深浅模式
DBB
一、本文介绍
本文带来的改进机制是YOLOv8模型与多元分支模块 （Diverse Branch Block）的结合，Diverse Branch Block (DBB) 是一种用于增强卷积神经网络性能的结构重新参数化技术。这种技术的核心在于结合多样化的分支，这些分支具有不同的尺度和复杂度，从而丰富特征空间。我将其放在了YOLOv8的不同位置上均有一定的涨点幅度，同时这个DBB模块的参数量并不会上涨太多，我添加三个该机制到模型中，GFLOPs上涨了0.4。
二、Diverse Branch Block原理
论文地址：论文官方地址
代码地址：官方代码地址
2.1 Diverse Branch Block的基本原理
Diverse Branch Block（DBB）的基本原理是在训练阶段增加卷积层的复杂性，通过引入不同尺寸和结构的卷积分支来丰富网络的特征表示能力。我们可以将基本原理可以概括为以下几点：
1. 多样化分支结构：DBB 结合了不同尺度和复杂度的分支，如不同大小的卷积核和平均池化，以增加单个卷积的特征表达能力。
2. 训练与推理分离：在训练阶段，DBB 采用复杂的分支结构，而在推理阶段，这些分支可以被等效地转换为单个卷积层，以保持高效推理。
3. 宏观架构不变：DBB 允许在不改变整体网络架构的情况下，作为常规卷积层的替代品插入到现有网络中。
下面我将为大家展示Diverse Branch Block（DBB）的设计示例：
在训练时（左侧），DBB由不同大小的卷积层和平均池化层组成，这些层以一种复杂的方式并行排列，并最终合并输出。训练完成后，这些复杂的结构会转换成单个卷积层，用于模型的推理阶段（右侧），以此保持推理时的效率。这种转换允许DBB在保持宏观架构不变的同时，增加训练时的微观结构复杂性。
2.2 多样化分支结构
多样化分支结构是在卷积神经网络中引入的一种结构，旨在通过多样化的分支来增强模型的特征提取能力。这些分支包含不同尺寸的卷积层和池化层，以及其他潜在的操作，它们并行工作以捕获不同的特征表示。在训练完成后，这些复杂的结构可以合并并简化为单个的卷积层，以便在推理时不增加额外的计算负担。这种设计使得DBB可以作为现有卷积层的直接替换，增强了现有网络架构的性能，而不需要修改整体架构。
下面我详细展示了如何通过六种转换方法将训练时的Diverse Branch Block（DBB）转换为推理时的常规卷积层，每一种转换对应于一种特定的操作：
1. Transform I：将具有批量规范化（batch norm）的卷积层融合。
2. Transform II：合并具有相同配置的卷积层的输出。
3. Transform III：合并序列卷积层。
4. Transform IV：通过深度串联（concat）来合并卷积层。
5. Transform V：将平均池化（AVG）操作融入卷积操作中。
6. Transform VI：结合不同尺度的卷积层。
可以看到右侧的框显示了经过这些转换后，可以实现的推理时DBB，其中包含了常规卷积、平均池化和批量规范化操作。这些转换确保了在不增加推理时负担的同时，能够在训练时利用DBB的多样化特征提取能力。
2.3 训练与推理分离
训练与推理分离的概念是指在模型训练阶段使用复杂的DBB结构，而在模型推理阶段则转换为简化的卷积结构。这种设计允许模型在训练时利用DBB的多样性来增强特征提取和学习能力，而在实际应用中，即推理时，通过减少计算量来保持高效。这样，模型在保持高性能的同时，也保证了运行速度和资源效率。
上面我将展示在训练阶段如何通过不同的卷积组合（如图中的1x1和KxK卷积），以及在推理阶段如何将这些组合转换成一个简化的结构（如图中的转换IV所示的拼接操作）：
经过分析，我们可以发现它说明了三种不同的情况：
A）组卷积（Groupwise conv）：将输入分成多个组，每个组使用不同的卷积核。
B）训练时的1x1-KxK结构：首先应用1x1的卷积（减少特征维度），然后是分组的KxK卷积。
C）从转换IV的角度看：这是将多个分组的卷积输出合并的视角。这里，组内卷积后的特征图先分别通过1x1卷积处理，然后再进行拼接（concat）。
2.4 宏观架构不变
宏观架构不变指的是DBB在设计时考虑到了与现有的网络架构兼容性，确保可以在不改变整体网络架构（如ResNet等流行架构）的前提下，将DBB作为一个模块嵌入。这意味着DBB增强了网络的特征提取能力，同时保持了原有网络结构的布局，确保了推理时的效率和性能。这样的设计允许研究者和开发者将DBB直接应用到现有的深度学习模型中，而无需进行大规模的架构调整。
三、Diverse Branch Block的核心代码
python
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
__all__ = ['DiverseBranchBlock', 'C2f_DBB']
def autopad(k, p=None, d=1): # kernel, padding, dilation
"""Pad to 'same' shape outputs."""
if d > 1:
k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size
if p is None:
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
return p
class Conv(nn.Module):
"""Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)."""
default_act = nn.SiLU() # default activation
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
"""Initialize Conv layer with given arguments including activation."""
super().__init__()
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
self.bn = nn.BatchNorm2d(c2)
self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
def forward(self, x):
"""Apply convolution, batch normalization and activation to input tensor."""
return self.act(self.bn(self.conv(x)))
def forward_fuse(self, x):
"""Perform transposed convolution of 2D data."""
return self.act(self.conv(x))
def transI_fusebn(kernel, bn):
gamma = bn.weight
std = (bn.running_var + bn.eps).sqrt()
return kernel * ((gamma / std).reshape(-1, 1, 1, 1)), bn.bias - bn.running_mean * gamma / std
def transII_addbranch(kernels, biases):
return sum(kernels), sum(biases)
def transIII_1x1_kxk(k1, b1, k2, b2, groups):
if groups == 1:
k = F.conv2d(k2, k1.permute(1, 0, 2, 3)) #
b_hat = (k2 * b1.reshape(1, -1, 1, 1)).sum((1, 2, 3))
else:
k_slices = []
b_slices = []
k1_T = k1.permute(1, 0, 2, 3)
k1_group_width = k1.size(0) // groups
k2_group_width = k2.size(0) // groups
for g in range(groups):
k1_T_slice = k1_T[:, g * k1_group_width:(g + 1) * k1_group_width, :, :]
k2_slice = k2[g * k2_group_width:(g + 1) * k2_group_width, :, :, :]
k_slices.append(F.conv2d(k2_slice, k1_T_slice))
b_slices.append(
(k2_slice * b1[g * k1_group_width:(g + 1) * k1_group_width].reshape(1, -1, 1, 1)).sum((1, 2, 3)))
k, b_hat = transIV_depthconcat(k_slices, b_slices)
return k, b_hat + b2
def transIV_depthconcat(kernels, biases):
return torch.cat(kernels, dim=0), torch.cat(biases)
def transV_avg(channels, kernel_size, groups):
input_dim = channels // groups
k = torch.zeros((channels, input_dim, kernel_size, kernel_size))
k[np.arange(channels), np.tile(np.arange(input_dim), groups), :, :] = 1.0 / kernel_size ** 2
return k
# This has not been tested with non-square kernels (kernel.size(2) != kernel.size(3)) nor even-size kernels
def transVI_multiscale(kernel, target_kernel_size):
H_pixels_to_pad = (target_kernel_size - kernel.size(2)) // 2
W_pixels_to_pad = (target_kernel_size - kernel.size(3)) // 2
return F.pad(kernel, [H_pixels_to_pad, H_pixels_to_pad, W_pixels_to_pad, W_pixels_to_pad])
def conv_bn(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1,
padding_mode='zeros'):
conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, dilation=dilation, groups=groups,
bias=False, padding_mode=padding_mode)
bn_layer = nn.BatchNorm2d(num_features=out_channels, affine=True)
se = nn.Sequential()
se.add_module('conv', conv_layer)
se.add_module('bn', bn_layer)
return se
class IdentityBasedConv1x1(nn.Conv2d):
def __init__(self, channels, groups=1):
super(IdentityBasedConv1x1, self).__init__(in_channels=channels, out_channels=channels, kernel_size=1, stride=1,
padding=0, groups=groups, bias=False)
assert channels % groups == 0
input_dim = channels // groups
id_value = np.zeros((channels, input_dim, 1, 1))
for i in range(channels):
id_value[i, i % input_dim, 0, 0] = 1
self.id_tensor = torch.from_numpy(id_value).type_as(self.weight)
nn.init.zeros_(self.weight)
def forward(self, input):
kernel = self.weight + self.id_tensor.to(self.weight.device).type_as(self.weight)
result = F.conv2d(input, kernel, None, stride=1, padding=0, dilation=self.dilation, groups=self.groups)
return result
def get_actual_kernel(self):
return self.weight + self.id_tensor.to(self.weight.device)
class BNAndPadLayer(nn.Module):
def __init__(self,
pad_pixels,
num_features,
eps=1e-5,
momentum=0.1,
affine=True,
track_running_stats=True):
super(BNAndPadLayer, self).__init__()
self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine, track_running_stats)
self.pad_pixels = pad_pixels
def forward(self, input):
output = self.bn(input)
if self.pad_pixels > 0:
if self.bn.affine:
pad_values = self.bn.bias.detach() - self.bn.running_mean * self.bn.weight.detach() / torch.sqrt(
self.bn.running_var + self.bn.eps)
else:
pad_values = - self.bn.running_mean / torch.sqrt(self.bn.running_var + self.bn.eps)
output = F.pad(output, [self.pad_pixels] * 4)
pad_values = pad_values.view(1, -1, 1, 1)
output[:, :, 0:self.pad_pixels, :] = pad_values
output[:, :, -self.pad_pixels:, :] = pad_values
output[:, :, :, 0:self.pad_pixels] = pad_values
output[:, :, :, -self.pad_pixels:] = pad_values
return output
@property
def weight(self):
return self.bn.weight
@property
def bias(self):
return self.bn.bias
@property
def running_mean(self):
return self.bn.running_mean
@property
def running_var(self):
return self.bn.running_var
@property
def eps(self):
return self.bn.eps
class DiverseBranchBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size,
stride=1, padding=None, dilation=1, groups=1,
internal_channels_1x1_3x3=None,
deploy=False, single_init=False):
super(DiverseBranchBlock, self).__init__()
self.deploy = deploy
self.nonlinear = Conv.default_act
self.kernel_size = kernel_size
self.out_channels = out_channels
self.groups = groups
if padding is None:
padding = autopad(kernel_size, padding, dilation)
assert padding == kernel_size // 2
if deploy:
self.dbb_reparam = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=True)
else:
self.dbb_origin = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, dilation=dilation, groups=groups)
self.dbb_avg = nn.Sequential()
if groups < out_channels:
self.dbb_avg.add_module('conv',
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1,
stride=1, padding=0, groups=groups, bias=False))
self.dbb_avg.add_module('bn', BNAndPadLayer(pad_pixels=padding, num_features=out_channels))
self.dbb_avg.add_module('avg', nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=0))
self.dbb_1x1 = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride,
padding=0, groups=groups)
else:
self.dbb_avg.add_module('avg', nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=padding))
self.dbb_avg.add_module('avgbn', nn.BatchNorm2d(out_channels))
if internal_channels_1x1_3x3 is None:
internal_channels_1x1_3x3 = in_channels if groups < out_channels else 2 * in_channels # For mobilenet, it is better to have 2X internal channels
self.dbb_1x1_kxk = nn.Sequential()
if internal_channels_1x1_3x3 == in_channels:
self.dbb_1x1_kxk.add_module('idconv1', IdentityBasedConv1x1(channels=in_channels, groups=groups))
else:
self.dbb_1x1_kxk.add_module('conv1',
nn.Conv2d(in_channels=in_channels, out_channels=internal_channels_1x1_3x3,
kernel_size=1, stride=1, padding=0, groups=groups, bias=False))
self.dbb_1x1_kxk.add_module('bn1', BNAndPadLayer(pad_pixels=padding, num_features=internal_channels_1x1_3x3,
affine=True))
self.dbb_1x1_kxk.add_module('conv2',
nn.Conv2d(in_channels=internal_channels_1x1_3x3, out_channels=out_channels,
kernel_size=kernel_size, stride=stride, padding=0, groups=groups,
bias=False))
self.dbb_1x1_kxk.add_module('bn2', nn.BatchNorm2d(out_channels))
# The experiments reported in the paper used the default initialization of bn.weight (all as 1). But changing the initialization may be useful in some cases.
if single_init:
# Initialize the bn.weight of dbb_origin as 1 and others as 0. This is not the default setting.
self.single_init()
def get_equivalent_kernel_bias(self):
k_origin, b_origin = transI_fusebn(self.dbb_origin.conv.weight, self.dbb_origin.bn)
if hasattr(self, 'dbb_1x1'):
k_1x1, b_1x1 = transI_fusebn(self.dbb_1x1.conv.weight, self.dbb_1x1.bn)
k_1x1 = transVI_multiscale(k_1x1, self.kernel_size)
else:
k_1x1, b_1x1 = 0, 0
if hasattr(self.dbb_1x1_kxk, 'idconv1'):
k_1x1_kxk_first = self.dbb_1x1_kxk.idconv1.get_actual_kernel()
else:
k_1x1_kxk_first = self.dbb_1x1_kxk.conv1.weight
k_1x1_kxk_first, b_1x1_kxk_first = transI_fusebn(k_1x1_kxk_first, self.dbb_1x1_kxk.bn1)
k_1x1_kxk_second, b_1x1_kxk_second = transI_fusebn(self.dbb_1x1_kxk.conv2.weight, self.dbb_1x1_kxk.bn2)
k_1x1_kxk_merged, b_1x1_kxk_merged = transIII_1x1_kxk(k_1x1_kxk_first, b_1x1_kxk_first, k_1x1_kxk_second,
b_1x1_kxk_second, groups=self.groups)
k_avg = transV_avg(self.out_channels, self.kernel_size, self.groups)
k_1x1_avg_second, b_1x1_avg_second = transI_fusebn(k_avg.to(self.dbb_avg.avgbn.weight.device),
self.dbb_avg.avgbn)
if hasattr(self.dbb_avg, 'conv'):
k_1x1_avg_first, b_1x1_avg_first = transI_fusebn(self.dbb_avg.conv.weight, self.dbb_avg.bn)
k_1x1_avg_merged, b_1x1_avg_merged = transIII_1x1_kxk(k_1x1_avg_first, b_1x1_avg_first, k_1x1_avg_second,
b_1x1_avg_second, groups=self.groups)
else:
k_1x1_avg_merged, b_1x1_avg_merged = k_1x1_avg_second, b_1x1_avg_second
return transII_addbranch((k_origin, k_1x1, k_1x1_kxk_merged, k_1x1_avg_merged),
(b_origin, b_1x1, b_1x1_kxk_merged, b_1x1_avg_merged))
def switch_to_deploy(self):
if hasattr(self, 'dbb_reparam'):
return
kernel, bias = self.get_equivalent_kernel_bias()
self.dbb_reparam = nn.Conv2d(in_channels=self.dbb_origin.conv.in_channels,
out_channels=self.dbb_origin.conv.out_channels,
kernel_size=self.dbb_origin.conv.kernel_size, stride=self.dbb_origin.conv.stride,
padding=self.dbb_origin.conv.padding, dilation=self.dbb_origin.conv.dilation,
groups=self.dbb_origin.conv.groups, bias=True)
self.dbb_reparam.weight.data = kernel
self.dbb_reparam.bias.data = bias
for para in self.parameters():
para.detach_()
self.__delattr__('dbb_origin')
self.__delattr__('dbb_avg')
if hasattr(self, 'dbb_1x1'):
self.__delattr__('dbb_1x1')
self.__delattr__('dbb_1x1_kxk')
def forward(self, inputs):
if hasattr(self, 'dbb_reparam'):
return self.nonlinear(self.dbb_reparam(inputs))
out = self.dbb_origin(inputs)
if hasattr(self, 'dbb_1x1'):
out += self.dbb_1x1(inputs)
out += self.dbb_avg(inputs)
out += self.dbb_1x1_kxk(inputs)
return self.nonlinear(out)
def init_gamma(self, gamma_value):
if hasattr(self, "dbb_origin"):
torch.nn.init.constant_(self.dbb_origin.bn.weight, gamma_value)
if hasattr(self, "dbb_1x1"):
torch.nn.init.constant_(self.dbb_1x1.bn.weight, gamma_value)
if hasattr(self, "dbb_avg"):
torch.nn.init.constant_(self.dbb_avg.avgbn.weight, gamma_value)
if hasattr(self, "dbb_1x1_kxk"):
torch.nn.init.constant_(self.dbb_1x1_kxk.bn2.weight, gamma_value)
def single_init(self):
self.init_gamma(0.0)
if hasattr(self, "dbb_origin"):
torch.nn.init.constant_(self.dbb_origin.bn.weight, 1.0)
class Bottleneck_DBB(nn.Module):
"""Standard bottleneck."""
def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):
"""Initializes a bottleneck module with given input/output channels, shortcut option, group, kernels, and
expansion.
"""
super().__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, k[0], 1)
self.cv2 = DiverseBranchBlock(c_, c2, k[1], 1, g)
self.add = shortcut and c1 == c2
def forward(self, x):
"""'forward()' applies the YOLO FPN to input data."""
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
class C2f_DBB(nn.Module):
"""Faster Implementation of CSP Bottleneck with 2 convolutions."""
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):
"""Initialize CSP bottleneck layer with two convolutions with arguments ch_in, ch_out, number, shortcut, groups,
expansion.
"""
super().__init__()
self.c = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, 2 * self.c, 1, 1)
self.cv2 = Conv((2 + n) * self.c, c2, 1) # optional act=FReLU(c2)
self.m = nn.ModuleList(Bottleneck_DBB(self.c, self.c, shortcut, g, k=(3, 3), e=1.0) for _ in range(n))
def forward(self, x):
"""Forward pass through C2f layer."""
x = self.cv1(x)
x = x.chunk(2, 1)
y = list(x)
# y = list(self.cv1(x).chunk(2, 1))
y.extend(m(y[-1]) for m in self.m)
return self.cv2(torch.cat(y, 1))
def forward_split(self, x):
"""Forward pass using split() instead of chunk()."""
y = list(self.cv1(x).split((self.c, self.c), 1))
y.extend(m(y[-1]) for m in self.m)
return self.cv2(torch.cat(y, 1))四、手把手教你添加Diverse Branch Block机制
4.1 Diverse Branch Block的添加教程
这个添加方式和之前的变了一下，以后的添加方法都按照这个来了，是为了和群内的文件适配。
4.1.1 修改一
第一还是建立文件，我们找到如下ultralytics/nn/modules文件夹下建立一个目录名字呢就是'Addmodules'文件夹(用群内的文件的话已经有了无需新建)！然后在其内部建立一个新的py文件将核心代码复制粘贴进去即可。
4.1.2 修改二
第二步我们在该目录下创建一个新的py文件名字为'__init__.py'(用群内的文件的话已经有了无需新建)，然后在其内部导入我们的检测头如下图所示。
4.1.3 修改三
第三步我门中到如下文件'ultralytics/nn/tasks.py'进行导入和注册我们的模块(用群内的文件的话已经有了无需重新导入直接开始第四步即可)！
从今天开始以后的教程就都统一成这个样子了，因为我默认大家用了我群内的文件来进行修改！！
4.1.4 修改四
按照我的添加在parse_model里添加即可。
到此就修改完成了，大家可以复制下面的yaml文件运行。
4.2 Diverse Branch Block的yaml文件和训练截图
下面推荐几个版本的yaml文件给大家，大家可以复制进行训练，但是组合用很多具体那种最有效果都不一定，针对不同的数据集效果也不一样，我不可每一种都做实验，所以我下面推荐了三种我自己认为可能有效果的配合方式，你也可以自己进行组合。
4.2.1 Diverse Branch Block的yaml版本一(推荐)
下面的配置文件为我修改的C2f-DBB****的位置(我的对比实验是用这个版本跑出来的)。
python
# 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
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
# 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
# YOLOv8.0n 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_DBB, [256]] # 15 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f_DBB, [512]] # 18 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f_DBB, [1024]] # 21 (P5/32-large)
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)4.2.2 Diverse Branch Block的yaml版本二
添加的版本二具体那种适合你需要大家自己多做实验来尝试。
python
# 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
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
# 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
# YOLOv8.0n 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_DBB, [256]] # 15 (P3/8-small)
- [-1, 1, DiverseBranchBlock, [256, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f_DBB, [512]] # 18 (P4/16-medium)
- [-1, 1, DiverseBranchBlock, [512, 3, 2]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f_DBB, [1024]] # 21 (P5/32-large)
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)4.2.3Diverse Branch Block的yaml版本三
python
# 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
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
# YOLOv8.0n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
- [-1, 1, DiverseBranchBlock, [128, 3, 2]] # 1-P2/4
- [-1, 3, C2f, [128, True]]
- [-1, 1, DiverseBranchBlock, [256, 3, 2]] # 3-P3/8
- [-1, 6, C2f, [256, True]]
- [-1, 1, DiverseBranchBlock, [512, 3, 2]] # 5-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, DiverseBranchBlock, [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, 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)4.2.2 Diverse Branch Block的训练过程截图
下面是添加了Diverse Branch Block的训练截图。
五、Diverse Branch Block可添加的位置
5.1 推荐Diverse Branch Block可添加的位置
Diverse Branch Block是一种即插即用的可替换卷积的模块，其可以添加的位置有很多，添加的位置不同效果也不同，所以我下面推荐几个添加的位，置大家可以进行参考，当然不一定要按照我推荐的地方添加。
残差连接中：在残差网络的残差连接中加入Diverse Branch Block(yaml文件一)。
Neck部分：YOLOv8的Neck部分负责特征融合，这里添加修改后的C2f_DBB可以帮助模型更有效地融合不同层次的特征（yaml文件二）
Backbone：可以替换中干网络中的卷积部分(yaml文件三)。