YOLOv5 第Y6周 模型改进
🍨 本文为[🔗365天深度学习训练营学习记录博客
🍦 参考文章：365天深度学习训练营
🍖 原作者：[K同学啊]
🚀 文章来源：[K同学的学习圈子](https://www.yuque.com/mingtian-fkmxf/zxwb45)
改进前模型框架图：
改进后模型框架图：
改进前：
改进后：
# YOLOv5 v6.0 backbone
backbone:# [from, number, module, args][[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2[-1, 1, Conv, [128, 3, 2]], # 1-P2/4[-1, 3, C3, [128]], # 2[-1, 1, Conv, [256, 3, 2]], # 3-P3/8[-1, 6, C2, [256]], # 4-修改为C2*2[-1, 1, Conv, [512, 3, 2]], # 5-P4/16[-1, 3, C3, [512]], # 6-修改为C3*1
# [-1, 1, Conv, [1024, 3, 2]], # 7-删除P5/32
# [-1, 3, C3, [1024]], # 8-删除[-1, 1, SPPF, [512, 5]], # 9-修改参数;层数变为7] 修改前：
修改后：
# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [512, 3, 2]], # 修改参数[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]], # cat backbone P4[-1, 3, C3, [512, False]], # 13->11[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 4], 1, Concat, [1]], # cat backbone P3[-1, 3, C3, [256, False]], # 17->15 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 12], 1, Concat, [1]], # cat head P4 修改层数-2[-1, 3, C3, [512, False]], # 20->18 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 8], 1, Concat, [1]], # cat head P5 修改层数-2[-1, 3, C3, [1024, False]], # 23->21 (P5/32-large)[[15, 18, 21], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 修改层数-2]执行命令行：
python train.py --img 900 --batch 2 --epoch 100 --data D:/yolov5-master/data/ab.yaml --cfg D:/yolov5-master/models/yolov5s.yaml --weights yolov5s.pt运行结果：
D:\yolov5-master>python train.py --img 900 --batch 2 --epoch 100 --data D:/yolov5-master/data/ab.yaml --cfg D:/yolov5-master/models/yolov5s.yaml --weights yolov5s.pt
train: weights=yolov5s.pt, cfg=D:/yolov5-master/models/yolov5s.yaml, data=D:/yolov5-master/data/ab.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=100, batch_size=2, imgsz=900, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5
YOLOv5 2023-10-15 Python-3.10.7 torch-2.0.1+cpu CPUhyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 runs in Comet
TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=4from n params module arguments0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]1 -1 1 18560 models.common.Conv [32, 64, 3, 2]2 -1 1 18816 models.common.C3 [64, 64, 1]3 -1 1 73984 models.common.Conv [64, 128, 3, 2]4 -1 2 115712 models.common.C2 [128, 128, 2]5 -1 1 295424 models.common.Conv [128, 256, 3, 2]6 -1 3 625152 models.common.C3 [256, 256, 3]7 -1 1 296192 models.common.SPPF [256, 512, 5]8 -1 1 131584 models.common.Conv [512, 256, 1, 1]9 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']10 [-1, 6] 1 0 models.common.Concat [1]11 -1 1 361984 models.common.C3 [512, 256, 1, False]12 -1 1 33024 models.common.Conv [256, 128, 1, 1]13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']14 [-1, 4] 1 0 models.common.Concat [1]15 -1 1 90880 models.common.C3 [256, 128, 1, False]16 -1 1 147712 models.common.Conv [128, 128, 3, 2]17 [-1, 12] 1 0 models.common.Concat [1]18 -1 1 296448 models.common.C3 [256, 256, 1, False]19 -1 1 590336 models.common.Conv [256, 256, 3, 2]20 [-1, 8] 1 0 models.common.Concat [1]21 -1 1 1182720 models.common.C3 [512, 512, 1, False]22 [15, 18, 21] 1 24273 models.yolo.Detect [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Traceback (most recent call last):File "D:\yolov5-master\train.py", line 647, in <module>main(opt)File "D:\yolov5-master\train.py", line 536, in maintrain(opt.hyp, opt, device, callbacks)File "D:\yolov5-master\train.py", line 130, in trainmodel = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # createFile "D:\yolov5-master\models\yolo.py", line 195, in __init__m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forwardFile "D:\yolov5-master\models\yolo.py", line 194, in <lambda>forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)File "D:\yolov5-master\models\yolo.py", line 209, in forwardreturn self._forward_once(x, profile, visualize) # single-scale inference, trainFile "D:\yolov5-master\models\yolo.py", line 121, in _forward_oncex = m(x) # runFile "D:\Python\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_implreturn forward_call(*args, **kwargs)File "D:\yolov5-master\models\common.py", line 336, in forwardreturn torch.cat(x, self.d)
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 32 but got size 16 for tensor number 1 in the list.D:\yolov5-master>python train.py --img 900 --batch 2 --epoch 100 --data D:/yolov5-master/data/ab.yaml --cfg D:/yolov5-master/models/yolov5s.yaml --weights yolov5s.pt
train: weights=yolov5s.pt, cfg=D:/yolov5-master/models/yolov5s.yaml, data=D:/yolov5-master/data/ab.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=100, batch_size=2, imgsz=900, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5
YOLOv5 2023-10-15 Python-3.10.7 torch-2.0.1+cpu CPUhyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 runs in Comet
TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=4from n params module arguments0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]1 -1 1 18560 models.common.Conv [32, 64, 3, 2]2 -1 1 18816 models.common.C3 [64, 64, 1]3 -1 1 73984 models.common.Conv [64, 128, 3, 2]4 -1 2 115712 models.common.C2 [128, 128, 2]5 -1 1 295424 models.common.Conv [128, 256, 3, 2]6 -1 1 296448 models.common.C3 [256, 256, 1]7 -1 1 164608 models.common.SPPF [256, 256, 5]8 -1 1 590336 models.common.Conv [256, 256, 3, 2]9 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']10 [-1, 6] 1 0 models.common.Concat [1]11 -1 1 361984 models.common.C3 [512, 256, 1, False]12 -1 1 33024 models.common.Conv [256, 128, 1, 1]13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']14 [-1, 4] 1 0 models.common.Concat [1]15 -1 1 90880 models.common.C3 [256, 128, 1, False]16 -1 1 147712 models.common.Conv [128, 128, 3, 2]17 [-1, 12] 1 0 models.common.Concat [1]18 -1 1 296448 models.common.C3 [256, 256, 1, False]19 -1 1 590336 models.common.Conv [256, 256, 3, 2]20 [-1, 8] 1 0 models.common.Concat [1]21 -1 1 1182720 models.common.C3 [512, 512, 1, False]22 [15, 18, 21] 1 24273 models.yolo.Detect [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
YOLOv5s summary: 179 layers, 4304785 parameters, 4304785 gradients, 13.4 GFLOPsTransferred 126/289 items from yolov5s.pt
WARNING --img-size 900 must be multiple of max stride 32, updating to 928
optimizer: SGD(lr=0.01) with parameter groups 47 weight(decay=0.0), 50 weight(decay=0.0005), 50 bias
train: Scanning D:\yolov5-master\Y2\train... 1 images, 0 backgrounds, 159 corrupt: 100%|██████████| 160/160 [00:13<00:0
train: WARNING D:\yolov5-master\Y2\images\fruit1.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit1.png'
train: WARNING D:\yolov5-master\Y2\images\fruit10.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit10.png'
train: WARNING D:\yolov5-master\Y2\images\fruit100.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit100.png'
train: WARNING D:\yolov5-master\Y2\images\fruit102.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit102.png'
train: WARNING D:\yolov5-master\Y2\images\fruit103.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit103.png'
train: WARNING D:\yolov5-master\Y2\images\fruit104.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit104.png'
train: WARNING D:\yolov5-master\Y2\images\fruit106.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit106.png'
train: WARNING D:\yolov5-master\Y2\images\fruit108.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit108.png'
train: WARNING D:\yolov5-master\Y2\images\fruit109.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit109.png'
train: WARNING D:\yolov5-master\Y2\images\fruit11.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit11.png'
train: WARNING D:\yolov5-master\Y2\images\fruit110.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit110.png'
train: WARNING D:\yolov5-master\Y2\images\fruit111.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit111.png'
train: WARNING D:\yolov5-master\Y2\images\fruit113.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit113.png'
train: WARNING D:\yolov5-master\Y2\images\fruit114.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit114.png'
train: WARNING D:\yolov5-master\Y2\images\fruit115.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit115.png'
train: WARNING D:\yolov5-master\Y2\images\fruit116.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit116.png'
train: WARNING D:\yolov5-master\Y2\images\fruit117.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit117.png'
train: WARNING D:\yolov5-master\Y2\images\fruit118.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit118.png'
train: WARNING D:\yolov5-master\Y2\images\fruit119.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit119.png'
train: WARNING D:\yolov5-master\Y2\images\fruit12.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit12.png'
train: WARNING D:\yolov5-master\Y2\images\fruit120.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit120.png'
train: WARNING D:\yolov5-master\Y2\images\fruit121.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit121.png'
train: WARNING D:\yolov5-master\Y2\images\fruit122.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit122.png'
train: WARNING D:\yolov5-master\Y2\images\fruit123.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit123.png'
train: WARNING D:\yolov5-master\Y2\images\fruit124.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit124.png'
train: WARNING D:\yolov5-master\Y2\images\fruit125.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit125.png'
train: WARNING D:\yolov5-master\Y2\images\fruit127.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit127.png'
train: WARNING D:\yolov5-master\Y2\images\fruit129.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit129.png'
train: WARNING D:\yolov5-master\Y2\images\fruit13.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit13.png'
train: WARNING D:\yolov5-master\Y2\images\fruit130.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit130.png'
train: WARNING D:\yolov5-master\Y2\images\fruit131.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit131.png'
train: WARNING D:\yolov5-master\Y2\images\fruit132.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit132.png'
train: WARNING D:\yolov5-master\Y2\images\fruit133.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit133.png'
train: WARNING D:\yolov5-master\Y2\images\fruit134.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit134.png'
train: WARNING D:\yolov5-master\Y2\images\fruit135.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit135.png'
train: WARNING D:\yolov5-master\Y2\images\fruit136.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit136.png'
train: WARNING D:\yolov5-master\Y2\images\fruit138.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit138.png'
train: WARNING D:\yolov5-master\Y2\images\fruit14.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit14.png'
train: WARNING D:\yolov5-master\Y2\images\fruit142.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit142.png'
train: WARNING D:\yolov5-master\Y2\images\fruit143.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit143.png'
train: WARNING D:\yolov5-master\Y2\images\fruit144.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit144.png'
train: WARNING D:\yolov5-master\Y2\images\fruit145.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit145.png'
train: WARNING D:\yolov5-master\Y2\images\fruit147.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit147.png'
train: WARNING D:\yolov5-master\Y2\images\fruit148.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit148.png'
train: WARNING D:\yolov5-master\Y2\images\fruit149.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit149.png'
train: WARNING D:\yolov5-master\Y2\images\fruit15.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit15.png'
train: WARNING D:\yolov5-master\Y2\images\fruit151.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit151.png'
train: WARNING D:\yolov5-master\Y2\images\fruit152.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit152.png'
train: WARNING D:\yolov5-master\Y2\images\fruit155.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit155.png'
train: WARNING D:\yolov5-master\Y2\images\fruit156.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit156.png'
train: WARNING D:\yolov5-master\Y2\images\fruit157.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit157.png'
train: WARNING D:\yolov5-master\Y2\images\fruit158.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit158.png'
train: WARNING D:\yolov5-master\Y2\images\fruit159.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit159.png'
train: WARNING D:\yolov5-master\Y2\images\fruit16.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit16.png'
train: WARNING D:\yolov5-master\Y2\images\fruit161.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit161.png'
train: WARNING D:\yolov5-master\Y2\images\fruit162.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit162.png'
train: WARNING D:\yolov5-master\Y2\images\fruit163.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit163.png'
train: WARNING D:\yolov5-master\Y2\images\fruit164.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit164.png'
train: WARNING D:\yolov5-master\Y2\images\fruit165.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit165.png'
train: WARNING D:\yolov5-master\Y2\images\fruit167.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit167.png'
train: WARNING D:\yolov5-master\Y2\images\fruit168.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit168.png'
train: WARNING D:\yolov5-master\Y2\images\fruit169.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit169.png'
train: WARNING D:\yolov5-master\Y2\images\fruit17.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit17.png'
train: WARNING D:\yolov5-master\Y2\images\fruit170.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit170.png'
train: WARNING D:\yolov5-master\Y2\images\fruit171.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit171.png'
train: WARNING D:\yolov5-master\Y2\images\fruit172.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit172.png'
train: WARNING D:\yolov5-master\Y2\images\fruit173.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit173.png'
train: WARNING D:\yolov5-master\Y2\images\fruit174.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit174.png'
train: WARNING D:\yolov5-master\Y2\images\fruit175.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit175.png'
train: WARNING D:\yolov5-master\Y2\images\fruit176.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit176.png'
train: WARNING D:\yolov5-master\Y2\images\fruit177.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit177.png'
train: WARNING D:\yolov5-master\Y2\images\fruit178.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit178.png'
train: WARNING D:\yolov5-master\Y2\images\fruit179.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit179.png'
train: WARNING D:\yolov5-master\Y2\images\fruit18.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit18.png'
train: WARNING D:\yolov5-master\Y2\images\fruit180.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit180.png'
train: WARNING D:\yolov5-master\Y2\images\fruit181.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit181.png'
train: WARNING D:\yolov5-master\Y2\images\fruit182.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit182.png'
train: WARNING D:\yolov5-master\Y2\images\fruit183.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit183.png'
train: WARNING D:\yolov5-master\Y2\images\fruit184.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit184.png'
train: WARNING D:\yolov5-master\Y2\images\fruit185.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit185.png'
train: WARNING D:\yolov5-master\Y2\images\fruit186.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit186.png'
train: WARNING D:\yolov5-master\Y2\images\fruit187.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit187.png'
train: WARNING D:\yolov5-master\Y2\images\fruit188.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit188.png'
train: WARNING D:\yolov5-master\Y2\images\fruit19.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit19.png'
train: WARNING D:\yolov5-master\Y2\images\fruit196.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit196.png'
train: WARNING D:\yolov5-master\Y2\images\fruit197.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit197.png'
train: WARNING D:\yolov5-master\Y2\images\fruit198.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit198.png'
train: WARNING D:\yolov5-master\Y2\images\fruit199.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit199.png'
train: WARNING D:\yolov5-master\Y2\images\fruit2.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit2.png'
train: WARNING D:\yolov5-master\Y2\images\fruit200.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit200.png'
train: WARNING D:\yolov5-master\Y2\images\fruit202.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit202.png'
train: WARNING D:\yolov5-master\Y2\images\fruit208.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit208.png'
train: WARNING D:\yolov5-master\Y2\images\fruit209.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit209.png'
train: WARNING D:\yolov5-master\Y2\images\fruit211.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit211.png'
train: WARNING D:\yolov5-master\Y2\images\fruit22.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit22.png'
train: WARNING D:\yolov5-master\Y2\images\fruit23.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit23.png'
train: WARNING D:\yolov5-master\Y2\images\fruit25.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit25.png'
train: WARNING D:\yolov5-master\Y2\images\fruit26.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit26.png'
train: WARNING D:\yolov5-master\Y2\images\fruit27.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit27.png'
train: WARNING D:\yolov5-master\Y2\images\fruit28.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit28.png'
train: WARNING D:\yolov5-master\Y2\images\fruit29.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit29.png'
train: WARNING D:\yolov5-master\Y2\images\fruit3.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit3.png'
train: WARNING D:\yolov5-master\Y2\images\fruit30.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit30.png'
train: WARNING D:\yolov5-master\Y2\images\fruit31.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit31.png'
train: WARNING D:\yolov5-master\Y2\images\fruit33.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit33.png'
train: WARNING D:\yolov5-master\Y2\images\fruit34.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit34.png'
train: WARNING D:\yolov5-master\Y2\images\fruit35.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit35.png'
train: WARNING D:\yolov5-master\Y2\images\fruit36.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit36.png'
train: WARNING D:\yolov5-master\Y2\images\fruit38.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit38.png'
train: WARNING D:\yolov5-master\Y2\images\fruit39.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit39.png'
train: WARNING D:\yolov5-master\Y2\images\fruit4.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit4.png'
train: WARNING D:\yolov5-master\Y2\images\fruit40.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit40.png'
train: WARNING D:\yolov5-master\Y2\images\fruit43.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit43.png'
train: WARNING D:\yolov5-master\Y2\images\fruit44.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit44.png'
train: WARNING D:\yolov5-master\Y2\images\fruit45.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit45.png'
train: WARNING D:\yolov5-master\Y2\images\fruit46.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit46.png'
train: WARNING D:\yolov5-master\Y2\images\fruit49.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit49.png'
train: WARNING D:\yolov5-master\Y2\images\fruit50.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit50.png'
train: WARNING D:\yolov5-master\Y2\images\fruit51.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit51.png'
train: WARNING D:\yolov5-master\Y2\images\fruit52.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit52.png'
train: WARNING D:\yolov5-master\Y2\images\fruit53.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit53.png'
train: WARNING D:\yolov5-master\Y2\images\fruit54.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit54.png'
train: WARNING D:\yolov5-master\Y2\images\fruit55.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit55.png'
train: WARNING D:\yolov5-master\Y2\images\fruit57.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit57.png'
train: WARNING D:\yolov5-master\Y2\images\fruit59.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit59.png'
train: WARNING D:\yolov5-master\Y2\images\fruit6.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit6.png'
train: WARNING D:\yolov5-master\Y2\images\fruit60.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit60.png'
train: WARNING D:\yolov5-master\Y2\images\fruit61.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit61.png'
train: WARNING D:\yolov5-master\Y2\images\fruit62.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit62.png'
train: WARNING D:\yolov5-master\Y2\images\fruit63.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit63.png'
train: WARNING D:\yolov5-master\Y2\images\fruit65.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit65.png'
train: WARNING D:\yolov5-master\Y2\images\fruit66.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit66.png'
train: WARNING D:\yolov5-master\Y2\images\fruit68.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit68.png'
train: WARNING D:\yolov5-master\Y2\images\fruit69.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit69.png'
train: WARNING D:\yolov5-master\Y2\images\fruit7.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit7.png'
train: WARNING D:\yolov5-master\Y2\images\fruit70.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit70.png'
train: WARNING D:\yolov5-master\Y2\images\fruit71.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit71.png'
train: WARNING D:\yolov5-master\Y2\images\fruit73.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit73.png'
train: WARNING D:\yolov5-master\Y2\images\fruit74.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit74.png'
train: WARNING D:\yolov5-master\Y2\images\fruit75.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit75.png'
train: WARNING D:\yolov5-master\Y2\images\fruit77.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit77.png'
train: WARNING D:\yolov5-master\Y2\images\fruit78.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit78.png'
train: WARNING D:\yolov5-master\Y2\images\fruit79.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit79.png'
train: WARNING D:\yolov5-master\Y2\images\fruit80.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit80.png'
train: WARNING D:\yolov5-master\Y2\images\fruit81.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit81.png'
train: WARNING D:\yolov5-master\Y2\images\fruit82.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit82.png'
train: WARNING D:\yolov5-master\Y2\images\fruit83.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit83.png'
train: WARNING D:\yolov5-master\Y2\images\fruit85.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit85.png'
train: WARNING D:\yolov5-master\Y2\images\fruit86.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit86.png'
train: WARNING D:\yolov5-master\Y2\images\fruit87.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit87.png'
train: WARNING D:\yolov5-master\Y2\images\fruit88.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit88.png'
train: WARNING D:\yolov5-master\Y2\images\fruit89.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit89.png'
train: WARNING D:\yolov5-master\Y2\images\fruit90.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit90.png'
train: WARNING D:\yolov5-master\Y2\images\fruit91.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit91.png'
train: WARNING D:\yolov5-master\Y2\images\fruit94.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit94.png'
train: WARNING D:\yolov5-master\Y2\images\fruit95.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit95.png'
train: WARNING D:\yolov5-master\Y2\images\fruit97.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit97.png'
train: WARNING D:\yolov5-master\Y2\images\fruit98.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit98.png'
train: WARNING D:\yolov5-master\Y2\images\fruit99.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit99.png'
train: WARNING Cache directory D:\yolov5-master\Y2 is not writeable: [WinError 183] : 'D:\\yolov5-master\\Y2\\train.cache.npy' -> 'D:\\yolov5-master\\Y2\\train.cache'
val: Scanning D:\yolov5-master\Y2\val.cache... 1 images, 0 backgrounds, 19 corrupt: 100%|██████████| 20/20 [00:00<?, ?i
val: WARNING D:\yolov5-master\Y2\images\fruit107.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit107.png'
val: WARNING D:\yolov5-master\Y2\images\fruit112.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit112.png'
val: WARNING D:\yolov5-master\Y2\images\fruit139.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit139.png'
val: WARNING D:\yolov5-master\Y2\images\fruit140.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit140.png'
val: WARNING D:\yolov5-master\Y2\images\fruit141.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit141.png'
val: WARNING D:\yolov5-master\Y2\images\fruit146.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit146.png'
val: WARNING D:\yolov5-master\Y2\images\fruit20.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit20.png'
val: WARNING D:\yolov5-master\Y2\images\fruit210.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit210.png'
val: WARNING D:\yolov5-master\Y2\images\fruit24.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit24.png'
val: WARNING D:\yolov5-master\Y2\images\fruit32.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit32.png'
val: WARNING D:\yolov5-master\Y2\images\fruit41.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit41.png'
val: WARNING D:\yolov5-master\Y2\images\fruit47.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit47.png'
val: WARNING D:\yolov5-master\Y2\images\fruit48.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit48.png'
val: WARNING D:\yolov5-master\Y2\images\fruit5.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit5.png'
val: WARNING D:\yolov5-master\Y2\images\fruit64.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit64.png'
val: WARNING D:\yolov5-master\Y2\images\fruit8.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit8.png'
val: WARNING D:\yolov5-master\Y2\images\fruit84.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit84.png'
val: WARNING D:\yolov5-master\Y2\images\fruit92.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit92.png'
val: WARNING D:\yolov5-master\Y2\images\fruit96.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit96.png'AutoAnchor: 4.33 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset
Plotting labels to runs\train\exp16\labels.jpg...
Image sizes 928 train, 928 val
Using 0 dataloader workers
Logging results to runs\train\exp16
Starting training for 100 epochs...Epoch GPU_mem box_loss obj_loss cls_loss Instances Size0/99 0G 0.1023 0.06894 0.0481 7 928: 0%| | 0/1 [00:01<?, ?it/s]WARNING TensorBoard graph visualization failure Sizes of tensors must match except in dimension 1. Expected size 58 but got size 57 for tensor number 1 in the list.0/99 0G 0.1023 0.06894 0.0481 7 928: 100%|██████████| 1/1 [00:02<00:00, 2.22Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size1/99 0G 0.116 0.0604 0.04635 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.19Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size2/99 0G 0.1082 0.05426 0.05132 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.25Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size3/99 0G 0.07671 0.04771 0.0333 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.14Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size4/99 0G 0.08278 0.04585 0.0296 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.10Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size5/99 0G 0.111 0.09066 0.04756 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.16Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size6/99 0G 0.116 0.06792 0.04824 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.25Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size7/99 0G 0.07378 0.05158 0.03187 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.23Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size8/99 0G 0.1157 0.05443 0.05176 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.27Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size9/99 0G 0.1144 0.05169 0.06036 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.20Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size10/99 0G 0.1124 0.09406 0.04572 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.24Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size11/99 0G 0.0766 0.04767 0.03086 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.19Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size12/99 0G 0.1054 0.08936 0.04594 10 928: 100%|██████████| 1/1 [00:01<00:00, 1.22Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size13/99 0G 0.1048 0.04886 0.05069 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.22Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size14/99 0G 0.07221 0.04882 0.02977 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.17Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size15/99 0G 0.1178 0.04965 0.05099 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.16Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0 0 0 0Epoch GPU_mem box_loss obj_loss cls_loss Instances Size16/99 0G 0.1314 0.05189 0.05638 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.21Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.0045 0.333 0.00582 0.00279Epoch GPU_mem box_loss obj_loss cls_loss Instances Size17/99 0G 0.0856 0.04364 0.02689 2 928: 100%|██████████| 1/1 [00:01<00:00, 1.14Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.0045 0.333 0.00582 0.00279Epoch GPU_mem box_loss obj_loss cls_loss Instances Size18/99 0G 0.1042 0.0573 0.04667 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.16Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.0045 0.333 0.00582 0.00279Epoch GPU_mem box_loss obj_loss cls_loss Instances Size19/99 0G 0.0898 0.05344 0.04976 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.14Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.0045 0.333 0.00582 0.00279Epoch GPU_mem box_loss obj_loss cls_loss Instances Size20/99 0G 0.1115 0.0723 0.04465 10 928: 100%|██████████| 1/1 [00:01<00:00, 1.20Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.0045 0.333 0.00582 0.00279Epoch GPU_mem box_loss obj_loss cls_loss Instances Size21/99 0G 0.0894 0.05696 0.04866 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.18Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.0045 0.333 0.00582 0.00279Epoch GPU_mem box_loss obj_loss cls_loss Instances Size22/99 0G 0.1064 0.09512 0.04609 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.13Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.0045 0.333 0.00582 0.00279Epoch GPU_mem box_loss obj_loss cls_loss Instances Size23/99 0G 0.1139 0.05224 0.04599 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.31Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.0045 0.333 0.00582 0.00279Epoch GPU_mem box_loss obj_loss cls_loss Instances Size24/99 0G 0.1062 0.06945 0.04768 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.26Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00433 0.333 0.0051 0.00251Epoch GPU_mem box_loss obj_loss cls_loss Instances Size25/99 0G 0.116 0.09317 0.04589 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.28Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00433 0.333 0.0051 0.00251Epoch GPU_mem box_loss obj_loss cls_loss Instances Size26/99 0G 0.1084 0.07551 0.04941 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.14Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00433 0.333 0.0051 0.00251Epoch GPU_mem box_loss obj_loss cls_loss Instances Size27/99 0G 0.09334 0.05819 0.04563 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.13Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00433 0.333 0.0051 0.00251Epoch GPU_mem box_loss obj_loss cls_loss Instances Size28/99 0G 0.07549 0.05148 0.0278 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.36Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00433 0.333 0.0051 0.00251Epoch GPU_mem box_loss obj_loss cls_loss Instances Size29/99 0G 0.1213 0.05355 0.05749 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.21Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00433 0.333 0.0051 0.00251Epoch GPU_mem box_loss obj_loss cls_loss Instances Size30/99 0G 0.09584 0.07607 0.0448 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.22Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00433 0.333 0.0051 0.00251Epoch GPU_mem box_loss obj_loss cls_loss Instances Size31/99 0G 0.09792 0.06158 0.04772 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.14Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00433 0.333 0.0051 0.00251Epoch GPU_mem box_loss obj_loss cls_loss Instances Size32/99 0G 0.07992 0.04641 0.03711 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.16Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00433 0.333 0.0051 0.00251Epoch GPU_mem box_loss obj_loss cls_loss Instances Size33/99 0G 0.1093 0.1033 0.04237 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.27Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00433 0.333 0.0051 0.00251Epoch GPU_mem box_loss obj_loss cls_loss Instances Size34/99 0G 0.0973 0.05861 0.05034 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.16Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00433 0.333 0.0051 0.00251Epoch GPU_mem box_loss obj_loss cls_loss Instances Size35/99 0G 0.1088 0.07091 0.05135 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.15Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00433 0.333 0.0051 0.00251Epoch GPU_mem box_loss obj_loss cls_loss Instances Size36/99 0G 0.106 0.09896 0.0442 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.16Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size37/99 0G 0.09571 0.06897 0.0473 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.11Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size38/99 0G 0.0849 0.04579 0.03352 2 928: 100%|██████████| 1/1 [00:01<00:00, 1.11Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size39/99 0G 0.09164 0.07926 0.04676 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.21Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size40/99 0G 0.1012 0.06744 0.04635 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.31Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size41/99 0G 0.09285 0.0705 0.05205 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.37Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size42/99 0G 0.09498 0.05619 0.04658 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.26Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size43/99 0G 0.09886 0.0651 0.05261 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.24Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size44/99 0G 0.1023 0.09061 0.04475 10 928: 100%|██████████| 1/1 [00:01<00:00, 1.29Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size45/99 0G 0.1112 0.05952 0.04528 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.25Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size46/99 0G 0.06881 0.04866 0.03377 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.24Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size47/99 0G 0.107 0.09679 0.0465 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.27Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size48/99 0G 0.0966 0.06407 0.05717 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.22Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size49/99 0G 0.1058 0.05406 0.04795 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.23Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size50/99 0G 0.1129 0.09446 0.04697 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.25Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size51/99 0G 0.1094 0.05599 0.04734 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.33Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size52/99 0G 0.1011 0.08309 0.0515 9 928: 100%|██████████| 1/1 [00:01<00:00, 1.22Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00337 0.333 0.00436 0.00247Epoch GPU_mem box_loss obj_loss cls_loss Instances Size53/99 0G 0.07754 0.04801 0.03401 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.26Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size54/99 0G 0.1096 0.09382 0.04247 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.25Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size55/99 0G 0.1049 0.06054 0.04536 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.29Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size56/99 0G 0.1158 0.06923 0.04261 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.27Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size57/99 0G 0.1096 0.05218 0.05424 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.22Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size58/99 0G 0.07191 0.06456 0.03113 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.24Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size59/99 0G 0.1026 0.09789 0.045 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.23Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size60/99 0G 0.09762 0.05207 0.0531 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.22Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size61/99 0G 0.07858 0.05155 0.03045 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.27Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size62/99 0G 0.1158 0.05363 0.04873 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.24Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size63/99 0G 0.1099 0.1002 0.04338 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.23Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size64/99 0G 0.1129 0.05755 0.04531 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.21Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size65/99 0G 0.1013 0.07252 0.04573 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.26Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size66/99 0G 0.09242 0.05776 0.05089 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.25Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size67/99 0G 0.1057 0.06878 0.04572 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.22Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size68/99 0G 0.1167 0.05181 0.04856 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.28Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size69/99 0G 0.09379 0.07217 0.0474 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.32Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size70/99 0G 0.0967 0.06586 0.04948 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.29Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size71/99 0G 0.1116 0.09719 0.04578 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.33Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size72/99 0G 0.09084 0.05456 0.04548 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.37Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size73/99 0G 0.111 0.05615 0.04613 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.31Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size74/99 0G 0.1121 0.09089 0.04653 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.32Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size75/99 0G 0.07513 0.04933 0.02963 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.34Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size76/99 0G 0.07868 0.04755 0.02927 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.24Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size77/99 0G 0.09763 0.05113 0.04474 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.20Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00366 0.333 0.00448 0.00245Epoch GPU_mem box_loss obj_loss cls_loss Instances Size78/99 0G 0.09904 0.05832 0.04777 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.31Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size79/99 0G 0.07866 0.05068 0.03276 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.24Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size80/99 0G 0.07645 0.05212 0.02934 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.29Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size81/99 0G 0.08324 0.04543 0.03031 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.23Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size82/99 0G 0.1052 0.06037 0.04437 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.25Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size83/99 0G 0.1036 0.06233 0.05686 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.26Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size84/99 0G 0.1042 0.09739 0.04465 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.28Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size85/99 0G 0.08622 0.05649 0.0523 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.26Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size86/99 0G 0.1084 0.1003 0.04353 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.26Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size87/99 0G 0.08857 0.06209 0.04712 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.22Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size88/99 0G 0.09376 0.05289 0.05011 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.26Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size89/99 0G 0.1045 0.05565 0.04718 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.25Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size90/99 0G 0.1034 0.05799 0.04736 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.28Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size91/99 0G 0.1023 0.06441 0.04832 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.24Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size92/99 0G 0.1109 0.08371 0.04649 10 928: 100%|██████████| 1/1 [00:01<00:00, 1.29Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size93/99 0G 0.1058 0.0641 0.05235 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.27Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size94/99 0G 0.1209 0.0516 0.04735 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.24Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size95/99 0G 0.11 0.09829 0.04451 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.26Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size96/99 0G 0.09733 0.05244 0.05601 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.27Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size97/99 0G 0.09427 0.05871 0.05337 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.29Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size98/99 0G 0.09102 0.1003 0.04611 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.25Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254Epoch GPU_mem box_loss obj_loss cls_loss Instances Size99/99 0G 0.07237 0.0536 0.03054 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.31Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00881 0.00254100 epochs completed in 0.053 hours.
Optimizer stripped from runs\train\exp16\weights\last.pt, 9.1MB
Optimizer stripped from runs\train\exp16\weights\best.pt, 9.1MBValidating runs\train\exp16\weights\best.pt...
Fusing layers...
YOLOv5s summary: 132 layers, 4298225 parameters, 0 gradients, 13.2 GFLOPsClass Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0all 1 3 0.00726 0.667 0.00891 0.00258banana 1 1 0.00943 1 0.00985 0.000985snake fruit 1 1 0 0 0 0pineapple 1 1 0.0123 1 0.0169 0.00675
Results saved to runs\train\exp16相关文章：
YOLOv5 第Y6周 模型改进
🍨 本文为[🔗365天深度学习训练营学习记录博客 🍦 参考文章：365天深度学习训练营 🍖 原作者：[K同学啊] 🚀 文章来源：[K同学的学习圈子](https://www.yuque.com/mingtian-fkmxf/zxwb4…...
Unity Android FireBase bugly报错查询
报错如下图，注意，标红的三处 使用的il2cpp和架构是arm64-v8a 那我们就可以根据这些去找对应的符号表，在unity安装目录下 Unity2020.3.33f1\Editor\Data\PlaybackEngines\AndroidPlayer\Variations\il2cpp\Release\Symbols\arm64-v8a 找到l…...
React中如何解决点击＜Tree＞节点前面三角区域不触发onClick事件
React中如何解决点击节点前面三角区域不触发onClick事件，如何区别‘左边’和‘右边’区域点击逻辑呢？（Tree引用开源组件TDesign） 只需要在onClick里面加限制一下就行： <TreeexpandMutexactivabletransitiondata{t…...
如何利用4G路由器构建茶饮连锁店物联网
随着年轻消费群体的增长，加上移动互联网营销的助推，各类新式奶茶消费风靡大街小巷，也促进了品牌奶茶连锁店的快速扩张。 在店铺快速扩张的局势下，品牌总部对于各间连锁店的零售统计、营销规划、物流调配、卫生监测、安全管理等事务…...
【2024系统架构设计】 系统架构设计师第二版-大数据架构理论设计与实践
目录 1 传统数据库的数据过载问题 2 大数据处理系统 3 Lambda架构 4 Kappa架构...
正整数分解
题目编号：Exp08-Basic01，GJBook3-12-05 题目名称：正整数分解 题目描述：正整数n，按第一项递减的顺序依次输出其和等于n的所有不增的正整数和式。 输入：一个正整数n（0<n≤15）。 …...
基于51单片机电子钟闹钟LCD1602显示proteus仿真设计
基于51单片机的LCD1602电子钟闹钟proteus仿真设计 基于51单片机的LCD1602电子钟闹钟proteus仿真设计功能介绍：仿真图：原理图：设计报告：程序：器件清单：资料清单&&下载链接： 基于51单片机…...
第三节-Android10.0 Binder通信原理（三）-ServiceManager篇
1、概述 在Android中，系统提供的服务被包装成一个个系统级service，这些service往往会在设备启动之时添加进Android系统，当某个应用想要调用系统某个服务的功能时，往往是向系统发出请求，调用该服务的外部接口。在上一节…...
使用XHProf查找PHP性能瓶颈
使用XHProf查找PHP性能瓶颈 XHProf是facebook 开发的一个测试php性能的扩展，本文记录了在PHP应用中使用XHProf对PHP进行性能优化，查找性能瓶颈的方法。 下载 网上很多是编译安装xhprof-0.9.4版本，应该是用php5，在php8.0下编译x…...
矩阵论（Matrix）
大纲 矩阵微积分：多元微积分的一种特殊表达，尤其是在矩阵空间上进行讨论的时候逆矩阵(inverse matrix)矩阵分解：特征分解（Eigendecomposition），又称谱分解（Spectral decomposition…...
解决Emmy Lua插件在IDEA或 Reder 没有代码提示的问题(设置文件关联 增加对.lua.txt文件的支持)
目录 Reder版本2019.x Reder版本2021.1.5x Reder版本2019.x 解决Emmy Lua插件在IDEA或 Reder 没有代码提示的问题(设置文件关联 增加对.lua.txt文件的支持) Reder版本2021.1.5x 解决Emmy Lua插件在IDEA或 Reder 没有代码提示的问题(设置文件关联 增加对.lua.txt文件的支持)…...
macos端文件夹快速访问工具 Default Folder X 最新for mac
Default Folder X 是一款实用的工具，提供了许多增强功能和快捷方式，使用户能够更高效地浏览和管理文件。它的快速导航、增强的文件对话框、自定义设置和快捷键等功能，可以大大提升用户的工作效率和文件管理体验。 快速导航和访问：…...
树形 DP：树的直径
leetCode 104.二叉树的最大深度104. 二叉树的最大深度 - 力扣（LeetCode） class Solution { public:int maxDepth(TreeNode* root) {if(root nullptr) return 0;int lDepth maxDepth(root->left);int rDepth maxDepth(root->right);return max(l…...
【Python百宝箱】第三维度的魔法：探索Python游戏世界
Python在游戏开发中的魔力 前言 游戏开发一直是计算机科学中最引人入胜和具有挑战性的领域之一。随着技术的不断进步，开发者们寻找着更快、更灵活的工具来实现他们的创意。在这个探索的过程中，Python以其简洁、易学和强大的特性成为了游戏开发的热门选…...
3ds Max 电脑配置建议 | 建模+渲染选专业显卡or游戏显卡？
使用3ds Max进行建模和渲染时，选择合适的电脑配置非常重要。比如在硬件选择上，究竟选购游戏显卡还是专业显卡呢？本文将为你详细介绍游戏显卡和专业显卡的区别，并提供配置建议，助你作出明智的决策。 &#…...
水淹七军（递归，又是递归）
北大2023级最强新生问我的，最后他的问题说是重写了一遍就解决了 乐死了，有的时候根本看不出源代码漏了哪里 我的思路是： 一个数组记录本次放水所经过的格子，经过的不再递归 一个数组记录地图上各地点的高度 一个数组记录地图…...
Stable Video Diffusion重磅发布，快来看看哪些功能
本周，有关 OpenAI 宫斗的报道占据了Ai圈版面的主导地位，吃够了奥特曼的大瓜。我们来看看Stability AI刚发布的Stable Video Diffusion，这是一种通过对现有图像进行动画处理来生成视频的 AI 模型。基于 Stability 现有的Stable Diffusion文本到…...
城市NOA到来时刻，车企密集上车NVIDIA
作者 |张祥威 编辑 |德新 基于双NVIDIA DRIVE Orin实现城市NOA，已是今天国内汽车行业的主流做法。 这款芯片获得广泛的市场认同，用时仅一年多。去年3月， NVIDIA DRIVE Orin正式投产，此后从造车新势力一路来到更多自主品牌的车内&…...
Linux后台运行Python的py文件，如何使ssh工具退出后仍能运行
常规运行 python3 mysqlbak.py ssh工具退出后，或ctrlc中断后，程序将不在运行 后台运行 nohup python3 mysqlbak.py > mysqlbak.log & > mysqlbak.log为可选项，输出日志到指定文件，如果不写，输出日志到nohup…...
Excel中出现“#NAME?”怎么办？（文本原因）
excel 单元格出现 #NAME? 错误的原因有二： 函数公式输入不对导致 #NAME? 错误。 在单元格中字符串的前面加了号，如下图中的--GoJG7sEe6RqgTnlUcitA，本身我们想要的是--GoJG7sEe6RqgTnlUcitA，但因为某些不当的操作在前面加了号&…...
python如何将word的doc另存为docx
将 DOCX 文件另存为 DOCX 格式（Python 实现） 在 Python 中，你可以使用 python-docx 库来操作 Word 文档。不过需要注意的是，.doc 是旧的 Word 格式，而 .docx 是新的基于 XML 的格式。python-docx 只能处理 .docx 格式…...
【git】把本地更改提交远程新分支feature_g
创建并切换新分支 git checkout -b feature_g 添加并提交更改 git add . git commit -m “实现图片上传功能” 推送到远程 git push -u origin feature_g...
全志A40i android7.1 调试信息打印串口由uart0改为uart3
一，概述 1. 目的 将调试信息打印串口由uart0改为uart3。 2. 版本信息 Uboot版本：2014.07； Kernel版本：Linux-3.10； 二，Uboot 1. sys_config.fex改动 使能uart3(TX:PH00 RX:PH01)，并让boo…...
selenium学习实战【Python爬虫】
selenium学习实战【Python爬虫】 文章目录 selenium学习实战【Python爬虫】一、声明二、学习目标三、安装依赖3.1 安装selenium库3.2 安装浏览器驱动3.2.1 查看Edge版本3.2.2 驱动安装 四、代码讲解4.1 配置浏览器4.2 加载更多4.3 寻找内容4.4 完整代码 五、报告文件爬取5.1 提…...
React---day11
14.4 react-redux第三方库 提供connect、thunk之类的函数 以获取一个banner数据为例子 store： 我们在使用异步的时候理应是要使用中间件的，但是configureStore 已经自动集成了 redux-thunk，注意action里面要返回函数 import { configureS…...
站群服务器的应用场景都有哪些？
站群服务器主要是为了多个网站的托管和管理所设计的，可以通过集中管理和高效资源的分配，来支持多个独立的网站同时运行，让每一个网站都可以分配到独立的IP地址，避免出现IP关联的风险，用户还可以通过控制面板进行管理功…...
接口自动化测试：HttpRunner基础
相关文档 HttpRunner V3.x中文文档 HttpRunner 用户指南 使用HttpRunner 3.x实现接口自动化测试 HttpRunner介绍 HttpRunner 是一个开源的 API 测试工具，支持 HTTP(S)/HTTP2/WebSocket/RPC 等网络协议，涵盖接口测试、性能测试、数字体验监测等测试类型…...
Go语言多线程问题
打印零与奇偶数（leetcode 1116） 方法1：使用互斥锁和条件变量 package mainimport ("fmt""sync" )type ZeroEvenOdd struct {n intzeroMutex sync.MutexevenMutex sync.MutexoddMutex sync.Mutexcurrent int…...
Axure 下拉框联动
实现选省、选完省之后选对应省份下的市区...
ubuntu22.04 安装docker 和docker-compose
首先你要确保没有docker环境或者使用命令删掉docker sudo apt-get remove docker docker-engine docker.io containerd runc安装docker 更新软件环境 sudo apt update sudo apt upgrade下载docker依赖和GPG 密钥 # 依赖 apt-get install ca-certificates curl gnupg lsb-rel…...