我正在使用 cpu 在 Windows 10 上使用暗网尝试 yolov3-tiny。但是，我一直平均损失 nan。我已经按照https://github.com/AlexeyAB/darknet.git上的指示遵循了所有指示。我编辑了我的 cfg 文件，将 yolo 的所有三个过滤器都设置为 21（因为我只有两个类。）我将细分设置为 8，批处理设置为 64。我使用了我自己和我制作的 500 多张图像'正在尝试进行自定义检测。我想让 yolo 确定图像是竖起大拇指还是竖起大拇指。我已经多次运行 train 命令，但我从来没有超过 100 次迭代
#config file:
[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=64
subdivisions=8
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
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=1
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
###########
[convolutional]
batch_normalize=1
filters=21
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=21
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=21
activation=linear
[yolo]
mask = 3,4,5
anchors = 38, 93, 55,120, 66,156, 90,259, 110,239, 118,283
classes=2
num=6
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=21
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 8
[convolutional]
batch_normalize=1
filters=21
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=21
activation=linear
[yolo]
mask = 0,1,2
anchors = 38, 93, 55,120, 66,156, 90,259, 110,239, 118,283
classes=2
num=6
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1