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论文链接： https://arxiv.org/abs/2502.12524
代码链接： https://github.com/sunsmarterjie/yolov12
长期以来，增强YOLO框架的网络架构一直至关重要，但一直专注于基于cnn的改进，尽管注意力机制在建模能力方面已被证明具有优越性。这是因为基于注意力的模型无法匹配基于cnn的模型的速度。本文提出了一种以注意力为中心的YOLO框架，即YOLOv12，与之前基于cnn的YOLO框架的速度相匹配，同时利用了注意力机制的性能优势。YOLOv12在精度和速度方面超越了所有流行的实时目标检测器。例如，YOLOv12-N在T4 GPU上以1.64ms的推理延迟实现了40.6% mAP，以相当的速度超过了高级的YOLOv10-N / YOLOv11-N 2.1%/1.2% mAP。这种优势可以扩展到其他模型规模。YOLOv12还超越了改善DETR的端到端实时检测器，如RT-DETR /RT-DETRv2: YOLOv12- s比RT-DETR- r18 / RT-DETRv2-r18运行更快42%，仅使用36%的计算和45%的参数。
总结：作者围提出YOLOv12目标检测模型，测试结果更快、更强，围绕注意力机制进行创新。
一、创新点总结
作者构建了一个以注意力为核心构建了YOLOv12检测模型，主要创新点创新点如下：
1、提出一种简单有效的区域注意力机制（area-attention）。
2、提出一种高效的聚合网络结构R-ELAN。
作者提出的area-attention代码如下：
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
结构上与YOLOv11里C2PSA中的模式相似，使用了Flash-attn进行运算加速。Flash-attn安装时需要找到与cuda、torch和python解释器对应的版本，Windows用户可用上述代码替换官方代码的AAttn代码，无需安装Flash-attn。
R-ELAN结构如下图所示：
作者基于该结构构建了A2C2f模块，与C2f/C3K2模块结构类似，代码如下：
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
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))
模型结构图如下：
二、使用教程
2.1 准备代码
首先，点击上方链接进入YOLOv12的GitHub仓库，按照图示流程下载打包好的YOLOv12代码与预训练权重文件到本地。
下载完成后解压， 使用PyCharm（或VsCode等IDE软件）打开，并将下载的预训练权重拷贝到解压的工程目录下，下文以PyCharm为例。
2.2 准备数据集
Ultralytics版本的YOLO所需格式的数据集标签为txt格式的文本文件，文本文件中保存的标签信息分别为：类别序号、中心点x/y坐标、标注框的归一化信息，每一行对应一个对象。图像中有几个标注的对象就有几行信息。
自制数据集标注教程可看此篇文章：
https://blog.csdn.net/StopAndGoyyy/article/details/139906637
如果没有自己的数据集，本文提供一个小型数据集（摘自SIMD公共数据集）以供测试代码，包含24张训练集以及20张测试集
下载链接：https://pan.quark.cn/s/f318a977f81c
提取码：LQ68
下载完成后将提供的datasets文件夹解压并复制到工程路径下。
创建 data.yaml文件保存数据集的相关信息，如果使用本文提供的数据集可使用以下代码：
# dataset path
train: ./images/train
val: ./images/test
test: ./images/test
# number of classes
nc: 15
# class names
names: ['car', 'Truck', 'Van', 'Long Vehicle','Bus', 'Airliner', 'Propeller Aircraft', 'Trainer Aircraft', 'Chartered Aircraft', 'Fighter Aircraft',\
'Others', 'Stair Truck', 'Pushback Truck', 'Helicopter', 'Boat']
2.3 模型训练
创建train.py文件，依次填入以下信息。epochs=2表示只训练两轮，通常设置为100-300之间，此处仅测试两轮。batch=1表示每批次仅训练一张图片，可按显存大小调整batchsize，一般24g卡可设置为16-64。
from ultralytics.models import YOLO
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
if __name__ == '__main__':
model = YOLO(model='ultralytics/cfg/models/11/yolo11.yaml')
# model.load('yolov8n.pt')
model.train(data='./data.yaml', epochs=2, batch=1, device='0', imgsz=640, workers=2, cache=False,
amp=True, mosaic=False, project='runs/train', name='exp')
待软件控制台打印如下信息即为运行成功。如果Flash_attn包报错，可使用本文的A2代码对原文代码进行更改。
训练完成后在runs/train文件夹下保存有训练好的权重及相关训练信息。 
2.4 模型预测
创建detect.py文件，填入训练好的权重路径及要检测的图片信息。
三、小结
浅谈一下YOLOv12的感受，相比前几代的YOLO，v12的改动较小，在结构上删除了SPPF模块，设计了A2C2f模块，在模型的几个位置进行了替换。从作者公布的实验数据来看，模型的计算量和参数量都有一定下降，同时检测性能有一定提升。也不得不感慨，YOLO更新换代的速度越来越快了。要说YOLO那个版本强，那当然是最新的最强。
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