一、导言
SimSPPF（Simplified Spatial Pyramid Pooling with Fixed-size kernel）模块是在YOLOv6架构中引入的一个关键组件，它旨在优化原始SPPF（Spatial Pyramid Pooling Fixed-size）模块的效率。以下是SimSPPF的一些优点和可能存在的缺点：
优点:
-
效率提升: SimSPPF通过简化原有的SPPF结构，使用固定大小的池化核（通常是5x5），从而显著减少了计算量和内存消耗，这使得模型在推断阶段运行得更快。
-
感受野扩大: 尽管使用了较小的池化核，SimSPPF仍然能够有效地捕获多尺度特征，因为其设计允许在网络中整合不同层级的信息，这对于物体检测任务来说非常重要。
-
易于实现: 由于其设计上的简化，SimSPPF比原始的SPPF更容易在不同的硬件平台上实现和优化。
-
适应性: 在YOLOv6的不同变体中（如YOLOv6-N/S/M），SimSPPF可以根据模型的大小和特定需求进行调整，例如在较小的模型中使用SimCSPSPPF以进一步加速，而在较大的模型中使用SimSPPF以保持良好的精度。
缺点:
-
可能的信息损失: 通过使用固定大小的池化核，SimSPPF可能会牺牲一些大尺度的特征信息，这是由于较大尺寸的池化核能够捕捉到更大范围内的上下文信息。
-
精度折衷: 虽然效率提高了，但这种简化可能会影响模型在某些场景下的检测精度，特别是对于那些需要非常精细的多尺度分析的情况。
-
灵活性降低: 固定大小的池化核限制了SimSPPF的灵活性，因为它不能像可变大小的SPPF那样自适应地调整其感受野的大小。
-
泛化能力: 理论上讲，固定的池化核可能会限制模型处理各种尺度输入的能力，尽管实践中这种影响可能被其他网络组件所弥补。
总体而言，SimSPPF模块是YOLOv6设计中为了平衡速度和精度而做出的一种选择，它通过牺牲一定的复杂性和理论上的精度，换取了实际应用中的高效率和快速响应。
二、准备工作
首先在YOLOv5/v7的models文件夹下新建文件morepooling.py，导入如下代码
import warningsfrom models.common import *
# simsppf
# yolov6
class SimConv(nn.Module):'''Normal Conv with ReLU activation'''def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1, bias=False):super().__init__()padding = kernel_size // 2self.conv = nn.Conv2d(in_channels,out_channels,kernel_size=kernel_size,stride=stride,padding=padding,groups=groups,bias=bias,)self.bn = nn.BatchNorm2d(out_channels)self.act = nn.ReLU()def forward(self, x):return self.act(self.bn(self.conv(x)))def forward_fuse(self, x):return self.act(self.conv(x))class SimSPPF(nn.Module):'''Simplified SPPF with ReLU activation'''def __init__(self, in_channels, out_channels, kernel_size=5):super().__init__()c_ = in_channels // 2 # hidden channelsself.cv1 = SimConv(in_channels, c_, 1, 1)self.cv2 = SimConv(c_ * 4, out_channels, 1, 1)self.m = nn.MaxPool2d(kernel_size=kernel_size, stride=1, padding=kernel_size // 2)def forward(self, x):x = self.cv1(x)with warnings.catch_warnings():warnings.simplefilter('ignore')y1 = self.m(x)y2 = self.m(y1)return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
其次在在YOLOv5/v7项目文件下的models/yolo.py中在文件首部添加代码
from models.morepooling import *并搜索def parse_model(d, ch)
定位到如下行添加以下代码
SimSPPF,
三、YOLOv7-tiny改进工作
完成二后，在YOLOv7项目文件下的models文件夹下创建新的文件yolov7-tiny-simsppf.yaml，导入如下代码。
# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple# anchors
anchors:- [10,13, 16,30, 33,23] # P3/8- [30,61, 62,45, 59,119] # P4/16- [116,90, 156,198, 373,326] # P5/32# yolov7-tiny backbone
backbone:# [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True[[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 0-P1/2[-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 1-P2/4[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 7[-1, 1, MP, []], # 8-P3/8[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 14[-1, 1, MP, []], # 15-P4/16[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 21[-1, 1, MP, []], # 22-P5/32[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 28]# yolov7-tiny head
head:[[-1, 1, SimSPPF, [256]], # 29[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[21, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4[[-1, -2], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 39[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[14, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3[[-1, -2], 1, Concat, [1]],[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 49[-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]],[[-1, 39], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 57[-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]],[[-1, 29], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[-1, -2, -3, -4], 1, Concat, [1]],[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 65[49, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[57, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[65, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]],[[66, 67, 68], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)]
from n params module arguments 0 -1 1 928 models.common.Conv [3, 32, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]1 -1 1 18560 models.common.Conv [32, 64, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]2 -1 1 2112 models.common.Conv [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]3 -2 1 2112 models.common.Conv [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]4 -1 1 9280 models.common.Conv [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]5 -1 1 9280 models.common.Conv [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]6 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 7 -1 1 8320 models.common.Conv [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]8 -1 1 0 models.common.MP [] 9 -1 1 4224 models.common.Conv [64, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]10 -2 1 4224 models.common.Conv [64, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]11 -1 1 36992 models.common.Conv [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]12 -1 1 36992 models.common.Conv [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]13 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 14 -1 1 33024 models.common.Conv [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]15 -1 1 0 models.common.MP [] 16 -1 1 16640 models.common.Conv [128, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]17 -2 1 16640 models.common.Conv [128, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]18 -1 1 147712 models.common.Conv [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]19 -1 1 147712 models.common.Conv [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]20 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 21 -1 1 131584 models.common.Conv [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]22 -1 1 0 models.common.MP [] 23 -1 1 66048 models.common.Conv [256, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]24 -2 1 66048 models.common.Conv [256, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]25 -1 1 590336 models.common.Conv [256, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]26 -1 1 590336 models.common.Conv [256, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]27 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 28 -1 1 525312 models.common.Conv [1024, 512, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]29 -1 1 394240 models.morepooling.SimSPPF [512, 256] 30 -1 1 33024 models.common.Conv [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]31 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 32 21 1 33024 models.common.Conv [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]33 [-1, -2] 1 0 models.common.Concat [1] 34 -1 1 16512 models.common.Conv [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]35 -2 1 16512 models.common.Conv [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]36 -1 1 36992 models.common.Conv [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]37 -1 1 36992 models.common.Conv [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]38 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 39 -1 1 33024 models.common.Conv [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]40 -1 1 8320 models.common.Conv [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]41 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 42 14 1 8320 models.common.Conv [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]43 [-1, -2] 1 0 models.common.Concat [1] 44 -1 1 4160 models.common.Conv [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]45 -2 1 4160 models.common.Conv [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]46 -1 1 9280 models.common.Conv [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]47 -1 1 9280 models.common.Conv [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]48 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 49 -1 1 8320 models.common.Conv [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]50 -1 1 73984 models.common.Conv [64, 128, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]51 [-1, 39] 1 0 models.common.Concat [1] 52 -1 1 16512 models.common.Conv [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]53 -2 1 16512 models.common.Conv [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]54 -1 1 36992 models.common.Conv [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]55 -1 1 36992 models.common.Conv [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]56 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 57 -1 1 33024 models.common.Conv [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]58 -1 1 295424 models.common.Conv [128, 256, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)]59 [-1, 29] 1 0 models.common.Concat [1] 60 -1 1 65792 models.common.Conv [512, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]61 -2 1 65792 models.common.Conv [512, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]62 -1 1 147712 models.common.Conv [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]63 -1 1 147712 models.common.Conv [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]64 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 65 -1 1 131584 models.common.Conv [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)]66 49 1 73984 models.common.Conv [64, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]67 57 1 295424 models.common.Conv [128, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]68 65 1 1180672 models.common.Conv [256, 512, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)]69 [66, 67, 68] 1 17132 models.yolo.IDetect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]Model Summary: 249 layers, 5751820 parameters, 5751820 gradients, 13.0 GFLOPS运行后若打印出如上文本代表改进成功。
四、YOLOv5s改进工作
完成二后，在YOLOv5项目文件下的models文件夹下创建新的文件yolov5s-simsppf.yaml，导入如下代码。
# Parameters
nc: 1 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
anchors:- [10,13, 16,30, 33,23] # P3/8- [30,61, 62,45, 59,119] # P4/16- [116,90, 156,198, 373,326] # P5/32# YOLOv5 v6.0 backbone
backbone:# [from, number, module, args][[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2[-1, 1, Conv, [128, 3, 2]], # 1-P2/4[-1, 3, C3, [128]],[-1, 1, Conv, [256, 3, 2]], # 3-P3/8[-1, 6, C3, [256]],[-1, 1, Conv, [512, 3, 2]], # 5-P4/16[-1, 9, C3, [512]],[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32[-1, 3, C3, [1024]],[-1, 1, SimSPPF, [1024]],]# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]], # cat backbone P4[-1, 3, C3, [512, False]], # 13[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 4], 1, Concat, [1]], # cat backbone P3[-1, 3, C3, [256, False]], # 17 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 14], 1, Concat, [1]], # cat head P4[-1, 3, C3, [512, False]], # 20 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]], # cat head P5[-1, 3, C3, [1024, False]], # 23 (P5/32-large)[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)]
from n params module arguments 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] 2 -1 1 18816 models.common.C3 [64, 64, 1] 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] 4 -1 2 115712 models.common.C3 [128, 128, 2] 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] 6 -1 3 625152 models.common.C3 [256, 256, 3] 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] 8 -1 1 1182720 models.common.C3 [512, 512, 1] 9 -1 1 656896 models.morepooling.SimSPPF [512, 512, 5] 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 models.common.Concat [1] 13 -1 1 361984 models.common.C3 [512, 256, 1, False] 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 16 [-1, 4] 1 0 models.common.Concat [1] 17 -1 1 90880 models.common.C3 [256, 128, 1, False] 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 1 296448 models.common.C3 [256, 256, 1, False] 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] 24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]Model Summary: 270 layers, 7022326 parameters, 7022326 gradients, 15.9 GFLOPs五、YOLOv5n改进工作
完成二后，在YOLOv5项目文件下的models文件夹下创建新的文件yolov5n-simsppf.yaml，导入如下代码。
# Parameters
nc: 1 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.25 # layer channel multiple
anchors:- [10,13, 16,30, 33,23] # P3/8- [30,61, 62,45, 59,119] # P4/16- [116,90, 156,198, 373,326] # P5/32# YOLOv5 v6.0 backbone
backbone:# [from, number, module, args][[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2[-1, 1, Conv, [128, 3, 2]], # 1-P2/4[-1, 3, C3, [128]],[-1, 1, Conv, [256, 3, 2]], # 3-P3/8[-1, 6, C3, [256]],[-1, 1, Conv, [512, 3, 2]], # 5-P4/16[-1, 9, C3, [512]],[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32[-1, 3, C3, [1024]],[-1, 1, SimSPPF, [1024]],]# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]], # cat backbone P4[-1, 3, C3, [512, False]], # 13[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 4], 1, Concat, [1]], # cat backbone P3[-1, 3, C3, [256, False]], # 17 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 14], 1, Concat, [1]], # cat head P4[-1, 3, C3, [512, False]], # 20 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]], # cat head P5[-1, 3, C3, [1024, False]], # 23 (P5/32-large)[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)]
from n params module arguments 0 -1 1 1760 models.common.Conv [3, 16, 6, 2, 2] 1 -1 1 4672 models.common.Conv [16, 32, 3, 2] 2 -1 1 4800 models.common.C3 [32, 32, 1] 3 -1 1 18560 models.common.Conv [32, 64, 3, 2] 4 -1 2 29184 models.common.C3 [64, 64, 2] 5 -1 1 73984 models.common.Conv [64, 128, 3, 2] 6 -1 3 156928 models.common.C3 [128, 128, 3] 7 -1 1 295424 models.common.Conv [128, 256, 3, 2] 8 -1 1 296448 models.common.C3 [256, 256, 1] 9 -1 1 164608 models.morepooling.SimSPPF [256, 256, 5] 10 -1 1 33024 models.common.Conv [256, 128, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 models.common.Concat [1] 13 -1 1 90880 models.common.C3 [256, 128, 1, False] 14 -1 1 8320 models.common.Conv [128, 64, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 16 [-1, 4] 1 0 models.common.Concat [1] 17 -1 1 22912 models.common.C3 [128, 64, 1, False] 18 -1 1 36992 models.common.Conv [64, 64, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 1 74496 models.common.C3 [128, 128, 1, False] 21 -1 1 147712 models.common.Conv [128, 128, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 1 296448 models.common.C3 [256, 256, 1, False] 24 [17, 20, 23] 1 8118 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [64, 128, 256]]Model Summary: 270 layers, 1765270 parameters, 1765270 gradients, 4.2 GFLOPs
六、代码解释
simsppf又称为“简单的”sppf.
定义了两个深度学习模块：SimConv 和 SimSPPF。它们是YOLOv6检测模型中的一部分，旨在简化网络结构和提高效率。
SimConv
这是一个简化版的卷积块，它包含一个卷积层、批量归一化（Batch Normalization）和ReLU激活函数。相比于标准的卷积块，这里没有使用更复杂的激活函数如SiLU或Leaky ReLU，而是选择了计算成本较低的ReLU。
优点:
- 简单性: 使用ReLU减少了计算复杂度，加速训练过程。
- 可融合性: 提供了
forward_fuse方法，允许在网络优化过程中融合BN和卷积操作，进一步加速推理速度。
缺点:
- 表达能力: 相比于更复杂的激活函数，ReLU可能在某些任务上限制了网络的表达能力。
- 梯度消失问题: 虽然ReLU解决了传统sigmoid和tanh函数的梯度消失问题，但在输入为负数时其梯度为零，这仍然可能导致部分神经元“死亡”。
SimSPPF
这是对空间金字塔池化（Spatial Pyramid Pooling, SPP）模块的简化版本，通常用于增强特征的尺度不变性。这个模块使用多个最大池化层来捕获不同尺度的信息，并通过1x1卷积进行通道数调整。
优点:
- 尺度不变性: 多尺度的最大池化有助于模型捕捉到不同大小的目标信息。
- 参数效率: 使用了较少的参数量，同时保持了SPP模块的大部分功能。
- 灵活性: 可以通过改变
kernel_size参数来适应不同的输入尺寸或特征图需求。
缺点:
- 计算资源: 尽管进行了简化，SPPF仍然涉及多次最大池化操作，这可能会增加计算负担，尤其是在高分辨率图像上。
- 信息损失: 最大池化操作可能会导致一些空间细节的丢失，这可能会影响最终的检测精度。
运行后若打印出如上文本代表改进成功。
运行后打印如上代码说明改进成功。
更多文章产出中，主打简洁和准确，欢迎关注我，共同探讨！
![[XCUITest] 处理iOS权限点击授权 有哪些权限？](http://pic.xiahunao.cn/[XCUITest] 处理iOS权限点击授权 有哪些权限？)
