用自己的数据集训练时ciou loss下降比giou loss慢，请问怎么解决呢？

[1343464520]

我的数据只有一类行人，我用的是AB版Darknet，我只调了iou_normalizer参数，从0.07改成0.15还是一样，基本没啥变化。。我是从头开始train的。求助大神！以下是我的cfg文件中参数设置：
[net]

Testing

#batch=1
#subdivisions=1

Training

batch=64
subdivisions=16
width=416
height=416
channels=3
momentum=0.9
#decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1

#learning_rate=0.001
learning_rate=0.01
burn_in=1000
max_batches = 50200
policy=steps
#steps=40000,45000
#scales=.1,.1
#steps=4000,8000,12000,16000,20000
steps=900,2000,3000,5000,20000
scales=.5,.5,.5,.5,.5

#cutmix=1
#mosaic=1

#:104x104 54:52x52 85:26x26 104:13x13 for 416

[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

Downsample

[convolutional]
batch_normalize=1
filters=32
size=3
stride=2
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[route]
layers = -2

[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=16
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[route]
layers = -1,-7

[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

Downsample

[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[route]
layers = -2

[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[route]
layers = -1,-10

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

Downsample

[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[route]
layers = -2

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[route]
layers = -1,-28

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

Downsample

[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[route]
layers = -2

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[route]
layers = -1,-28

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

Downsample

[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[route]
layers = -2

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
#activation=leaky
activation=leaky

[shortcut]
from=-3
activation=linear

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

[route]
layers = -1,-16

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
#activation=leaky
activation=leaky

##########################

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

SPP

[maxpool]
stride=1
size=5

[route]
layers=-2

[maxpool]
stride=1
size=9

[route]
layers=-4

[maxpool]
stride=1
size=13

[route]
layers=-1,-3,-5,-6

End SPP

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[upsample]
stride=2

[route]
layers = 85

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[route]
layers = -1, -3

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky

[upsample]
stride=2

[route]
layers = 54

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky

[route]
layers = -1, -3

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=128
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=128
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky

##########################

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=128
activation=leaky

[convolutional]
size=1
stride=1
pad=1
filters=18
activation=linear

[yolo]
mask = 0,1,2
anchors = 89,100, 103,136, 118,181, 132,106, 141,147, 162,194, 182,134, 211,179, 233,243 # 9488 + 2825 = 12313
classes=1
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
scale_x_y = 1.2
iou_thresh=0.213
cls_normalizer=1.0
#iou_normalizer=0.07
iou_normalizer=0.15
iou_loss=ciou
nms_kind=greedynms
beta_nms=0.6
max_delta=5

[route]
layers = -4

[convolutional]
batch_normalize=1
size=3
stride=2
pad=1
filters=128
activation=leaky

[route]
layers = -1, -16

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky

[convolutional]
size=1
stride=1
pad=1
filters=18
activation=linear

[yolo]
mask = 3,4,5
anchors = 89,100, 103,136, 118,181, 132,106, 141,147, 162,194, 182,134, 211,179, 233,243 # 9488 + 2825 = 12313
classes=1
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
scale_x_y = 1.1
iou_thresh=0.213
cls_normalizer=1.0
#iou_normalizer=0.07
iou_normalizer=0.15
iou_loss=ciou
nms_kind=greedynms
beta_nms=0.6
max_delta=5

[route]
layers = -4

[convolutional]
batch_normalize=1
size=3
stride=2
pad=1
filters=256
activation=leaky

[route]
layers = -1, -37

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky

[convolutional]
size=1
stride=1
pad=1
filters=18
activation=linear

[yolo]
mask = 6,7,8
anchors = 89,100, 103,136, 118,181, 132,106, 141,147, 162,194, 182,134, 211,179, 233,243 # 9488 + 2825 = 12313
classes=1
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
scale_x_y = 1.05
iou_thresh=0.213
cls_normalizer=1.0
#iou_normalizer=0.07
iou_normalizer=0.15
iou_loss=ciou
nms_kind=greedynms
beta_nms=0.6
max_delta=5

[Zzh-tju]

具体的工程需要你自己来调试完成，我只能给一些建议。

1. 尝试更多的系数，iou_normalizer∈[0.07,0.5]
2. 观察数据集的物体多为中大型还是小型，小型建议使用DIoU
3. 控制变量的情况下，如果D/CIoU均比GIoU差，那毫无疑问使用GIoU，说明D/CIoU对该数据集失效。当然也可以看看IoU loss是何情况。我们无法保证所提出的trick在任何情况下都最优，事实上一些经典的trick如focal loss，Soft-NMS也都存在失效的情况

[1343464520]

非常感谢作者的中肯建议！通过控制变量法去尝试后可能是我的数据更适合giou. 曾经也尝试过focal loss也是几乎没有提升，回头换别的任务数据集再试试。再次感谢作者！

[djwilv]

你画loss曲线代码能发一下吗？

[1343464520]

可以直接画的，我用的AlexyAB版darknet。------------------&nbsp;原始邮件&nbsp;------------------ 发件人:&nbsp;&quot;djwilv&quot;<notifications@github.com&gt; 发送时间:&nbsp;2020年6月17日(星期三) 晚上7:54 收件人:&nbsp;&quot;Zzh-tju/DIoU-darknet&quot;<DIoU-darknet@noreply.github.com&gt;; 抄送:&nbsp;&quot;1343464520&quot;<1343464520@qq.com&gt;;&quot;Author&quot;<author@noreply.github.com&gt;; 主题:&nbsp;Re: [Zzh-tju/DIoU-darknet] 用自己的数据集训练时ciou loss下降比giou loss慢，请问怎么解决呢？ (#30)