🍨 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 CPU
hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
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=4
from n params module arguments
0 -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 main
train(opt.hyp, opt, device, callbacks)
File "D:\yolov5-master\train.py", line 130, in train
model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
File "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))]) # forward
File "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 forward
return self._forward_once(x, profile, visualize) # single-scale inference, train
File "D:\yolov5-master\models\yolo.py", line 121, in _forward_once
x = m(x) # run
File "D:\Python\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "D:\yolov5-master\models\common.py", line 336, in forward
return 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 CPU
hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
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=4
from n params module arguments
0 -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 GFLOPs
Transferred 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 Size
0/99 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, 2.22
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
1/99 0G 0.116 0.0604 0.04635 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.19
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
2/99 0G 0.1082 0.05426 0.05132 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.25
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
3/99 0G 0.07671 0.04771 0.0333 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.14
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
4/99 0G 0.08278 0.04585 0.0296 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.10
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
5/99 0G 0.111 0.09066 0.04756 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.16
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
6/99 0G 0.116 0.06792 0.04824 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.25
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
7/99 0G 0.07378 0.05158 0.03187 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.23
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
8/99 0G 0.1157 0.05443 0.05176 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.27
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
9/99 0G 0.1144 0.05169 0.06036 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.20
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
10/99 0G 0.1124 0.09406 0.04572 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.24
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
11/99 0G 0.0766 0.04767 0.03086 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.19
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
12/99 0G 0.1054 0.08936 0.04594 10 928: 100%|██████████| 1/1 [00:01<00:00, 1.22
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
13/99 0G 0.1048 0.04886 0.05069 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.22
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
14/99 0G 0.07221 0.04882 0.02977 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.17
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
15/99 0G 0.1178 0.04965 0.05099 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.16
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
16/99 0G 0.1314 0.05189 0.05638 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.21
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.0045 0.333 0.00582 0.00279
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
17/99 0G 0.0856 0.04364 0.02689 2 928: 100%|██████████| 1/1 [00:01<00:00, 1.14
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.0045 0.333 0.00582 0.00279
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
18/99 0G 0.1042 0.0573 0.04667 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.16
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.0045 0.333 0.00582 0.00279
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
19/99 0G 0.0898 0.05344 0.04976 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.14
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.0045 0.333 0.00582 0.00279
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
20/99 0G 0.1115 0.0723 0.04465 10 928: 100%|██████████| 1/1 [00:01<00:00, 1.20
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.0045 0.333 0.00582 0.00279
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
21/99 0G 0.0894 0.05696 0.04866 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.18
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.0045 0.333 0.00582 0.00279
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
22/99 0G 0.1064 0.09512 0.04609 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.13
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.0045 0.333 0.00582 0.00279
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
23/99 0G 0.1139 0.05224 0.04599 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.31
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.0045 0.333 0.00582 0.00279
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
24/99 0G 0.1062 0.06945 0.04768 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.26
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00433 0.333 0.0051 0.00251
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
25/99 0G 0.116 0.09317 0.04589 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.28
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00433 0.333 0.0051 0.00251
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
26/99 0G 0.1084 0.07551 0.04941 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.14
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00433 0.333 0.0051 0.00251
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
27/99 0G 0.09334 0.05819 0.04563 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.13
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00433 0.333 0.0051 0.00251
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
28/99 0G 0.07549 0.05148 0.0278 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.36
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00433 0.333 0.0051 0.00251
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
29/99 0G 0.1213 0.05355 0.05749 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.21
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00433 0.333 0.0051 0.00251
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
30/99 0G 0.09584 0.07607 0.0448 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.22
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00433 0.333 0.0051 0.00251
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
31/99 0G 0.09792 0.06158 0.04772 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.14
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00433 0.333 0.0051 0.00251
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
32/99 0G 0.07992 0.04641 0.03711 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.16
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00433 0.333 0.0051 0.00251
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
33/99 0G 0.1093 0.1033 0.04237 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.27
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00433 0.333 0.0051 0.00251
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
34/99 0G 0.0973 0.05861 0.05034 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.16
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00433 0.333 0.0051 0.00251
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
35/99 0G 0.1088 0.07091 0.05135 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.15
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00433 0.333 0.0051 0.00251
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
36/99 0G 0.106 0.09896 0.0442 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.16
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
37/99 0G 0.09571 0.06897 0.0473 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.11
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
38/99 0G 0.0849 0.04579 0.03352 2 928: 100%|██████████| 1/1 [00:01<00:00, 1.11
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
39/99 0G 0.09164 0.07926 0.04676 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.21
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
40/99 0G 0.1012 0.06744 0.04635 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.31
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
41/99 0G 0.09285 0.0705 0.05205 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.37
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
42/99 0G 0.09498 0.05619 0.04658 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.26
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
43/99 0G 0.09886 0.0651 0.05261 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.24
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
44/99 0G 0.1023 0.09061 0.04475 10 928: 100%|██████████| 1/1 [00:01<00:00, 1.29
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
45/99 0G 0.1112 0.05952 0.04528 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.25
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
46/99 0G 0.06881 0.04866 0.03377 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.24
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
47/99 0G 0.107 0.09679 0.0465 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.27
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
48/99 0G 0.0966 0.06407 0.05717 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.22
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
49/99 0G 0.1058 0.05406 0.04795 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.23
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
50/99 0G 0.1129 0.09446 0.04697 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.25
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
51/99 0G 0.1094 0.05599 0.04734 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.33
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
52/99 0G 0.1011 0.08309 0.0515 9 928: 100%|██████████| 1/1 [00:01<00:00, 1.22
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00337 0.333 0.00436 0.00247
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
53/99 0G 0.07754 0.04801 0.03401 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.26
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
54/99 0G 0.1096 0.09382 0.04247 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.25
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
55/99 0G 0.1049 0.06054 0.04536 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.29
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
56/99 0G 0.1158 0.06923 0.04261 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.27
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
57/99 0G 0.1096 0.05218 0.05424 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.22
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
58/99 0G 0.07191 0.06456 0.03113 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.24
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
59/99 0G 0.1026 0.09789 0.045 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.23
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
60/99 0G 0.09762 0.05207 0.0531 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.22
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
61/99 0G 0.07858 0.05155 0.03045 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.27
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
62/99 0G 0.1158 0.05363 0.04873 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.24
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
63/99 0G 0.1099 0.1002 0.04338 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.23
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
64/99 0G 0.1129 0.05755 0.04531 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.21
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
65/99 0G 0.1013 0.07252 0.04573 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.26
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
66/99 0G 0.09242 0.05776 0.05089 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.25
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
67/99 0G 0.1057 0.06878 0.04572 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.22
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
68/99 0G 0.1167 0.05181 0.04856 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.28
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
69/99 0G 0.09379 0.07217 0.0474 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.32
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
70/99 0G 0.0967 0.06586 0.04948 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.29
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
71/99 0G 0.1116 0.09719 0.04578 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.33
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
72/99 0G 0.09084 0.05456 0.04548 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.37
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
73/99 0G 0.111 0.05615 0.04613 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.31
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
74/99 0G 0.1121 0.09089 0.04653 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.32
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
75/99 0G 0.07513 0.04933 0.02963 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.34
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
76/99 0G 0.07868 0.04755 0.02927 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.24
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
77/99 0G 0.09763 0.05113 0.04474 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.20
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00366 0.333 0.00448 0.00245
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
78/99 0G 0.09904 0.05832 0.04777 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.31
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
79/99 0G 0.07866 0.05068 0.03276 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.24
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
80/99 0G 0.07645 0.05212 0.02934 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.29
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
81/99 0G 0.08324 0.04543 0.03031 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.23
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
82/99 0G 0.1052 0.06037 0.04437 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.25
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
83/99 0G 0.1036 0.06233 0.05686 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.26
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
84/99 0G 0.1042 0.09739 0.04465 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.28
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
85/99 0G 0.08622 0.05649 0.0523 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.26
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
86/99 0G 0.1084 0.1003 0.04353 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.26
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
87/99 0G 0.08857 0.06209 0.04712 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.22
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
88/99 0G 0.09376 0.05289 0.05011 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.26
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
89/99 0G 0.1045 0.05565 0.04718 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.25
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
90/99 0G 0.1034 0.05799 0.04736 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.28
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
91/99 0G 0.1023 0.06441 0.04832 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.24
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
92/99 0G 0.1109 0.08371 0.04649 10 928: 100%|██████████| 1/1 [00:01<00:00, 1.29
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
93/99 0G 0.1058 0.0641 0.05235 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.27
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
94/99 0G 0.1209 0.0516 0.04735 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.24
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
95/99 0G 0.11 0.09829 0.04451 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.26
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
96/99 0G 0.09733 0.05244 0.05601 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.27
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
97/99 0G 0.09427 0.05871 0.05337 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.29
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
98/99 0G 0.09102 0.1003 0.04611 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.25
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
99/99 0G 0.07237 0.0536 0.03054 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.31
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00881 0.00254
100 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, 9.1MB
Validating runs\train\exp16\weights\best.pt...
Fusing layers...
YOLOv5s summary: 132 layers, 4298225 parameters, 0 gradients, 13.2 GFLOPs
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00726 0.667 0.00891 0.00258
banana 1 1 0.00943 1 0.00985 0.000985
snake fruit 1 1 0 0 0 0
pineapple 1 1 0.0123 1 0.0169 0.00675
Results saved to runs\train\exp16
