YOLOv12上线！纽约州立大学联合中科院，再造目标检测新高度

2025年2月19日，YOLOv12发布，YOLOv12与其它YOLO模型的对比如下：

论文地址：https://arxiv.org/pdf/2502.12524

代码地址：https://github.com/sunsmarterjie/yolov12

YOLOv12在继承YOLO系列高效性的同时，引入了注意力机制（attention mechanisms），显著提升了检测精度，同时保持了快速的推理速度。YOLOv12通过一系列创新的设计和架构改进，打破了传统卷积神经网络（CNN）在YOLO系列中的主导地位，证明了注意力机制在实时目标检测中的潜力。

YOLOv12的主要贡献包括：

• 提出了一种以注意力为中心的YOLO框架，通过方法论创新和架构改进，打破了CNN在YOLO系列中的主导地位。
• 在不依赖额外预训练技术的情况下，YOLOv12实现了更快的推理速度和更高的检测精度，展现出其在实时目标检测中的潜力。

1 Area Attention

YOLOv12提出了一种名为“Area Attention”的注意力机制，将注意力分解为水平和垂直两个方向。

A2模块代码实现如下：

class AAttn(nn.Module):
    """
    Area-attention module with the requirement of flash attention.

    Attributes:
        dim (int): Number of hidden channels;
        num_heads (int): Number of heads into which the attention mechanism is divided;
        area (int, optional): Number of areas the feature map is divided. Defaults to 1.

    Methods:
        forward: Performs a forward process of input tensor and outputs a tensor after the execution of the area attention mechanism.

    Examples:
        >>> import torch
        >>> from ultralytics.nn.modules import AAttn
        >>> model = AAttn(dim=64, num_heads=2, area=4)
        >>> x = torch.randn(2, 64, 128, 128)
        >>> output = model(x)
        >>> print(output.shape)

    Notes: 
        recommend that dim//num_heads be a multiple of 32 or 64.

    """

    def __init__(self, dim, num_heads, area=1):
        """Initializes the area-attention module, a simple yet efficient attention module for YOLO."""
        super().__init__()
        self.area = area

        self.num_heads = num_heads
        self.head_dim = head_dim = dim // num_heads
        all_head_dim = head_dim * self.num_heads

        self.qkv = Conv(dim, all_head_dim * 3, 1, act=False)
        self.proj = Conv(all_head_dim, dim, 1, act=False)
        self.pe = Conv(all_head_dim, dim, 7, 1, 3, g=dim, act=False)


    def forward(self, x):
        """Processes the input tensor 'x' through the area-attention"""
        B, C, H, W = x.shape
        N = H * W

        qkv = self.qkv(x).flatten(2).transpose(1, 2)
        if self.area > 1:
            qkv = qkv.reshape(B * self.area, N // self.area, C * 3)
            B, N, _ = qkv.shape
        q, k, v = qkv.view(B, N, self.num_heads, self.head_dim * 3).split(
            [self.head_dim, self.head_dim, self.head_dim], dim=3
        )

        if x.is_cuda:
            x = flash_attn_func(
                q.contiguous().half(),
                k.contiguous().half(),
                v.contiguous().half()
            ).to(q.dtype)
        else:
            q = q.permute(0, 2, 3, 1)
            k = k.permute(0, 2, 3, 1)
            v = v.permute(0, 2, 3, 1)
            attn = (q.transpose(-2, -1) @ k) * (self.head_dim ** -0.5)
            max_attn = attn.max(dim=-1, keepdim=True).values 
            exp_attn = torch.exp(attn - max_attn)
            attn = exp_attn / exp_attn.sum(dim=-1, keepdim=True)
            x = (v @ attn.transpose(-2, -1))
            x = x.permute(0, 3, 1, 2)
            v = v.permute(0, 3, 1, 2)

        if self.area > 1:
            x = x.reshape(B // self.area, N * self.area, C)
            v = v.reshape(B // self.area, N * self.area, C)
            B, N, _ = x.shape

        x = x.reshape(B, H, W, C).permute(0, 3, 1, 2)
        v = v.reshape(B, H, W, C).permute(0, 3, 1, 2)

        x = x + self.pe(v)
        x = self.proj(x)
        return x


class ABlock(nn.Module):
    """
    ABlock class implementing a Area-Attention block with effective feature extraction.

    This class encapsulates the functionality for applying multi-head attention with feature map are dividing into areas
    and feed-forward neural network layers.

    Attributes:
        dim (int): Number of hidden channels;
        num_heads (int): Number of heads into which the attention mechanism is divided;
        mlp_ratio (float, optional): MLP expansion ratio (or MLP hidden dimension ratio). Defaults to 1.2;
        area (int, optional): Number of areas the feature map is divided.  Defaults to 1.

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

    Examples:
        Create a ABlock and perform a forward pass
        >>> model = ABlock(dim=64, num_heads=2, mlp_ratio=1.2, area=4)
        >>> x = torch.randn(2, 64, 128, 128)
        >>> output = model(x)
        >>> print(output.shape)

    Notes: 
        recommend that dim//num_heads be a multiple of 32 or 64.
    """

    def __init__(self, dim, num_heads, mlp_ratio=1.2, area=1):
        """Initializes the ABlock with area-attention and feed-forward layers for faster feature extraction."""
        super().__init__()

        self.attn = AAttn(dim, num_heads=num_heads, area=area)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = nn.Sequential(Conv(dim, mlp_hidden_dim, 1), Conv(mlp_hidden_dim, dim, 1, act=False))

        self.apply(self._init_weights)

    def _init_weights(self, m):
        """Initialize weights using a truncated normal distribution."""
        if isinstance(m, nn.Conv2d):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Conv2d) and m.bias is not None:
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        """Executes a forward pass through ABlock, applying area-attention and feed-forward layers to the input tensor."""
        x = x + self.attn(x)
        x = x + self.mlp(x)
        return x

2 R-ELAN

并利用Area Attention(A2)作为主要的特征提取模块，提出了R-ELAN。

R-ELAN代码实现如下：

我们发现，并非所有的特征提取模块都替换为了A2C2f，而是在较小的尺度上使用A2C2f，这可能是出于时间复杂度的考虑，在大的尺度上使用A2C2f，将会很大程度的增加计算量。

class A2C2f(nn.Module):  
    """
    A2C2f module with residual enhanced feature extraction using ABlock blocks with area-attention. Also known as R-ELAN

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

    Attributes:
        c1 (int): Number of input channels;
        c2 (int): Number of output channels;
        n (int, optional): Number of 2xABlock modules to stack. Defaults to 1;
        a2 (bool, optional): Whether use area-attention. Defaults to True;
        area (int, optional): Number of areas the feature map is divided. Defaults to 1;
        residual (bool, optional): Whether use the residual (with layer scale). Defaults to False;
        mlp_ratio (float, optional): MLP expansion ratio (or MLP hidden dimension ratio). Defaults to 1.2;
        e (float, optional): Expansion ratio for R-ELAN modules. Defaults to 0.5.
        g (int, optional): Number of groups for grouped convolution. Defaults to 1;
        shortcut (bool, optional): Whether to use shortcut connection. Defaults to True;

    Methods:
        forward: Performs a forward pass through the A2C2f module.

    Examples:
        >>> import torch
        >>> from ultralytics.nn.modules import A2C2f
        >>> model = A2C2f(c1=64, c2=64, n=2, a2=True, area=4, residual=True, e=0.5)
        >>> x = torch.randn(2, 64, 128, 128)
        >>> output = model(x)
        >>> print(output.shape)
    """

    def __init__(self, c1, c2, n=1, a2=True, area=1, residual=False, mlp_ratio=2.0, e=0.5, g=1, shortcut=True):
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        assert c_ % 32 == 0, "Dimension of ABlock be a multiple of 32."

        # num_heads = c_ // 64 if c_ // 64 >= 2 else c_ // 32
        num_heads = c_ // 32

        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv((1 + n) * c_, c2, 1)  # optional act=FReLU(c2)

        init_values = 0.01  # or smaller
        self.gamma = nn.Parameter(init_values * torch.ones((c2)), requires_grad=True) if a2 and residual else None

        self.m = nn.ModuleList(
            nn.Sequential(*(ABlock(c_, num_heads, mlp_ratio, area) for _ in range(2))) if a2 else C3k(c_, c_, 2, shortcut, g) for _ in range(n)
        )

    def forward(self, x):
        """Forward pass through R-ELAN layer."""
        y = [self.cv1(x)]
        y.extend(m(y[-1]) for m in self.m)
        if self.gamma is not None:
            return x + (self.gamma * self.cv2(torch.cat(y, 1)).permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
        return self.cv2(torch.cat(y, 1))

YOLOv12的模型结构如下：

# YOLOv12 🚀, AGPL-3.0 license
# YOLOv12 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov12n.yaml' will call yolov12.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.50, 0.25, 1024] # summary: 465 layers, 2,603,056 parameters, 2,603,040 gradients, 6.7 GFLOPs
  s: [0.50, 0.50, 1024] # summary: 465 layers, 9,285,632 parameters, 9,285,616 gradients, 21.7 GFLOPs
  m: [0.50, 1.00, 512] # summary: 501 layers, 20,201,216 parameters, 20,201,200 gradients, 68.1 GFLOPs
  l: [1.00, 1.00, 512] # summary: 831 layers, 26,454,880 parameters, 26,454,864 gradients, 89.7 GFLOPs
  x: [1.00, 1.50, 512] # summary: 831 layers, 59,216,928 parameters, 59,216,912 gradients, 200.3 GFLOPs

# YOLO12n 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, 4, A2C2f, [512, True, 4]]
  - [-1, 1, Conv,  [1024, 3, 2]] # 7-P5/32
  - [-1, 4, A2C2f, [1024, True, 1]] # 8

# YOLO12n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, A2C2f, [512, False, -1]] # 11

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
  - [-1, 2, A2C2f, [256, False, -1]] # 14

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 11], 1, Concat, [1]] # cat head P4
  - [-1, 2, A2C2f, [512, False, -1]] # 17

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 8], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [1024, True]] # 20 (P5/32-large)

  - [[14, 17, 20], 1, Detect, [nc]] # Detect(P3, P4, P5)

可以发现，并非所有的特征提取模块都替换为了A2C2f，而是在较小的尺度上使用A2C2f，这可能是出于时间复杂度的考虑，在大的尺度上使用A2C2f，将会很大程度的增加计算量。

3 对比实验

以下是YOLOv12与其它几个版本YOLO的对比。

可以发现，YOLOv12在AP上达到了YOLO模型的最佳水平。美中不足的是，YOLO12在推理速度上略低于YOLO11模型，但零点几毫秒的延迟几乎可以忽略不记。

论文还对不同模型的特征图做了可视化，YOLOv12的注意力能够更加有效地保持目标的特征。

YOLOv12成功地将注意力机制引入实时目标检测框架，通过Area Attention、R-ELAN和架构优化等创新设计，实现了精度与效率的双重提升。该研究不仅挑战了CNN在YOLO系列中的主导地位，还为未来实时目标检测的发展提供了新的方向。