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YOLOv10改进策略【卷积层】| 利用MobileNetv4中的UIB、ExtraDW优化C2fCIB

news 2024/11/5 15:12:11

一、本文介绍

本文记录的是利用ExtraDW优化YOLOv10中的RepNCSPELAN4，详细说明了优化原因，注意事项等。ExtraDW是MobileNetv4模型中提出的新模块，允许以低成本增加网络深度和感受野，具有ConvNext和IB的组合优势。可以在提高模型精度的同时降低一定量的模型参数。

专栏目录：YOLOv10改进目录一览 | 涉及卷积层、轻量化、注意力、损失函数、Backbone、SPPF、Neck、检测头等全方位改进

专栏地址：YOLOv10改进专栏——以发表论文的角度，快速准确的找到有效涨点的创新点！

文章目录

  • 一、本文介绍
  • 二、UIB介绍
    • 2.1 UIB结构设计
    • 2.2 ExtraDW结构组成
    • 2.3 ExtraDW特点
  • 三、ExtraDW的实现代码
  • 四、添加步骤
    • 4.1 改进点1
    • 4.2 改进点2⭐
  • 五、添加步骤
    • 5.1 修改一
    • 5.2 修改二
    • 5.3 修改三
  • 六、yaml模型文件
    • 6.1 模型改进版本一
    • 6.2 模型改进版本二⭐
  • 七、成功运行结果

二、UIB介绍

Universal Inverted Bottleneck（UIB）通用反向瓶颈结构。

2.1 UIB结构设计

  1. 基于MobileNetV4

    • UIB建立在MobileNetV4之上，即采用深度可分离卷积和逐点扩展及投影的反向瓶颈结构。
    • 在反向瓶颈块（IB）中引入两个可选的深度可分离卷积，一个在扩展层之前，另一个在扩展层和投影层之间。

  2. UIB有四种可能的实例化形式：

    • Inverted Bottleneck (IB)：对扩展后的特征激活进行空间混合，以增加成本为代价提供更大的模型容量。
    • ConvNext：通过在扩展之前进行空间混合，使用更大的核尺寸实现更便宜的空间混合。
    • ExtraDW：文中引入的新变体，允许以低成本增加网络深度和感受野，具有ConvNext和IB的组合优势。
    • FFN：由两个1x1逐点卷积（PW）组成的栈，中间有激活和归一化层。

在这里插入图片描述

2.2 ExtraDW结构组成

结构组成：

  • 在IB块中加入两个可选的深度可分离卷积，一个在扩展层之前，另一个在扩展层和投影层之间。

2.3 ExtraDW特点

  1. 灵活性：

    • 在每个网络阶段，可以灵活地进行空间和通道混合的权衡调整，根据需要扩大感受野，并最大化计算利用率，增强模型对输入特征的感知能力。

  2. 效率提升：

    • 提供了一种廉价增加网络深度和感受野的方式。相比其他结构，它在增加网络深度和感受野的同时，不会带来过高的计算成本。
    • 在论文中，与其他注意力机制结合时，能有效提高模型的运算强度，减少内存访问需求，从而提高模型效率。

论文：http://arxiv.org/abs/2404.10518
源码：https://github.com/tensorflow/models/blob/master/official/vision/modeling/backbones/mobilenet.py

三、ExtraDW的实现代码

ExtraDW模块的实现代码如下：参考代码


import torch
import torch.nn as nn
from typing import Optionaldef make_divisible(value: float,divisor: int,min_value: Optional[float] = None,round_down_protect: bool = True,
) -> int:"""This function is copied from here"https://github.com/tensorflow/models/blob/master/official/vision/modeling/layers/nn_layers.py"This is to ensure that all layers have channels that are divisible by 8.Args:value: A `float` of original value.divisor: An `int` of the divisor that need to be checked upon.min_value: A `float` of  minimum value threshold.round_down_protect: A `bool` indicating whether round down more than 10%will be allowed.Returns:The adjusted value in `int` that is divisible against divisor."""if min_value is None:min_value = divisornew_value = max(min_value, int(value + divisor / 2) // divisor * divisor)# Make sure that round down does not go down by more than 10%.if round_down_protect and new_value < 0.9 * value:new_value += divisorreturn int(new_value)def conv2d(in_channels, out_channels, kernel_size=3, stride=1, groups=1, bias=False, norm=True, act=True):conv = nn.Sequential()padding = (kernel_size - 1) // 2conv.append(nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=bias, groups=groups))if norm:conv.append(nn.BatchNorm2d(out_channels))if act:conv.append(nn.ReLU6())return convclass UniversalInvertedBottleneckBlock(nn.Module):def __init__(self, in_channels, out_channels, start_dw_kernel_size, middle_dw_kernel_size, middle_dw_downsample,stride, expand_ratio):"""An inverted bottleneck block with optional depthwises.Referenced from here https://github.com/tensorflow/models/blob/master/official/vision/modeling/layers/nn_blocks.py"""super(UniversalInvertedBottleneckBlock, self).__init__()# starting depthwise convself.start_dw_kernel_size = start_dw_kernel_sizeif self.start_dw_kernel_size:stride_ = stride if not middle_dw_downsample else 1self._start_dw_ = conv2d(in_channels, in_channels, kernel_size=start_dw_kernel_size, stride=stride_, groups=in_channels, act=False)# expansion with 1x1 convsexpand_filters = make_divisible(in_channels * expand_ratio, 8)self._expand_conv = conv2d(in_channels, expand_filters, kernel_size=1)# middle depthwise convself.middle_dw_kernel_size = middle_dw_kernel_sizeif self.middle_dw_kernel_size:stride_ = stride if middle_dw_downsample else 1self._middle_dw = conv2d(expand_filters, expand_filters, kernel_size=middle_dw_kernel_size, stride=stride_, groups=expand_filters)# projection with 1x1 convsself._proj_conv = conv2d(expand_filters, out_channels, kernel_size=1, stride=1, act=False)# expand depthwise conv (not used)# _end_dw_kernel_size = 0# self._end_dw = conv2d(out_channels, out_channels, kernel_size=_end_dw_kernel_size, stride=stride, groups=in_channels, act=False)def forward(self, x):if self.start_dw_kernel_size:x = self._start_dw_(x)# print("_start_dw_", x.shape)x = self._expand_conv(x)# print("_expand_conv", x.shape)if self.middle_dw_kernel_size:x = self._middle_dw(x)# print("_middle_dw", x.shape)x = self._proj_conv(x)# print("_proj_conv", x.shape)return xdef autopad(k, p=None, d=1):  # kernel, padding, dilation"""Pad to 'same' shape outputs."""if d > 1:k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-sizeif p is None:p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-padreturn pclass Conv(nn.Module):"""Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)."""default_act = nn.SiLU()  # default activationdef __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):"""Initialize Conv layer with given arguments including activation."""super().__init__()self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)self.bn = nn.BatchNorm2d(c2)self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()def forward(self, x):"""Apply convolution, batch normalization and activation to input tensor."""return self.act(self.bn(self.conv(x)))def forward_fuse(self, x):"""Perform transposed convolution of 2D data."""return self.act(self.conv(x))class Bottleneck(nn.Module):"""Standard bottleneck."""def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):"""Initializes a standard bottleneck module with optional shortcut connection and configurable parameters."""super().__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, k[0], 1)self.cv2 = Conv(c_, c2, k[1], 1, g=g)self.add = shortcut and c1 == c2def forward(self, x):"""Applies the YOLO FPN to input data."""return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))class C2f(nn.Module):"""Faster Implementation of CSP Bottleneck with 2 convolutions."""def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):"""Initializes a CSP bottleneck with 2 convolutions and n Bottleneck blocks for faster processing."""super().__init__()self.c = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, 2 * self.c, 1, 1)self.cv2 = Conv((2 + n) * self.c, c2, 1)  # optional act=FReLU(c2)self.m = nn.ModuleList(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n))def forward(self, x):"""Forward pass through C2f layer."""y = list(self.cv1(x).chunk(2, 1))y.extend(m(y[-1]) for m in self.m)return self.cv2(torch.cat(y, 1))def forward_split(self, x):"""Forward pass using split() instead of chunk()."""y = list(self.cv1(x).split((self.c, self.c), 1))y.extend(m(y[-1]) for m in self.m)return self.cv2(torch.cat(y, 1))class RepVGGDW(torch.nn.Module):def __init__(self, ed) -> None:super().__init__()self.conv = Conv(ed, ed, 7, 1, 3, g=ed, act=False)self.conv1 = Conv(ed, ed, 3, 1, 1, g=ed, act=False)self.dim = edself.act = nn.SiLU()def forward(self, x):return self.act(self.conv(x) + self.conv1(x))def forward_fuse(self, x):return self.act(self.conv(x))@torch.no_grad()def fuse(self):conv = fuse_conv_and_bn(self.conv.conv, self.conv.bn)conv1 = fuse_conv_and_bn(self.conv1.conv, self.conv1.bn)conv_w = conv.weightconv_b = conv.biasconv1_w = conv1.weightconv1_b = conv1.biasconv1_w = torch.nn.functional.pad(conv1_w, [2,2,2,2])final_conv_w = conv_w + conv1_wfinal_conv_b = conv_b + conv1_bconv.weight.data.copy_(final_conv_w)conv.bias.data.copy_(final_conv_b)self.conv = convdel self.conv1class CIB(nn.Module):"""Standard bottleneck."""def __init__(self, c1, c2, shortcut=True, e=0.5, lk=False):"""Initializes a bottleneck module with given input/output channels, shortcut option, group, kernels, andexpansion."""super().__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = nn.Sequential(Conv(c1, c1, 3, g=c1),Conv(c1, 2 * c_, 1),Conv(2 * c_, 2 * c_, 3, g=2 * c_) if not lk else RepVGGDW(2 * c_),Conv(2 * c_, c2, 1),Conv(c2, c2, 3, g=c2),UniversalInvertedBottleneckBlock(c2, c2, 5, 3, True, 1, 4))self.add = shortcut and c1 == c2def forward(self, x):"""'forward()' applies the YOLO FPN to input data."""return x + self.cv1(x) if self.add else self.cv1(x)class C2fCIB_UIB(C2f):"""Faster Implementation of CSP Bottleneck with 2 convolutions."""def __init__(self, c1, c2, n=1, shortcut=False, lk=False, g=1, e=0.5):"""Initialize CSP bottleneck layer with two convolutions with arguments ch_in, ch_out, number, shortcut, groups,expansion."""super().__init__(c1, c2, n, shortcut, g, e)self.m = nn.ModuleList(CIB(self.c, self.c, shortcut, e=1.0, lk=lk) for _ in range(n))

四、添加步骤

4.1 改进点1

模块改进方法1️⃣：直接加入UniversalInvertedBottleneckBlock模块。
UniversalInvertedBottleneckBlock模块添加后如下：

在这里插入图片描述

注意❗：在5.2和5.3小节中需要声明的模块名称为：UniversalInvertedBottleneckBlock。

4.2 改进点2⭐

模块改进方法2️⃣：基于UniversalInvertedBottleneckBlock模块的C2fCIB。

第二种改进方法是对YOLOv10中的C2fCIB模块进行改进。UIB中的ExtraDW模块与C2fCIB结合后，可以为YOLOv10提供更丰富的特征表示，更好地调整特征的空间分布和通道信息，使得模型能够更有效地聚焦于目标相关的特征，减少无关信息的干扰，进而提高检测精度。

改进代码如下：

首先添加UniversalInvertedBottleneckBlock模块改进CIB模块。

class CIB(nn.Module):"""Standard bottleneck."""def __init__(self, c1, c2, shortcut=True, e=0.5, lk=False):"""Initializes a bottleneck module with given input/output channels, shortcut option, group, kernels, andexpansion."""super().__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = nn.Sequential(Conv(c1, c1, 3, g=c1),Conv(c1, 2 * c_, 1),Conv(2 * c_, 2 * c_, 3, g=2 * c_) if not lk else RepVGGDW(2 * c_),Conv(2 * c_, c2, 1),Conv(c2, c2, 3, g=c2),UniversalInvertedBottleneckBlock(c2, c2, 5, 3, True, 1, 4))self.add = shortcut and c1 == c2def forward(self, x):"""'forward()' applies the YOLO FPN to input data."""return x + self.cv1(x) if self.add else self.cv1(x)

在这里插入图片描述

再添加如下代码将C2fCIB重命名为C2fCIB_UIB

class C2fCIB_UIB(C2f):"""Faster Implementation of CSP Bottleneck with 2 convolutions."""def __init__(self, c1, c2, n=1, shortcut=False, lk=False, g=1, e=0.5):"""Initialize CSP bottleneck layer with two convolutions with arguments ch_in, ch_out, number, shortcut, groups,expansion."""super().__init__(c1, c2, n, shortcut, g, e)self.m = nn.ModuleList(CIB(self.c, self.c, shortcut, e=1.0, lk=lk) for _ in range(n))

在这里插入图片描述

注意❗：在5.2和5.3小节中需要声明的模块名称为：C2fCIB_UIB。

五、添加步骤

5.1 修改一

① 在ultralytics/nn/目录下新建AddModules文件夹用于存放模块代码

② 在AddModules文件夹下新建UIB.py，将第三节中的代码粘贴到此处

在这里插入图片描述

5.2 修改二

在AddModules文件夹下新建__init__.py（已有则不用新建），在文件内导入模块：from .UIB import *

在这里插入图片描述

5.3 修改三

在ultralytics/nn/modules/tasks.py文件中，需要在两处位置添加各模块类名称。

首先：导入模块

在这里插入图片描述

其次：在parse_model函数中注册UniversalInvertedBottleneckBlock和C2fCIB_UIB模块

在这里插入图片描述
在这里插入图片描述

六、yaml模型文件

6.1 模型改进版本一

在代码配置完成后，配置模型的YAML文件。

此处以ultralytics/cfg/models/v10/yolov10m.yaml为例，在同目录下创建一个用于自己数据集训练的模型文件yolov10m-UIB.yaml。

将yolov10m.yaml中的内容复制到yolov10m-UIB.yaml文件下，修改nc数量等于自己数据中目标的数量。
在骨干网络中添加UniversalInvertedBottleneckBlock模块，。

# Parameters
nc: 1 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'# [depth, width, max_channels]m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPsbackbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 3, UniversalInvertedBottleneckBlock, [128, 0, 3, True, 1, 2]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 6, UniversalInvertedBottleneckBlock, [256, 0, 3, True, 1, 2]]- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16- [-1, 6, UniversalInvertedBottleneckBlock, [512, 5, 3, True, 1, 4]]- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32- [-1, 3, UniversalInvertedBottleneckBlock, [1024, 5, 3, True, 1, 4]]- [-1, 1, SPPF, [1024, 5]] # 9- [-1, 1, PSA, [1024]] # 10# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 3, C2f, [512]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 3, C2f, [256]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 3, C2f, [512]] # 19 (P4/16-medium)- [-1, 1, SCDown, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

6.2 模型改进版本二⭐

此处同样以ultralytics/cfg/models/v10/yolov11m.yaml为例，在同目录下创建一个用于自己数据集训练的模型文件yolov10m-C2fCIB_UIB.yaml。

将yolov10m.yaml中的内容复制到yolov10m-C2fCIB_UIB.yaml文件下，修改nc数量等于自己数据中目标的数量。

📌 模型的修改方法是将骨干网络中的所有C2fCIB模块替换成C2fCIB_UIB模块。

# Parameters
nc: 1 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'# [depth, width, max_channels]m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPsbackbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 3, C2f, [128, True]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32- [-1, 3, C2fCIB_UIB, [1024, True, True]]- [-1, 1, SPPF, [1024, 5]] # 9- [-1, 1, PSA, [1024]] # 10# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 3, C2f, [512]] # 13- [-1, 1, nn.Upsample, [None, 2, "nearest"]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 3, C2f, [256]] # 16 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 3, C2f, [512]] # 19 (P4/16-medium)- [-1, 1, SCDown, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

七、成功运行结果

分别打印网络模型可以看到UniversalInvertedBottleneckBlock和C2fCIB_UIB已经加入到模型中，并可以进行训练了。

YOLOv10m-UIB：

YOLOv10m-UIB summary: 522 layers, 22,630,438 parameters, 22,630,422 gradients, 71.3 GFLOPs

                   from  n    params  module                                       arguments                     0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                2                  -1  2     79104  ultralytics.nn.modules.block.UniversalInvertedBottleneckBlock[96, 96, 0, 3, True, 1, 2]    3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               4                  -1  4    611328  ultralytics.nn.modules.block.UniversalInvertedBottleneckBlock[192, 192, 0, 3, True, 1, 2]  5                  -1  1     78720  ultralytics.nn.modules.block.SCDown          [192, 384, 3, 2]              6                  -1  4   4843008  ultralytics.nn.modules.block.UniversalInvertedBottleneckBlock[384, 384, 5, 3, True, 1, 4]  7                  -1  1    228672  ultralytics.nn.modules.block.SCDown          [384, 576, 3, 2]              8                  -1  2   5401728  ultralytics.nn.modules.block.UniversalInvertedBottleneckBlock[576, 576, 5, 3, True, 1, 4]  9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 10                  -1  1   1253088  ultralytics.nn.modules.block.PSA             [576, 576]                    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  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 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  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 17                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           19                  -1  2   1846272  ultralytics.nn.modules.block.C2f             [576, 384, 2]                 20                  -1  1    152448  ultralytics.nn.modules.block.SCDown          [384, 384, 3, 2]              21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           22                  -1  2   1969920  ultralytics.nn.modules.block.C2fCIB          [960, 576, 2, True, True]     23        [16, 19, 22]  1   2282134  ultralytics.nn.modules.head.v10Detect        [1, [192, 384, 576]]          
YOLOv10m-UIB summary: 522 layers, 22,630,438 parameters, 22,630,422 gradients, 71.3 GFLOPs

YOLOv10m-C2fCIB_UIB：

YOLOv10m-C2fCIB_UIB summary: 532 layers, 19,598,182 parameters, 19,598,166 gradients, 68.9 GFLOPs

                   from  n    params  module                                       arguments                     0                  -1  1      1392  ultralytics.nn.modules.conv.Conv             [3, 48, 3, 2]                 1                  -1  1     41664  ultralytics.nn.modules.conv.Conv             [48, 96, 3, 2]                2                  -1  2    111360  ultralytics.nn.modules.block.C2f             [96, 96, 2, True]             3                  -1  1    166272  ultralytics.nn.modules.conv.Conv             [96, 192, 3, 2]               4                  -1  4    813312  ultralytics.nn.modules.block.C2f             [192, 192, 4, True]           5                  -1  1     78720  ultralytics.nn.modules.block.SCDown          [192, 384, 3, 2]              6                  -1  4   3248640  ultralytics.nn.modules.block.C2f             [384, 384, 4, True]           7                  -1  1    228672  ultralytics.nn.modules.block.SCDown          [384, 576, 3, 2]              8                  -1  2   3729600  ultralytics.nn.AddModules.UIB.C2fCIB_UIB     [576, 576, True, True]        9                  -1  1    831168  ultralytics.nn.modules.block.SPPF            [576, 576, 5]                 10                  -1  1   1253088  ultralytics.nn.modules.block.PSA             [576, 576]                    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  2   1993728  ultralytics.nn.modules.block.C2f             [960, 384, 2]                 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  2    517632  ultralytics.nn.modules.block.C2f             [576, 192, 2]                 17                  -1  1    332160  ultralytics.nn.modules.conv.Conv             [192, 192, 3, 2]              18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           19                  -1  2   1846272  ultralytics.nn.modules.block.C2f             [576, 384, 2]                 20                  -1  1    152448  ultralytics.nn.modules.block.SCDown          [384, 384, 3, 2]              21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           22                  -1  2   1969920  ultralytics.nn.modules.block.C2fCIB          [960, 576, 2, True, True]     23        [16, 19, 22]  1   2282134  ultralytics.nn.modules.head.v10Detect        [1, [192, 384, 576]]          
YOLOv10m-C2fCIB_UIB summary: 532 layers, 19,598,182 parameters, 19,598,166 gradients, 68.9 GFLOPs

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