YOLOv8-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
本周任务
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，英语调整模型的规模，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表所示：
二、模型整体结构
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
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。
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)
head部分的数据格式和backbone一样。
3.模型结构输出
注意结合参数配置中depth、width、max_channels三个参数对比模型结构输出的异同。
yolov8n.yaml模型：
yolo task=detect mode=train model=yolov8n.yaml data=/root/autodl-tmp/ultralytics/ultralytics/fruit_data/config.yaml epochs=100 batch=4
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, 3011628 parameters, 3011612 gradients, 8.2 GFLOPs
yolov8s.yaml模型：
yolo task=detect mode=train model=yolov8s.yaml data=/root/autodl-tmp/ultralytics/ultralytics/fruit_data/config.yaml epochs=100 batch=4
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, 11137148 parameters, 11137132 gradients, 28.7 GFLOPs
yolov8l.yaml模型：
yolo task=detect mode=train model=yolov8l.yaml data=/root/autodl-tmp/ultralytics/ultralytics/fruit_data/config.yaml epochs=100 batch=4
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, 43632924 parameters, 43632908 gradients, 165.4 GFLOPs
