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mit网站可以做app,如何优化百度seo排名,石狮建设局网站,网络域名的网站🍨 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: #…

🍨 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. 'model=yolov8n.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 GFLOPs

nc 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]]  # 9

from 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 GFLOPs

yolov8s

                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 GFLOPs

yolov8l

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]]