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]
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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

class C2(nn.Module):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c1, c_, 1, 1)self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))def forward(self, x):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

def parse_model(d, ch):  # model_dict, input_channels(3)# Parse a YOLOv5 model.yaml dictionaryLOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')if act:Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()LOGGER.info(f"{colorstr('activation:')} {act}")  # printna = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchorsno = na * (nc + 5)  # number of outputs = anchors * (classes + 5)layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch outfor i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, argsm = eval(m) if isinstance(m, str) else m  # eval stringsfor j, a in enumerate(args):with contextlib.suppress(NameError):args[j] = eval(a) if isinstance(a, str) else a  # eval stringsn = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gainif m in {Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:c1, c2 = ch[f], args[0]if c2 != no:  # if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:args.insert(2, n)  # number of repeatsn = 1elif m is nn.BatchNorm2d:args = [ch[f]]elif m is Concat:c2 = sum(ch[x] for x in f)# TODO: channel, gw, gdelif m in {Detect, Segment}:args.append([ch[x] for x in f])if isinstance(args[1], int):  # number of anchorsargs[1] = [list(range(args[1] * 2))] * len(f)if m is Segment:args[3] = make_divisible(args[3] * gw, 8)elif m is Contract:c2 = ch[f] * args[0] ** 2elif m is Expand:c2 = ch[f] // args[0] ** 2else:c2 = ch[f]m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # modulet = str(m)[8:-2].replace('__main__.', '')  # module typenp = sum(x.numel() for x in m_.parameters())  # number paramsm_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number paramsLOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}')  # printsave.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelistlayers.append(m_)if i == 0:ch = []ch.append(c2)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：

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                                  arguments
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 185, in __init__self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelistFile "D:\yolov5-master\models\yolo.py", line 319, in parse_modelBottleneckCSP, C2, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
NameError: name 'C2' is not defined. Did you mean: 'c2'?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     18816  models.common.C2                        [64, 64, 1]4                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]5                -1  2    115712  models.common.C3                        [128, 128, 2]6                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]7                -1  3    625152  models.common.C3                        [256, 256, 3]8                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]9                -1  1   1182720  models.common.C3                        [512, 512, 1]10                -1  1    656896  models.common.SPPF                      [512, 512, 5]11                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]12                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']13           [-1, 6]  1         0  models.common.Concat                    [1]14                -1  1    361984  models.common.C3                        [512, 256, 1, False]15                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]16                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']17           [-1, 4]  1         0  models.common.Concat                    [1]18                -1  1     90880  models.common.C3                        [256, 128, 1, False]19                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]20          [-1, 14]  1         0  models.common.Concat                    [1]21                -1  1    329216  models.common.C3                        [384, 256, 1, False]22                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]23          [-1, 10]  1         0  models.common.Concat                    [1]24                -1  1   1313792  models.common.C3                        [768, 512, 1, False]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]]
YOLOv5s summary: 232 layers, 7226897 parameters, 7226897 gradients, 17.2 GFLOPsTransferred 49/379 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 62 weight(decay=0.0), 65 weight(decay=0.0005), 65 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\exp12\labels.jpg...
Image sizes 928 train, 928 val
Using 0 dataloader workers
Logging results to runs\train\exp12
Starting training for 100 epochs...Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size0/99         0G     0.1123    0.06848    0.04815          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     0.1123    0.06848    0.04815          7        928: 100%|██████████| 1/1 [00:02<00:00,  2.97Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00439      0.333     0.0474     0.0121Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size1/99         0G     0.1105    0.06846    0.04628          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.51Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00926      0.333     0.0332     0.0154Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size2/99         0G     0.1139    0.05816    0.04684          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.45Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3    0.00926      0.333     0.0332     0.0154Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size3/99         0G    0.07328    0.05078    0.03088          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.51Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3     0.0119      0.333     0.0123    0.00369Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size4/99         0G    0.06693    0.05186    0.03044          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.47Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3     0.0119      0.333     0.0123    0.00369Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size5/99         0G     0.1102    0.09702    0.04647         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.44Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3     0.0119      0.333     0.0123    0.00369Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size6/99         0G     0.1147    0.07053    0.04376          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.48Class     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    0.06716    0.05544    0.02962          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.43Class     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     0.1161    0.05993    0.04253          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.44Class     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     0.1187    0.05657     0.0432          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.45Class     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     0.1163    0.09305    0.04868         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.50Class     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    0.07575    0.04969    0.03171          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.42Class     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     0.1092    0.09129      0.045         10        928: 100%|██████████| 1/1 [00:01<00:00,  1.43Class     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     0.1003    0.05476    0.04605          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.44Class     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    0.07006    0.05166    0.03166          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.43Class     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     0.1156    0.05315    0.04495          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.43Class     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     0.1143     0.0559      0.045          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.48Class     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       Size17/99         0G    0.08845     0.0449    0.02645          2        928: 100%|██████████| 1/1 [00:01<00:00,  1.43Class     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       Size18/99         0G     0.1189    0.05909    0.04975          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.44Class     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       Size19/99         0G     0.1113    0.05739    0.04547          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.46Class     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       Size20/99         0G      0.117    0.07437    0.04842         10        928: 100%|██████████| 1/1 [00:01<00:00,  1.45Class     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       Size21/99         0G      0.109    0.06155     0.0505          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.44Class     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       Size22/99         0G     0.1073     0.1035    0.04515         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.46Class     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       Size23/99         0G     0.1257     0.0527    0.04264          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.45Class     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       Size24/99         0G     0.1036     0.0745    0.04745          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.50Class     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       Size25/99         0G     0.1112     0.1054    0.04881         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.45Class     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       Size26/99         0G     0.1053    0.08021    0.04656          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.44Class     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       Size27/99         0G     0.1208    0.05651    0.04577          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.45Class     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       Size28/99         0G    0.07633     0.0537    0.03023          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.46Class     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       Size29/99         0G     0.1162    0.05969    0.04597          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.44Class     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       Size30/99         0G     0.1117    0.07415    0.04961          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.45Class     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       Size31/99         0G     0.1132    0.06359    0.04704          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.46Class     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       Size32/99         0G    0.08006    0.05026    0.02591          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.45Class     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       Size33/99         0G     0.1117      0.104    0.04704         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.45Class     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       Size34/99         0G     0.1135    0.06241    0.04401          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.45Class     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       Size35/99         0G     0.1117    0.07476    0.04524          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.45Class     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       Size36/99         0G     0.1134    0.09759    0.04479         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.51Class     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       Size37/99         0G     0.1184    0.06637    0.04515          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.45Class     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       Size38/99         0G    0.08484    0.04526    0.02921          2        928: 100%|██████████| 1/1 [00:01<00:00,  1.50Class     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       Size39/99         0G    0.09749     0.0813    0.04582          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.57Class     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       Size40/99         0G     0.1117    0.07415      0.046          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.63Class     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       Size41/99         0G     0.1117    0.07245    0.04489          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.68Class     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       Size42/99         0G     0.1094    0.05986    0.04839          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.71Class     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       Size43/99         0G     0.1097     0.0697    0.04865          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.65Class     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       Size44/99         0G     0.1108    0.09187    0.04328         10        928: 100%|██████████| 1/1 [00:01<00:00,  1.57Class     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       Size45/99         0G     0.1126    0.05993      0.047          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.52Class     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       Size46/99         0G     0.0688    0.05024    0.03075          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.53Class     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       Size47/99         0G      0.112    0.09688    0.04424         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.51Class     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       Size48/99         0G     0.1166    0.06569    0.04565          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.53Class     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       Size49/99         0G     0.1118    0.05801    0.04417          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.51Class     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       Size50/99         0G     0.1097     0.1048    0.04665         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.51Class     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       Size51/99         0G     0.1218    0.06085    0.04525          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.83Class     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       Size52/99         0G     0.1056    0.08698    0.04532          9        928: 100%|██████████| 1/1 [00:01<00:00,  1.66Class     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       Size53/99         0G    0.06761    0.05242    0.03217          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.68Class     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       Size54/99         0G     0.1044     0.1022     0.0441         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.60Class     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       Size55/99         0G     0.1269    0.05652    0.04289          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.87Class     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       Size56/99         0G     0.1112     0.0772    0.04683          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.86Class     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       Size57/99         0G     0.1144    0.05499    0.04611          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.78Class     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       Size58/99         0G    0.07043     0.0666     0.0297          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.71Class     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       Size59/99         0G     0.1092    0.09867    0.04592         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.72Class     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       Size60/99         0G       0.12    0.05285    0.04611          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.71Class     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       Size61/99         0G     0.0728    0.05391    0.02953          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.75Class     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       Size62/99         0G     0.1164    0.05441    0.04357          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.91Class     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       Size63/99         0G     0.1123     0.1039     0.0476         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.82Class     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       Size64/99         0G     0.1089      0.064    0.04559          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.69Class     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       Size65/99         0G     0.1152    0.07665    0.04802          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.64Class     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       Size66/99         0G     0.1186    0.06205     0.0432          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.74Class     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       Size67/99         0G      0.114    0.06644    0.04486          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.88Class     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       Size68/99         0G     0.1118    0.05814    0.04571          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.89Class     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       Size69/99         0G      0.106     0.0762    0.04522          8        928: 100%|██████████| 1/1 [00:01<00:00,  1.88Class     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       Size70/99         0G     0.1068    0.06769      0.048          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.71Class     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       Size71/99         0G       0.11     0.1035    0.04768         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.64Class     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       Size72/99         0G     0.1071    0.05783    0.04588          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.71Class     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       Size73/99         0G      0.107    0.06332    0.04598          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.72Class     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       Size74/99         0G     0.1127    0.09514    0.04832         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.71Class     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       Size75/99         0G    0.07471    0.05085    0.03363          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.62Class     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       Size76/99         0G    0.07295    0.05077    0.03028          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.68Class     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       Size77/99         0G     0.1221     0.0522     0.0502          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.73Class     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       Size78/99         0G     0.1159    0.05984    0.04441          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.86Class     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       Size79/99         0G     0.0764    0.05256    0.03172          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.81Class     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       Size80/99         0G    0.07563    0.05452    0.03032          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.73Class     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       Size81/99         0G    0.06719     0.0531    0.02945          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.67Class     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       Size82/99         0G     0.1076    0.06686    0.04691          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.68Class     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       Size83/99         0G     0.1112    0.07135    0.04413          7        928: 100%|██████████| 1/1 [00:01<00:00,  1.70Class     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       Size84/99         0G     0.1116    0.09399    0.04413         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.63Class     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       Size85/99         0G     0.1116    0.06021    0.04635          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.67Class     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       Size86/99         0G     0.1096     0.1032    0.04634         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.66Class     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       Size87/99         0G     0.1143    0.05941    0.04396          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.66Class     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       Size88/99         0G     0.1161     0.0518    0.04673          3        928: 100%|██████████| 1/1 [00:01<00:00,  1.66Class     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       Size89/99         0G     0.1106    0.05528    0.04363          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.65Class     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       Size90/99         0G     0.1238    0.05427    0.04809          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.66Class     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       Size91/99         0G     0.1104    0.06561    0.04492          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.67Class     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       Size92/99         0G     0.1137    0.08532    0.04445         10        928: 100%|██████████| 1/1 [00:01<00:00,  1.70Class     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       Size93/99         0G     0.1125    0.07016    0.04628          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.65Class     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       Size94/99         0G     0.1116    0.05724    0.04418          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.63Class     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       Size95/99         0G     0.1124     0.1026    0.04744         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.77Class     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       Size96/99         0G      0.117    0.05599    0.04682          5        928: 100%|██████████| 1/1 [00:01<00:00,  1.71Class     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       Size97/99         0G      0.124     0.0617    0.04387          6        928: 100%|██████████| 1/1 [00:01<00:00,  1.75Class     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       Size98/99         0G     0.1126     0.1009    0.04399         12        928: 100%|██████████| 1/1 [00:01<00:00,  1.64Class     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       Size99/99         0G    0.06937    0.05515    0.03017          4        928: 100%|██████████| 1/1 [00:01<00:00,  1.68Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3          0          0          0          0100 epochs completed in 0.067 hours.
Optimizer stripped from runs\train\exp12\weights\last.pt, 15.0MB
Optimizer stripped from runs\train\exp12\weights\best.pt, 15.0MBValidating runs\train\exp12\weights\best.pt...
Fusing layers...
YOLOv5s summary: 170 layers, 7217201 parameters, 0 gradients, 17.0 GFLOPsClass     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<0all          1          3     0.0115      0.333     0.0369      0.012banana          1          1          0          0          0          0snake fruit          1          1          0          0          0          0pineapple          1          1     0.0345          1      0.111     0.0359
Results saved to runs\train\exp12