【YOLOv8多模态融合改进】（可见光+红外）涉及前期、中期、中后期、后期融合方式的完整配置步骤以及二次改进方案
前言
主题： YOLOv8的多模态融合改进
方式： 前期融合、中期融合、中-后期融合、后期融合。
内容： 包含融合方式详解和完整的项目包和配置步骤以及二次改进建议，开箱即用，一键运行。
一、融合方式
1.1 前期融合方法及结构图
定义： 在网络输入阶段将多模态数据直接合并，形成统一的特征表示。
实现方式： 将 RGB（3 通道）与红外（3 通道）图像直接拼接为 6 通道输入，以保留原始模态的细节信息。
结构示意图：
1.2 中期融合方法及结构图
定义： 在网络中间层（骨干网络与颈部网络之间）对多模态特征进行融合。
实现方式： 每个模态通过独立的骨干网络提取特征，融合时采用Concat操作合并特征图，送入颈部网络。
结构示意图：
1.3 中-后期融合方法及结构图
定义： 在颈部网络的上采样之后对多模态特征进行融合。
实现方式： 每个模态通过独立的骨干网络和颈部的FPN网络提取特征，融合时采用Concat操作合并特征图，送入检测头。
结构示意图：
1.4 后期融合方法及结构图
定义： 在网络输出阶段（如检测头或分类器前）对多模态特征进行融合。
实现方式： 每个模态通过独立的骨干网络和颈部网络提取特征，融合时采用Concat操作合并特征图，送入检测头。
结构示意图：
二、完整配置步骤
！！！ 私信获取的项目包就已经把相关的多模态输入、训练等改动都已经配好了，只需要新建模型yaml文件，粘贴对应的模型，进行训练即可。 项目包获取及使用教程可参考链接： 《YOLO系列模型的多模态项目》配置使用教程
在什么地方新建，n,s,m,l,x,用哪个版本按自己的需求来即可，和普通的训练步骤一致。
除了模型结构方面的改动，在yaml文件中还传入了一个通道数
ch: 6
表示传入的是双模态，6通道 ，前三个是可见光，后三个是红外。
在default.yaml中也配置了这个参数。
2.1 前期融合
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters
ch: 6
nc: 1 # 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
# YOLOv8.0n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, MF, [64]] # 0-P1/2
- [-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
# YOLOv8.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 7], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 12
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 5], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
- [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)
2.2 中期融合
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters
ch: 6
nc: 1 # 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
# YOLOv8.0n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, IN, []] # 0
- [-1, 1, Multiin, [1]] # 1
- [-2, 1, Multiin, [2]] # 2
- [1, 1, Conv, [64, 3, 2]] # 3-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 4-P2/4
- [-1, 3, C2f, [128, True]]
- [-1, 1, Conv, [256, 3, 2]] # 6-P3/8
- [-1, 6, C2f, [256, True]]
- [-1, 1, Conv, [512, 3, 2]] # 8-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 10-P5/32
- [-1, 3, C2f, [1024, True]]
- [2, 1, Conv, [64, 3, 2]] # 12-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 13-P2/4
- [-1, 3, C2f, [128, True]]
- [-1, 1, Conv, [256, 3, 2]] # 15-P3/8
- [-1, 6, C2f, [256, True]]
- [-1, 1, Conv, [512, 3, 2]] # 17-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 19-P5/32
- [-1, 3, C2f, [1024, True]]
- [[7, 16], 1, Concat, [1]] # 21 cat backbone P3
- [[9, 18], 1, Concat, [1]] # 22 cat backbone P4
- [[11, 20], 1, Concat, [1]] # 23 cat backbone P5
- [-1, 1, SPPF, [1024, 5]] # 24
# YOLOv8.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 22], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 27
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 21], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 30 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 27], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 33 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 24], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 36 (P5/32-large)
- [[30, 33, 36], 1, Detect, [nc]] # Detect(P3, P4, P5)
2.3 中-后期融合
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters
ch: 6
nc: 1 # 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
# YOLOv8.0n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, IN, []] # 0
- [-1, 1, Multiin, [1]] # 1
- [-2, 1, Multiin, [2]] # 2
- [1, 1, Conv, [64, 3, 2]] # 3-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 4-P2/4
- [-1, 3, C2f, [128, True]]
- [-1, 1, Conv, [256, 3, 2]] # 6-P3/8
- [-1, 6, C2f, [256, True]]
- [-1, 1, Conv, [512, 3, 2]] # 8-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 10-P5/32
- [-1, 3, C2f, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 12
- [2, 1, Conv, [64, 3, 2]] # 13-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 14-P2/4
- [-1, 3, C2f, [128, True]]
- [-1, 1, Conv, [256, 3, 2]] # 16-P3/8
- [-1, 6, C2f, [256, True]]
- [-1, 1, Conv, [512, 3, 2]] # 18-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 20-P5/32
- [-1, 3, C2f, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 22
# YOLOv8.0n head
head:
- [12, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 9], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 25
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 7], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 28 (P3/8-small)
- [22, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 19], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 31
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 17], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 34 (P3/8-small)
- [ [ 12, 22 ], 1, Concat, [ 1 ] ] # cat head P5 35
- [ [ 25, 31 ], 1, Concat, [ 1 ] ] # cat head P5 36
- [ [ 28, 34 ], 1, Concat, [ 1 ] ] # cat head P5 37
- [37, 1, Conv, [256, 3, 2]]
- [[-1, 36], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 40 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 35], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 43 (P5/32-large)
- [[37, 40, 43], 1, Detect, [nc]] # Detect(P3, P4, P5)
2.4 后期融合
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters
ch: 6
nc: 1 # 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
# YOLOv8.0n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, IN, []] # 0
- [-1, 1, Multiin, [1]] # 1
- [-2, 1, Multiin, [2]] # 2
- [1, 1, Conv, [64, 3, 2]] # 3-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 4-P2/4
- [-1, 3, C2f, [128, True]]
- [-1, 1, Conv, [256, 3, 2]] # 6-P3/8
- [-1, 6, C2f, [256, True]]
- [-1, 1, Conv, [512, 3, 2]] # 8-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 10-P5/32
- [-1, 3, C2f, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 12
- [2, 1, Conv, [64, 3, 2]] # 13-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 14-P2/4
- [-1, 3, C2f, [128, True]]
- [-1, 1, Conv, [256, 3, 2]] # 16-P3/8
- [-1, 6, C2f, [256, True]]
- [-1, 1, Conv, [512, 3, 2]] # 18-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 20-P5/32
- [-1, 3, C2f, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 22
# YOLOv8.0n head
head:
- [12, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 9], 1, Concat, [1] ] # cat backbone P4
- [-1, 3, C2f, [512]] # 25
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[ -1, 7], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 28 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 25], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 31 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 34 (P5/32-large)
- [22, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 19], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 37
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[ -1, 17 ], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 40 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 37], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 43 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 22], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 46 (P5/32-large)
- [[28, 40], 1, Concat, [1]] # cat head P5 47
- [[31, 43], 1, Concat, [1]] # cat head P5 48
- [[34, 46], 1, Concat, [1]] # cat head P5 49
- [[47, 48, 49], 1, Detect, [nc]] # Detect(P3, P4, P5)
三、成功运行结果
前期融合结果： 可以看到输入的通道数为6，表明可见光图像和红外图像均输入到了模型中进行融合训练。
YOLOv8-earlyfusion summary: 262 layers, 2,692,747 parameters, 2,692,731 gradients, 7.7 GFLOPs
from n params module arguments
0 -1 1 472 ultralytics.nn.AddModules.multimodal.MF [6, 16]
1 -1 1 2336 ultralytics.nn.modules.conv.Conv [16, 16, 3, 2]
2 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
3 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
4 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
5 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
6 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
7 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
8 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
9 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
10 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 7] 1 0 ultralytics.nn.modules.conv.Concat [1]
13 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
15 [-1, 5] 1 0 ultralytics.nn.modules.conv.Concat [1]
16 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1]
19 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1]
22 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
23 [16, 19, 22] 1 430867 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]]
YOLOv8-earlyfusion summary: 262 layers, 2,692,747 parameters, 2,692,731 gradients, 7.7 GFLOPs
中期融合结果：
YOLOv8-midfusion summary: 344 layers, 4,048,563 parameters, 4,048,547 gradients, 10.3 GFLOPs
from n params module arguments
0 -1 1 0 ultralytics.nn.AddModules.multimodal.IN []
1 -1 1 0 ultralytics.nn.AddModules.multimodal.Multiin [1]
2 -2 1 0 ultralytics.nn.AddModules.multimodal.Multiin [2]
3 1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
4 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
5 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
6 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
7 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
8 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
9 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
10 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
11 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
12 2 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
13 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
14 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
15 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
16 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
17 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
18 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
19 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
20 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
21 [7, 16] 1 0 ultralytics.nn.modules.conv.Concat [1]
22 [9, 18] 1 0 ultralytics.nn.modules.conv.Concat [1]
23 [11, 20] 1 0 ultralytics.nn.modules.conv.Concat [1]
24 -1 1 394240 ultralytics.nn.modules.block.SPPF [512, 256, 5]
25 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
26 [-1, 22] 1 0 ultralytics.nn.modules.conv.Concat [1]
27 -1 1 164608 ultralytics.nn.modules.block.C2f [512, 128, 1]
28 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
29 [-1, 21] 1 0 ultralytics.nn.modules.conv.Concat [1]
30 -1 1 41344 ultralytics.nn.modules.block.C2f [256, 64, 1]
31 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
32 [-1, 27] 1 0 ultralytics.nn.modules.conv.Concat [1]
33 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
34 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
35 [-1, 24] 1 0 ultralytics.nn.modules.conv.Concat [1]
36 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
37 [30, 33, 36] 1 430867 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]]
YOLOv8-midfusion summary: 344 layers, 4,048,563 parameters, 4,048,547 gradients, 10.3 GFLOPs
中-后期融合结果：
YOLOv8-mid-to-late-fusion summary: 386 layers, 4,381,683 parameters, 4,381,667 gradients, 12.3 GFLOPs
from n params module arguments
0 -1 1 0 ultralytics.nn.AddModules.multimodal.IN []
1 -1 1 0 ultralytics.nn.AddModules.multimodal.Multiin [1]
2 -2 1 0 ultralytics.nn.AddModules.multimodal.Multiin [2]
3 1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
4 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
5 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
6 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
7 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
8 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
9 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
10 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
11 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
12 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
13 2 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
14 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
15 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
16 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
17 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
18 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
19 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
20 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
21 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
22 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
23 12 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
24 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
25 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
26 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
27 [-1, 7] 1 0 ultralytics.nn.modules.conv.Concat [1]
28 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
29 22 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
30 [-1, 19] 1 0 ultralytics.nn.modules.conv.Concat [1]
31 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
32 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
33 [-1, 17] 1 0 ultralytics.nn.modules.conv.Concat [1]
34 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
35 [12, 22] 1 0 ultralytics.nn.modules.conv.Concat [1]
36 [25, 31] 1 0 ultralytics.nn.modules.conv.Concat [1]
37 [28, 34] 1 0 ultralytics.nn.modules.conv.Concat [1]
38 37 1 73856 ultralytics.nn.modules.conv.Conv [128, 64, 3, 2]
39 [-1, 36] 1 0 ultralytics.nn.modules.conv.Concat [1]
40 -1 1 140032 ultralytics.nn.modules.block.C2f [320, 128, 1]
41 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
42 [-1, 35] 1 0 ultralytics.nn.modules.conv.Concat [1]
43 -1 1 558592 ultralytics.nn.modules.block.C2f [640, 256, 1]
44 [37, 40, 43] 1 545235 ultralytics.nn.modules.head.Detect [1, [128, 128, 256]]
YOLOv8-mid-to-late-fusion summary: 386 layers, 4,381,683 parameters, 4,381,667 gradients, 12.3 GFLOPs
后期融合结果：
YOLOv8-latefusion summary: 424 layers, 5,338,867 parameters, 5,338,851 gradients, 13.5 GFLOPs
from n params module arguments
0 -1 1 0 ultralytics.nn.AddModules.multimodal.IN []
1 -1 1 0 ultralytics.nn.AddModules.multimodal.Multiin [1]
2 -2 1 0 ultralytics.nn.AddModules.multimodal.Multiin [2]
3 1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
4 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
5 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
6 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
7 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
8 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
9 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
10 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
11 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
12 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
13 2 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
14 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
15 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
16 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
17 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
18 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
19 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
20 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
21 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
22 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
23 12 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
24 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
25 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
26 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
27 [-1, 7] 1 0 ultralytics.nn.modules.conv.Concat [1]
28 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
29 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
30 [-1, 25] 1 0 ultralytics.nn.modules.conv.Concat [1]
31 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
32 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
33 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
34 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
35 22 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
36 [-1, 19] 1 0 ultralytics.nn.modules.conv.Concat [1]
37 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
38 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
39 [-1, 17] 1 0 ultralytics.nn.modules.conv.Concat [1]
40 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
41 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
42 [-1, 37] 1 0 ultralytics.nn.modules.conv.Concat [1]
43 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
44 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
45 [-1, 22] 1 0 ultralytics.nn.modules.conv.Concat [1]
46 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
47 [28, 40] 1 0 ultralytics.nn.modules.conv.Concat [1]
48 [31, 43] 1 0 ultralytics.nn.modules.conv.Concat [1]
49 [34, 46] 1 0 ultralytics.nn.modules.conv.Concat [1]
50 [47, 48, 49] 1 819795 ultralytics.nn.modules.head.Detect [1, [128, 256, 512]]
YOLOv8-latefusion summary: 424 layers, 5,338,867 parameters, 5,338,851 gradients, 13.5 GFLOPs
四、二次改进方案
-
多模态模型的二次改进和普通模型的改进一致，主要涉及到C2f、颈部结构、上采样、下采样等，可以增加或替换成其它模块，可以换成其它的颈部结构在进行融合。若有需要可查看主页的模块改进专栏。
-
两个骨干中均可以再次添加其它模块，需要注意的是融合的时候层数要对应上，即两层的特征图大小要一致。