有关网站建设的知识,广州网站建设厂家,技术支持广州骏域网站建设专家,淘宝关闭网站建设类目#x1f368; 本文为#x1f517;365天深度学习训练营 中的学习记录博客#x1f356; 原作者#xff1a;K同学啊 | 接辅导、项目定制 位置
该文件的位置位于 ./ultralytics/cfg/models/v8/yolov8.yaml
模型参数配置
# Parameters
nc: 80 # number of classes
scales: #… 本文为365天深度学习训练营 中的学习记录博客 原作者K同学啊 | 接辅导、项目定制 位置
该文件的位置位于 ./ultralytics/cfg/models/v8/yolov8.yaml
模型参数配置
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. modelyolov8n.yaml will call yolov8.yaml with scale n# [depth, width, max_channels]n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPss: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPsm: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPsl: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPsnc 是分类的数量scales 下设置了不同模型的规模权重depth 深度控制子模块的数量 int(number * depth)width 宽度控制卷积核的数量 int(number * width)max_channels 最大通道数
backbone 模块配置
# YOLOv8.0n backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 3, C2f, [128, True]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 3, C2f, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9from 表示当前模块的输入来自哪一层的输出 -1表示来自上一层的输出 层编号从0开始计repeats 表示当前模块的理论重复次数实际的重复次数正是要根据上面的规模权重来计算后得到这个参数会影响网络的整体深度module 模块类名通过这个类名在common.py中寻找相应的类进行模块化的搭建网络args 是一个列表提供了模块搭建所需要的参数channel, kernel_size, stride, padding, bias等。
head 模块配置
# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 3, C2f, [512]] # 12- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 3, C2f, [256]] # 15 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 12], 1, Concat, [1]] # cat head P4- [-1, 3, C2f, [512]] # 18 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 9], 1, Concat, [1]] # cat head P5- [-1, 3, C2f, [1024]] # 21 (P5/32-large)- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
这里配置的是模型的head部分其结构和使用规则与backbone一致
任务
根据提供的yolov8n yolov8s的模型输出推测yolov8l的模型输出
yolov8n
from n params module arguments 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] 22 [15, 18, 21] 1 897664 ultralytics.nn.modules.head.Detect [80, [64, 128, 256]]
YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPsyolov8s from n params module arguments 0 -1 1 928 ultralytics.nn.modules.conv.Conv [3, 32, 3, 2] 1 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 2 -1 1 29056 ultralytics.nn.modules.block.C2f [64, 64, 1, True] 3 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 4 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] 5 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 6 -1 2 788480 ultralytics.nn.modules.block.C2f [256, 256, 2, True] 7 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] 8 -1 1 1838080 ultralytics.nn.modules.block.C2f [512, 512, 1, True] 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 1 591360 ultralytics.nn.modules.block.C2f [768, 256, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] 16 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] 19 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 1 1969152 ultralytics.nn.modules.block.C2f [768, 512, 1] 22 [15, 18, 21] 1 2147008 ultralytics.nn.modules.head.Detect [80, [128, 256, 512]]
YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPsyolov8l
通过对比最上面的scales和上面两个输出可以发现卷积核大小被width来控制模块重复次数由depth来控制对照可以写下v8l的输出 from n params module arguments 0 -1 1 1856 ultralytics.nn.modules.conv.Conv [3, 64, 3, 2] 1 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 2 -1 3 279808 ultralytics.nn.modules.block.C2f [128, 128, 3, True] 3 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 4 -1 6 2101248 ultralytics.nn.modules.block.C2f [256, 256, 6, True] 5 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] 6 -1 6 8396800 ultralytics.nn.modules.block.C2f [512, 512, 6, True] 7 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] 8 -1 3 4461568 ultralytics.nn.modules.block.C2f [512, 512, 3, True] 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 3 4723712 ultralytics.nn.modules.block.C2f [1024, 512, 3] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 3 1247744 ultralytics.nn.modules.block.C2f [768, 256, 3] 16 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 3 4592640 ultralytics.nn.modules.block.C2f [768, 512, 3] 19 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 3 4723712 ultralytics.nn.modules.block.C2f [1024, 512, 3] 22 [15, 18, 21] 1 5644480 ultralytics.nn.modules.head.Detect [80, [256, 512, 512]]
