Open hvudeshi opened 3 years ago
`[net] batch=64 subdivisions=32
width=608 height=608 channels=3 momentum=0.949 decay=0.0005 angle=0 saturation = 1.5 exposure = 1.5 hue=.1
learning_rate=0.000561 burn_in=1000 max_batches = 500500 policy=steps steps=400000,450000 scales=.1,.1
mosaic=1
[convolutional] batch_normalize=1 filters=32 size=3 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=64 size=3 stride=2 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish
[route] layers = -2
[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=32 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish
[route] layers = -1,-7
[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=128 size=3 stride=2 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish
[route] layers = -2
[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=64 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=64 size=1 stride=1 pad=1 activation=mish
[route] layers = -1,-10
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=256 size=3 stride=2 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish
[route] layers = -2
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=128 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=128 size=1 stride=1 pad=1 activation=mish
[route] layers = -1,-28
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=512 size=3 stride=2 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish
[route] layers = -2
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=256 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=mish
[route] layers = -1,-28
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=1024 size=3 stride=2 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish
[route] layers = -2
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish
[convolutional] batch_normalize=1 filters=512 size=3 stride=1 pad=1 activation=mish
[shortcut] from=-3 activation=linear
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=mish
[route] layers = -1,-16
[convolutional] batch_normalize=1 filters=1024 size=1 stride=1 pad=1 activation=mish
##########################
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky
[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
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[upsample] stride=2
[route] layers = 85
[convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky
[route] layers = -1, -3
[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 filters=128 size=1 stride=1 pad=1 activation=leaky
[upsample] stride=2
[route] layers = 54
[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 size=3 stride=1 pad=1 filters=256 activation=leaky
[convolutional] size=1 stride=1 pad=1 filters=18 activation=linear
[yolo] mask = 0,1,2 anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 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_loss=ciou nms_kind=greedynms beta_nms=0.6
[route] layers = -4
[convolutional] batch_normalize=1 size=3 stride=2 pad=1 filters=256 activation=leaky
[route] layers = -1, -16
[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 = 3,4,5 anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 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_loss=ciou nms_kind=greedynms beta_nms=0.6
[route] layers = -4
[convolutional] batch_normalize=1 size=3 stride=2 pad=1 filters=512 activation=leaky
[route] layers = -1, -37
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky
[convolutional] batch_normalize=1 filters=512 size=1 stride=1 pad=1 activation=leaky
[convolutional] batch_normalize=1 size=3 stride=1 pad=1 filters=1024 activation=leaky
[convolutional] size=1 stride=1 pad=1 filters=18 activation=linear
[yolo]
mask = 6,7,8
anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
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_loss=ciou
nms_kind=greedynms
beta_nms=0.6
`
Run command: ./darknet detector train
Hello @AlexeyAB,
I have trained yolov4 on my custom dataset two days before. Now, I am again training yolov4 on the subset of the same custom dataset. I have used your yolov4 cfg and changed classes and filters of yolov4 and convolution layer above the yolov4 layer respectively. But, the loss is going nan. I have reduced the learning rate by 20 times, but still facing the same issue. Also, bad.list is also not generated by the check_mistakes flag that I have already enabled. As I have already trained on this dataset with more number of images for more than 100000 iterations, I don't think so there is an issue with the images. So, I want to know is there any bug or I am missing something? I want you to verify this.
Thank you.
There was a bug. I resolved it.
Hello @AlexeyAB,
I have trained yolov4 on my custom dataset two days before. Now, I am again training yolov4 on the subset of the same custom dataset. I have used your yolov4 cfg and changed classes and filters of yolov4 and convolution layer above the yolov4 layer respectively. But, the loss is going nan. I have reduced the learning rate by 20 times, but still facing the same issue. Also, bad.list is also not generated by the check_mistakes flag that I have already enabled. As I have already trained on this dataset with more number of images for more than 100000 iterations, I don't think so there is an issue with the images. So, I want to know is there any bug or I am missing something? I want you to verify this.
Thank you.