RT-DETR融合YOLOv12中的R-ELAN结构

RT-DETR使用教程： RT-DETR使用教程

RT-DETR改进汇总贴：RT-DETR更新汇总贴

《YOLOv12: Attention-Centric Real-Time Object Detectors》

一、模块介绍

**论文链接：**https://arxiv.org/abs/2502.12524

代码链接： https://gitcode.com/gh_mirrors/yo/yolov12

论文速览：

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。更多的比较见图1。

**总结：本文将其中的R-ELAN思想融入其他模块。**

二、二创融合模块

2.1 相关二创模块及所需参数

该模块可如图加入到RepNCSPELAN4、RepC3与自研等模块中，代码见群文件，所需参数如下。

RepNCSPELAN4-变式模块所需参数：(c1, c2, c3 , c4, n**)**

CCRI及变式模块所需参数：( c1, c2 , k, n, lightconv, shortcut, scale, e, act**)**

RepC4及变式模块所需参数：(c1, c2, n, e**)**

其中，RepNCSPELAN4模块的代码如下：

class RepNCSPELAN4_R_ELAN(nn.Module):
    """CSP-ELAN."""

    def __init__(self, c1, c2, c3, c4, n=1):
        """Initializes CSP-ELAN layer with specified channel sizes, repetitions, and convolutions."""
        super().__init__()
        self.c = c3 // 2
        self.cv1 = Conv(c1, self.c, 1, 1)
        self.cv2 = nn.Sequential(RepCSP(c3 // 2, c4, n), Conv(c4, c4, 3, 1))
        self.cv3 = nn.Sequential(RepCSP(c4, c4, n), Conv(c4, c4, 3, 1))
        self.cv4 = Conv(self.c + (2 * c4), c2, 1, 1)

    def forward(self, x):
        """Forward pass through RepNCSPELAN4 layer."""
        y = [self.cv1(x)]
        y.extend((m(y[-1])) for m in [self.cv2, self.cv3])
        return self.cv4(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.cv2, self.cv3])
        return self.cv4(torch.cat(y, 1))

2.2 更改yaml文件（以自研模型加入为例）

yam文件解读：YOLO系列 ".yaml"文件解读_yolo yaml文件-CSDN博客

打开更改ultralytics/cfg/models/rt-detr路径下的rtdetr-l.yaml文件，替换原有模块。

# Ultralytics YOLO 🚀, AGPL-3.0 license
# RT-DETR-l object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr
# ⭐⭐Powered by https://blog.csdn.net/StopAndGoyyy,  技术指导QQ:2668825911⭐⭐

# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
  # [depth, width, max_channels]
  l: [1.00, 1.00, 512]
#  n: [ 0.33, 0.25, 1024 ]
#  s: [ 0.33, 0.50, 1024 ]
#  m: [ 0.67, 0.75, 768 ]
#  l: [ 1.00, 1.00, 512 ]
#  x: [ 1.00, 1.25, 512 ]
# ⭐⭐Powered by https://blog.csdn.net/StopAndGoyyy,  技术指导QQ:2668825911⭐⭐

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, CCRI, [128, 5, True, False]]
  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
  - [-1, 4, CCRI, [256, 3, True, True]]
  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
  - [-1, 4, RepNCSPELAN4_R_ELAN, [512, 512, 256, 1]]
  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
  - [-1, 2, CCRI, [1024, 3, True, False]]

head:
  - [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9 input_proj.2
  - [-1, 1, AIFI, [1024, 8]]
  - [-1, 1, Conv, [256, 1, 1]] # 11, Y5, lateral_convs.0

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [6, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 13 input_proj.1
  - [[-2, -1], 1, Concat, [1]]
  - [-1, 2, RepC4, [256]] # 15, fpn_blocks.0
  - [-1, 1, Conv, [256, 1, 1]] # 16, Y4, lateral_convs.1

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [4, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 18 input_proj.0
  - [[-2, -1], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, RepC4, [256]] # X3 (20), fpn_blocks.1

  - [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0
  - [[-1, 16], 1, Concat, [1]] # cat Y4
  - [-1, 2, RepC4, [256]] # F4 (23), pan_blocks.0

  - [-1, 1, Conv, [256, 3, 2]] # 24, downsample_convs.1
  - [[-1, 11], 1, Concat, [1]] # cat Y5
  - [-1, 2, RepC4, [256]] # F5 (26), pan_blocks.1

  - [[20, 23, 26], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
# ⭐⭐Powered by https://blog.csdn.net/StopAndGoyyy,  技术指导QQ:2668825911⭐⭐

2.2 修改train.py文件

创建Train_RT脚本用于训练。

from ultralytics.models import RTDETR
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'

if __name__ == '__main__':
    model = RTDETR(model='ultralytics/cfg/models/rt-detr/rtdetr-l.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')

在train.py脚本中填入修改好的yaml路径，运行即可训。

RT-DETR融合YOLOv12中的R-ELAN结构

RT-DETR使用教程： RT-DETR使用教程

RT-DETR改进汇总贴：RT-DETR更新汇总贴