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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，...

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YOLOv11没有改进前P,R,MAP50,MAP50-95的结果分别如下：0.923，0.828，0.89，0.719，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，怎么解决呢

Gedanke

Analyzing feature interactions and attention mechanisms

I need to analyze several factors like interaction, training stability, and changes in speed distribution. SPD changes could conflict with MSCA's attention method, potentially distorting features. The addition of SPDConvLite and MSCA might impact detection, and issues with YAML indices could cause errors in layer references.

Revising layer configurations and indices

It looks like there’s a mismatch in layer numbers after modifications to the config. The combined config should have correct indices, but they might be off due to the changes in layers and concat indices. It seems the detection logic works if indices are counted properly, but the concat layers are inconsistent with SPD-only versions.

Troubleshooting MSCA integration and concat issues

After adding MSCA, the P4 layer index stays the same, and concatenating P4_td with the P3 downsample seems correct. However, there’s a performance drop when concatenating layer 11 (MSCA output) with [-1,11] as it leads to a large decrease in performance. It suggests a miswire on the concat behavior.

Evaluating challenges of combining modules

The potential synergy is negatively impacted by the increased complexity and risk of overfitting. Adding modules to a small dataset with limited classes may require adjustments, like more epochs or different learning rates. SPDConvLite and MSCA affect feature scales and may cause mismatch when concatenated, especially with EMA attention.

Considering which module to prioritize for maximum performance

The results suggest that keeping the improved detection head yields the biggest performance gains, especially for mAP50-95. SPDConvLite improved precision, while MSCA helped slightly with mAP50, but combined performance decreased. I recommend keeping the detection head and adjusting SPDConvLite or MSCA for better compatibility.

Analyzing combined module issues and proposing experiments

The modules aren't delivering independent improvements due to changes in feature distribution. Combining them may require hyperparameter adjustments (like learning rate and augmentation) and more epochs. Also, SPDConvLite disrupts alignment with backbone features, while MSCA might saturate features. I propose testing different adjustments, including adding alignment layers or modifying SPDConvLite.

Choosing between MSCA and SPDConvLite

I'm exploring whether to keep MSCA or SPDConvLite in the model. MSCA boosts mAP50, while SPD improves precision but not mAP. The detection head already improved, so SPD might be unnecessary. I'll likely keep MSCA since it complements the multi-scale head better, and rework SPDConvLite accordingly.

Analyzing capacity misalignment and optimization issues

The combined model seems to have some interplay between SPD and MSCA that leads to under-activation, reducing precision. Platform recall is similar for SPD and MSCA, but precision drops drastically when combined. The issue might be misaligned feature detection or incorrect concatenation indices. There could also be YAML indentation errors causing issues in the detection configuration.

Analyzing non-additivity and feature mismatch

The SPD configuration appears fine when using indices [16, 19, 22], but in the combined config, indices shift due to MSCA. The non-additivity seems to stem from optimization issues and feature distribution redundancy. To improve performance: I recommend keeping the detection head for larger mAP gains, and preferring MSCA over SPDConvLite. MSCA has better compatibility by not altering tensor shapes, unlike SPD, which changes downsampling and could cause mismatches across scales.

Fixing SPDConvLite and adjusting MSCA

To fix SPDConvLite: I recommend removing or reducing the EMA attention factor, adding 1x1 normalization after SPD, and using stride2 followed by a 3x3 Conv for better receptive fields. Alternatively, consider replacing SPDConvLite with a standard Conv method. For MSCA, I suggest placing it after P4 or adjusting smaller dilations at P3/P4, reducing edge addition, and applying stronger regularization if needed. Lastly, retune hyperparameters and anchors when combining with the detection head. Prioritize adding MSCA and SPDConvLite to the improved baseline.

Addressing SPDConvLite integration issues with MSCA

To enhance SPDConvLite with MSCA, either remove EMA or reduce its impact. MSCA placement should be at P4, not P5. For SPDConvLite, adjust its compression ratio or increase the hidden layer size to prevent feature loss. Ensure normalization consistency between EMA and other BN layers to avoid instability. Consider using GroupNorm with a lower number of groups or LayerNorm for improved stability. Also, optimize and tune hyperparameters, and balance training with potential overfitting remedies like freezing layers.

Thought for 1m 56s

Antwort

你这组消融其实已经把“问题在哪”暴露得很清楚了：单加 MSCA 有提升、单加 SPDConvLite 有提升、单加检测头（P2 + DetectLSDECD）提升最大，但三者一起反而退化。这不是玄学，基本属于典型的“模块叠加非独立、分布不匹配、优化难度上升”的组合冲突。

下面我按你问的几个点逐条给结论 + 原因 + 怎么改。

1）如果必须“保留一个不改”，我建议保留：你的检测头改进（P2 + DetectLSDECD）

理由很硬：

  • 你给的数据里，它带来的增益是最明显的：
    mAP50-95：0.719 → 0.781（+0.062）
    这是“质量提升”，不只是 P 或 R 的波动。
  • MSCA 和 SPDConvLite 单独提升都很小（0.725 左右徘徊），而检测头直接把整体拉上一个台阶。
  • 工程角度：检测头是“输出层级的表达能力 + 小目标层(P2)”的改变，通常对数据集更稳、更泛化。

结论：以“检测头改进版”当你的新 baseline，然后再考虑 MSCA / SPD 的兼容性改造。

2）MSCA vs SPDConvLite：更像“该修谁”？

如果你非要在 MSCA 和 SPDConvLite 里选一个“保留不动”，我更倾向：

✅ 保留 MSCA（更容易融进系统），优先去修 SPDConvLite

原因：

  • MSCA 不改张量几何结构（H/W 不变、通道不变），本质是一个残差注意力增强块；兼容性通常比“改变下采样方式”的模块更好。
  • SPDConvLite 改的是下采样算子（space-to-depth），它会改变：
    • 频谱/混叠特性（stride conv vs pixel-unshuffle 的差异）
    • 通道语义组织（2×2 子像素被搬到通道维，后续融合会“语义错位”）
    • 与旁路特征 concat 时的“统计分布/语义对齐”

你现在的冲突现象很像：SPD 路径产出的特征，和 MSCA 强化过的 P5 特征在 PAN/FPN 融合时“不在一个分布里”，导致融合后的特征“看起来很强但不稳定”，最后表现为：** P 掉得厉害、mAP50-95 回退**。

3）为什么会出现“1 + 1 < 2”？（你现在就是这种典型）

模块的提升不是线性可叠加的，尤其你这三类模块叠加时会出现三种“互相打架”的机制：

(A) 注意力/门控叠加 → 梯度和信息被反复“筛掉”

你现在至少有：

  • C2PSA（注意力/选择性聚合）
  • MSCA（sigmoid gate + 残差门控）
  • SPDConvLite 里还有 EMA（再来一次 sigmoid 权重）

多次 sigmoid/gate 叠加，很容易出现两种情况：

  • 早期训练 gate 进入饱和区（接近 0 或 1）→ 梯度变小
  • 信息被“过度筛选”→ recall/定位质量下降（mAP50-95 更敏感）

(B) 融合点的“分布不匹配”

你的 concat 里，常见是两路来源：

  • 一路来自 backbone（尤其 MSCA 后的 P5，带边缘/多尺度强化）
  • 一路来自 head 的下采样（SPDConvLite，通道语义是“子像素搬运”后的）

这两路的统计差异会让后面的 C3k2 很难同时兼顾，出现“谁也没融合好”的情况。

(C) 你 SPDConvLite 的瓶颈压缩很激进

以第一处 SPDConvLite 为例（典型 c1=256, s=2）：

  • SPD 后通道：cin = 256 * 4 = 1024
  • 你 hidden = int(c2 * ratio) = 256 * 0.5 = 128
    等于 1024 → 128 → 256，压得非常狠。

单独用它可能还能“靠别的层补回来”，但一旦你又叠了 MSCA + 新检测头（尤其 P2 增加了监督/梯度路径），这种信息瓶颈更容易成为系统短板。

4）怎么解决？（优先级从高到低，按你现状最可能有效的来）

方案 1：以“检测头改进版”为 baseline，只加 MSCA，不加 SPD（最稳）

你已经知道检测头很强；MSCA 单独也不坏。
很多数据集上这就能拿到最稳的收益。

方案 2：保留检测头 + MSCA，但**“弱化/改造 SPDConvLite”**（推荐你重点改这里）

给你几个非常具体、可直接动手的改法（你不需要全做，按顺序试）：

2.1 先把 SPDConvLite 的瓶颈放宽（最可能立刻止血）

把 ratio 从 0.5 提到 0.75 或 1.0，例如：

yaml
- [-1, 1, SPDConvLite, [256, 3, 2, 0.75, 16]]
...
- [-1, 1, SPDConvLite, [512, 3, 2, 0.75, 16]]

或干脆让 hidden ≥ c2（别让它太瘦）。

2.2 暂时移除 SPDConvLite 里的 EMA（只保留 SPD + DW/PW）

你现在系统里注意力太密了。
先把 SPDConvLite 里的 EMA 注释掉，看看三模块叠加是否立刻恢复提升——这一步能快速验证“注意力叠加导致的退化”是不是主因。

2.3 SPD 只用一处，不要两处都替换

比如只替换 P4→P5 那个下采样，P3→P4 仍用原 Conv stride2。
（低层特征更敏感，SPD 更容易造成融合语义错位。）

2.4 给 concat 后加一个“对齐层”（让两路分布先对齐再进 C3k2）

思路：Concat 后先 Conv(1x1) 或 Conv(3x3) 做一次轻量整形，再进 C3k2。
这对“SPD 分布 vs backbone 分布”很有效。

5）要不要去改 C3k2？

不建议你现在优先动 C3k2。 原因很简单：你现在的退化更像“融合/分布/门控叠加”的系统问题，不像 C3k2 表达不足。

你应该先把：

  • 注意力密度降低（至少别 3 套 gate 叠一起）
  • SPD 的语义/统计对齐（瓶颈、对齐层、只用一处）
  • 以检测头为 baseline 重新消融

做完后如果还有瓶颈，再考虑把 MSCA“下放”到 P4 或放进 C3k2 内部（例如只在某个 stage 的最后一个 block 加）。

6）我给你一个“最推荐的落地路线”（不需要问你更多信息）

  1. Baseline 换成：检测头改进版（你 0.781 的那套）
  2. 在这套上：只加 MSCA（先验证是否还能涨）
  3. 在“检测头 + MSCA”上，加 SPDConvLite，但按顺序做：
    • 先 ratio=0.75/1.0
    • 再 去掉 EMA
    • 再 SPD 只用一处
    • 再 Concat 后加对齐 Conv

做到其中任意一步，只要你看到 mAP50-95 开始恢复上升，就说明你抓到冲突点了。

如果你愿意把“三者一起使用时的完整 YAML（检测头那版 + MSCA + SPDConvLite）”贴出来（尤其是 DetectLSDECD 那一版你到底把 SPD 插在哪几层），我可以帮你把层索引、融合点、以及最小改动的对齐层位置一次性给你改成一份“更不容易打架”的结构。

Frage

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

Gedanke

Proposing experimental procedure in Chinese

I'll provide a step-by-step experimental procedure. First, ensure the same training settings and record seeds. Then, verify the baseline run, which yields 0.781 for detection head alone. Afterward, propose modifications to MSCA, including adjusting placement or strength. Consider testing MSCA earlier or modifying its weight. I'll also follow the requested "每次的操作" structure for the sequence.

Proposing MSCA conflict resolution steps

Next, for MSCA conflict, I'll first make it "lite" by disabling the edge branch, reducing dilation, and reducing the gate. I’ll try using small fixed gamma values or allow gradual adjustments with warmup. Then, I’ll test two experiments: replacing C2PSA and MSCA with only MSCA, or only C2PSA, to improve performance without degradation. Finally, I'll incorporate SPDConvLite after stabilizing the baseline, ensuring no EMA and applying a higher ratio at one stage.

Providing YAML-based experiment plan

Let's implement a step-by-step experiment plan with clear YAML snips:

Step 0: Copy baseline YAML to yolo11n_head.yaml.
Step 1: Remove MSCA and confirm performance (0.781).
Step 2: Swap C2PSA with MSCA (add MSCA after SPPF output 1024). Example YAML:

yaml
- [-1, 1, SPPF, [1024, 5]]    
- [-1, 1, MSCA, [1024]]    # remove C2PSA (optional: keep C2PSA?)

Step 3: If Step 2 improves results, keep.

Proposing SPDConvLite adjustments

For SPDConvLite, I'll start by replacing the Conv at layer 26 (P4->P5) with SPDConvLite. This should help since higher layers are less sensitive. The YAML for this step would be:

yaml
- [-1, 1, SPDConvLite, [1024, 3, 2, 0.75, 16]]

If this works, I'll try replacing layer 23 next. If conflicts arise, I'll add alignment by inserting a 1x1 Conv after concatenation at layers 24 and 27. Finally, I'll propose each operation within a step-by-step process.

Structuring algorithm change steps

I'll provide a concrete, concise step-by-step plan.

  • Step A: Establish baseline.
  • Step B: Modify MSCA placement (B1) replace C2PSA, (B2) move MSCA to P4, with exact YAML edits.
  • Step C: Gradually add SPDConvLite with use_attn parameter. Disable EMA by setting use_attn=False.
  • Step D: Add Conv alignment after concatenation with correct YAML.

Each step targets isolated changes, optimizing the algorithm sequentially without altering training parameters.

Thought for 34s

Antwort

你现在这个结果链路很关键：

  • 检测头改进版（P2 + DetectLSDECD）单独： mAP50-95 = 0.781
  • 检测头 + 你现在放在 P5 的 MSCA： mAP50-95 = 0.763（反而掉了）

所以接下来不要“继续堆模块”，而是按一次只改一处的方式，把 MSCA 从“冲突位置”挪开/减弱，再尝试把 SPD 以“低冲突形态”加进去。

下面给你一套每次具体操作（建议按顺序跑，别跳步；每次训练设置保持完全一致）。

实验 0（基线锁定）

操作： 用你目前“检测头改进版 YAML（P2 + DetectLSDECD）”，不要 MSCA、不要 SPDConvLite。
目的： 把 0.781 当成新 baseline（你后面每次改动都必须 ≥0.781 才算有效）。

实验 1（确认 MSCA 的“位置冲突”）

你现在 MSCA 放在 backbone 的最深层（P5/32，还是在 C2PSA 之后），这类“深层注意力 + 检测头增强（尤其加了 P2）”经常会互相抢表达/抢梯度，导致整体回退。

1A：把 MSCA 从 backbone P5 移走（先不删，挪到 neck）

操作（YAML改法）：

  1. backbone 里删掉这一行：
yaml
- [-1, 1, MSCA, [1024]]
  1. 在 neck 中间加 1 个 MSCA（建议先加在 P4_out 后面，比较稳）
    在你改进检测头那份里，P4_out 是：
yaml
- [-1, 2, C3k2, [512, False]]  # 25: P4_out

紧跟着加：

yaml
- [-1, 1, MSCA, [512]]

只做这一个改动去训练。

✅ 你要看的现象：

  • 如果 mAP50-95 回到接近 0.781 或超过，说明“MSCA 放在 P5 的位置”确实是主要冲突点。

实验 2（如果 1A 还不够，再把 MSCA 放到更浅一层）

2A：MSCA 改放在 P3_out 后面（对小目标更友好）

P3_out 在你的 head 里是：

yaml
- [-1, 2, C3k2, [256, False]]  # 22: P3_out

在它后面加：

yaml
- [-1, 1, MSCA, [256]]

注意：这一轮只做“位置改变”，不要同时保留 P4_out 的 MSCA。
也就是：要么加在 P4_out，要么加在 P3_out，先别两处都加。

实验 3（MSCA 仍然拖后腿的话：改成“替换 C2PSA”，别叠加）

你的 backbone 里原本就有 C2PSA（注意力类），你又叠一个 MSCA，很容易“门控过密”。

3A：用 MSCA 替换 C2PSA（只留一种注意力）

操作： 把 backbone 的

yaml
- [-1, 2, C2PSA, [1024]]

替换为

yaml
- [-1, 1, MSCA, [1024]]

（不要同时再加 MSCA 到 neck，保持“只动一处”）

✅ 这步的意义：验证“注意力叠加”是不是根因。

  • 如果这步比 0.781 更好：以后就走“MSCA 取代 C2PSA”路线
  • 如果更差：说明 MSCA 本身对你数据集/训练配置不占优，那就应当放弃 MSCA 或把它做轻量化（见实验 4）

实验 4（MSCA 轻量化：先去掉你 MSCA 里最容易造成干扰的部分）

你 MSCA 里这段：

  • edge = depthwise 3x3
  • gate = sigmoid(attn)
  • out = identity + gamma_add*attn + gamma_gate*identity*gate

在“检测头已经增强”的情况下，很可能出现过度门控。

4A：先禁用 gate 项（最常见止血）

操作（改代码）： 把这一行：

python
out = identity + self.gamma_add*attn + self.gamma_gate*identity*gate

改成：

python
out = identity + self.gamma_add*attn

先跑一轮。

（这一步只改 MSCA forward 的最后一行，别动别的）

实验 5（开始加 SPD，但用“低冲突版本”）

你之前 SPDConvLite 单独能涨，但一旦和别的注意力/增强叠加就容易掉。原因一般是两点：

  1. SPD 的信息重排 + concat 融合带来分布不对齐
  2. SPDConvLite 自带 EMA，注意力又叠了一层（你系统里已经很多 gate 了）

所以 SPD 要按“最保守方式”加：

5A：SPDConvLite 先“去掉 EMA”（只保留 SPD + PW/DW/PW）

操作（改代码）： 给 SPDConvLite 增加一个开关，比如 use_attn=False，或最简单直接：forward 里先把注意力关掉：

把：

python
y_attn = self.attn(y)
return y + self.alpha*(y_attn-y)

临时改成：

python
return y

（只做这一处，先验证“EMA 叠加导致退化”是不是主因）

5B：SPD 只替换一处下采样（先替换最深的那一次）

在你的检测头改进版里，下采样有三次：P2→P3、P3→P4、P4→P5。
第一轮只替换 P4→P5 那个（最不容易破坏小目标特征）。

把这行：

yaml
- [-1, 1, Conv, [1024, 3, 2]]   # 26: P4 -> P5

改成：

yaml
- [-1, 1, SPDConvLite, [1024, 3, 2, 0.75, 16]]

（ratio 从 0.5 提到 0.75，避免 1024→128 这种过强瓶颈）

实验 6（如果 5B 有提升，再加第二处；否则先做“对齐层”）

6A：如果 5B 提升了

再把 P3→P4 那个也替换：

yaml
- [-1, 1, Conv, [512, 3, 2]]

→

yaml
- [-1, 1, SPDConvLite, [512, 3, 2, 0.75, 16]]

6B：如果 5B 没提升或下降

不要继续替换第二处，先加“对齐层”再试（解决 concat 两路分布不一致）：

在每个 Concat 后面插一个 1×1 Conv（通道不变），例如：

yaml
- [[-1, 13], 1, Concat, [1]]     # 24
- [-1, 1, Conv, [512, 1, 1]]     # ★对齐层
- [-1, 2, C3k2, [512, False]]    # 25

同理在另一个 Concat 后也插。

你照这个流程跑，最后会得到一个清晰结论

  • MSCA 是“放错位置”还是“机制不适配你数据集”
  • SPD 的问题是“EMA叠加”还是“融合分布不对齐”
  • 最终你会得到一个组合：
    ✅（检测头改进） + ✅（MSCA 的正确位置/轻量版） + ✅（低冲突 SPD）

如果你想让我把“检测头改进版 YAML + 你要加的 MSCA/SPD”直接合成一份我认为最稳的最终结构（带好层号、插对齐层的位置都写好），你把你当前正在用的那份“检测头改进版 YAML（完整 head 部分）”原样贴我就能直接给你一份可跑的最终 YAML。

Frage

你说的方案1这个修改位置后，我的检测头需要改吗实验 1（确认 MSCA 的“位置冲突”）
你现在 MSCA 放在 backbone 的最深层（P5/32，还是在 C2PSA 之后），这类“深层注意力 + 检测头增强（尤其加了 P2）”经常会互相抢表达/抢梯度，导致整体回退。

1A：把 MSCA 从 backbone P5 移走（先不删，挪到 neck）
操作（YAML改法）：比如我的配置还保持下面的吗：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]] # 2
  • [-1, 1, Conv, [256, 3, 2]] # 3 P3/8
  • [-1, 2, C3k2, [512, False, 0.25]] # 4
  • [-1, 1, Conv, [512, 3, 2]] # 5 P4/16
  • [-1, 2, C3k2, [512, True]] # 6
  • [-1, 1, SPDConvLite, [1024, 3, 2]] # 7 P5/32
  • [-1, 2, C3k2, [1024, True]] # 8
  • [-1, 1, SPPF, [1024, 5]] # 9
  • [-1, 2, C2PSA, [1024]] # 10

Head (P2-P5, 4 heads)

head:

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

  • [-1, 1, nn.Upsample, [None, 2, "nearest"]] # 12: P5 -> P4

  • [[-1, 6], 1, Concat, [1]] # 13: cat backbone P4 (layer 6)

  • [-1, 2, C3k2, [512, False]] # 14: P4_td

  • [-1, 1, nn.Upsample, [None, 2, "nearest"]] # 15: P4 -> P3

  • [[-1, 4], 1, Concat, [1]] # 16: cat backbone P3 (layer 4)

  • [-1, 2, C3k2, [256, False]] # 17: P3_td

-------- add P2 detection layer --------

  • [-1, 1, nn.Upsample, [None, 2, "nearest"]] # 18: P3 -> P2
  • [[-1, 2], 1, Concat, [1]] # 19: cat backbone P2 (layer 2)
  • [-1, 2, C3k2, [128, False]] # 20: P2_out (P2/4)

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

  • [-1, 1, Conv, [256, 3, 2]] # 21: ✅ P2 -> P3 下采样(替换Conv)

  • [[-1, 17], 1, Concat, [1]] # 22: cat P3_td (layer 17)

  • [-1, 2, C3k2, [256, False]] # 23: P3_out (P3/8)

  • [-1, 1, Conv, [512, 3, 2]] # 24: ✅ P3 -> P4 下采样(替换Conv)

  • [[-1, 14], 1, Concat, [1]] # 25: cat P4_td (layer 14)

  • [-1, 2, C3k2, [512, False]] # 26: P4_out (P4/16)

  • [-1, 1, MSCA, [512]] # 11 ✅ 加入 MSCA (P5增强)

  • [-1, 1, Conv, [1024, 3, 2]] # 27: ✅ P4 -> P5 下采样(替换Conv)

  • [[-1, 11], 1, Concat, [1]] # 28: cat backbone P5 (layer 11, MSCA输出)

  • [-1, 2, C3k2, [1024, True]] # 29: P5_out (P5/32)

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

  • [[20, 23, 26, 29], 1, DetectLSDECD, [nc, 16, 16]]

Gedanke

Analyzing MSCA and YAML errors

I need to reply in Chinese regarding whether to change the detection head when moving MSCA from the backbone to the neck. I'll also address errors in the provided YAML: mismatched indices, incorrect feature map references, and mistakes in concatenation with incorrect layers. Specifically, they used SPDConvLite in the wrong layer and misreferenced the feature maps.

Clarifying experiment setup and YAML adjustments

For experiment 1A, MSCA should be moved from the backbone to the neck without affecting the detection head or other components. SPDConvLite is likely part of the backbone and should be excluded to avoid confounding the results. The YAML should be adjusted to move MSCA after P4_out and update layer indices as needed, with the final Concat citing the correct backbone layer (P5).

Clarifying experiment 1A setup and adjustments

For experiment 1A, stick to the baseline architecture: keep the original detection head and the backbone convolution (either stride-2 conv or SPDConvLite, based on the baseline). Do not include SPDConvLite for P5 if it wasn't in the original setup. Adjust only MSCA placement. Also, correct layer indices and 'from' references, using the proper numeric index post-modification.

Clarifying indexing and relative referencing

Due to auto-indexing, I recommend using relative referencing (e.g., [-1, ...]) for clarity. Explicitly reference backbone outputs with indices: P4 from layer 6, P3 from layer 4, P2 from layer 2, and P5 from layer 10. Adding new layers won’t affect backbone indices but may shift others. For P5 concat, use [-1, 10], not [-1, 11].

Clarifying MSCA placement and YAML corrections

In experiment 1A, MSCA should stay after P4_out as the config specifies, with the downsampling happening after MSCA output. There’s no need to change the detection head. For the YAML, remove MSCA from the backbone, adjust indices,

Thought for 27s

Antwort

不需要改你的检测头（P2 + DetectLSDECD 那套保持不动）。实验 1A 的目的只是验证“MSCA 放在 backbone 最深处（P5）是否和增强检测头冲突”，所以你应当做到：** 只动 MSCA 的位置，其它结构尽量保持和 baseline（0.781 那套检测头）一致**。

你贴的这份 YAML 里有两点我建议你立刻纠正/注意，否则实验结论会被“混变量”污染：

  1. 你在 backbone 里用了 SPDConvLite 做 P5 下采样（layer 7），这已经不是“只移动 MSCA”了，会干扰实验 1A 的判断。
  2. 你 head 里最后的 Concat 写成了 [[ -1, 11], ...]，但 backbone 的 P5 特征是 C2PSA 输出那层（你这里是第 10 层），不应该去 concat 你 head 里的 MSCA 层号。

✅ 实验 1A：把 MSCA 从 backbone 移到 neck（不改检测头，只改 MSCA）

下面给你一份**“只移动 MSCA”**的写法（同时我把 backbone 的 SPDConvLite 改回普通 Conv，避免混变量；如果你坚持要保留 SPDConvLite，那应该单独做另一个实验）。

Backbone（不放 MSCA；保持和检测头 baseline 一致）

yaml
backbone:
  - [-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]]      # 2
  - [-1, 1, Conv, [256, 3, 2]]             # 3  P3/8
  - [-1, 2, C3k2, [512, False, 0.25]]      # 4
  - [-1, 1, Conv, [512, 3, 2]]             # 5  P4/16
  - [-1, 2, C3k2, [512, True]]             # 6
  - [-1, 1, Conv, [1024, 3, 2]]            # 7  P5/32  ✅先别用SPDConvLite，避免混变量
  - [-1, 2, C3k2, [1024, True]]            # 8
  - [-1, 1, SPPF, [1024, 5]]               # 9
  - [-1, 2, C2PSA, [1024]]                 # 10  (backbone P5 输出)

Head（检测头结构不变；MSCA 放到 P4_out 后面）

你说的“挪到 neck”，我建议先放在 P4_out（PAN 融合后的 P4/16），最稳。

yaml
head:
  # -------- top-down FPN --------
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 6], 1, Concat, [1]]
  - [-1, 2, C3k2, [512, False]]                 # P4_td

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 4], 1, Concat, [1]]
  - [-1, 2, C3k2, [256, False]]                 # P3_td

  # -------- add P2 detection layer --------
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 2], 1, Concat, [1]]
  - [-1, 2, C3k2, [128, False]]                 # P2_out

  # -------- bottom-up PAN --------
  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, -6], 1, Concat, [1]]                  # cat P3_td（这里用相对索引避免你插层后层号错乱）
  - [-1, 2, C3k2, [256, False]]                 # P3_out

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, -9], 1, Concat, [1]]                  # cat P4_td
  - [-1, 2, C3k2, [512, False]]                 # P4_out

  - [-1, 1, MSCA, [512]]                        # ✅MSCA moved here（作用在P4_out）

  - [-1, 1, Conv, [1024, 3, 2]]                 # P4 -> P5
  - [[-1, 10], 1, Concat, [1]]                  # ✅一定是 concat backbone P5 (= layer 10)
  - [-1, 2, C3k2, [1024, True]]                 # P5_out

  - [[-12, -6, -3, -1], 1, DetectLSDECD, [nc, 16, 16]]

关键点：

  • 检测头（P2 层 + DetectLSDECD）不用改。
  • 你最后 cat 的 backbone P5 必须是 [10]（C2PSA 输出），不是你 head 里的 MSCA 层号。
  • 我在 head 的 Concat 里用了相对索引 -6/-9 这种写法，是为了防止你插入 MSCA 后层号整体偏移导致引用错层。

你接下来每次“操作”的标准（避免越改越乱）

  1. 一次只改一个点：本实验只移动 MSCA，SPDConvLite 先不要出现。
  2. 训练超参、数据增强、seed、epoch 全部保持一致。
  3. 记录四个指标（P/R/mAP50/mAP50-95）+ 最好再看一下各类别 AP，判断是不是小目标受损。

如果你希望我把你“现在真实在跑的检测头 baseline YAML（0.781 那套）”按你项目里实际层号完全对齐（不用相对索引、每个 from 都精准），你把那份 baseline YAML 原样贴出来（不要混入 SPD/MSCA），我就能直接给你一份“可直接复制跑、不会引用错层”的 1A 版本。

Frage

代码报错如下：/home/jd/anaconda3/envs/Yolov11/lib/python3.8/site-packages/timm/models/layers/init.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers
warnings.warn(f"Importing from {name} is deprecated, please import via timm.layers", FutureWarning)
/home/jd/anaconda3/envs/Yolov11/lib/python3.8/site-packages/timm/models/helpers.py:7: FutureWarning: Importing from timm.models.helpers is deprecated, please import via timm.models
warnings.warn(f"Importing from {name} is deprecated, please import via timm.models", FutureWarning)
Traceback (most recent call last):
File "train.py", line 18, in <module>
model = YOLO(model='ultralytics/cfg/models/11/yolo11n.yaml')
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/models/yolo/model.py", line 53, in init
super().init(model=model, task=task, verbose=verbose)
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/engine/model.py", line 146, in init
self._new(model, task=task, verbose=verbose)
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/engine/model.py", line 258, in _new
self.model = (model or self._smart_load("model"))(cfg_dict, verbose=verbose and RANK == -1) # build model
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/nn/tasks.py", line 364, in init
m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))]) # forward
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/nn/tasks.py", line 362, in _forward
return self.forward(x)[0] if isinstance(m, (Segment, YOLOESegment, Pose, OBB)) else self.forward(x)
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/nn/tasks.py", line 127, in forward
return self.predict(x, *args, **kwargs)
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/nn/tasks.py", line 145, in predict
return self._predict_once(x, profile, visualize, embed)
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/nn/tasks.py", line 166, in _predict_once
x = m(x) # run
File "/home/jd/anaconda3/envs/Yolov11/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/jd/anaconda3/envs/Yolov11/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/nn/modules/conv.py", line 797, in forward
return torch.cat(x, self.d)
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 8 but got size 16 for tensor number 1 in the list.
(Yolov11) jd@jd-x11dai-n:/media/jd/4997BB1603CFE2C4/lw/ultralytic$ python train.py
/home/jd/anaconda3/envs/Yolov11/lib/python3.8/site-packages/timm/models/layers/init.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers
warnings.warn(f"Importing from {name} is deprecated, please import via timm.layers", FutureWarning)
/home/jd/anaconda3/envs/Yolov11/lib/python3.8/site-packages/timm/models/helpers.py:7: FutureWarning: Importing from timm.models.helpers is deprecated, please import via timm.models
warnings.warn(f"Importing from {name} is deprecated, please import via timm.models", FutureWarning)
Traceback (most recent call last):
File "train.py", line 18, in <module>
model = YOLO(model='ultralytics/cfg/models/11/yolo11n.yaml')
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/models/yolo/model.py", line 53, in init
super().init(model=model, task=task, verbose=verbose)
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/engine/model.py", line 146, in init
self._new(model, task=task, verbose=verbose)
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/engine/model.py", line 258, in _new
self.model = (model or self._smart_load("model"))(cfg_dict, verbose=verbose and RANK == -1) # build model
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/nn/tasks.py", line 364, in init
m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))]) # forward
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/nn/tasks.py", line 362, in _forward
return self.forward(x)[0] if isinstance(m, (Segment, YOLOESegment, Pose, OBB)) else self.forward(x)
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/nn/tasks.py", line 127, in forward
return self.predict(x, *args, **kwargs)
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/nn/tasks.py", line 145, in predict
return self._predict_once(x, profile, visualize, embed)
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/nn/tasks.py", line 166, in _predict_once
x = m(x) # run
File "/home/jd/anaconda3/envs/Yolov11/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/jd/anaconda3/envs/Yolov11/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/media/jd/4997BB1603CFE2C4/lw/ultralytic/ultralytics/nn/modules/conv.py", line 797, in forward
return torch.cat(x, self.d)
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 16 but got size 32 for tensor number 1 in the list.
如何解决呢

Gedanke

Fixing index error in YAML for Concat layer

It seems like the issue stems from incorrect indexing when referencing layers in YAML. The "from" indices are absolute and shift as layers are added. It looks like the user used incorrect references for the Concat layer. To fix this, print the shapes during model construction or set verbosity to troubleshoot the mismatch at Concat.

Guiding Concat layer resolution match and index correction

To fix the issue, ensure that each Concat layer's inputs have the same resolution. For example:

  • After upsample P5->P4, concat with backbone layer 6 (P4) is correct.
  • Similarly for other layers (P4->P3, P3->P2). In the bottom-up process, verify that conv stride and indices are accurate. Check if layer 17 is indeed the correct one. Always ensure consistent resolutions when references are adjusted.

Fixing layer index shifting and Concat references in YAML

After adding upsampling layers in the head, the layer numbers shift, which is likely the cause of the issue. Instead of using absolute indices, it's better to use relative references for backbone layers (like 6, 4, or 2) and negative indices for head layers. Also, I'll map out the corrected YAML and ensure the absolute indices are aligned from the start to prevent mismatches.

Resolving Concat layer reference errors and numbering layers

The Concat issue may come from incorrect references, especially for P5 and layer 6, due to the SPDConvLite in the backbone. In the bottom-up approach, the indices like [-1, -6] might also be wrong due to insufficient layers. I suggest using absolute indices to avoid these mismatches. To debug, use yolo model=... verbose=True and examine layer shapes using model.info() to see where the problem lies.

Fixing Concat layer mismatches and index errors

In the detection head YAML, by keeping Concat layer connections as originally planned, everything should align. The user’s issue likely arises from incorrect indexing after adding layers like MSCA. Ensure Concat layers use the absolute correct references (e.g., [[-1, 10]]). The mismatched sizes suggest an issue with padding between layers; these should be adjusted to avoid stride mismatches. Consider removing any conflicting layers like SPDConvLite or ensure they don’t pad differently than other layers.

Addressing Concatenation Mismatch in FPN

The issue seems to stem from mismatched feature map sizes when concatenating in the FPN. SPD might pad at certain layers, but since P5 uses a stride of 256 to yield a feature map size of 8, it’s unlikely padding is to blame. The error suggests an upsample issue in the concatenation; the output might be referencing the wrong layer. Concat of upsampled P5 (16) should align with backbone P4 (16).

Troubleshooting Concat Mismatch in YAML

It seems the issue may arise from a missing upsample or indentation errors in the YAML. The error is within the Concat module, likely caused by inconsistencies in indices. I suggest debugging using verbose mode to identify which specific Concat layer fails. The steps could be: 1. Check YAML indentation; 2. Use absolute indices; 3. Print shapes in Concat to locate the problem.

Fixing Concat Layer Mismatch in YAML

The issue with their Concat at [[ -1, 11], 1, Concat, [1]] is that it should refer to the backbone's P5 (stride 32). If they want to use MS output, the stride needs to be adjusted. I suggest replacing the Concat layer to [[ -1, 10], 1, Concat, [1]] and updating the detection layer indices accordingly.

Correcting Concat Layer Mismatch and Indices

The issue comes from mis-indexed Concat layers, particularly with layer references like 11 and 14. Layers need to correctly sequence from P2_out to P4_out, with features drawn after MSCA (layer 26) for enhanced detection. The YAML must avoid handwritten numbers; instead, use proper indices for clarity, ensuring consistent stride matches.

Thought for 1m 11s

Antwort

这个报错不是 timm 的 warning（那两个 FutureWarning 可以先忽略），真正的错误是：

RuntimeError: Sizes of tensors must match except in dimension 1 ... torch.cat

也就是 Concat 的两个输入特征图 H/W 不一样（你看到的 8 vs 16、16 vs 32，都是差 2 倍），说明你在 YAML 里某个 Concat 把 不同尺度（不同 stride） 的层拼到了一起了——几乎都是 from 索引写错 导致的。

一、你这份 YAML 里“必错”的地方（就是它触发的 8↔16 / 16↔32）

你 head 里这段：

yaml
- [-1, 1, MSCA, [512]]                  # 11  ✅ 加入 MSCA (P5增强)

- [-1, 1, Conv, [1024, 3, 2]]           # 27: P4 -> P5 下采样
- [[-1, 11], 1, Concat, [1]]            # 28: cat backbone P5 (layer 11, MSCA输出)

这里 -1 是 P5（stride=32，特征图更小），而你拿去拼的 11 是 MSCA 输出（它在 P4_out 上，stride=16，更大）。

所以就会出现：

  • 256 输入时：P5 是 8×8，P4 是 16×16 → 8 vs 16 报错
  • 512 输入时：P5 是 16×16，P4 是 32×32 → 16 vs 32 报错

✅ 修复原则：P5_out 的 concat 必须 concat stride=32 的 backbone P5（也就是你的 layer 10：C2PSA 输出），不能 concat stride=16 的 MSCA 输出。

二、你按下面这几步改，立刻能跑通（不改检测头结构，只修索引）

操作 1：把 P5 那个 Concat 改成 concat backbone 的 P5（layer 10）

把这行：

yaml
- [[-1, 11], 1, Concat, [1]]

改成：

yaml
- [[-1, 10], 1, Concat, [1]]   # ✅ concat backbone P5（C2PSA输出，stride=32）

10 是你 backbone 的最后一层：- [-1, 2, C2PSA, [1024]]
这层输出就是 P5/32。

操作 2：Detect 的输入层号也要跟着对齐（否则虽然能 build，检测头可能接错层）

按你这份 head 的逻辑，正确的 4 个输出应该是：

  • P2_out：C3k2 [128] 那层（你注释里是 20）
  • P3_out：C3k2 [256] 那层（你注释里是 23）
  • P4_out（建议用 MSCA 后）：MSCA 那层（你注释里是 11，但实际层号会变化）
  • P5_out：最后 C3k2 [1024] 那层（你注释里是 29）

为了不被“你写的注释层号”误导，我给你一份完全按实际顺序重排后的 head（可直接替换 head 整段），并且把 Detect 输入写死正确：

yaml
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]   # 11
  - [[-1, 6], 1, Concat, [1]]                    # 12
  - [-1, 2, C3k2, [512, False]]                  # 13  P4_td

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]   # 14
  - [[-1, 4], 1, Concat, [1]]                    # 15
  - [-1, 2, C3k2, [256, False]]                  # 16  P3_td

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]   # 17
  - [[-1, 2], 1, Concat, [1]]                    # 18
  - [-1, 2, C3k2, [128, False]]                  # 19  P2_out

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

  - [-1, 1, Conv, [512, 3, 2]]                   # 23  P3->P4
  - [[-1, 13], 1, Concat, [1]]                   # 24  cat P4_td（注意是13）
  - [-1, 2, C3k2, [512, False]]                  # 25  P4_out
  - [-1, 1, MSCA, [512]]                         # 26  P4_out_msca（stride=16）

  - [-1, 1, Conv, [1024, 3, 2]]                  # 27  P4->P5
  - [[-1, 10], 1, Concat, [1]]                   # 28  ✅ cat backbone P5（layer10, stride=32）
  - [-1, 2, C3k2, [1024, True]]                  # 29  P5_out

  - [[19, 22, 26, 29], 1, DetectLSDECD, [nc, 16, 16]]

✅ 这样能保证每个 Concat 都是同尺度（stride 对齐），Detect 也吃到正确的 4 个层。

三、你现在这版 backbone 里还有 SPDConvLite：先别在“验证 MSCA 位置”实验里用

你贴的 backbone 第 7 层是：

yaml
- [-1, 1, SPDConvLite, [1024, 3, 2]]  # 7  P5/32

这会让你的“实验 1A（只移动 MSCA）”变成“移动 MSCA + 改下采样”，实验结论会被污染。

建议：

  • 如果你的目标是“先验证 MSCA 放 neck 是否更好”，那这一层先改回：
yaml
- [-1, 1, Conv, [1024, 3, 2]]
  • SPDConvLite 留到后面的 SPD 专门实验再做。

四、你怎么快速定位以后到底是哪一个 Concat 出错（避免反复猜）

把模型构建改成 verbose：

python
model = YOLO(model='ultralytics/cfg/models/11/yolo11n.yaml', verbose=True)

它会把每层的 from/shape 打出来，你就能一眼看到是哪个 Concat 拼了 8 和 16。

如果你把你“当前正在用、报错的完整 YAML 文件”原样贴出来（不要只贴片段），我可以帮你把 所有 from 索引一次性校正成“不会错层号”的版本（包括你后面要加 SPD 的那套）。

Frage

现在的结果如下：0.968，0.85，0.909，0.763，下一步怎么办，每次给出完整的修改后的代码或者配置文件

Gedanke

Planning next steps for improving experiment

The user provided new results, and the next step is to improve the configuration based on current metrics (P=0.968, R=0.85, mAP50=0.909, mAP50-95=0.763). Next, I'll test moving MSCA to P3_out or replacing C2PSA, providing the full modified head config for each experiment.

Outlining next steps for experiment and configurations

I'll propose keeping the detection head as the baseline. First, remove MSCA to verify the current baseline. Then, experiment steps are: move MSCA to P3_out, replace C2PSA with MSCA, and modify MSCA gating. I'll give a full YAML for experiment 2A and include notes.

Providing next step plan and config

They asked for a complete, step-by-step plan, along with the full modified config. For Experiment 2A, I'll provide both the baseline and the modified YAML config, including parameters, backbone, and head. I'll ensure the MSCA is placed after P3_out and explain the layer indices explicitly.

Adjusting layer numbering and head configuration

The layer numbering will shift once we downsample from P3 to P4. The Concat P4_td remains at layer 13. However, the earlier P4_out now becomes layer 26. I'll recalculate the indices and adjust the head configuration accordingly, ensuring that all references are explicit and accurate.

Thought for 23s

Antwort

你现在这套（检测头 + MSCA@P4_out）mAP50-95 = 0.763，仍然 低于你的检测头基线 0.781，说明：** MSCA 目前这个放置点（P4_out）对你这套 4-head 检测头是负作用**。

下一步（只改一处，继续验证“MSCA 位置冲突”）

把 MSCA 从 P4_out 移到 P3_out（更贴近小目标/细粒度特征），其它全部不动。下面给你一份可直接替换的完整 YAML（不包含 SPDConvLite，避免混变量；只移动 MSCA）：

✅ yolo11n_p2head_msca_p3.yaml（完整文件）

yaml
# Parameters
nc: 5
scales:
  n: [0.50, 0.25, 1024]
  s: [0.50, 0.50, 1024]
  m: [0.50, 1.00, 512]
  l: [1.00, 1.00, 512]
  x: [1.00, 1.50, 512]

# Backbone (保持原始下采样Conv，先别引入SPDConvLite，避免混变量)
backbone:
  - [-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]]      # 2
  - [-1, 1, Conv, [256, 3, 2]]             # 3  P3/8
  - [-1, 2, C3k2, [512, False, 0.25]]      # 4
  - [-1, 1, Conv, [512, 3, 2]]             # 5  P4/16
  - [-1, 2, C3k2, [512, True]]             # 6
  - [-1, 1, Conv, [1024, 3, 2]]            # 7  P5/32
  - [-1, 2, C3k2, [1024, True]]            # 8
  - [-1, 1, SPPF, [1024, 5]]               # 9
  - [-1, 2, C2PSA, [1024]]                 # 10 backbone P5输出

# Head (P2-P5, 4 heads) —— 只把MSCA放到P3_out后面
head:
  # -------- top-down FPN --------
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]   # 11: P5 -> P4
  - [[-1, 6], 1, Concat, [1]]                    # 12: cat backbone P4
  - [-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
  - [-1, 2, C3k2, [256, False]]                  # 16: P3_td

  # -------- add P2 detection layer --------
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]   # 17: P3 -> P2
  - [[-1, 2], 1, Concat, [1]]                    # 18: cat backbone P2
  - [-1, 2, C3k2, [128, False]]                  # 19: P2_out

  # -------- 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

  - [-1, 1, MSCA, [256]]                         # 23: ✅MSCA放到P3_out（stride=8）

  - [-1, 1, Conv, [512, 3, 2]]                   # 24: P3 -> P4
  - [[-1, 13], 1, Concat, [1]]                   # 25: cat P4_td
  - [-1, 2, C3k2, [512, False]]                  # 26: P4_out

  - [-1, 1, Conv, [1024, 3, 2]]                  # 27: P4 -> P5
  - [[-1, 10], 1, Concat, [1]]                   # 28: ✅cat backbone P5（layer10，stride=32）
  - [-1, 2, C3k2, [1024, True]]                  # 29: P5_out

  # -------- Detect (4 heads) --------
  - [[19, 23, 26, 29], 1, DetectLSDECD, [nc, 16, 16]]

你跑完这一轮后怎么判断“下一步”

  • 如果 mAP50-95 ≥ 0.781：说明 MSCA 的问题主要是“放置点”，后面就可以在这个版本上再做 SPD 的低冲突引入。
  • 如果 mAP50-95 仍 < 0.781：基本可以判定 MSCA 在你这套检测头里不适配（或与你的 C2PSA/LSDECD 门控叠加冲突），下一步我会让你做 “MSCA 替换 C2PSA（只留一种注意力）”，我会再给你完整 YAML（不需要你自己改）。

你先按上面这份 YAML 跑一次，把四个指标发我（P/R/mAP50/mAP50-95），我再给你下一次的完整配置文件（保证一次只改一个点）。

Frage

结果一句不好，我尝试了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

Gedanke

Considering YAML config for SPDConvLite and EMA

I’m suggesting we reduce the bottleneck ratio and remove EMA. Specific configuration for the next experiment could include baseline head + SPDConvLite only, with a ratio of 0.75/1.0 and ema_factor of 16. If we disable attention via a new argument and set EMA based on the channels, that might work well!

Considering next step for SPDConvLite and detection head

For c2=128, applying factor 16 seems okay. But introducing SPDConvLite as an alternative downsample in the backbone could cause a conflict, as it might disrupt the gradient flow. I suggest reintroducing SPD not as a replacement but as an addition, and tweaking attention stacking to avoid redundancy.

Testing removing C2PSA and SPD adjustment

I’ll try removing C2PSA and replacing it with Identity or C3k2 to check if C2PSA is over-attentioning when the head improves. This could help open room for SPD/MSCA while ensuring better performance than the baseline. Also, I'll test SPDConvLite with a ratio of 1.0 and without attention.

Modifying SPDConvLite to reduce loss and add skip connection

The next step is to modify SPDConvLite by reducing information loss and using a ratio of 1.0. I’m thinking of adding a residual skip by using a strided convolution path, like adding conv_skip(x) with stride2. I'll provide the code snippets with the "skip" and "use_attn" options, then offer the YAML configuration using SPDConvLiteV2.

Modifying SPDConvLite without EMA for next experiment

For the next experiment, I'll try SPDConvLite without EMA, high ratio, and apply it to just one location in the head. I'll create a class SPDConvLiteNA, using strided convolution for the skip connection. We’ll replace only one layer (P4->P5) in the baseline head and maintain other layers, while considering slight improvements and adjusting for the user’s previous feedback.

Planning the SPDConvLiteV2 experiment

Let’s break this down step by step: 1) Revert to the baseline detect head. 2) Implement SPDConvLiteV2 with a skip connection and no attention. 3) Prepare a full YAML and code class for the experiment. We’ll ensure the concatenation of dimensions is correct and handle any runtime errors. Adjust args accordingly for the experiment while ensuring proper module definition and inclusion.

Clarifying the approach for the next experiment

It seems like the best way forward is to implement a use_attn switch, though an alternative could be removing EMA in SPDConvLite (via setting ema_factor large). This could help with SPD degradation issues. We’ll reduce SPD aggressiveness, remove extra attention, and add residual skip. I'll propose code and YAML for these adjustments and highlight potential training needs like a longer warmup or lower learning rate. We'll focus on one layer for SPDConvLiteV2 and ensure proper module registration.

Thought for 53s

Antwort

你现在的现象已经很明确了：检测头（P2 + DetectLSDECD）单独最强（mAP50-95=0.781），但你一把 SPDConvLite 或 MSCA 加进去就掉，说明冲突主要来自：

  • 门控/注意力过密：DetectLSDECD + C2PSA +（SPDConvLite里还有EMA）/MSCA → 多重 sigmoid/gate 叠加，训练更难、特征被过度筛。
  • SPDConvLite瓶颈太狠 + 分布不对齐：尤其你之前是“替换多处下采样”，会让 PAN/FPN 融合时统计分布更不一致。

所以下一步不要继续“堆”，而是做一个低冲突版 SPD：
✅ 只替换一处（P4→P5） + ✅ 去掉 SPDConvLite 里的注意力（EMA） + ✅ 加一条 stride2 skip 残差（保证不丢信息）
这样通常能把“SPD带来的不稳定/信息损失”压住。

下面按你要求：每次给出完整代码/配置。

下一步实验：SPDConvLiteV2（去注意力 + 加skip），只替换 P4→P5 一处

1）新增模块代码（完整）

把下面代码放到你自定义模块文件（例如 ultralytics/nn/modules/conv.py 或你项目里注册模块的地方），与 SPD 同文件可直接用：

python
import torch
import torch.nn as nn
import torch.nn.functional as F

# 你已有的 SPD 保持不变
class SPD(nn.Module):
    def __init__(self, scale=2):
        super().__init__()
        assert scale >= 2 and int(scale) == scale
        self.scale = int(scale)

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

        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 SPDConvLiteV2(nn.Module):
    """
    低冲突版本：
    - SPD(space-to-depth) 做下采样
    - 不使用 EMA 注意力（避免与 DetectLSDECD/C2PSA/MSCA 门控叠加）
    - 加 stride2 的 skip 分支（防止信息瓶颈导致性能塌）
    YAML args: [c2, k, s, ratio, act, skip]
      - ratio: 中间通道比例（建议 >=1.0）
      - act: True/False
      - skip: True/False
    """
    def __init__(self, c1, c2, k=3, s=2, ratio=1.0, act=True, skip=True):
        super().__init__()
        self.spd = SPD(scale=s)
        cin = c1 * s * s
        hidden = max(16, int(c2 * ratio))

        # 注意：这里用的是 ultralytics 的 Conv（带BN+act），你项目里应该已存在
        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)

        self.use_skip = skip
        self.skip = Conv(c1, c2, 3, s, act=False) if skip else nn.Identity()

    def forward(self, x):
        x_in = x
        x = self.spd(x)
        x = self.pw1(x)
        x = self.dw(x)
        y = self.pw2(x)
        if self.use_skip:
            y = y + self.skip(x_in)
        return y

关键点：这版 没有 EMA，并且有 skip 残差，通常能把你现在 0.726 这种“SPD+检测头崩掉”的情况救回来。

另外你需要确保 Ultralytics 能解析到这个类名（不同版本略不同，一般做法是把类放在 ultralytics/nn/modules/conv.py 并在对应 __init__.py 里 import 出来）。

2）完整 YAML（只动一处：P4→P5 下采样换成 SPDConvLiteV2）

保存为例如：yolo11n_p2_lsdecd_spdv2_p5.yaml

yaml
# Parameters
nc: 5
scales:
  n: [0.50, 0.25, 1024]
  s: [0.50, 0.50, 1024]
  m: [0.50, 1.00, 512]
  l: [1.00, 1.00, 512]
  x: [1.00, 1.50, 512]

# YOLO11n backbone (保持原样，别再引入MSCA/SPDConvLite/其它变化)
backbone:
  - [-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]]      # 2
  - [-1, 1, Conv, [256, 3, 2]]             # 3  P3/8
  - [-1, 2, C3k2, [512, False, 0.25]]      # 4
  - [-1, 1, Conv, [512, 3, 2]]             # 5  P4/16
  - [-1, 2, C3k2, [512, True]]             # 6
  - [-1, 1, Conv, [1024, 3, 2]]            # 7  P5/32
  - [-1, 2, C3k2, [1024, True]]            # 8
  - [-1, 1, SPPF, [1024, 5]]               # 9
  - [-1, 2, C2PSA, [1024]]                 # 10 backbone P5输出

# Head (P2-P5, 4 heads) + DetectLSDECD
head:
  # -------- top-down FPN --------
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]   # 11: P5 -> P4
  - [[-1, 6], 1, Concat, [1]]                    # 12: cat backbone P4
  - [-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
  - [-1, 2, C3k2, [256, False]]                  # 16: P3_td

  # -------- add P2 detection layer --------
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]   # 17: P3 -> P2
  - [[-1, 2], 1, Concat, [1]]                    # 18: cat backbone P2
  - [-1, 2, C3k2, [128, False]]                  # 19: P2_out

  # -------- 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

  - [-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

  # ✅ 只替换这一处：P4 -> P5
  - [-1, 1, SPDConvLiteV2, [1024, 3, 2, 1.0, True, True]]   # 26: P4 -> P5 (SPD + skip, no attn)
  - [[-1, 10], 1, Concat, [1]]                               # 27: cat backbone P5 (layer10)
  - [-1, 2, C3k2, [1024, True]]                              # 28: P5_out

  - [[19, 22, 25, 28], 1, DetectLSDECD, [nc, 16, 16]]

为什么这一步是你现在“最该做”的？

  • 你已经验证：SPDConvLite（原版带EMA、且多处替换）+ 检测头会崩（0.726）。
  • 所以 SPD 要想活下来，必须：
    1. 减少替换位置（先只动最深的一处，不动 P2/P3 这类敏感层）
    2. 去掉 EMA（避免门控叠加）
    3. 加 skip（避免 SPD 分支瓶颈造成信息损失）

这三点是“系统级冲突”里最常见的止血组合。

这轮跑完你怎么走下一步（不需要你自己猜）

  • 如果这轮 mAP50-95 回到 ≥0.781：下一步再考虑把 P3→P4 那一处也换成 SPDConvLiteV2（仍然只加一处，不要三处全换）。
  • 如果这轮仍然 远低于 0.781：那基本可以判定“你的检测头增强后，不需要 SPD 类下采样”，下一步我会让你做 “去掉 backbone 的 C2PSA（只留检测头的增强）” 或 “DetectLSDECD 参数/分配的校正”，我会继续给你完整 YAML。

你把这轮的四个指标（P/R/mAP50/mAP50-95）发我，我就按上面分支给你“下一次完整配置文件”。

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