YoloR的 YoloR_p6.cfg 如何修改成自己想要的訓練資料?

此文會繼承我之前寫的這篇~~

💛YOLOv4 training 訓練完整教學(以口罩辨識為範例) 💛

💛如何運用darknet 在yolov4 上面計算anchors 💛

# 207

[implicit_mul]

filters=258   #change

# 208

[implicit_mul]

filters=258   #change

# 209

[implicit_mul]

filters=258   #change

# 210

[implicit_mul]

filters=258  #change

# ============ Head ============ #

# YOLO-3

[route]

layers = 163

[convolutional]

batch_normalize=1

size=3

stride=1

pad=1

filters=256

activation=silu

[shift_channels]

from=203

[convolutional]

size=1

stride=1

pad=1

filters=258    #change

activation=linear

[control_channels]

from=207

[yolo]

mask = 0,1,2

anchors = 19,27,  44,40,  38,94,  96,68,  86,152,  180,137,  140,301,  303,264,  238,542,  436,615,  739,380,  925,792

classes=81    #change

num=12

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

# YOLO-4

[route]

layers = 176

[convolutional]

batch_normalize=1

size=3

stride=1

pad=1

filters=384

activation=silu

[shift_channels]

from=204

[convolutional]

size=1

stride=1

pad=1

filters=258  #change

activation=linear

[control_channels]

from=208

[yolo]

mask = 3,4,5

anchors = 19,27,  44,40,  38,94,  96,68,  86,152,  180,137,  140,301,  303,264,  238,542,  436,615,  739,380,  925,792

classes=81  #change

num=12

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

# YOLO-5

[route]

layers = 189

[convolutional]

batch_normalize=1

size=3

stride=1

pad=1

filters=512

activation=silu

[shift_channels]

from=205

[convolutional]

size=1

stride=1

pad=1

filters=258 #change

activation=linear

[control_channels]

from=209

[yolo]

mask = 6,7,8

anchors = 19,27,  44,40,  38,94,  96,68,  86,152,  180,137,  140,301,  303,264,  238,542,  436,615,  739,380,  925,792

classes=81  #change

num=12

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

# YOLO-6

[route]

layers = 202

[convolutional]

batch_normalize=1

size=3

stride=1

pad=1

filters=640

activation=silu

[shift_channels]

from=206

[convolutional]

size=1

stride=1

pad=1 

filters=258    #change

activation=linear

[control_channels]

from=210

[yolo]

mask = 9,10,11

anchors = 19,27,  44,40,  38,94,  96,68,  86,152,  180,137,  140,301,  303,264,  238,542,  436,615,  739,380,  925,792

classes=81  #change

num=12

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

# ============ End of Head ============ #

只要你把上面Highlight的地方改成你要的就可以了!!

記得請把註解#change刪除掉,

cfg檔案裡面不能打註解,

我剛剛測試過,打完註解就不能跑了

filters的公式是(classes + 5)x3能,

詳情可以看我yolov4的文章,

或者去github的AlexeyAB/darknet看介紹~

💙記得當你的classes要擴充時, 要進去你的.cfg檔案裡面調整參數, 

像是你有62個classes的話, 就去把它改成62.

• change line classes=80 to your number of objects in each of 3 [yolo]-layers:
  • https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610
  • https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696
  • https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L783
• change [filters=255] to filters=(classes + 5)x3 in the 3 [convolutional] before each [yolo] layer, keep in mind that it only has to be the last [convolutional] before each of the [yolo] layers.
  • https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603
  • https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689
  • https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776