🍨 本文为[🔗365天深度学习训练营学习记录博客
🍦 参考文章：365天深度学习训练营
🍖 原作者：[K同学啊]
🚀 文章来源：[K同学的学习圈子](https://www.yuque.com/mingtian-fkmxf/zxwb45)
目标：在YOLOv5s网络模型中，修改common.py、yolo.py、yolov5s.yaml文件，将C2模块插入第2层与第3层之间，且跑通YOLOv5s。
操作步骤：
1.在common.py文件中插入C2模块
class C2(nn.Module):
# CSP Bottleneck with 3 convolutions
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c1, c_, 1, 1)
self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2)
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
def forward(self, x):
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
2.修改yolo.py文件，改动模型框架
def parse_model(d, ch): # model_dict, input_channels(3)
# Parse a YOLOv5 model.yaml dictionary
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
if act:
Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU()
LOGGER.info(f"{colorstr('activation:')} {act}") # print
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
m = eval(m) if isinstance(m, str) else m # eval strings
for j, a in enumerate(args):
with contextlib.suppress(NameError):
args[j] = eval(a) if isinstance(a, str) else a # eval strings
n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
if m in {
Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
c1, c2 = ch[f], args[0]
if c2 != no: # if not output
c2 = make_divisible(c2 * gw, 8)
args = [c1, c2, *args[1:]]
if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:
args.insert(2, n) # number of repeats
n = 1
elif m is nn.BatchNorm2d:
args = [ch[f]]
elif m is Concat:
c2 = sum(ch[x] for x in f)
# TODO: channel, gw, gd
elif m in {Detect, Segment}:
args.append([ch[x] for x in f])
if isinstance(args[1], int): # number of anchors
args[1] = [list(range(args[1] * 2))] * len(f)
if m is Segment:
args[3] = make_divisible(args[3] * gw, 8)
elif m is Contract:
c2 = ch[f] * args[0] ** 2
elif m is Expand:
c2 = ch[f] // args[0] ** 2
else:
c2 = ch[f]
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
t = str(m)[8:-2].replace('__main__.', '') # module type
np = sum(x.numel() for x in m_.parameters()) # number params
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # print
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_)
if i == 0:
ch = []
ch.append(c2)
return nn.Sequential(*layers), sorted(save)函数用于将模型的模块拼接起来，搭建完成的网络模型。后续如果需要动模型框架的话，需要对这个函数做相应的改动。
修改前：
修改后：
3.yolov5s.yaml文件中加入C2层
4.命令窗运行
python train.py --img 900 --batch 2 --epoch 100 --data D:/yolov5-master/data/ab.yaml --cfg D:/yolov5-master/models/yolov5s.yaml --weights yolov5s.pt运行结果：
D:\yolov5-master>python train.py --img 900 --batch 2 --epoch 100 --data D:/yolov5-master/data/ab.yaml --cfg D:/yolov5-master/models/yolov5s.yaml --weights yolov5s.pt
train: weights=yolov5s.pt, cfg=D:/yolov5-master/models/yolov5s.yaml, data=D:/yolov5-master/data/ab.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=100, batch_size=2, imgsz=900, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5
YOLOv5 2023-10-15 Python-3.10.7 torch-2.0.1+cpu 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
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 185, in __init__
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
File "D:\yolov5-master\models\yolo.py", line 319, in parse_model
BottleneckCSP, C2, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
NameError: name 'C2' is not defined. Did you mean: 'c2'?
D:\yolov5-master>python train.py --img 900 --batch 2 --epoch 100 --data D:/yolov5-master/data/ab.yaml --cfg D:/yolov5-master/models/yolov5s.yaml --weights yolov5s.pt
train: weights=yolov5s.pt, cfg=D:/yolov5-master/models/yolov5s.yaml, data=D:/yolov5-master/data/ab.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=100, batch_size=2, imgsz=900, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5
YOLOv5 2023-10-15 Python-3.10.7 torch-2.0.1+cpu 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 18816 models.common.C2 [64, 64, 1]
4 -1 1 73984 models.common.Conv [64, 128, 3, 2]
5 -1 2 115712 models.common.C3 [128, 128, 2]
6 -1 1 295424 models.common.Conv [128, 256, 3, 2]
7 -1 3 625152 models.common.C3 [256, 256, 3]
8 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
9 -1 1 1182720 models.common.C3 [512, 512, 1]
10 -1 1 656896 models.common.SPPF [512, 512, 5]
11 -1 1 131584 models.common.Conv [512, 256, 1, 1]
12 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
13 [-1, 6] 1 0 models.common.Concat [1]
14 -1 1 361984 models.common.C3 [512, 256, 1, False]
15 -1 1 33024 models.common.Conv [256, 128, 1, 1]
16 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
17 [-1, 4] 1 0 models.common.Concat [1]
18 -1 1 90880 models.common.C3 [256, 128, 1, False]
19 -1 1 147712 models.common.Conv [128, 128, 3, 2]
20 [-1, 14] 1 0 models.common.Concat [1]
21 -1 1 329216 models.common.C3 [384, 256, 1, False]
22 -1 1 590336 models.common.Conv [256, 256, 3, 2]
23 [-1, 10] 1 0 models.common.Concat [1]
24 -1 1 1313792 models.common.C3 [768, 512, 1, False]
25 [17, 20, 23] 1 38097 models.yolo.Detect [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [256, 384, 768]]
YOLOv5s summary: 232 layers, 7226897 parameters, 7226897 gradients, 17.2 GFLOPs
Transferred 49/379 items from yolov5s.pt
WARNING --img-size 900 must be multiple of max stride 32, updating to 928
optimizer: SGD(lr=0.01) with parameter groups 62 weight(decay=0.0), 65 weight(decay=0.0005), 65 bias
train: Scanning D:\yolov5-master\Y2\train... 1 images, 0 backgrounds, 159 corrupt: 100%|██████████| 160/160 [00:13<00:0
train: WARNING D:\yolov5-master\Y2\images\fruit1.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit1.png'
train: WARNING D:\yolov5-master\Y2\images\fruit10.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit10.png'
train: WARNING D:\yolov5-master\Y2\images\fruit100.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit100.png'
train: WARNING D:\yolov5-master\Y2\images\fruit102.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit102.png'
train: WARNING D:\yolov5-master\Y2\images\fruit103.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit103.png'
train: WARNING D:\yolov5-master\Y2\images\fruit104.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit104.png'
train: WARNING D:\yolov5-master\Y2\images\fruit106.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit106.png'
train: WARNING D:\yolov5-master\Y2\images\fruit108.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit108.png'
train: WARNING D:\yolov5-master\Y2\images\fruit109.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit109.png'
train: WARNING D:\yolov5-master\Y2\images\fruit11.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit11.png'
train: WARNING D:\yolov5-master\Y2\images\fruit110.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit110.png'
train: WARNING D:\yolov5-master\Y2\images\fruit111.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit111.png'
train: WARNING D:\yolov5-master\Y2\images\fruit113.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit113.png'
train: WARNING D:\yolov5-master\Y2\images\fruit114.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit114.png'
train: WARNING D:\yolov5-master\Y2\images\fruit115.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit115.png'
train: WARNING D:\yolov5-master\Y2\images\fruit116.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit116.png'
train: WARNING D:\yolov5-master\Y2\images\fruit117.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit117.png'
train: WARNING D:\yolov5-master\Y2\images\fruit118.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit118.png'
train: WARNING D:\yolov5-master\Y2\images\fruit119.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit119.png'
train: WARNING D:\yolov5-master\Y2\images\fruit12.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit12.png'
train: WARNING D:\yolov5-master\Y2\images\fruit120.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit120.png'
train: WARNING D:\yolov5-master\Y2\images\fruit121.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit121.png'
train: WARNING D:\yolov5-master\Y2\images\fruit122.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit122.png'
train: WARNING D:\yolov5-master\Y2\images\fruit123.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit123.png'
train: WARNING D:\yolov5-master\Y2\images\fruit124.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit124.png'
train: WARNING D:\yolov5-master\Y2\images\fruit125.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit125.png'
train: WARNING D:\yolov5-master\Y2\images\fruit127.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit127.png'
train: WARNING D:\yolov5-master\Y2\images\fruit129.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit129.png'
train: WARNING D:\yolov5-master\Y2\images\fruit13.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit13.png'
train: WARNING D:\yolov5-master\Y2\images\fruit130.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit130.png'
train: WARNING D:\yolov5-master\Y2\images\fruit131.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit131.png'
train: WARNING D:\yolov5-master\Y2\images\fruit132.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit132.png'
train: WARNING D:\yolov5-master\Y2\images\fruit133.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit133.png'
train: WARNING D:\yolov5-master\Y2\images\fruit134.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit134.png'
train: WARNING D:\yolov5-master\Y2\images\fruit135.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit135.png'
train: WARNING D:\yolov5-master\Y2\images\fruit136.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit136.png'
train: WARNING D:\yolov5-master\Y2\images\fruit138.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit138.png'
train: WARNING D:\yolov5-master\Y2\images\fruit14.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit14.png'
train: WARNING D:\yolov5-master\Y2\images\fruit142.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit142.png'
train: WARNING D:\yolov5-master\Y2\images\fruit143.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit143.png'
train: WARNING D:\yolov5-master\Y2\images\fruit144.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit144.png'
train: WARNING D:\yolov5-master\Y2\images\fruit145.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit145.png'
train: WARNING D:\yolov5-master\Y2\images\fruit147.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit147.png'
train: WARNING D:\yolov5-master\Y2\images\fruit148.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit148.png'
train: WARNING D:\yolov5-master\Y2\images\fruit149.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit149.png'
train: WARNING D:\yolov5-master\Y2\images\fruit15.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit15.png'
train: WARNING D:\yolov5-master\Y2\images\fruit151.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit151.png'
train: WARNING D:\yolov5-master\Y2\images\fruit152.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit152.png'
train: WARNING D:\yolov5-master\Y2\images\fruit155.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit155.png'
train: WARNING D:\yolov5-master\Y2\images\fruit156.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit156.png'
train: WARNING D:\yolov5-master\Y2\images\fruit157.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit157.png'
train: WARNING D:\yolov5-master\Y2\images\fruit158.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit158.png'
train: WARNING D:\yolov5-master\Y2\images\fruit159.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit159.png'
train: WARNING D:\yolov5-master\Y2\images\fruit16.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit16.png'
train: WARNING D:\yolov5-master\Y2\images\fruit161.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit161.png'
train: WARNING D:\yolov5-master\Y2\images\fruit162.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit162.png'
train: WARNING D:\yolov5-master\Y2\images\fruit163.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit163.png'
train: WARNING D:\yolov5-master\Y2\images\fruit164.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit164.png'
train: WARNING D:\yolov5-master\Y2\images\fruit165.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit165.png'
train: WARNING D:\yolov5-master\Y2\images\fruit167.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit167.png'
train: WARNING D:\yolov5-master\Y2\images\fruit168.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit168.png'
train: WARNING D:\yolov5-master\Y2\images\fruit169.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit169.png'
train: WARNING D:\yolov5-master\Y2\images\fruit17.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit17.png'
train: WARNING D:\yolov5-master\Y2\images\fruit170.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit170.png'
train: WARNING D:\yolov5-master\Y2\images\fruit171.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit171.png'
train: WARNING D:\yolov5-master\Y2\images\fruit172.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit172.png'
train: WARNING D:\yolov5-master\Y2\images\fruit173.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit173.png'
train: WARNING D:\yolov5-master\Y2\images\fruit174.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit174.png'
train: WARNING D:\yolov5-master\Y2\images\fruit175.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit175.png'
train: WARNING D:\yolov5-master\Y2\images\fruit176.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit176.png'
train: WARNING D:\yolov5-master\Y2\images\fruit177.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit177.png'
train: WARNING D:\yolov5-master\Y2\images\fruit178.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit178.png'
train: WARNING D:\yolov5-master\Y2\images\fruit179.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit179.png'
train: WARNING D:\yolov5-master\Y2\images\fruit18.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit18.png'
train: WARNING D:\yolov5-master\Y2\images\fruit180.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit180.png'
train: WARNING D:\yolov5-master\Y2\images\fruit181.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit181.png'
train: WARNING D:\yolov5-master\Y2\images\fruit182.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit182.png'
train: WARNING D:\yolov5-master\Y2\images\fruit183.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit183.png'
train: WARNING D:\yolov5-master\Y2\images\fruit184.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit184.png'
train: WARNING D:\yolov5-master\Y2\images\fruit185.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit185.png'
train: WARNING D:\yolov5-master\Y2\images\fruit186.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit186.png'
train: WARNING D:\yolov5-master\Y2\images\fruit187.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit187.png'
train: WARNING D:\yolov5-master\Y2\images\fruit188.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit188.png'
train: WARNING D:\yolov5-master\Y2\images\fruit19.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit19.png'
train: WARNING D:\yolov5-master\Y2\images\fruit196.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit196.png'
train: WARNING D:\yolov5-master\Y2\images\fruit197.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit197.png'
train: WARNING D:\yolov5-master\Y2\images\fruit198.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit198.png'
train: WARNING D:\yolov5-master\Y2\images\fruit199.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit199.png'
train: WARNING D:\yolov5-master\Y2\images\fruit2.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit2.png'
train: WARNING D:\yolov5-master\Y2\images\fruit200.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit200.png'
train: WARNING D:\yolov5-master\Y2\images\fruit202.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit202.png'
train: WARNING D:\yolov5-master\Y2\images\fruit208.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit208.png'
train: WARNING D:\yolov5-master\Y2\images\fruit209.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit209.png'
train: WARNING D:\yolov5-master\Y2\images\fruit211.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit211.png'
train: WARNING D:\yolov5-master\Y2\images\fruit22.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit22.png'
train: WARNING D:\yolov5-master\Y2\images\fruit23.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit23.png'
train: WARNING D:\yolov5-master\Y2\images\fruit25.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit25.png'
train: WARNING D:\yolov5-master\Y2\images\fruit26.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit26.png'
train: WARNING D:\yolov5-master\Y2\images\fruit27.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit27.png'
train: WARNING D:\yolov5-master\Y2\images\fruit28.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit28.png'
train: WARNING D:\yolov5-master\Y2\images\fruit29.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit29.png'
train: WARNING D:\yolov5-master\Y2\images\fruit3.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit3.png'
train: WARNING D:\yolov5-master\Y2\images\fruit30.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit30.png'
train: WARNING D:\yolov5-master\Y2\images\fruit31.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit31.png'
train: WARNING D:\yolov5-master\Y2\images\fruit33.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit33.png'
train: WARNING D:\yolov5-master\Y2\images\fruit34.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit34.png'
train: WARNING D:\yolov5-master\Y2\images\fruit35.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit35.png'
train: WARNING D:\yolov5-master\Y2\images\fruit36.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit36.png'
train: WARNING D:\yolov5-master\Y2\images\fruit38.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit38.png'
train: WARNING D:\yolov5-master\Y2\images\fruit39.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit39.png'
train: WARNING D:\yolov5-master\Y2\images\fruit4.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit4.png'
train: WARNING D:\yolov5-master\Y2\images\fruit40.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit40.png'
train: WARNING D:\yolov5-master\Y2\images\fruit43.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit43.png'
train: WARNING D:\yolov5-master\Y2\images\fruit44.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit44.png'
train: WARNING D:\yolov5-master\Y2\images\fruit45.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit45.png'
train: WARNING D:\yolov5-master\Y2\images\fruit46.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit46.png'
train: WARNING D:\yolov5-master\Y2\images\fruit49.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit49.png'
train: WARNING D:\yolov5-master\Y2\images\fruit50.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit50.png'
train: WARNING D:\yolov5-master\Y2\images\fruit51.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit51.png'
train: WARNING D:\yolov5-master\Y2\images\fruit52.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit52.png'
train: WARNING D:\yolov5-master\Y2\images\fruit53.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit53.png'
train: WARNING D:\yolov5-master\Y2\images\fruit54.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit54.png'
train: WARNING D:\yolov5-master\Y2\images\fruit55.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit55.png'
train: WARNING D:\yolov5-master\Y2\images\fruit57.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit57.png'
train: WARNING D:\yolov5-master\Y2\images\fruit59.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit59.png'
train: WARNING D:\yolov5-master\Y2\images\fruit6.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit6.png'
train: WARNING D:\yolov5-master\Y2\images\fruit60.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit60.png'
train: WARNING D:\yolov5-master\Y2\images\fruit61.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit61.png'
train: WARNING D:\yolov5-master\Y2\images\fruit62.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit62.png'
train: WARNING D:\yolov5-master\Y2\images\fruit63.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit63.png'
train: WARNING D:\yolov5-master\Y2\images\fruit65.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit65.png'
train: WARNING D:\yolov5-master\Y2\images\fruit66.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit66.png'
train: WARNING D:\yolov5-master\Y2\images\fruit68.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit68.png'
train: WARNING D:\yolov5-master\Y2\images\fruit69.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit69.png'
train: WARNING D:\yolov5-master\Y2\images\fruit7.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit7.png'
train: WARNING D:\yolov5-master\Y2\images\fruit70.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit70.png'
train: WARNING D:\yolov5-master\Y2\images\fruit71.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit71.png'
train: WARNING D:\yolov5-master\Y2\images\fruit73.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit73.png'
train: WARNING D:\yolov5-master\Y2\images\fruit74.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit74.png'
train: WARNING D:\yolov5-master\Y2\images\fruit75.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit75.png'
train: WARNING D:\yolov5-master\Y2\images\fruit77.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit77.png'
train: WARNING D:\yolov5-master\Y2\images\fruit78.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit78.png'
train: WARNING D:\yolov5-master\Y2\images\fruit79.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit79.png'
train: WARNING D:\yolov5-master\Y2\images\fruit80.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit80.png'
train: WARNING D:\yolov5-master\Y2\images\fruit81.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit81.png'
train: WARNING D:\yolov5-master\Y2\images\fruit82.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit82.png'
train: WARNING D:\yolov5-master\Y2\images\fruit83.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit83.png'
train: WARNING D:\yolov5-master\Y2\images\fruit85.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit85.png'
train: WARNING D:\yolov5-master\Y2\images\fruit86.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit86.png'
train: WARNING D:\yolov5-master\Y2\images\fruit87.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit87.png'
train: WARNING D:\yolov5-master\Y2\images\fruit88.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit88.png'
train: WARNING D:\yolov5-master\Y2\images\fruit89.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit89.png'
train: WARNING D:\yolov5-master\Y2\images\fruit90.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit90.png'
train: WARNING D:\yolov5-master\Y2\images\fruit91.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit91.png'
train: WARNING D:\yolov5-master\Y2\images\fruit94.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit94.png'
train: WARNING D:\yolov5-master\Y2\images\fruit95.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit95.png'
train: WARNING D:\yolov5-master\Y2\images\fruit97.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit97.png'
train: WARNING D:\yolov5-master\Y2\images\fruit98.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit98.png'
train: WARNING D:\yolov5-master\Y2\images\fruit99.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit99.png'
train: WARNING Cache directory D:\yolov5-master\Y2 is not writeable: [WinError 183] : 'D:\\yolov5-master\\Y2\\train.cache.npy' -> 'D:\\yolov5-master\\Y2\\train.cache'
val: Scanning D:\yolov5-master\Y2\val.cache... 1 images, 0 backgrounds, 19 corrupt: 100%|██████████| 20/20 [00:00<?, ?i
val: WARNING D:\yolov5-master\Y2\images\fruit107.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit107.png'
val: WARNING D:\yolov5-master\Y2\images\fruit112.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit112.png'
val: WARNING D:\yolov5-master\Y2\images\fruit139.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit139.png'
val: WARNING D:\yolov5-master\Y2\images\fruit140.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit140.png'
val: WARNING D:\yolov5-master\Y2\images\fruit141.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit141.png'
val: WARNING D:\yolov5-master\Y2\images\fruit146.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit146.png'
val: WARNING D:\yolov5-master\Y2\images\fruit20.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit20.png'
val: WARNING D:\yolov5-master\Y2\images\fruit210.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit210.png'
val: WARNING D:\yolov5-master\Y2\images\fruit24.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit24.png'
val: WARNING D:\yolov5-master\Y2\images\fruit32.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit32.png'
val: WARNING D:\yolov5-master\Y2\images\fruit41.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit41.png'
val: WARNING D:\yolov5-master\Y2\images\fruit47.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit47.png'
val: WARNING D:\yolov5-master\Y2\images\fruit48.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit48.png'
val: WARNING D:\yolov5-master\Y2\images\fruit5.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit5.png'
val: WARNING D:\yolov5-master\Y2\images\fruit64.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit64.png'
val: WARNING D:\yolov5-master\Y2\images\fruit8.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit8.png'
val: WARNING D:\yolov5-master\Y2\images\fruit84.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit84.png'
val: WARNING D:\yolov5-master\Y2\images\fruit92.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit92.png'
val: WARNING D:\yolov5-master\Y2\images\fruit96.png: ignoring corrupt image/label: [Errno 22] Invalid argument: ' D:\\yolov5-master\\Y2\\images\\fruit96.png'
AutoAnchor: 4.33 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset
Plotting labels to runs\train\exp12\labels.jpg...
Image sizes 928 train, 928 val
Using 0 dataloader workers
Logging results to runs\train\exp12
Starting training for 100 epochs...
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
0/99 0G 0.1123 0.06848 0.04815 7 928: 0%| | 0/1 [00:01<?, ?it/s]WARNING TensorBoard graph visualization failure Sizes of tensors must match except in dimension 1. Expected size 58 but got size 57 for tensor number 1 in the list.
0/99 0G 0.1123 0.06848 0.04815 7 928: 100%|██████████| 1/1 [00:02<00:00, 2.97
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00439 0.333 0.0474 0.0121
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
1/99 0G 0.1105 0.06846 0.04628 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.51
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00926 0.333 0.0332 0.0154
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
2/99 0G 0.1139 0.05816 0.04684 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.45
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.00926 0.333 0.0332 0.0154
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
3/99 0G 0.07328 0.05078 0.03088 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.51
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.0119 0.333 0.0123 0.00369
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
4/99 0G 0.06693 0.05186 0.03044 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.47
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.0119 0.333 0.0123 0.00369
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
5/99 0G 0.1102 0.09702 0.04647 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.44
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.0119 0.333 0.0123 0.00369
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
6/99 0G 0.1147 0.07053 0.04376 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.48
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 0.06716 0.05544 0.02962 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.43
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 0.1161 0.05993 0.04253 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.44
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 0.1187 0.05657 0.0432 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.45
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 0.1163 0.09305 0.04868 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.50
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 0.07575 0.04969 0.03171 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.42
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 0.1092 0.09129 0.045 10 928: 100%|██████████| 1/1 [00:01<00:00, 1.43
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 0.1003 0.05476 0.04605 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.44
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 0.07006 0.05166 0.03166 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.43
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 0.1156 0.05315 0.04495 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.43
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 0.1143 0.0559 0.045 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.48
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
17/99 0G 0.08845 0.0449 0.02645 2 928: 100%|██████████| 1/1 [00:01<00:00, 1.43
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
18/99 0G 0.1189 0.05909 0.04975 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.44
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
19/99 0G 0.1113 0.05739 0.04547 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.46
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
20/99 0G 0.117 0.07437 0.04842 10 928: 100%|██████████| 1/1 [00:01<00:00, 1.45
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
21/99 0G 0.109 0.06155 0.0505 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.44
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
22/99 0G 0.1073 0.1035 0.04515 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.46
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
23/99 0G 0.1257 0.0527 0.04264 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.45
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
24/99 0G 0.1036 0.0745 0.04745 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.50
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
25/99 0G 0.1112 0.1054 0.04881 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.45
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
26/99 0G 0.1053 0.08021 0.04656 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.44
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
27/99 0G 0.1208 0.05651 0.04577 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.45
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
28/99 0G 0.07633 0.0537 0.03023 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.46
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
29/99 0G 0.1162 0.05969 0.04597 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.44
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
30/99 0G 0.1117 0.07415 0.04961 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.45
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
31/99 0G 0.1132 0.06359 0.04704 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.46
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
32/99 0G 0.08006 0.05026 0.02591 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.45
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
33/99 0G 0.1117 0.104 0.04704 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.45
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
34/99 0G 0.1135 0.06241 0.04401 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.45
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
35/99 0G 0.1117 0.07476 0.04524 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.45
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
36/99 0G 0.1134 0.09759 0.04479 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.51
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
37/99 0G 0.1184 0.06637 0.04515 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.45
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
38/99 0G 0.08484 0.04526 0.02921 2 928: 100%|██████████| 1/1 [00:01<00:00, 1.50
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
39/99 0G 0.09749 0.0813 0.04582 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.57
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
40/99 0G 0.1117 0.07415 0.046 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.63
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
41/99 0G 0.1117 0.07245 0.04489 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.68
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
42/99 0G 0.1094 0.05986 0.04839 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.71
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
43/99 0G 0.1097 0.0697 0.04865 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.65
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
44/99 0G 0.1108 0.09187 0.04328 10 928: 100%|██████████| 1/1 [00:01<00:00, 1.57
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
45/99 0G 0.1126 0.05993 0.047 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.52
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
46/99 0G 0.0688 0.05024 0.03075 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.53
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
47/99 0G 0.112 0.09688 0.04424 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.51
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
48/99 0G 0.1166 0.06569 0.04565 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.53
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
49/99 0G 0.1118 0.05801 0.04417 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.51
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
50/99 0G 0.1097 0.1048 0.04665 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.51
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
51/99 0G 0.1218 0.06085 0.04525 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.83
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
52/99 0G 0.1056 0.08698 0.04532 9 928: 100%|██████████| 1/1 [00:01<00:00, 1.66
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
53/99 0G 0.06761 0.05242 0.03217 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.68
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
54/99 0G 0.1044 0.1022 0.0441 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.60
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
55/99 0G 0.1269 0.05652 0.04289 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.87
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
56/99 0G 0.1112 0.0772 0.04683 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.86
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
57/99 0G 0.1144 0.05499 0.04611 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.78
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
58/99 0G 0.07043 0.0666 0.0297 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.71
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
59/99 0G 0.1092 0.09867 0.04592 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.72
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
60/99 0G 0.12 0.05285 0.04611 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.71
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
61/99 0G 0.0728 0.05391 0.02953 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.75
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
62/99 0G 0.1164 0.05441 0.04357 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.91
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
63/99 0G 0.1123 0.1039 0.0476 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.82
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
64/99 0G 0.1089 0.064 0.04559 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.69
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
65/99 0G 0.1152 0.07665 0.04802 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.64
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
66/99 0G 0.1186 0.06205 0.0432 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.74
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
67/99 0G 0.114 0.06644 0.04486 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.88
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
68/99 0G 0.1118 0.05814 0.04571 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.89
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
69/99 0G 0.106 0.0762 0.04522 8 928: 100%|██████████| 1/1 [00:01<00:00, 1.88
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
70/99 0G 0.1068 0.06769 0.048 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.71
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
71/99 0G 0.11 0.1035 0.04768 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.64
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
72/99 0G 0.1071 0.05783 0.04588 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.71
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
73/99 0G 0.107 0.06332 0.04598 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.72
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
74/99 0G 0.1127 0.09514 0.04832 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.71
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
75/99 0G 0.07471 0.05085 0.03363 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.62
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
76/99 0G 0.07295 0.05077 0.03028 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.68
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
77/99 0G 0.1221 0.0522 0.0502 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.73
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
78/99 0G 0.1159 0.05984 0.04441 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.86
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
79/99 0G 0.0764 0.05256 0.03172 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.81
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
80/99 0G 0.07563 0.05452 0.03032 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.73
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
81/99 0G 0.06719 0.0531 0.02945 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.67
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
82/99 0G 0.1076 0.06686 0.04691 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.68
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
83/99 0G 0.1112 0.07135 0.04413 7 928: 100%|██████████| 1/1 [00:01<00:00, 1.70
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
84/99 0G 0.1116 0.09399 0.04413 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.63
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
85/99 0G 0.1116 0.06021 0.04635 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.67
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
86/99 0G 0.1096 0.1032 0.04634 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.66
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
87/99 0G 0.1143 0.05941 0.04396 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.66
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
88/99 0G 0.1161 0.0518 0.04673 3 928: 100%|██████████| 1/1 [00:01<00:00, 1.66
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
89/99 0G 0.1106 0.05528 0.04363 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.65
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
90/99 0G 0.1238 0.05427 0.04809 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.66
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
91/99 0G 0.1104 0.06561 0.04492 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.67
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
92/99 0G 0.1137 0.08532 0.04445 10 928: 100%|██████████| 1/1 [00:01<00:00, 1.70
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
93/99 0G 0.1125 0.07016 0.04628 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.65
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
94/99 0G 0.1116 0.05724 0.04418 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.63
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
95/99 0G 0.1124 0.1026 0.04744 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.77
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
96/99 0G 0.117 0.05599 0.04682 5 928: 100%|██████████| 1/1 [00:01<00:00, 1.71
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
97/99 0G 0.124 0.0617 0.04387 6 928: 100%|██████████| 1/1 [00:01<00:00, 1.75
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
98/99 0G 0.1126 0.1009 0.04399 12 928: 100%|██████████| 1/1 [00:01<00:00, 1.64
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
99/99 0G 0.06937 0.05515 0.03017 4 928: 100%|██████████| 1/1 [00:01<00:00, 1.68
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0 0 0 0
100 epochs completed in 0.067 hours.
Optimizer stripped from runs\train\exp12\weights\last.pt, 15.0MB
Optimizer stripped from runs\train\exp12\weights\best.pt, 15.0MB
Validating runs\train\exp12\weights\best.pt...
Fusing layers...
YOLOv5s summary: 170 layers, 7217201 parameters, 0 gradients, 17.0 GFLOPs
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 1/1 [00:00<0
all 1 3 0.0115 0.333 0.0369 0.012
banana 1 1 0 0 0 0
snake fruit 1 1 0 0 0 0
pineapple 1 1 0.0345 1 0.111 0.0359
Results saved to runs\train\exp12
