YOLOv11训练自己的数据集，YOLOv11网络解析

1 训练自己的数据集

在github搜索ultralytics并下载。

 GitHub - ultralytics/ultralytics: Ultralytics YOLO11 🚀

环境配置不再赘述，本地配置自行搜索教程，若使用云服务器配置更为简单。

数据标注

pip install labelimg

启动标注工具

labelimg

标注格式设置为yolo

数据集划分比例 train:val:test  建议8:1:1 or 7:2:1

 ultralytics提供了从小到大的五个v11模型，一般默认使用yolo11n

并且没有提供训练脚本，你可以再命令行指定训练参数，或创建train.py，将参数提前设置好。

tips：

1. 若想训练yolo11n，yaml文件指定为yolo11n.yaml，若想训练yolo11s，yaml文件指定为yolo11s.yaml，以此类推
2. 若不想加载预训练权重 ，model.load('') # loading pretrain weights保持注释状态，加载预训练权重的话，权重与yaml文件对应。
3. Windows系统中将workers设置为大于1的值可能会报错
4. 需要修改为自己的路径

import warnings
warnings.filterwarnings('ignore')
from ultralytics import YOLO


if __name__ == '__main__':
    model = YOLO('./ultralytics/cfg/models/11/yolo11s.yaml')
    # model.load('') # loading pretrain weights
    model.train(data='./dataset/pest.yaml',
                cache=False,
                imgsz=640,
                epochs=150,
                batch=8,
                close_mosaic=0,
                workers=11,
                # device='0',
                optimizer='SGD', # using SGD
                patience=50, # close earlystop
                # resume=True, # 断点续训,YOLO初始化时选择last.pt
                # amp=False, # close amp
                # fraction=0.2,
                project='runs/train',
                name='exp',
                )

训练完成后，进行验证

若你没有测试集，split需设置为val

import warnings
warnings.filterwarnings('ignore')
from ultralytics import YOLO


if __name__ == '__main__':
    model = YOLO('./runs/train/exp/weights/best.pt')
    model.val(data='./dataset/pest.yaml',
              split='test',
              imgsz=640,
              batch=16,
              # iou=0.7,
              # rect=False,
              # save_json=True, # if you need to cal coco metrice
              project='runs/test',
              name='exp',
              )

 在本人的数据集中，yolo11实现了优于目前大部分主流模型的性能。

2 YOLO11网络解析

yolo11网络的yaml文件

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLO11 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=yolo11n.yaml' will call yolo11.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
  s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
  m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
  l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
  x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs

# YOLO11n 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, 2, C3k2, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
  - [-1, 2, C3k2, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]] # 9
  - [-1, 2, C2PSA, [1024]] # 10

# YOLO11n head
head:
  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
  - [-1, 2, C3k2, [512, False]] # 13

  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 13], 1, Concat, [1]] # cat head P4
  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 10], 1, Concat, [1]] # cat head P5
  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)

  - [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)

总体上的网络对比

 在网络结构中，未发生变化的模块为白色。

YOLO11网络同样由backbone 、neck和head三部分组成。相较于YOLOv8，backbone部分有10层，C2f替换为C3k2，SPPF后新增了C2PSA模块；neck部分的C2f同样替换为C3k2，其余模块无变动；head部分将原始检测头优化为更为轻量化的检测头。

模块对比

C2f improvement

code:

class C3(nn.Module):
    """CSP Bottleneck with 3 convolutions."""

    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        """Initialize the CSP Bottleneck with given channels, number, shortcut, groups, and expansion values."""
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, k=((1, 1), (3,