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YOLOv11没有改进前P,R,MAP50,MAP50-95的结果分别如下：0.923，0.828，...

Créé le : 26 décembre 2025

Répondu en utilisant GPT-5.2 Pro par Chat01

Question

YOLOv11没有改进前P,R,MAP50,MAP50-95的结果分别如下：0.923，0.828，0.89，0.719，我现在的代码如下：class SPD(nn.Module):
def init(self, scale=2):
super().init()
assert scale >= 2 and int(scale) == scale
self.scale = int(scale)

text
def forward(self, x):
    b, c, h, w = x.shape
    s = self.scale

    # 若你训练时可能遇到非整除尺寸（不常见，但做个保护），这里右/下补齐到能整除
    pad_h = (s - h % s) % s
    pad_w = (s - w % s) % s
    if pad_h or pad_w:
        x = F.pad(x, (0, pad_w, 0, pad_h))  # (left,right,top,bottom)
        b, c, h, w = x.shape

    # [B,C,H,W] -> [B, C*s*s, H/s, W/s]
    x = x.view(b, c, h // s, s, w // s, s)
    x = x.permute(0, 1, 3, 5, 2, 4).contiguous()
    return x.view(b, c * s * s, h // s, w // s)

class EMA(nn.Module):
def init(self, channels, c2=None, factor=32):
super(EMA, self).init()
self.groups = factor
assert channels % factor == 0
assert channels // self.groups > 0
self.softmax = nn.Softmax(-1)
self.agp = nn.AdaptiveAvgPool2d((1, 1))
self.pool_h = nn.AdaptiveAvgPool2d((None, 1))
self.pool_w = nn.AdaptiveAvgPool2d((1, None))
self.gn = nn.GroupNorm(channels // self.groups, channels // self.groups)
self.conv1x1 = nn.Conv2d(channels // self.groups, channels // self.groups, kernel_size=1, stride=1, padding=0)
self.conv3x3 = nn.Conv2d(channels // self.groups, channels // self.groups, kernel_size=3, stride=1, padding=1)

text
def forward(self, x):
    b, c, h, w = x.size()
    group_x = x.reshape(b * self.groups, -1, h, w)  # b*g,c//g,h,w
    x_h = self.pool_h(group_x)
    x_w = self.pool_w(group_x).permute(0, 1, 3, 2)
    hw = self.conv1x1(torch.cat([x_h, x_w], dim=2))
    x_h, x_w = torch.split(hw, [h, w], dim=2)
    x1 = self.gn(group_x * x_h.sigmoid() * x_w.permute(0, 1, 3, 2).sigmoid())
    x2 = self.conv3x3(group_x)
    x11 = self.softmax(self.agp(x1).reshape(b * self.groups, -1, 1).permute(0, 2, 1))
    x12 = x2.reshape(b * self.groups, c // self.groups, -1)  # b*g, c//g, hw
    x21 = self.softmax(self.agp(x2).reshape(b * self.groups, -1, 1).permute(0, 2, 1))
    x22 = x1.reshape(b * self.groups, c // self.groups, -1)  # b*g, c//g, hw
    weights = (torch.matmul(x11, x12) + torch.matmul(x21, x22)).reshape(b * self.groups, 1, h, w)
    return (group_x * weights.sigmoid()).reshape(b, c, h, w)

class SPDConvLite(nn.Module):
"""
SPDConvLite + EMA
args 兼容你 YAML: [c2, k, s, ratio, ema_factor]
"""
def init(self, c1, c2, k=3, s=2, ratio=0.5, ema_factor=16, act=True):
super().init()
self.spd = SPD(scale=s) # 你已有的 SPD（或 space_to_depth/PixelUnshuffle）
cin = c1 * s * s
hidden = max(16, int(c2 * ratio))

text
    self.pw1 = Conv(cin, hidden, 1, 1, act=act)
    self.dw  = Conv(hidden, hidden, k, 1, g=hidden, act=act)
    self.pw2 = Conv(hidden, c2, 1, 1, act=act)

    # EMA 注意力：作用在输出通道 c2 上
    # 如果你的 EMA 定义是 EMA(channels, c2=None, factor=32)，这里传 channels=c2 即可
    self.attn = EMA(c2, factor=ema_factor)
    self.alpha=nn.Parameter(torch.zeros((1)))

def forward(self, x):
    x = self.spd(x)
    x = self.pw1(x)
    x = self.dw(x)
    y = self.pw2(x)
    y_attn = self.attn(y)
    return y+self.alpha*(y_attn-y)

class MSCA(nn.Module):
def init(self, c1, c2, reduction=8):
super().init()
assert c1 == c2, "MSCAPlus 目前假设输入输出通道相同"
dim = c2

text
    # 1. 多尺度空间分支
    # 分支0: 局部 5x5 深度卷积
    self.branch0 = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)

    # 分支1: 条带卷积 (1x7 -> 7x1)
    self.b1_1 = nn.Conv2d(dim, dim, (1, 7), padding=(0, 3), groups=dim)
    self.b1_2 = nn.Conv2d(dim, dim, (7, 1), padding=(3, 0), groups=dim)

    # 分支2: 3x3 空洞卷积, dilation=2 (中等感受野)
    self.branch2 = nn.Conv2d(dim, dim, 3, padding=2, dilation=2, groups=dim)

    # 分支3: 3x3 空洞卷积, dilation=3 (大感受野, 替代 21x21)
    self.branch3 = nn.Conv2d(dim, dim, 3, padding=3, dilation=3, groups=dim)

    # 2. 组卷积融合多尺度特征
    # 4 个分支 concat → 通道数 4*dim，groups=4 保证参数 ≈ 原来的 1x1 conv
    self.fuse = nn.Conv2d(4 * dim, dim, kernel_size=1, groups=4, bias=False)
    self.edge=nn.Conv2d(dim, dim, 3, padding=1,  groups=dim, bias=False)

    # 3. 通道注意力 (SE-like)
    hidden = max(dim // reduction, 4)
    self.avg_pool = nn.AdaptiveAvgPool2d(1)
    self.fc1 = nn.Conv2d(dim, hidden, kernel_size=1, bias=True)
    self.fc2 = nn.Conv2d(hidden, dim, kernel_size=1, bias=True)

    # 激活与门控
    self.act = nn.SiLU()
    self.sigmoid = nn.Sigmoid()

    self.gamma_add = nn.Parameter(torch.zeros(1))
    self.gamma_gate = nn.Parameter(torch.zeros(1))

def forward(self, x):
    identity = x

    # 多尺度空间分支
    b0 = self.branch0(x)

    b1 = self.b1_1(x)
    b1 = self.b1_2(b1)

    b2 = self.branch2(x)
    b3 = self.branch3(x)

    # concat + 融合
    ms = torch.cat([b0, b1, b2, b3], dim=1)   # [B, 4C, H, W]
    ms = self.fuse(ms)                        # [B, C, H, W]
    edge=self.edge(x)
    ms=ms+edge

    # 通道注意力
    ca = self.avg_pool(ms)                    # [B, C, 1, 1]
    ca = self.fc2(self.act(self.fc1(ca)))     # [B, C, 1, 1]
    ca = self.sigmoid(ca)

    attn = ms * ca                            # 空间 + 通道联合
    gate = self.sigmoid(attn)                 # [0,1]

    # 残差门控：避免特征被破坏性缩放
    out = identity + self.gamma_add*attn + self.gamma_gate*identity*gate
    return out

class EMA(nn.Module):
def init(self, channels, c2=None, factor=32):
super(EMA, self).init()
self.groups = factor
assert channels // self.groups > 0
self.softmax = nn.Softmax(-1)
self.agp = nn.AdaptiveAvgPool2d((1, 1))
self.pool_h = nn.AdaptiveAvgPool2d((None, 1))
self.pool_w = nn.AdaptiveAvgPool2d((1, None))
self.gn = nn.GroupNorm(channels // self.groups, channels // self.groups)
self.conv1x1 = nn.Conv2d(channels // self.groups, channels // self.groups, kernel_size=1, stride=1, padding=0)
self.conv3x3 = nn.Conv2d(channels // self.groups, channels // self.groups, kernel_size=3, stride=1, padding=1)

text
def forward(self, x):
    b, c, h, w = x.size()
    group_x = x.reshape(b * self.groups, -1, h, w)  # b*g,c//g,h,w
    x_h = self.pool_h(group_x)
    x_w = self.pool_w(group_x).permute(0, 1, 3, 2)
    hw = self.conv1x1(torch.cat([x_h, x_w], dim=2))
    x_h, x_w = torch.split(hw, [h, w], dim=2)
    x1 = self.gn(group_x * x_h.sigmoid() * x_w.permute(0, 1, 3, 2).sigmoid())
    x2 = self.conv3x3(group_x)
    x11 = self.softmax(self.agp(x1).reshape(b * self.groups, -1, 1).permute(0, 2, 1))
    x12 = x2.reshape(b * self.groups, c // self.groups, -1)  # b*g, c//g, hw
    x21 = self.softmax(self.agp(x2).reshape(b * self.groups, -1, 1).permute(0, 2, 1))
    x22 = x1.reshape(b * self.groups, c // self.groups, -1)  # b*g, c//g, hw
    weights = (torch.matmul(x11, x12) + torch.matmul(x21, x22)).reshape(b * self.groups, 1, h, w)
    return (group_x * weights.sigmoid()).reshape(b, c, h, w)我的配置文件如下：当我只增加MSCA模块时

nc: 5 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'

[depth, width, max_channels]

n: [0.50, 0.25, 1024] # summary: 181 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
s: [0.50, 0.50, 1024] # summary: 181 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 231 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
l: [1.00, 1.00, 512] # summary: 357 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
x: [1.00, 1.50, 512] # summary: 357 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs

YOLO11n 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, 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
  • [-1, 1, MSCA, [1024]]

YOLO11n 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, 14], 1, Concat, [1]] # cat head P4

  • [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)

  • [-1, 1, Conv, [512, 3, 2]]

  • [[-1, 11], 1, Concat, [1]] # cat head P5

  • [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)

  • [[17, 20, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)
    代码结果如下：0.935，0.866，0.914，0.725，我现在用SPD的配置文件：
    nc: 5 # number of classes
    scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'

[depth, width, max_channels]

n: [0.50, 0.25, 1024] # summary: 181 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
s: [0.50, 0.50, 1024] # summary: 181 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 231 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
l: [1.00, 1.00, 512] # summary: 357 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
x: [1.00, 1.50, 512] # summary: 357 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs

YOLO11n 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, 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

YOLO11n 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, SPDConvLite, [256, 3, 2]]

  • [[-1, 13], 1, Concat, [1]]

  • [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)

  • [-1, 1, SPDConvLite, [512, 3, 2]]

  • [[-1, 10], 1, Concat, [1]]

  • [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)

  • [[16, 19, 22], 1, Detect, [nc]]
    结果如下：0.959，0.865，0.91，0.725
    但是当我两者同时添加后配置文件如下：# Parameters
    nc: 5 # number of classes
    scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'

[depth, width, max_channels]

n: [0.50, 0.25, 1024] # summary: 181 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
s: [0.50, 0.50, 1024] # summary: 181 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 231 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
l: [1.00, 1.00, 512] # summary: 357 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
x: [1.00, 1.50, 512] # summary: 357 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs

YOLO11n 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, 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
  • [-1, 1, MSCA, [1024]]

YOLO11n 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, SPDConvLite, [256, 3, 2]]

  • [[-1, 14], 1, Concat, [1]] # cat head P4

  • [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)

  • [-1, 1, SPDConvLite, [512, 3, 2]]

  • [[-1, 11], 1, Concat, [1]] # cat head P5

  • [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)

  • [[17, 20, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)
    代码结果如下：0.909，0.864，0.896，0.719，我现在还想把我的检测头的改进点融入进去，我现在的检测头的改进配置文件如下：# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

Ultralytics YOLO11 object detection model with P3/8 - P5/32 outputs

Model docs: https://docs.ultralytics.com/models/yolo11

Task docs: https://docs.ultralytics.com/tasks/detect

Parameters

nc: 5 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'

[depth, width, max_channels]

n: [0.50, 0.25, 1024] # summary: 181 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
s: [0.50, 0.50, 1024] # summary: 181 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
m: [0.50, 1.00, 512] # summary: 231 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
l: [1.00, 1.00, 512] # summary: 357 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
x: [1.00, 1.50, 512] # summary: 357 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs

YOLO11n 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, 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

#head:

# -------- top-down FPN --------

  • [-1, 1, nn.Upsample, [None, 2, "nearest"]] # 11: P5 -> P4
  • [[-1, 6], 1, Concat, [1]] # 12: cat backbone P4 (layer 6)
  • [-1, 2, C3k2, [512, False]] # 13: P4_td

  • [-1, 1, nn.Upsample, [None, 2, "nearest"]] # 14: P4 -> P3
  • [[-1, 4], 1, Concat, [1]] # 15: cat backbone P3 (layer 4)
  • [-1, 2, C3k2, [256, False]] # 16: P3_td

# -------- add P2 detection layer (paper) --------

  • [-1, 1, nn.Upsample, [None, 2, "nearest"]] # 17: P3 -> P2
  • [[-1, 2], 1, Concat, [1]] # 18: cat backbone P2 (layer 2, stride=4)
  • [-1, 2, C3k2, [128, False]] # 19: P2_out (P2/4)

# -------- bottom-up PAN --------

  • [-1, 1, Conv, [256, 3, 2]] # 20: P2 -> P3
  • [[-1, 16], 1, Concat, [1]] # 21: cat P3_td
  • [-1, 2, C3k2, [256, False]] # 22: P3_out (P3/8)

  • [-1, 1, Conv, [512, 3, 2]] # 23: P3 -> P4
  • [[-1, 13], 1, Concat, [1]] # 24: cat P4_td
  • [-1, 2, C3k2, [512, False]] # 25: P4_out (P4/16)

  • [-1, 1, Conv, [1024, 3, 2]] # 26: P4 -> P5
  • [[-1, 10], 1, Concat, [1]] # 27: cat backbone P5 (layer 10)
  • [-1, 2, C3k2, [1024, True]] # 28: P5_out (P5/32)

# -------- Detect (4 heads) --------

  • [[19, 22, 25, 28], 1, DetectLSDECD, [nc, 16, 16]] # Detect(P2,P3,P4,P5)
    只增加检测头结果是0.951，0.888，0.927，0.781，现在单独使用每个模块都没问题，我同时使用MSCA、SPDConvLite更改检测头效果就变差，假如我必须选一个作为不改的，你认为我应该保留那个呢，去修正哪个呢，一定要分析这其中的原因，还是说应该更改其他模块呢，去改C3K2还是其他呢，为什么1+1<2了呢，模块相加还冲突导致检测效果差呢，我现在使用检测头+MSCA的结果如下：0.949，0.874，0.918，0.763，你现在告诉我根据目前的结果我如何改算法，具体给出每次的操作，尝试了SPDConvLite+检测头但是效果也不好，结果如下：0.916，0.838，0.873，0.726，下一步怎么办呢，怎么解决呢

Réflexion

Analyzing SPDConvLite integration

OK, let's see: SPDConvLite harmonizing with Detection Head is tricky, upgrading you to 0.916 precision and 0.838 recall. Testing different tweaks to hit that sweet spot.

Updosing YOLO layers

Correcting misreferenced layers in the head, ensuring accurate upsample, concat, and convolution operations for better alignment with backbone architecture. This adjustment sharpens fusion and performance.

Assessing feature integration

SPDConvLite combined with MSCA appears to disrupt feature concats due to altered dimensions and channels. Adjusting hyperparameters might be key to solving this integration issue.

Laying out the options

SPDConvLite clashes with the detection head due to spatial info reordering. Downsampling mishaps could lead to false positives, and the EMA attention module might further muddy things.

Assessing SPDConvLite synergy

Interestingly enough, SPDConvLite's integration with the detection head leads to negative synergy. This is likely due to over-parameterization or conflicting biases, resulting in precision and recall issues.

Refining integration

Focusing on the detection head's pivotal role, SPDConvLite's revision or replacement seems necessary. Analyzing module interactions will further align optimization, address feature mismatches, and reduce redundancy.

Weighing benefits

SPDConvLite may add noise for medium/large objects, especially with P2 detection head. Its advantages might be negligible if P2 is used, so careful assessment is essential.

Assessing SPDConvLite's impact

It's evident that incorporating SPDConvLite with the detection head results in weaker mAP and recall. It seems SPDConvLite's value diminishes when the detection head is already optimized.

Refining SPDConvLite

Increasing the hidden ratio or incorporating standard convolution instead of depthwise convolution with SPDConvLite could enhance capacity and minimize underfitting in more intricate detection heads.

Validating configurations

I’ll test each SPDConvLite combination with the detection head, considering different placements and evaluating stability over multiple seeds. Addressing duplicate EMA definitions and hidden parameter adjustments will ensure optimal performance.

Evaluating SPDConvLite's impact

I'm mapping out SPDConvLite's integration in early-stage backbone downsampling to enhance small object features while minimizing noise propagation. This approach could optimize overall performance.

Repositioning MSCA

Revisiting MSCA placement to P2 or P3 might enhance small object detection and head performance without causing negative synergy with the head's improvements.

Evaluating module synergy

It's interesting to see that SPDConvLite might disrupt the detection head's performance. So, I suggest focusing on the detection head, tweaking SPDConvLite, and adjusting MSCA's positioning for optimal results.

Ensuring seamless integration

To address SPDConvLite's interference, tweak gamma parameters, maintain a consistent training schedule, and confirm module compatibility and reduced noise.

Tweaking configurations

I’m piecing together ways to align MSCA and SPD settings, ensuring they boost head performance without causing conflicts or noise that could degrade results. Adjustments are being proposed methodically.

Balancing features

OK, let me see. I’m considering adding an extra convolution layer post-SPDConv to ensure semantic integrity and prevent feature collapse, enhancing the model's ability to process high-level semantics.

Evaluating MSCA's positioning

OK, let me see. Placing MSCA after C3k2 outputs in the neck rather than the backbone might refine features efficiently and address conflicts with SPDConvLite or P2 detection head.

Enhancing SPDConvLite's approach

I’m gathering info on adjusting SPDConvLite to streamline training and improve small object detection by tweaking resolutions and removing unneeded branches. Results will show progress and effects.

Navigating the choices

After analyzing performance metrics, it seems integrating the detection head with MSCA, while modifying SPDConvLite, yields the best results. SPDConvLite doesn't quite fit.

Aligning feature integration

I’m working on harmonizing MSCA with SPDConvLite outputs, considering thorough normalization or unified scaling post-concatenation to ensure seamless feature integration.

Taking a closer look

I'm mapping out a refined SPDConvLite design to boost small object detection by tweaking the hidden dimension ratio or computing it with c2. This should enhance output integration.

Adjusting the course

I’m honing the plan to improve feature integration by tweaking SPDConvLite design, aligning MSCA placement, and fine-tuning training settings for optimal outcomes. Progress is unseasonably steady.

Balancing parameters

I’m piecing together how SPDConvLite and MSCA combinations might strain memory and batch size, leading to performance drops. Keeping batch sizes steady or using methods like SyncBN or GroupNorm is crucial.

Shaping the strategy

I’m pulling together advice to keep the detection head, remix MSCA in the neck, tweak SPDConvLite design, and ensure consistent training hyperparameters for better feature alignment and reduced redundancy.

Ensuring accurate YAML references

I'm thinking through precise YAML indices for the neck MSCA integration to maintain feature alignment. It’s interesting to see how relative and negative indices facilitate this process.

Shifting indices

Updating detection head YAML indices to reflect MSCA integration, ensuring feature alignment post-insertion. This involves meticulously computing and adjusting relative indices for precise referencing.

Repositioning MSCA

I’m narrowing down MSCA's position, considering P5_out after layer28, and assessing a step-by-step plan to integrate or move MSCA and SPDConvLite for potential improvements.

Tweaking SPDConvLite

I’m adjusting SPDConvLite parameters like ratio and ema_factor to address burnout. Modifying YAML configurations to ensure seamless integration is crucial. Each step aims to refine these settings.

Evaluating detection gain

I'm assessing the significant impact on the detection head's performance and noting SPDConvLite's primary conflict in the network enhancements.

Adjusting parameters

I'm focusing on tweaking code and adjusting parameters for optimal results.

Piecing together integration

I’m thinking about combining modules by possibly reducing C2PSA or SPDConvLite with EMA, while keeping the detection head and exploring other modular changes.

Crafting SPDZero
I'm mapping out experiments to refine SPDConvLite by removing EMA attention and tweaking hidden ratios, ensuring minimal modifications while isolating SPD effects. This could lead to more precise models.

Mapping out integration paths
I'm considering SPDConv for downsample convs in the detection head, while examining MSCA's role in feature fusion. This sets the stage for exploring strategic modular setups.

Taking a closer look

I'm thinking about how SPDConvLite might disrupt feature hierarchy and cause semantic abstraction issues. Adjusting training settings might help, but this needs careful consideration. Let's dig into this together.

Reasoned for 13m 30s

Réponse

你现在这组实验，其实已经把“该保留谁、该修谁”基本跑出来了：
检测头（你加 P2 + DetectLSDECD 那套）是最强、最稳定的增益来源；SPDConvLite 是冲突源头；MSCA 是“单独有用，但和检测头叠加后对定位质量有轻微副作用”的模块。

下面我按你给的数字，先把结论讲清楚，再给你一个下一步每次该怎么改、改哪里、为什么这么改的可执行路线。

1）先用你自己的结果做“决策”：谁必须保留？谁必须修？

你现在的关键结果（按 mAP50-95 这个最能反映综合定位质量的指标看）：

  • 原始 YOLOv11：0.719
  • +MSCA（单独）：0.725（+0.006）
  • +SPDConvLite（单独）：0.725（+0.006）
  • MSCA+SPDConvLite：0.719（回到原点，甚至 P 掉很多）
  • 只改检测头（P2 + DetectLSDECD）：0.781（+0.062，巨大提升）
  • 检测头 + MSCA：0.763（比 0.781 低 -0.018，但仍远高于基线）
  • 检测头 + SPDConvLite：0.726（比 0.781 低 -0.055，基本把检测头增益废了）

因此：如果必须选一个“完全不改、必须保留”的，毫无疑问选你的检测头。
因为它给的增益量级远大于 MSCA / SPDConvLite，而且更稳定（单独使用就是最高）。

必须优先修正/重构的是 SPDConvLite（尤其是它用来替代 PAN/FPN 下采样 Conv 的方式）。
因为它一旦和“多尺度更密的检测头（P2）”叠加，直接把性能打回去。

MSCA 怎么办？

  • 它单独有效（0.725）。
  • 和检测头叠加后 mAP50-95 从 0.781 → 0.763（下降但还可以）。
    这说明 MSCA不是“不能用”，而是“放的位置/作用强度可能影响定位高 IoU”。

2）为什么会出现 1 + 1 < 2？（你现在这种就是典型的“结构增益冲突”）

你遇到的不是玄学，基本可以归因到下面几类“可验证的结构冲突”：

A. 下采样“相位/对齐方式”变化，破坏了多尺度特征融合

你的 SPD 是 PixelUnshuffle（Space-to-Depth）。
它不是像 stride=2 的 Conv 那样做“带卷积核的下采样”，而是把 2×2 的空间像素直接搬到通道维。

这会带来两个非常现实的问题：

  1. 特征对齐相位不同
  • stride=2 的 Conv（k=3,p=1）输出的每个点，来自输入 3×3 区域的“平滑组合”。
  • SPD 输出的每个点，对应输入严格的 2×2 块“重排”。
    两者在融合（Concat）时对齐假设不同，很容易让后面的 C3k2 学不到稳定的跨尺度对应关系。
  1. 低通滤波缺失导致“高频噪声”被带下去
    stride conv 本质上带一点低通效果（尤其 3×3）。SPD 是硬重排，不会抑制 aliasing。
    当你又引入 MSCA 的 edge 分支时（你 ms=ms+edge），高频边缘被进一步放大，最终就是：
  • 假阳性增加（Precision 下降）
    你 MSCA+SPD 时 P 从 0.959/0.935 级别直接掉到 0.909，就是这个味道。

B. 注意力/门控“叠加过度”，导致特征分布漂移（尤其影响定位质量）

  • MSCA：空间多尺度 + 通道注意力 + gate（sigmoid）+ 残差门控
  • SPDConvLite：又加 EMA 注意力（而且是组注意力）+ alpha 插值

当你把两者都叠上去，模型会出现：

  • 某些通道被过度抑制/过度强调
  • 分布漂移导致 Neck 的融合变得“更敏感、更不稳”
  • 常见表现就是 mAP50 还行，但 mAP50-95（高 IoU）掉：
    因为定位回归对特征的“平滑一致性”更敏感。

这也解释了你：

  • 检测头单独 0.781（很好）
  • +MSCA 后变 0.763（定位质量变差一点）
  • +SPDConvLite 后变 0.726（定位大幅变差）

C. 如果你在用预训练权重：权重迁移比例下降会放大冲突

这个我必须提醒你：Ultralytics 载入预训练很多时候是按 key 名字对齐的。
你插入层、替换模块，会导致大量 head/neck 权重不再匹配，等价于“头从头训练”。

  • 单独改一个模块时，尚可训练回来
  • 多个模块一起改时，初始化更随机，默认 epoch 不够就容易整体掉

你可以直接在训练日志里看类似： Transferred xxx/yyy items from pretrained weights 如果组合模型 transferred 数显著更低，那冲突会被放大。

3）你问“必须选一个不改”：我建议的保留与修正顺序

保留不改：检测头（P2 + DetectLSDECD）
理由：它给你带来的提升最大（mAP50-95 +0.062），而且结构上是“补充检测尺度”，属于强增益。

优先修正：SPDConvLite（尤其是“替换 stride conv 下采样”的用法）
理由：它和“更密集的多尺度头”天然容易产生对齐/aliasing/分布冲突；你实验也验证了它是主要负贡献。

MSCA：不是删，而是“换位置/减强度/改成更温和的形式”
理由：它与检测头叠加只掉了 0.018（还能接受），说明是“可以共存但要调”。

4）下一步怎么办：我给你一个“每次只动一个点”的可执行路线

你现在不要再“全都叠一起”；你要按 最可能修复冲突的方向做最少次数实验。

下面每一步我都写清：改哪里、怎么改、你期望看到什么、如果没提升下一步怎么走。

路线总原则

以“检测头方案（0.781）”作为主干 baseline。
因为它是当前最强结果。所有新尝试都围绕它做最小增量。

Step 1：把 SPDConvLite 里的 EMA 注意力先拿掉（先解决注意力叠加冲突）

你现在 SPDConvLite 本质上同时做了：

  • SPD downsample（改变对齐）
  • 深度可分离卷积（可）
  • EMA attention（又一套门控）

在“检测头 + SPDConvLite”里表现极差，我首先怀疑 EMA 与 DetectLSDECD + 多尺度融合叠加过强。

操作 1：做一个无注意力版 SPDConvLite（最小改动）

把 SPDConvLite 改成：

python
class SPDConvLite_NoAttn(nn.Module):
    def __init__(self, c1, c2, k=3, s=2, ratio=0.5, act=True):
        super().__init__()
        self.spd = SPD(scale=s)
        cin = c1 * s * s
        hidden = max(16, int(c2 * ratio))
        self.pw1 = Conv(cin, hidden, 1, 1, act=act)
        self.dw  = Conv(hidden, hidden, k, 1, g=hidden, act=act)
        self.pw2 = Conv(hidden, c2, 1, 1, act=act)

    def forward(self, x):
        x = self.spd(x)
        x = self.pw1(x)
        x = self.dw(x)
        return self.pw2(x)

YAML 里把 SPDConvLite 换成 SPDConvLite_NoAttn。

你期望看到的现象

  • 如果冲突主要来自注意力叠加：
    P 应该回升/稳定，mAP50-95 至少不再崩。

如果 Step1 仍然不好

说明主要问题不是 EMA，而是SPD 下采样对齐/aliasing本身 → 继续 Step2。

Step 2：不要用 SPD 去替换所有下采样，只替换“最该替换的那一层”（从 P2→P3 开始）

你的检测头多了 P2（stride=4），它最关心的是“小目标细节”。
SPD 保留细节的特性更适合放在浅层。

操作 2：只替换一层，下采样只改 “P2→P3” 那个 Conv

在你的检测头 YAML 里，原来 bottom-up PAN 有：

yaml
- [-1, 1, Conv, [256, 3, 2]]   # P2 -> P3

你只把这一条改成：

yaml
- [-1, 1, SPDConvLite_NoAttn, [256, 3, 2, 0.25]]  # ratio=0.25

注意：

  • ratio 调小到 0.25，避免 SPD 分支太重引起过拟合/分布震荡。
  • 其它 P3→P4、P4→P5 仍用原始 Conv（保持高层特征稳定）。

你期望看到的现象

  • 小目标召回（R）可能上升，mAP50-95 不要明显掉。
  • 如果 mAP50-95 还是掉，说明 SPD 的相位不适合你的 Neck 融合方式 → 继续 Step3。

Step 3：把 SPD 做成“可退化到原始 Conv”的双路下采样（最关键一步，解决 1+1<2 的结构冲突）

这是我最推荐你做的“工程化解决办法”。

你现在的问题是：
一旦你把 Conv 换成 SPD，下采样方式彻底变了，网络很难在多模块叠加时稳定收敛。

解决思路：
保留原来的 stride=2 Conv 作为主路（能加载预训练、对齐稳定），SPD 作为辅路，用一个可学习系数 β（初始为 0）慢慢加入。

操作 3：写一个 HybridDown（Conv 主路 + SPD 辅路，β=0 初始化）

python
class HybridDown(nn.Module):
    def __init__(self, c1, c2, k=3, s=2, ratio=0.25, act=True):
        super().__init__()
        assert s == 2, "当前建议先固定 s=2"
        self.conv = Conv(c1, c2, k, s, act=act)   # 主路：原始下采样（可迁移权重）
        self.spd = SPD(scale=s)
        cin = c1 * s * s
        hidden = max(16, int(c2 * ratio))
        self.pw1 = Conv(cin, hidden, 1, 1, act=act)
        self.dw  = Conv(hidden, hidden, 3, 1, g=hidden, act=act)
        self.pw2 = Conv(hidden, c2, 1, 1, act=act)
        self.beta = nn.Parameter(torch.zeros(1))  # 初始=0：一开始等价于原Conv

    def forward(self, x):
        y0 = self.conv(x)
        xs = self.spd(x)
        y1 = self.pw2(self.dw(self.pw1(xs)))
        return y0 + self.beta * y1

YAML 替换策略（仍然“一次只动一层”）

先把检测头里 P2→P3 的 Conv 换成 HybridDown：

yaml
- [-1, 1, HybridDown, [256, 3, 2, 0.25]]

跑完如果 OK，再考虑替换 P3→P4，最后才是 P4→P5。

你期望看到的现象

  • 不会再出现 SPDConvLite+检测头那种“直接崩掉”
    因为初始模型几乎等于原模型（β=0），训练过程中如果 SPD 有用，β 会自然变大；没用就保持接近 0，不会伤害主干。

这一步基本就是你要的“解决模块冲突”的最强手段。

Step 4：MSCA 不要放在 backbone 最后（P5），改放在 Neck 的 P3/P4（减少对高 IoU 定位的破坏）

你现在 “检测头+MSCA” mAP50-95 下降的特征很典型：
定位质量（高 IoU）下降，多发生在“高层语义特征被门控/边缘强化破坏平滑性”。

操作 4：MSCA 从 backbone P5 挪到 neck 的 P3_td 或 P4_td

举例：放在 P3_td（通常对小目标更友好）：

你的检测头里有：

yaml
- [-1, 2, C3k2, [256, False]]  # P3_td

改成：

yaml
- [-1, 2, C3k2, [256, False]]  
- [-1, 1, MSCA, [256]]

同时把 backbone 最后的 MSCA 删除（如果你之前加了的话）。

你期望看到的现象

  • mAP50-95 更可能回升（定位更稳）
  • P/R 不会大幅震荡

Step 5：如果你坚持要 MSCA + HybridDown + 检测头三者共存，顺序是这样叠

不要直接全上。按这个顺序最稳：

  1. 检测头（你当前最优 0.781）
    • MSCA(放 Neck 的 P3_td)
    • HybridDown 只替换 P2→P3
  4. 若还有收益，再替换 P3→P4（谨慎），最后才考虑 P4→P5

每加一步，只动一个点，看变化趋势。这样你能明确是谁在拉胯。

5）你现在“下一步我建议你立刻跑的 3 个实验”（最少次数，最大信息量）

你已经跑了很多组合了，接下来别再盲试。我建议你接下来只跑这 3 次：

实验 A（强烈建议先做）

检测头 + HybridDown(只替换 P2→P3)
目标：验证“SPD 冲突是否来自硬替换下采样”。

预期：显著优于你现在的 “检测头+SPDConvLite=0.726”，至少回到 0.76+，最好接近 0.78。

实验 B

检测头 + MSCA(移到 P3_td，不放 P5)
目标：验证 MSCA 的负作用是不是“放错位置导致定位变差”。

预期：mAP50-95 应该比你当前 “检测头+MSCA=0.763” 更高（有机会靠近 0.781）。

实验 C

检测头 + MSCA(P3_td) + HybridDown(只替换 P2→P3)
目标：最终验证三者是否可共存。

预期：如果 HybridDown 的 β 学到了正贡献，你可能在 0.78 基础上微增；
即使不增，也不该像 SPDConvLite 那样崩盘。

6）额外两个“非常容易忽略但很关键”的检查点

检查点 1：你代码里 EMA 类定义了两次

你贴的代码里 class EMA(nn.Module): 出现了两遍。
Python 里后面的会覆盖前面的。你最好保留一个，避免你以为在用 A，实际训练在用 B。

检查点 2：看训练日志的 transferred 比例（如果你用预训练）

如果你组合后 transferred 比例明显下降，那你需要：

  • 要么延长训练（更长 epoch）
  • 要么使用 HybridDown 这种“保留原 conv 主路”的结构来最大化迁移

这能显著减少“组合就掉点”的情况。

结论一句话

  • 保留不改：检测头（P2 + DetectLSDECD）
  • 优先修：SPDConvLite —— 不要硬替换下采样，改成 HybridDown（Conv 主路 + SPD 辅路，β=0）
  • MSCA：别放 backbone P5，挪到 neck 的 P3/P4，或者减弱 edge/gate 的影响

如果你愿意，我可以按你“检测头 YAML”的具体层号，把 HybridDown 替换哪一条、MSCA 插在哪两条 直接给你一份“可直接跑的最终 YAML”（不需要你自己对 index）。你只要告诉我：你现在检测头那份 YAML 训练时的 imgsz 和 pretrained 是否开启（用不用 yolo11n.pt）。

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