depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: - [12,16, 19,36, 40,28] # P3/8 - [36,75, 76,55, 72,146] # P4/16 - [142,110, 192,243, 459,401] # P5/32 # yolov7 backbone backbone: # [from, number,module, arg...
摘要 YoloV7虽然和YoloV5、YoloV8一脉相承,但是其配置文件及其复杂,对修改造成一定的难度。 yolov7.yaml配置文件如下: 代码语言:javascript 复制 # parametersnc:80# numberofclassesdepth_multiple:1.0# model depth multiplewidth_multiple:1.0# layer channel multiple # anchorsanchors:-[12,16,19,36,40,28]#...
depth_multiple: 0.33 # 更改模型的深度,这里主要影响的是网络里面的number参数,最少为一,其他的则是会变为原来的三分之一 width_multiple: 0.50 # 更改网络的宽度, 这里主要影响的是网络里面的输入通道数,变为原来的二分之一 anchors: # 这个先验框的配置 - [10,13, 16,30, 33,23] # P3/8 大尺度 -...
xmin = int(x_centre * width - w * width / 2) # 坐标转换 ymin = int(y_centre * height - h * height / 2) xmax = int(x_centre * width + w * width / 2) ymax = int(y_centre * height + h * height / 2) tmp = [name, xmin, ymin, xmax, ymax] # 单个检测框 ob.ap...
depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: - [12,16, 19,36, 40,28] # P3/8 - [36,75, 76,55, 72,146] # P4/16 - [142,110, 192,243, 459,401] # P5/32 # yolov7 backbone backbone: # [from, number, module, ...
depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple # anchors anchors: - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] na = (len(anchors[0]) //2)ifisinstance(anchors,list)elseanchors# number of anchorsno = na * (nc +5)# number of outputs = anchors * (classes + 5)layers, save, c2 = [], [], ch...
# Parametersnc:1# numberofclassesdepth_multiple:0.33# model depth multiplewidth_multiple:0.50# layer channel multipleanchors:-[10,13,16,30,33,23]#P3/8-[30,61,62,45,59,119]#P4/16-[116,90,156,198,373,326]#P5/32# YOLOv5 v6.0backbonebackbone:#[from,number,module,args][[-1,1,Conv...
depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: - [12,16, 19,36, 40,28] # P3/8 - [36,75, 76,55, 72,146] # P4/16 - [142,110, 192,243, 459,401] # P5/32
depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple 其中nc是检测个数,depth_multiple是模型深度,width_multiple表示卷积通道的缩放因子,就是将配置里面的backbone和head部分有关Conv通道的设置,全部乘以该系数。通过这两个参数就可以实现不同复杂度的模型设计。然后是添加数据索...