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YOLO11 全新发布！（原理介绍+代码详见+结构框图）

1.YOLO11介绍

Ultralytics YOLO11是一款尖端的、最先进的模型，它在之前YOLO版本成功的基础上进行了构建，并引入了新功能和改进，以进一步提升性能和灵活性。YOLO11设计快速、准确且易于使用，使其成为各种物体检测和跟踪、实例分割、图像分类以及姿态估计任务的绝佳选择。

目标检测性能

语义分割性能

Pose关键点检测性能

结构图如下：

1.1 C3k2

C3k2，结构图如下

C3k2，继承自类C2f，其中通过c3k设置False或者Ture来决定选择使用C3k还是Bottleneck

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实现代码ultralytics/nn/modules/block.py

代码语言：javascript代码运行次数：0运行复制
class C3k2(C2f):
    """Faster Implementation of CSP Bottleneck with 2 convolutions."""

    def __init__(self, c1, c2, n=1, c3k=False, e=0.5, g=1, shortcut=True):
        """Initializes the C3k2 module, a faster CSP Bottleneck with 2 convolutions and optional C3k blocks."""
        super().__init__(c1, c2, n, shortcut, g, e)
        self.m = nn.ModuleList(
            C3k(self.c, self.c, 2, shortcut, g) if c3k else Bottleneck(self.c, self.c, shortcut, g) for _ in range(n)
        )


class C3k(C3):
    """C3k is a CSP bottleneck module with customizable kernel sizes for feature extraction in neural networks."""

    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, k=3):
        """Initializes the C3k module with specified channels, number of layers, and configurations."""
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)  # hidden channels
        # self.m = nn.Sequential(*(RepBottleneck(c_, c_, shortcut, g, k=(k, k), e=1.0) for _ in range(n)))
        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, k=(k, k), e=1.0) for _ in range(n)))

1.2 C2PSA介绍

借鉴V10 PSA结构，实现了C2PSA和C2fPSA，最终选择了基于C2的C2PSA（可能涨点更好？）

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实现代码ultralytics/nn/modules/block.py

代码语言：javascript代码运行次数：0运行复制
class PSABlock(nn.Module):
    """
    PSABlock class implementing a Position-Sensitive Attention block for neural networks.

    This class encapsulates the functionality for applying multi-head attention and feed-forward neural network layers
    with optional shortcut connections.

    Attributes:
        attn (Attention): Multi-head attention module.
        ffn (nn.Sequential): Feed-forward neural network module.
        add (bool): Flag indicating whether to add shortcut connections.

    Methods:
        forward: Performs a forward pass through the PSABlock, applying attention and feed-forward layers.

    Examples:
        Create a PSABlock and perform a forward pass
        >>> psablock = PSABlock(c=128, attn_ratio=0.5, num_heads=4, shortcut=True)
        >>> input_tensor = torch.randn(1, 128, 32, 32)
        >>> output_tensor = psablock(input_tensor)
    """

    def __init__(self, c, attn_ratio=0.5, num_heads=4, shortcut=True) -> None:
        """Initializes the PSABlock with attention and feed-forward layers for enhanced feature extraction."""
        super().__init__()

        self.attn = Attention(c, attn_ratio=attn_ratio, num_heads=num_heads)
        self.ffn = nn.Sequential(Conv(c, c * 2, 1), Conv(c * 2, c, 1, act=False))
        self.add = shortcut

    def forward(self, x):
        """Executes a forward pass through PSABlock, applying attention and feed-forward layers to the input tensor."""
        x = x + self.attn(x) if self.add else self.attn(x)
        x = x + self.ffn(x) if self.add else self.ffn(x)
        return x





class C2PSA(nn.Module):
    """
    C2PSA module with attention mechanism for enhanced feature extraction and processing.

    This module implements a convolutional block with attention mechanisms to enhance feature extraction and processing
    capabilities. It includes a series of PSABlock modules for self-attention and feed-forward operations.

    Attributes:
        c (int): Number of hidden channels.
        cv1 (Conv): 1x1 convolution layer to reduce the number of input channels to 2*c.
        cv2 (Conv): 1x1 convolution layer to reduce the number of output channels to c.
        m (nn.Sequential): Sequential container of PSABlock modules for attention and feed-forward operations.

    Methods:
        forward: Performs a forward pass through the C2PSA module, applying attention and feed-forward operations.

    Notes:
        This module essentially is the same as PSA module, but refactored to allow stacking more PSABlock modules.

    Examples:
        >>> c2psa = C2PSA(c1=256, c2=256, n=3, e=0.5)
        >>> input_tensor = torch.randn(1, 256, 64, 64)
        >>> output_tensor = c2psa(input_tensor)
    """

    def __init__(self, c1, c2, n=1, e=0.5):
        """Initializes the C2PSA module with specified input/output channels, number of layers, and expansion ratio."""
        super().__init__()
        assert c1 == c2
        self.c = int(c1 * e)
        self.cv1 = Conv(c1, 2 * self.c, 1, 1)
        self.cv2 = Conv(2 * self.c, c1, 1)

        self.m = nn.Sequential(*(PSABlock(self.c, attn_ratio=0.5, num_heads=self.c // 64) for _ in range(n)))

    def forward(self, x):
        """Processes the input tensor 'x' through a series of PSA blocks and returns the transformed tensor."""
        a, b = self.cv1(x).split((self.c, self.c), dim=1)
        b = self.m(b)
        return self.cv2(torch.cat((a, b), 1))


class C2fPSA(C2f):
    """
    C2fPSA module with enhanced feature extraction using PSA blocks.

    This class extends the C2f module by incorporating PSA blocks for improved attention mechanisms and feature extraction.

    Attributes:
        c (int): Number of hidden channels.
        cv1 (Conv): 1x1 convolution layer to reduce the number of input channels to 2*c.
        cv2 (Conv): 1x1 convolution layer to reduce the number of output channels to c.
        m (nn.ModuleList): List of PSA blocks for feature extraction.

    Methods:
        forward: Performs a forward pass through the C2fPSA module.
        forward_split: Performs a forward pass using split() instead of chunk().

    Examples:
        >>> import torch
        >>> from ultralytics.modelsmon import C2fPSA
        >>> model = C2fPSA(c1=64, c2=64, n=3, e=0.5)
        >>> x = torch.randn(1, 64, 128, 128)
        >>> output = model(x)
        >>> print(output.shape)
    """

    def __init__(self, c1, c2, n=1, e=0.5):
        """Initializes the C2fPSA module, a variant of C2f with PSA blocks for enhanced feature extraction."""
        assert c1 == c2
        super().__init__(c1, c2, n=n, e=e)
        self.m = nn.ModuleList(PSABlock(self.c, attn_ratio=0.5, num_heads=self.c // 64) for _ in range(n))

1.3 11 Detect介绍

分类检测头引入了DWConv（更加轻量级，为后续二次创新提供了改进点），结构图如下（和V8的区别）：

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实现代码ultralytics/nn/modules/head.py

代码语言：javascript代码运行次数：0运行复制
        self.cv2 = nn.ModuleList(
            nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch
        )
        self.cv3 = nn.ModuleList(
            nn.Sequential(
                nn.Sequential(DWConv(x, x, 3), Conv(x, c3, 1)),
                nn.Sequential(DWConv(c3, c3, 3), Conv(c3, c3, 1)),
                nn.Conv2d(c3, self.nc, 1),
            )
            for x in ch
        )

1.4 YOLO11和 YOLOv8的区别

代码语言：javascript代码运行次数：0运行复制
-------------------------------   YOLO11   ----------------------------------
# Ultralytics YOLO             
            
            

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                                                YOLO11 全新发布！（原理介绍代码详见结构框图）
                                            


            

                

                    

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