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YOLO11框架使用

news 来源：原创 2025/7/4 12:56:36

YOLO11

- 1. Frame Understanding
- 2. What can YOLO11 do?
- 3.如何训练自己数据集？
- - 3.1 配置环境
  - 3.2 制作自己数据集
  - 3.3 配置文件
  - - 3.3.1 数据集配置文件
    - 3.3.2 网络模块配置文件
- 4.修改训练参数配置文件
- 5. 训练脚本编写
- 6.结果展示

1. Frame Understanding

在这里插入图片描述

2. What can YOLO11 do?

Ultralytics YOLO11 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLO11 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
在这里插入图片描述 Compared to previous YOLO,YOLO11 exhibites the high mAP.

3.如何训练自己数据集？

3.1 配置环境

这里推荐
深度学习环境配置

3.2 制作自己数据集

看这篇就够了
制作YOLO数据集

3.3 配置文件

3.3.1 数据集配置文件

# Ultralytics YOLO 🚀, AGPL-3.0 license
# Signature dataset by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/detect/signature/
# Example usage: yolo train data=signature.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── signature  ← downloads here (11.2 MB)# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]train:  "F:/paperDatas/dataSum/yolo11Data/images/train"val: "F:/paperDatas/dataSum/yolo11Data/images/val"
# val images (relative to 'path') 35 images# Classes
names:0: Commodity

3.3.2 网络模块配置文件

不做改进，改下nc即可

# 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: 1 # 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 GFLOPss: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPsm: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPsl: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPsx: [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