WARNING   D:\yolov5-master\Y2\images\fruit57.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit57.png'
    192. train: WARNING   D:\yolov5-master\Y2\images\fruit59.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit59.png'
    193. train: WARNING   D:\yolov5-master\Y2\images\fruit6.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit6.png'
    194. train: WARNING   D:\yolov5-master\Y2\images\fruit60.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit60.png'
    195. train: WARNING   D:\yolov5-master\Y2\images\fruit61.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit61.png'
    196. train: WARNING   D:\yolov5-master\Y2\images\fruit62.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit62.png'
    197. train: WARNING   D:\yolov5-master\Y2\images\fruit63.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit63.png'
    198. train: WARNING   D:\yolov5-master\Y2\images\fruit65.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit65.png'
    199. train: WARNING   D:\yolov5-master\Y2\images\fruit66.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit66.png'
    200. train: WARNING   D:\yolov5-master\Y2\images\fruit68.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit68.png'
    201. train: WARNING   D:\yolov5-master\Y2\images\fruit69.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit69.png'
    202. train: WARNING   D:\yolov5-master\Y2\images\fruit7.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit7.png'
    203. train: WARNING   D:\yolov5-master\Y2\images\fruit70.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit70.png'
    204. train: WARNING   D:\yolov5-master\Y2\images\fruit71.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit71.png'
    205. train: WARNING   D:\yolov5-master\Y2\images\fruit73.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit73.png'
    206. train: WARNING   D:\yolov5-master\Y2\images\fruit74.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit74.png'
    207. train: WARNING   D:\yolov5-master\Y2\images\fruit75.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit75.png'
    208. train: WARNING   D:\yolov5-master\Y2\images\fruit77.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit77.png'
    209. train: WARNING   D:\yolov5-master\Y2\images\fruit78.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit78.png'
    210. train: WARNING   D:\yolov5-master\Y2\images\fruit79.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit79.png'
    211. train: WARNING   D:\yolov5-master\Y2\images\fruit80.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit80.png'
    212. train: WARNING   D:\yolov5-master\Y2\images\fruit81.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit81.png'
    213. train: WARNING   D:\yolov5-master\Y2\images\fruit82.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit82.png'
    214. train: WARNING   D:\yolov5-master\Y2\images\fruit83.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit83.png'
    215. train: WARNING   D:\yolov5-master\Y2\images\fruit85.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit85.png'
    216. train: WARNING   D:\yolov5-master\Y2\images\fruit86.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit86.png'
    217. train: WARNING   D:\yolov5-master\Y2\images\fruit87.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit87.png'
    218. train: WARNING   D:\yolov5-master\Y2\images\fruit88.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit88.png'
    219. train: WARNING   D:\yolov5-master\Y2\images\fruit89.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit89.png'
    220. train: WARNING   D:\yolov5-master\Y2\images\fruit90.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit90.png'
    221. train: WARNING   D:\yolov5-master\Y2\images\fruit91.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit91.png'
    222. train: WARNING   D:\yolov5-master\Y2\images\fruit94.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit94.png'
    223. train: WARNING   D:\yolov5-master\Y2\images\fruit95.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit95.png'
    224. train: WARNING   D:\yolov5-master\Y2\images\fruit97.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit97.png'
    225. train: WARNING   D:\yolov5-master\Y2\images\fruit98.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit98.png'
    226. train: WARNING   D:\yolov5-master\Y2\images\fruit99.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit99.png'
    227. 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'
    228. val: Scanning D:\yolov5-master\Y2\val.cache... 1 images, 0 backgrounds, 19 corrupt: 100%|██████████| 20/20 [00:00
    229. val: WARNING   D:\yolov5-master\Y2\images\fruit107.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit107.png'
    230. val: WARNING   D:\yolov5-master\Y2\images\fruit112.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit112.png'
    231. val: WARNING   D:\yolov5-master\Y2\images\fruit139.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit139.png'
    232. val: WARNING   D:\yolov5-master\Y2\images\fruit140.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit140.png'
    233. val: WARNING   D:\yolov5-master\Y2\images\fruit141.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit141.png'
    234. val: WARNING   D:\yolov5-master\Y2\images\fruit146.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit146.png'
    235. val: WARNING   D:\yolov5-master\Y2\images\fruit20.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit20.png'
    236. val: WARNING   D:\yolov5-master\Y2\images\fruit210.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit210.png'
    237. val: WARNING   D:\yolov5-master\Y2\images\fruit24.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit24.png'
    238. val: WARNING   D:\yolov5-master\Y2\images\fruit32.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit32.png'
    239. val: WARNING   D:\yolov5-master\Y2\images\fruit41.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit41.png'
    240. val: WARNING   D:\yolov5-master\Y2\images\fruit47.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit47.png'
    241. val: WARNING   D:\yolov5-master\Y2\images\fruit48.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit48.png'
    242. val: WARNING   D:\yolov5-master\Y2\images\fruit5.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit5.png'
    243. val: WARNING   D:\yolov5-master\Y2\images\fruit64.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit64.png'
    244. val: WARNING   D:\yolov5-master\Y2\images\fruit8.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit8.png'
    245. val: WARNING   D:\yolov5-master\Y2\images\fruit84.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit84.png'
    246. val: WARNING   D:\yolov5-master\Y2\images\fruit92.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit92.png'
    247. val: WARNING   D:\yolov5-master\Y2\images\fruit96.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit96.png'
    248.  
    249. AutoAnchor: 4.33 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset
    250. Plotting labels to runs\train\exp12\labels.jpg...
    251. Image sizes 928 train, 928 val
    252. Using 0 dataloader workers
    253. Logging results to runs\train\exp12
    254. Starting training for 100 epochs...
    255.  
    256.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    257.        0/99         0G     0.1123    0.06848    0.04815          7        928:   0%|          | 0/1 [00:01
    258.        0/99         0G     0.1123    0.06848    0.04815          7        928: 100%|██████████| 1/1 [00:02<00:00,  2.97
    259.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    260.                    all          1          3    0.00439      0.333     0.0474     0.0121
    261.  
    262.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    263.        1/99         0G     0.1105    0.06846    0.04628          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.51
    264.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    265.                    all          1          3    0.00926      0.333     0.0332     0.0154
    266.  
    267.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    268.        2/99         0G     0.1139    0.05816    0.04684          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.45
    269.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    270.                    all          1          3    0.00926      0.333     0.0332     0.0154
    271.  
    272.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    273.        3/99         0G    0.07328    0.05078    0.03088          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.51
    274.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    275.                    all          1          3     0.0119      0.333     0.0123    0.00369
    276.  
    277.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    278.        4/99         0G    0.06693    0.05186    0.03044          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.47
    279.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    280.                    all          1          3     0.0119      0.333     0.0123    0.00369
    281.  
    282.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    283.        5/99         0G     0.1102    0.09702    0.04647         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.44
    284.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    285.                    all          1          3     0.0119      0.333     0.0123    0.00369
    286.  
    287.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    288.        6/99         0G     0.1147    0.07053    0.04376          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.48
    289.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    290.                    all          1          3          0          0          0          0
    291.  
    292.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    293.        7/99         0G    0.06716    0.05544    0.02962          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.43
    294.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    295.                    all          1          3          0          0          0          0
    296.  
    297.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    298.        8/99         0G     0.1161    0.05993    0.04253          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.44
    299.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    300.                    all          1          3          0          0          0          0
    301.  
    302.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    303.        9/99         0G     0.1187    0.05657     0.0432          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.45
    304.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    305.                    all          1          3          0          0          0          0
    306.  
    307.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    308.       10/99         0G     0.1163    0.09305    0.04868         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.50
    309.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    310.                    all          1          3          0          0          0          0
    311.  
    312.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    313.       11/99         0G    0.07575    0.04969    0.03171          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.42
    314.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    315.                    all          1          3          0          0          0          0
    316.  
    317.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    318.       12/99         0G     0.1092    0.09129      0.045         10        928: 100%|██████████| 1/1 [00:01<00:00,  1.43
    319.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    320.                    all          1          3          0          0          0          0
    321.  
    322.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    323.       13/99         0G     0.1003    0.05476    0.04605          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.44
    324.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    325.                    all          1          3          0          0          0          0
    326.  
    327.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    328.       14/99         0G    0.07006    0.05166    0.03166          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.43
    329.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    330.                    all          1          3          0          0          0          0
    331.  
    332.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    333.       15/99         0G     0.1156    0.05315    0.04495          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.43
    334.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    335.                    all          1          3          0          0          0          0
    336.  
    337.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    338.       16/99         0G     0.1143     0.0559      0.045          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.48
    339.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    340.                    all          1          3          0          0          0          0
    341.  
    342.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    343.       17/99         0G    0.08845     0.0449    0.02645          2        928: 100%|██████████| 1/1 [00:01<00:00,  1.43
    344.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    345.                    all          1          3          0          0          0          0
    346.  
    347.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    348.       18/99         0G     0.1189    0.05909    0.04975          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.44
    349.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    350.                    all          1          3          0          0          0          0
    351.  
    352.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    353.       19/99         0G     0.1113    0.05739    0.04547          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.46
    354.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    355.                    all          1          3          0          0          0          0
    356.  
    357.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    358.       20/99         0G      0.117    0.07437    0.04842         10        928: 100%|██████████| 1/1 [00:01<00:00,  1.45
    359.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    360.                    all          1          3          0          0          0          0
    361.  
    362.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    363.       21/99         0G      0.109    0.06155     0.0505          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.44
    364.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    365.                    all          1          3          0          0          0          0
    366.  
    367.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    368.       22/99         0G     0.1073     0.1035    0.04515         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.46
    369.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    370.                    all          1          3          0          0          0          0
    371.  
    372.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    373.       23/99         0G     0.1257     0.0527    0.04264          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.45
    374.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    375.                    all          1          3          0          0          0          0
    376.  
    377.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    378.       24/99         0G     0.1036     0.0745    0.04745          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.50
    379.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    380.                    all          1          3          0          0          0          0
    381.  
    382.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    383.       25/99         0G     0.1112     0.1054    0.04881         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.45
    384.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    385.                    all          1          3          0          0          0          0
    386.  
    387.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    388.       26/99         0G     0.1053    0.08021    0.04656          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.44
    389.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    390.                    all          1          3          0          0          0          0
    391.  
    392.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    393.       27/99         0G     0.1208    0.05651    0.04577          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.45
    394.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    395.                    all          1          3          0          0          0          0
    396.  
    397.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    398.       28/99         0G    0.07633     0.0537    0.03023          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.46
    399.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    400.                    all          1          3          0          0          0          0
    401.  
    402.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    403.       29/99         0G     0.1162    0.05969    0.04597          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.44
    404.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    405.                    all          1          3          0          0          0          0
    406.  
    407.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    408.       30/99         0G     0.1117    0.07415    0.04961          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.45
    409.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    410.                    all          1          3          0          0          0          0
    411.  
    412.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    413.       31/99         0G     0.1132    0.06359    0.04704          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.46
    414.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    415.                    all          1          3          0          0          0          0
    416.  
    417.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    418.       32/99         0G    0.08006    0.05026    0.02591          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.45
    419.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    420.                    all          1          3          0          0          0          0
    421.  
    422.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    423.       33/99         0G     0.1117      0.104    0.04704         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.45
    424.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    425.                    all          1          3          0          0          0          0
    426.  
    427.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    428.       34/99         0G     0.1135    0.06241    0.04401          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.45
    429.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    430.                    all          1          3          0          0          0          0
    431.  
    432.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    433.       35/99         0G     0.1117    0.07476    0.04524          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.45
    434.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    435.                    all          1          3          0          0          0          0
    436.  
    437.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    438.       36/99         0G     0.1134    0.09759    0.04479         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.51
    439.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    440.                    all          1          3          0          0          0          0
    441.  
    442.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    443.       37/99         0G     0.1184    0.06637    0.04515          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.45
    444.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    445.                    all          1          3          0          0          0          0
    446.  
    447.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    448.       38/99         0G    0.08484    0.04526    0.02921          2        928: 100%|██████████| 1/1 [00:01<00:00,  1.50
    449.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    450.                    all          1          3          0          0          0          0
    451.  
    452.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    453.       39/99         0G    0.09749     0.0813    0.04582          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.57
    454.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    455.                    all          1          3          0          0          0          0
    456.  
    457.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    458.       40/99         0G     0.1117    0.07415      0.046          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.63
    459.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    460.                    all          1          3          0          0          0          0
    461.  
    462.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    463.       41/99         0G     0.1117    0.07245    0.04489          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.68
    464.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    465.                    all          1          3          0          0          0          0
    466.  
    467.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    468.       42/99         0G     0.1094    0.05986    0.04839          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.71
    469.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    470.                    all          1          3          0          0          0          0
    471.  
    472.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    473.       43/99         0G     0.1097     0.0697    0.04865          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.65
    474.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    475.                    all          1          3          0          0          0          0
    476.  
    477.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    478.       44/99         0G     0.1108    0.09187    0.04328         10        928: 100%|██████████| 1/1 [00:01<00:00,  1.57
    479.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    480.                    all          1          3          0          0          0          0
    481.  
    482.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    483.       45/99         0G     0.1126    0.05993      0.047          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.52
    484.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    485.                    all          1          3          0          0          0          0
    486.  
    487.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    488.       46/99         0G     0.0688    0.05024    0.03075          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.53
    489.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    490.                    all          1          3          0          0          0          0
    491.  
    492.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    493.       47/99         0G      0.112    0.09688    0.04424         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.51
    494.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    495.                    all          1          3          0          0          0          0
    496.  
    497.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    498.       48/99         0G     0.1166    0.06569    0.04565          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.53
    499.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    500.                    all          1          3          0          0          0          0
    501.  
    502.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    503.       49/99         0G     0.1118    0.05801    0.04417          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.51
    504.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    505.                    all          1          3          0          0          0          0
    506.  
    507.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    508.       50/99         0G     0.1097     0.1048    0.04665         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.51
    509.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    510.                    all          1          3          0          0          0          0
    511.  
    512.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    513.       51/99         0G     0.1218    0.06085    0.04525          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.83
    514.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    515.                    all          1          3          0          0          0          0
    516.  
    517.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    518.       52/99         0G     0.1056    0.08698    0.04532          9        928: 100%|██████████| 1/1 [00:01<00:00,  1.66
    519.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    520.                    all          1          3          0          0          0          0
    521.  
    522.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    523.       53/99         0G    0.06761    0.05242    0.03217          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.68
    524.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    525.                    all          1          3          0          0          0          0
    526.  
    527.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    528.       54/99         0G     0.1044     0.1022     0.0441         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.60
    529.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    530.                    all          1          3          0          0          0          0
    531.  
    532.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    533.       55/99         0G     0.1269    0.05652    0.04289          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.87
    534.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    535.                    all          1          3          0          0          0          0
    536.  
    537.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    538.       56/99         0G     0.1112     0.0772    0.04683          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.86
    539.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    540.                    all          1          3          0          0          0          0
    541.  
    542.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    543.       57/99         0G     0.1144    0.05499    0.04611          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.78
    544.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    545.                    all          1          3          0          0          0          0
    546.  
    547.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    548.       58/99         0G    0.07043     0.0666     0.0297          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.71
    549.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    550.                    all          1          3          0          0          0          0
    551.  
    552.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    553.       59/99         0G     0.1092    0.09867    0.04592         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.72
    554.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    555.                    all          1          3          0          0          0          0
    556.  
    557.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    558.       60/99         0G       0.12    0.05285    0.04611          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.71
    559.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    560.                    all          1          3          0          0          0          0
    561.  
    562.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    563.       61/99         0G     0.0728    0.05391    0.02953          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.75
    564.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    565.                    all          1          3          0          0          0          0
    566.  
    567.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    568.       62/99         0G     0.1164    0.05441    0.04357          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.91
    569.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    570.                    all          1          3          0          0          0          0
    571.  
    572.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    573.       63/99         0G     0.1123     0.1039     0.0476         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.82
    574.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    575.                    all          1          3          0          0          0          0
    576.  
    577.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    578.       64/99         0G     0.1089      0.064    0.04559          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.69
    579.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    580.                    all          1          3          0          0          0          0
    581.  
    582.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    583.       65/99         0G     0.1152    0.07665    0.04802          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.64
    584.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    585.                    all          1          3          0          0          0          0
    586.  
    587.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    588.       66/99         0G     0.1186    0.06205     0.0432          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.74
    589.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    590.                    all          1          3          0          0          0          0
    591.  
    592.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    593.       67/99         0G      0.114    0.06644    0.04486          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.88
    594.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    595.                    all          1          3          0          0          0          0
    596.  
    597.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    598.       68/99         0G     0.1118    0.05814    0.04571          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.89
    599.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    600.                    all          1          3          0          0          0          0
    601.  
    602.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    603.       69/99         0G      0.106     0.0762    0.04522          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.88
    604.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    605.                    all          1          3          0          0          0          0
    606.  
    607.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    608.       70/99         0G     0.1068    0.06769      0.048          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.71
    609.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    610.                    all          1          3          0          0          0          0
    611.  
    612.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    613.       71/99         0G       0.11     0.1035    0.04768         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.64
    614.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    615.                    all          1          3          0          0          0          0
    616.  
    617.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    618.       72/99         0G     0.1071    0.05783    0.04588          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.71
    619.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    620.                    all          1          3          0          0          0          0
    621.  
    622.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    623.       73/99         0G      0.107    0.06332    0.04598          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.72
    624.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    625.                    all          1          3          0          0          0          0
    626.  
    627.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    628.       74/99         0G     0.1127    0.09514    0.04832         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.71
    629.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    630.                    all          1          3          0          0          0          0
    631.  
    632.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    633.       75/99         0G    0.07471    0.05085    0.03363          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.62
    634.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    635.                    all          1          3          0          0          0          0
    636.  
    637.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    638.       76/99         0G    0.07295    0.05077    0.03028          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.68
    639.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    640.                    all          1          3          0          0          0          0
    641.  
    642.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    643.       77/99         0G     0.1221     0.0522     0.0502          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.73
    644.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    645.                    all          1          3          0          0          0          0
    646.  
    647.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    648.       78/99         0G     0.1159    0.05984    0.04441          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.86
    649.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    650.                    all          1          3          0          0          0          0
    651.  
    652.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    653.       79/99         0G     0.0764    0.05256    0.03172          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.81
    654.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    655.                    all          1          3          0          0          0          0
    656.  
    657.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    658.       80/99         0G    0.07563    0.05452    0.03032          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.73
    659.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    660.                    all          1          3          0          0          0          0
    661.  
    662.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    663.       81/99         0G    0.06719     0.0531    0.02945          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.67
    664.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    665.                    all          1          3          0          0          0          0
    666.  
    667.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    668.       82/99         0G     0.1076    0.06686    0.04691          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.68
    669.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    670.                    all          1          3          0          0          0          0
    671.  
    672.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    673.       83/99         0G     0.1112    0.07135    0.04413          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.70
    674.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    675.                    all          1          3          0          0          0          0
    676.  
    677.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    678.       84/99         0G     0.1116    0.09399    0.04413         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.63
    679.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    680.                    all          1          3          0          0          0          0
    681.  
    682.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    683.       85/99         0G     0.1116    0.06021    0.04635          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.67
    684.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    685.                    all          1          3          0          0          0          0
    686.  
    687.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    688.       86/99         0G     0.1096     0.1032    0.04634         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.66
    689.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    690.                    all          1          3          0          0          0          0
    691.  
    692.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    693.       87/99         0G     0.1143    0.05941    0.04396          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.66
    694.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    695.                    all          1          3          0          0          0          0
    696.  
    697.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    698.       88/99         0G     0.1161     0.0518    0.04673          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.66
    699.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    700.                    all          1          3          0          0          0          0
    701.  
    702.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    703.       89/99         0G     0.1106    0.05528    0.04363          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.65
    704.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    705.                    all          1          3          0          0          0          0
    706.  
    707.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    708.       90/99         0G     0.1238    0.05427    0.04809          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.66
    709.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    710.                    all          1          3          0          0          0          0
    711.  
    712.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    713.       91/99         0G     0.1104    0.06561    0.04492          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.67
    714.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    715.                    all          1          3          0          0          0          0
    716.  
    717.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    718.       92/99         0G     0.1137    0.08532    0.04445         10        928: 100%|██████████| 1/1 [00:01<00:00,  1.70
    719.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    720.                    all          1          3          0          0          0          0
    721.  
    722.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    723.       93/99         0G     0.1125    0.07016    0.04628          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.65
    724.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    725.                    all          1          3          0          0          0          0
    726.  
    727.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    728.       94/99         0G     0.1116    0.05724    0.04418          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.63
    729.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    730.                    all          1          3          0          0          0          0
    731.  
    732.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    733.       95/99         0G     0.1124     0.1026    0.04744         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.77
    734.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    735.                    all          1          3          0          0          0          0
    736.  
    737.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    738.       96/99         0G      0.117    0.05599    0.04682          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.71
    739.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    740.                    all          1          3          0          0          0          0
    741.  
    742.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    743.       97/99         0G      0.124     0.0617    0.04387          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.75
    744.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    745.                    all          1          3          0          0          0          0
    746.  
    747.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    748.       98/99         0G     0.1126     0.1009    0.04399         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.64
    749.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    750.                    all          1          3          0          0          0          0
    751.  
    752.       Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    753.       99/99         0G    0.06937    0.05515    0.03017          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.68
    754.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    755.                    all          1          3          0          0          0          0
    756.  
    757. 100 epochs completed in 0.067 hours.
    758. Optimizer stripped from runs\train\exp12\weights\last.pt, 15.0MB
    759. Optimizer stripped from runs\train\exp12\weights\best.pt, 15.0MB
    760.  
    761. Validating runs\train\exp12\weights\best.pt...
    762. Fusing layers...
    763. YOLOv5s summary: 170 layers, 7217201 parameters, 0 gradients, 17.0 GFLOPs
    764.                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0
    765.                    all          1          3     0.0115      0.333     0.0369      0.012
    766.                 banana          1          1          0          0          0          0
    767.            snake fruit          1          1          0          0          0          0
    768.              pineapple          1          1     0.0345          1      0.111     0.0359
    769. Results saved to runs\train\exp12

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  • 原文地址：https://blog.csdn.net/qq_60245590/article/details/134469096