YOLOv8白皮书-第Y8周：yolov8.yaml文件解读

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

请根据YOLOv8n、YOLOv8s模型的结构输出，手写出YOLOv8l的模型输出

文件位置：./ultralytics/cfg/models/v8/yolov8.yaml

一、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 GFLOPs   s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPs   m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs   l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs   x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs  

Parameters:

• nc：80 是类别数量，即模型可以识别的物体类别数。
• scales：包含了不同模型配置的尺度参数，用于调整模型的规模，通过尺度参数可以实现不同复杂度的模型设计。YOLOv8n、YOLOv8s、YOLOv8m、YOLOv8l、YOLOv8x五种模型的区别在于depth、width、max_channels这三个参数的不同。
  • 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

五种模型性能的详细参数如下所示：

二、模型整体结构
YOLOv5模型：

YOLO-v8整体模型结构：

1. Backbone模块

# 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  

这是YOLOv8的backbone，每一个模块算一行，每行由四个参数构成。分别是：

• from：表示当前模块的输入来自那一层的输出，-1表示来自上一层的输出，层编号由0开始计数。

• repeats：表示当前模块的理论重复次数，实际的重复次数还要由上面的参数depth_multiple共同决定，该参数影响整体网络模型的深度。

• model：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list，模块搭建所需参数，channel，kernel_size，stride，padding，bias等。

  这个模块是YOLOv8的主干网络(backbone)，用于提取输入图像的特征以便后续的目标检测任务。

YOLOv8的主干网络包括卷积层(Conv)、深度可分离卷积层(C2f)以及空间金字塔池化层(SPPF)等卷积部分。它们在不同层数级上增强了网络的表示能力和视野范围，可以更好地适应各种尺寸的输入图像。

网络的输入为一幅图像，输出为多个不同层数级的特征图(feature maps)，将输出的特征图传递给头部(head)以产生物体检测的结果。

2. head模块

# 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)  

这是YOLOv8s的head，数据格式和backbone一样。

3. 模型结构输出

cmd命令行输入：

yolo task=detect mode=train model=yolov8n.yaml data=mydata.yaml epochs=100 batch=4 

yolov8n.yaml可以换成其他模型的yaml，如yolov8s.yaml、yolov8l.yaml、yolov8m.yaml、yolov8x.yaml
（1）yolov8n.yaml模型：

                   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    752092  ultralytics.nn.modules.head.Detect           [4, [64, 128, 256]] YOLOv8n summary: 225 layers, 3,011,628 parameters, 3,011,612 gradients, 8.2 GFLOPs 

（2）yolov8s.yaml模型：

                   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   2117596  ultralytics.nn.modules.head.Detect           [4, [128, 256, 512]] YOLOv8s summary: 225 layers, 11,137,148 parameters, 11,137,132 gradients, 28.7 GFLOPs 

（3）yolov8l.yaml模型：

                   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   5585884  ultralytics.nn.modules.head.Detect           [4, [256, 512, 512]] YOLOv8l summary: 365 layers, 43,632,924 parameters, 43,632,908 gradients, 165.4 GFLOPs 

三、总结
了解了YOLOv8的各种模型结构，了解了YOLOv8各种模型的参数配置。