💡💡💡本专栏所有程序均经过测试，可成功执行💡💡💡
本文给大家带来的教程是将YOLO26的Conv替换为FDConv来提取特征。文章在介绍主要的原理后，将手把手教学如何进行模块的代码添加和修改，并将修改后的完整代码放在文章的最后，方便大家一键运行，小白也可轻松上手实践。以帮助您更好地学习深度学习目标检测YOLO系列的挑战。
专栏地址：YOLO26改进-论文涨点------点击跳转看所有内容，关注不迷路！****
目录
[2. FDConv代码实现](#2. FDConv代码实现)
[2.1 将FDConv添加到YOLO26中](#2.1 将FDConv添加到YOLO26中)
[2.2 更改init.py文件](#2.2 更改init.py文件)
[2.3 添加yaml文件](#2.3 添加yaml文件)
[2.4 在task.py中进行注册](#2.4 在task.py中进行注册)
[2.5 执行程序](#2.5 执行程序)
[3. 完整代码分享](#3. 完整代码分享)
[4. GFLOPs](#4. GFLOPs)
[5. 进阶](#5. 进阶)
1.论文
论文地址： Frequency Dynamic Convolution for Dense Image Prediction
官方代码： 官方代码仓库点击即可跳转
2. FDConv代码实现
2.1 将FDConv添加到YOLO26中
**关键步骤一：**在ultralytics\ultralytics\nn\modules下面新建文件夹models，在文件夹下新建FDConv.py，粘贴下面代码
python
import torch
import torch.nn as nn
import torch.nn.functional as F
from ultralytics.nn.modules.conv import autopad
class StarReLU(nn.Module):
"""
StarReLU: s * relu(x) ** 2 + b
"""
def __init__(self, scale_value=1.0, bias_value=0.0,
scale_learnable=True, bias_learnable=True,
inplace=False):
super().__init__()
self.relu = nn.ReLU(inplace=inplace)
self.scale = nn.Parameter(torch.tensor(scale_value), requires_grad=scale_learnable)
self.bias = nn.Parameter(torch.tensor(bias_value), requires_grad=bias_learnable)
def forward(self, x):
return self.scale * self.relu(x).pow(2) + self.bias
def get_fft2freq(d1, d2, use_rfft=False):
freq_h = torch.fft.fftfreq(d1)
freq_w = torch.fft.rfftfreq(d2) if use_rfft else torch.fft.fftfreq(d2)
grid = torch.stack(torch.meshgrid(freq_h, freq_w), dim=-1)
dist = torch.norm(grid, dim=-1)
flat = dist.view(-1)
_, idx = torch.sort(flat)
if use_rfft:
d2_ = d2 // 2 + 1
else:
d2_ = d2
coords = torch.stack([idx // d2_, idx % d2_], dim=1)
return coords.t(), grid
class KernelSpatialModulation_Global(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size,
reduction=0.0625, kernel_num=4, min_channel=16,
temp=1.0, kernel_temp=None, att_multi=2.0,
ksm_only_kernel_att=False, stride=1,
spatial_freq_decompose=False, act_type='sigmoid'):
super().__init__()
attention_channel = max(int(in_planes * reduction), min_channel)
self.kernel_size = kernel_size
self.temperature = temp
self.kernel_temp = kernel_temp
self.att_multi = att_multi
self.ksm_only_kernel_att = ksm_only_kernel_att
self.act_type = act_type
self.fc = nn.Conv2d(in_planes, attention_channel, 1, bias=False)
self.bn = nn.BatchNorm2d(attention_channel)
self.relu = StarReLU()
# Channel
if ksm_only_kernel_att:
self.func_channel = lambda x: 1.0
else:
out_c = in_planes * 2 if spatial_freq_decompose and kernel_size>1 else in_planes
self.channel_fc = nn.Conv2d(attention_channel, out_c, 1)
self.func_channel = self._get_channel
# Filter
if in_planes==out_planes or ksm_only_kernel_att:
self.func_filter = lambda x: 1.0
else:
out_f = out_planes * 2 if spatial_freq_decompose and stride>1 else out_planes
self.filter_fc = nn.Conv2d(attention_channel, out_f, 1, stride=stride)
self.func_filter = self._get_filter
# Spatial
if kernel_size==1 or ksm_only_kernel_att:
self.func_spatial = lambda x: 1.0
else:
self.spatial_fc = nn.Conv2d(attention_channel, kernel_size*kernel_size,1)
self.func_spatial = self._get_spatial
# Kernel mixing
if kernel_num==1:
self.func_kernel = lambda x: 1.0
else:
self.kernel_fc = nn.Conv2d(attention_channel, kernel_num,1)
self.func_kernel = self._get_kernel
def _get_channel(self, x):
att = self.channel_fc(x).view(x.size(0),1,1,-1,1,1)
if self.act_type=='sigmoid': return torch.sigmoid(att/self.temperature)*self.att_multi
elif self.act_type=='tanh': return 1+torch.tanh(att/self.temperature)
raise
def _get_filter(self,x):
att = self.filter_fc(x).view(x.size(0),1,-1,1,1,1)
if self.act_type=='sigmoid': return torch.sigmoid(att/self.temperature)*self.att_multi
elif self.act_type=='tanh': return 1+torch.tanh(att/self.temperature)
raise
def _get_spatial(self,x):
att = self.spatial_fc(x).view(x.size(0),1,1,1,self.kernel_size,self.kernel_size)
if self.act_type=='sigmoid': return torch.sigmoid(att/self.temperature)*self.att_multi
elif self.act_type=='tanh': return 1+torch.tanh(att/self.temperature)
raise
def _get_kernel(self,x):
att = self.kernel_fc(x).view(x.size(0),-1,1,1,1,1)
return F.softmax(att/self.kernel_temp,dim=1)
def forward(self,x):
g = self.relu(self.bn(self.fc(x)))
return self.func_channel(g), self.func_filter(g), self.func_spatial(g), self.func_kernel(g)
class KernelSpatialModulation_Local(nn.Module):
def __init__(self, channel, kernel_num=1, out_n=1, k_size=3, use_global=False):
super().__init__()
self.kn, self.out_n, self.use_global = kernel_num, out_n, use_global
self.conv1d = nn.Conv1d(1, kernel_num*out_n, k_size, padding=(k_size-1)//2)
self.norm = nn.LayerNorm(channel)
if use_global:
self.complex_w = nn.Parameter(torch.randn(1,channel//2+1,2)*1e-6)
def forward(self,x):
y = x.mean(-1,keepdim=True).transpose(-1,-2)
if self.use_global:
fft = torch.fft.rfft(y.float(),dim=-1)
real = fft.real*self.complex_w[...,0]
imag = fft.imag*self.complex_w[...,1]
y = y+torch.fft.irfft(torch.complex(real,imag),dim=-1)
y = self.norm(y)
att = self.conv1d(y).reshape(y.size(0),self.kn,self.out_n,-1)
return att.permute(0,1,3,2)
class FrequencyBandModulation(nn.Module):
def __init__(self,in_channels,k_list=[2,4,8],lowfreq_att=False,fs_feat='feat',act='sigmoid',spatial='conv',spatial_group=1,spatial_kernel=3,init='zero'):
super().__init__()
self.k_list,self.lowfreq_att,self.act= k_list,lowfreq_att,act
# n_freq_groups used here
self.spatial_group = spatial_group
self.convs = nn.ModuleList()
for _ in range(len(k_list)+int(lowfreq_att)):
conv=nn.Conv2d(in_channels, self.spatial_group, spatial_kernel, padding=spatial_kernel//2, groups=self.spatial_group)
if init=='zero': nn.init.normal_(conv.weight, std=1e-6); nn.init.zeros_(conv.bias)
self.convs.append(conv)
def forward(self,x):
b,c,h,w=x.shape
x_fft=torch.fft.rfft2(x,norm='ortho')
out=0
for i,f in enumerate(self.k_list):
mask=torch.zeros_like(x_fft[:,:1])
coords,_=get_fft2freq(h,w,use_rfft=True)
mask[:,:,coords.max(-1)[0]<0.5/f]=1
low=torch.fft.irfft2(x_fft*mask,s=(h,w),norm='ortho')
high=x-low; x=low
w_att=self.convs[i](x)
w_att = (w_att.sigmoid()*2) if self.act=='sigmoid' else (1+w_att.tanh())
out+= (w_att.reshape(b,self.spatial_group,-1,h,w)*high.reshape(b,self.spatial_group,-1,h,w)).reshape(b,-1,h,w)
if self.lowfreq_att:
w_att=self.convs[-1](x)
w_att=(w_att.sigmoid()*2) if self.act=='sigmoid' else (1+w_att.tanh())
out+= (w_att.reshape(b,self.spatial_group,-1,h,w)*x.reshape(b,self.spatial_group,-1,h,w)).reshape(b,-1,h,w)
else:
out+=x
return out
class FDConv(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size,
stride=1, padding=None, dilation=1, groups=1, bias=True,
reduction=0.0625, kernel_num=4, n_freq_groups=1,
kernel_temp=1.0, temp=None, att_multi=2.0,
param_ratio=1, param_reduction=1.0,
ksm_only_kernel_att=False, spatial_freq_decompose=False,
use_ksm_local=True, ksm_local_act='sigmoid', ksm_global_act='sigmoid',
fbm_cfg=None):
padding = (kernel_size-1)//2 if padding is None else padding
super().__init__(in_channels, out_channels, kernel_size,
stride, padding, dilation, groups, bias)
self.kernel_num = kernel_num
self.att_multi = att_multi
self.param_ratio = param_ratio
self.param_reduction = param_reduction
self.n_freq_groups = n_freq_groups
# 全域調製模組
if temp is None:
temp = kernel_temp
self.KSMG = KernelSpatialModulation_Global(
in_planes=in_channels,
out_planes=out_channels,
kernel_size=kernel_size,
reduction=reduction,
kernel_num=kernel_num,
temp=temp,
kernel_temp=kernel_temp,
att_multi=att_multi,
ksm_only_kernel_att=ksm_only_kernel_att,
spatial_freq_decompose=spatial_freq_decompose,
act_type=ksm_global_act
)
# 局部調製模組
self.use_local = use_ksm_local
if use_ksm_local:
self.KSML = KernelSpatialModulation_Local(
channel=in_channels,
kernel_num=1,
out_n=out_channels * kernel_size * kernel_size
)
# 頻帶調製模組
if fbm_cfg is None:
fbm_cfg = {}
fbm_cfg['spatial_group'] = n_freq_groups
self.FBM = FrequencyBandModulation(in_channels, **fbm_cfg)
# 準備 DFT 權重
self._prepare_dft()
def _prepare_dft(self):
# 1) 計算 out×in 大矩陣的 DFT
d1, d2 = self.out_channels, self.in_channels
k = self.kernel_size[0]
flat = self.weight.permute(0,2,1,3).reshape(d1*k, d2*k)
freq = torch.fft.rfft2(flat, dim=(0,1))
w = torch.stack([freq.real, freq.imag], dim=-1) # shape = (d1*k, d2*k, 2)
# 2) 將它做成可訓練參數
# shape → (param_ratio, d1*k, d2*k, 2)
self.dft_weight = nn.Parameter(
w[None].repeat(self.param_ratio, 1, 1, 1) // (min(d1, d2)//2)
)
# **不要再刪除 self.weight**
# if self.bias is not None:
# self.bias = nn.Parameter(torch.zeros_like(self.bias))
def forward(self, x):
# fallback to plain conv if小channel 或 非1/3kernel
if min(self.in_channels, self.out_channels) <= 16 or self.kernel_size[0] not in [1,3]:
# 這裡 super().forward 會呼叫 nn.Conv2d.forward，必須有 self.weight
return super().forward(x)
b, _, h, w = x.shape
# 計算各種 attention map
ch, fl, sp, kn = self.KSMG(F.adaptive_avg_pool2d(x, 1))
hr = 1
if self.use_local:
local = self.KSML(F.adaptive_avg_pool2d(x, 1))
hr = (local.sigmoid() * self.att_multi) if self.KSML.use_global else (1 + local.tanh())
# 建 DFT map 並還原到 spatial domain
coords, _ = get_fft2freq(
self.out_channels * self.kernel_size[0],
self.in_channels * self.kernel_size[1],
use_rfft=True
)
DFTmap = torch.zeros(
(b, self.out_channels * self.kernel_size[0], self.in_channels * self.kernel_size[1]//2+1, 2),
device=x.device
)
for i in range(self.param_ratio):
w = self.dft_weight[i][coords[0], coords[1]][None]
DFTmap[...,0] += w[...,0] * kn[:, i]
DFTmap[...,1] += w[...,1] * kn[:, i]
adapt = torch.fft.irfft2(torch.view_as_complex(DFTmap), dim=(1,2))
adapt = adapt.reshape(
b, 1, self.out_channels, self.kernel_size[0],
self.in_channels, self.kernel_size[1]
).permute(0,1,2,4,3,5)
# 頻帶模組
x_fbm = self.FBM(x)
x_in = x_fbm if hasattr(self, 'FBM') else x
# 聚合所有 attention
agg = sp * ch * fl * adapt * hr
agg = agg.sum(1).view(
-1,
self.in_channels // self.groups,
self.kernel_size[0],
self.kernel_size[1]
)
# Depthwise batch conv trick
xcat = x_in.reshape(1, -1, h, w)
out = F.conv2d(
xcat, agg, None,
self.stride, self.padding,
self.dilation, self.groups * b
)
out = out.view(b, self.out_channels, out.size(-2), out.size(-1))
if self.bias is not None:
out += self.bias.view(1, -1, 1, 1)
return out
class FDConv_cfg(nn.Module):
"""Standard convolution Block with Batch Norm and Activation, using FDConv."""
default_act = nn.SiLU() # Default activation
def __init__(self, c1, c2, k=1, s=1, freq_groups=1, p=None, g=1, d=1, act=True):
"""
Args:
c1 (int): input channels
c2 (int): output channels
k (int): kernel size
s (int): stride
p (int or None): padding; if None uses autopad
g (int): groups
d (int): dilation
act (bool or nn.Module): activation to use (True→default_act, False→Identity, or custom)
"""
super().__init__()
padding = autopad(k, p, d)
# replace nn.Conv2d with FDConv
self.conv = FDConv(
in_channels=c1,
out_channels=c2,
kernel_size=k,
stride=s,
n_freq_groups=freq_groups,
padding=padding,
dilation=d,
groups=g,
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):
"""Conv → BatchNorm → Activation"""
x = self.conv(x)
x = self.bn(x)
return self.act(x)
def forward_fuse(self, x):
"""Conv only (for fused deployments) → Activation"""
return self.act(self.conv(x))
if __name__ == "__main__":
# Usage example:
fd = FDConv(in_channels=64, out_channels=64, kernel_size=3, n_freq_groups=64)
print(fd)
2.2 更改init.py文件
**关键步骤二：**在文件ultralytics\ultralytics\nn\modules\models文件夹下新建__init__.py文件，先导入函数
然后在下面的__all__中声明函数
2.3 添加yaml文件
**关键步骤三：**在/ultralytics/ultralytics/cfg/models/26下面新建文件yolo26_FDConv.yaml文件，粘贴下面的内容
- 目标检测
python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLO26 object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolo26
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
end2end: True # whether to use end-to-end mode
reg_max: 1 # DFL bins
scales: # model compound scaling constants, i.e. 'model=yolo26n.yaml' will call yolo26.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 260 layers, 2,572,280 parameters, 2,572,280 gradients, 6.1 GFLOPs
s: [0.50, 0.50, 1024] # summary: 260 layers, 10,009,784 parameters, 10,009,784 gradients, 22.8 GFLOPs
m: [0.50, 1.00, 512] # summary: 280 layers, 21,896,248 parameters, 21,896,248 gradients, 75.4 GFLOPs
l: [1.00, 1.00, 512] # summary: 392 layers, 26,299,704 parameters, 26,299,704 gradients, 93.8 GFLOPs
x: [1.00, 1.50, 512] # summary: 392 layers, 58,993,368 parameters, 58,993,368 gradients, 209.5 GFLOPs
# YOLO26n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, FDConv_cfg, [64, 3, 2, 3]] # 0-P1/2
- [-1, 1, FDConv_cfg, [128, 3, 2, 16]] # 1-P2/4
- [-1, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 2, C3k2, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 2, C3k2, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 2, C2PSA, [1024]] # 10
# YOLO26n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, C3k2, [512, False]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)- 语义分割
python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLO26 object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolo26
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
end2end: True # whether to use end-to-end mode
reg_max: 1 # DFL bins
scales: # model compound scaling constants, i.e. 'model=yolo26n.yaml' will call yolo26.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 260 layers, 2,572,280 parameters, 2,572,280 gradients, 6.1 GFLOPs
s: [0.50, 0.50, 1024] # summary: 260 layers, 10,009,784 parameters, 10,009,784 gradients, 22.8 GFLOPs
m: [0.50, 1.00, 512] # summary: 280 layers, 21,896,248 parameters, 21,896,248 gradients, 75.4 GFLOPs
l: [1.00, 1.00, 512] # summary: 392 layers, 26,299,704 parameters, 26,299,704 gradients, 93.8 GFLOPs
x: [1.00, 1.50, 512] # summary: 392 layers, 58,993,368 parameters, 58,993,368 gradients, 209.5 GFLOPs
# YOLO26n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, FDConv_cfg, [64, 3, 2, 3]] # 0-P1/2
- [-1, 1, FDConv_cfg, [128, 3, 2, 16]] # 1-P2/4
- [-1, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 2, C3k2, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 2, C3k2, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 2, C2PSA, [1024]] # 10
# YOLO26n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, C3k2, [512, False]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, Segment, [nc, 32, 256]]- 旋转目标检测
python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Ultralytics YOLO26 object detection model with P3/8 - P5/32 outputs
# Model docs: https://docs.ultralytics.com/models/yolo26
# Task docs: https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
end2end: True # whether to use end-to-end mode
reg_max: 1 # DFL bins
scales: # model compound scaling constants, i.e. 'model=yolo26n.yaml' will call yolo26.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 260 layers, 2,572,280 parameters, 2,572,280 gradients, 6.1 GFLOPs
s: [0.50, 0.50, 1024] # summary: 260 layers, 10,009,784 parameters, 10,009,784 gradients, 22.8 GFLOPs
m: [0.50, 1.00, 512] # summary: 280 layers, 21,896,248 parameters, 21,896,248 gradients, 75.4 GFLOPs
l: [1.00, 1.00, 512] # summary: 392 layers, 26,299,704 parameters, 26,299,704 gradients, 93.8 GFLOPs
x: [1.00, 1.50, 512] # summary: 392 layers, 58,993,368 parameters, 58,993,368 gradients, 209.5 GFLOPs
# YOLO26n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, FDConv_cfg, [64, 3, 2, 3]] # 0-P1/2
- [-1, 1, FDConv_cfg, [128, 3, 2, 16]] # 1-P2/4
- [-1, 2, C3k2, [256, False, 0.25]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 2, C3k2, [512, False, 0.25]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 2, C3k2, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 2, C3k2, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
- [-1, 2, C2PSA, [1024]] # 10
# YOLO26n head
head:
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 2, C3k2, [512, False]] # 13
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
- [[16, 19, 22], 1, OBB, [nc, 1]]温馨提示：本文只是对yolo26基础上添加模块，如果要对yolo26 n/l/m/x进行添加则只需要指定对应的depth_multiple 和 width_multiple
python
end2end: True # whether to use end-to-end mode
reg_max: 1 # DFL bins
scales: # model compound scaling constants, i.e. 'model=yolo26n.yaml' will call yolo26.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.50, 0.25, 1024] # summary: 260 layers, 2,572,280 parameters, 2,572,280 gradients, 6.1 GFLOPs
s: [0.50, 0.50, 1024] # summary: 260 layers, 10,009,784 parameters, 10,009,784 gradients, 22.8 GFLOPs
m: [0.50, 1.00, 512] # summary: 280 layers, 21,896,248 parameters, 21,896,248 gradients, 75.4 GFLOPs
l: [1.00, 1.00, 512] # summary: 392 layers, 26,299,704 parameters, 26,299,704 gradients, 93.8 GFLOPs
x: [1.00, 1.50, 512] # summary: 392 layers, 58,993,368 parameters, 58,993,368 gradients, 209.5 GFLOPs2.4 在task.py中进行注册
**关键步骤四：**在parse_model函数中进行注册，添加FDConv
先在task.py导入函数
然后在task.py文件下找到parse_model这个函数，如下图，添加FDConv
2.5 执行程序
关键步骤五: 在ultralytics文件中新建train.py，将model的参数路径设置为yolo26_FDConv .yaml的路径即可 【注意是在外边的Ultralytics下新建train.py】
python
from ultralytics import YOLO
import warnings
warnings.filterwarnings('ignore')
from pathlib import Path
if __name__ == '__main__':
# 加载模型
model = YOLO("ultralytics/cfg/26/yolo26.yaml") # 你要选择的模型yaml文件地址
# Use the model
results = model.train(data=r"你的数据集的yaml文件地址",
epochs=100, batch=16, imgsz=640, workers=4, name=Path(model.cfg).stem) # 训练模型🚀运行程序，如果出现下面的内容则说明添加成功🚀
python
from n params module arguments
0 -1 1 2242 ultralytics.nn.models.FDConv.FDConv_cfg [3, 16, 3, 2, 3]
1 -1 1 12463 ultralytics.nn.models.FDConv.FDConv_cfg [16, 32, 3, 2, 16]
2 -1 1 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25]
3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25]
5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False]
14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False]
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 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False]
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 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True]
23 [16, 19, 22] 1 309656 ultralytics.nn.modules.head.Detect [80, 1, True, [64, 128, 256]]
YOLO26_FDConv summary: 249 layers, 2,478,393 parameters, 2,478,393 gradients, 5.6 GFLOPs3. 完整代码分享
++主页侧边++
4. GFLOPs
关于GFLOPs的计算方式可以查看 ：百面算法工程师 | 卷积基础知识------Convolution
未改进的YOLO26n GFLOPs
改进后的GFLOPs
5. 进阶
可以与其他的注意力机制或者损失函数等结合，进一步提升检测效果
6.总结
通过以上的改进方法，我们成功提升了模型的表现。这只是一个开始，未来还有更多优化和技术深挖的空间。在这里，我想隆重向大家推荐我的专栏------<专栏地址： YOLO26改进-论文涨点------点击跳转看所有内容，关注不迷路！>。这个专栏专注于前沿的深度学习技术，特别是目标检测领域的最新进展，不仅包含对YOLO26的深入解析和改进策略，还会定期更新来自各大顶会（如CVPR、NeurIPS等）的论文复现和实战分享。
为什么订阅我的专栏？ ------专栏地址：YOLO26改进-论文涨点------点击跳转看所有内容，关注不迷路！****
-
前沿技术解读：专栏不仅限于YOLO系列的改进，还会涵盖各类主流与新兴网络的最新研究成果，帮助你紧跟技术潮流。
-
详尽的实践分享 ：所有内容实践性也极强。每次更新都会附带代码和具体的改进步骤，保证每位读者都能迅速上手。
-
问题互动与答疑 ：订阅我的专栏后，你将可以随时向我提问，获取及时的答疑。
-
实时更新，紧跟行业动态：不定期发布来自全球顶会的最新研究方向和复现实验报告，让你时刻走在技术前沿。
专栏适合人群：
-
对目标检测、YOLO系列网络有深厚兴趣的同学
-
希望在用YOLO算法写论文的同学
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对YOLO算法感兴趣的同学等
