【算法介绍】
基于YOLOv11的扑克牌识别检测系统是一种利用深度学习技术进行扑克牌种类和牌面信息识别的系统。该系统采用YOLOv11（You Only Look Once version 11）作为核心算法，这是一种高效的目标检测算法，能够将目标检测任务转化为回归问题，直接输出目标的类别和位置。
YOLOv11是YOLO系列的最新版本，相比之前的版本，它在检测精度、速度和适用性上都有所提升。这使得基于YOLOv11的扑克牌识别检测系统能够更快速、准确地识别扑克牌。
该系统可应用于多个领域，如智能纸牌游戏、线上扑克防作弊、教学辅助工具等。在智能纸牌游戏中，系统能够自动识别玩家手中的牌，实现自动计分、判断胜负等功能，提高游戏的趣味性和效率。在线上扑克防作弊方面，系统可以实时监测牌局，检测是否存在作弊行为，保障游戏的公平性和公正性。
此外，扑克牌识别检测系统还可以用于扑克牌生产质量检测、自动化包装与分拣、特殊场所的安全监控等场景。例如，在扑克牌生产过程中，系统可以对生产的扑克牌进行自动质量检测，确保产品质量符合标准。
总的来说，基于YOLOv11的扑克牌识别检测系统具有广泛的应用前景和重要的实用价值。
【效果展示】
【测试环境】
windows10
anaconda3+python3.8
torch==2.3.0
ultralytics==8.3.81
onnxruntime==1.16.3
【模型可以检测出52类别】
10C,10D,3H,3S,4C,4D,4H,4S,5C,5D,5H,5S,10H,6C,6D,6H,6S,7C,7D,7H,7S,8C,8D,10S,8H,8S,9C,9D,9H,9S,AC,AD,AH,AS,2C,JC,JD,JH,JS,KC,KD,KH,KS,QC,QD,2D,QH,QS,2H,2S,3C,3D
【训练数据集介绍】
数据集格式：Pascal VOC格式+YOLO格式(不包含分割路径的txt文件，仅仅包含jpg图片以及对应的VOC格式xml文件和yolo格式txt文件)
图片数量(jpg文件个数)：24233
标注数量(xml文件个数)：24233
标注数量(txt文件个数)：24233
标注类别数：52
标注类别名称(注意yolo格式类别顺序不和这个对应，而以labels文件夹classes.txt为准):["2C","2D","2H","2S","3C","3D","3H","3S","4C","4D","4H","4S","5C","5D","5H","5S","6C","6D","6H","6S","7C","7D","7H","7S","8C","8D","8H","8S","9C","9D","9H","9S","10C","10D","10H","10S","AC","AD","AH","AS","JC","JD","JH","JS","KC","KD","KH","KS","QC","QD","QH","QS"]
每个类别标注的框数：
2C 框数 = 1835
2D 框数 = 1829
2H 框数 = 1784
2S 框数 = 1843
3C 框数 = 1946
3D 框数 = 1747
3H 框数 = 1804
3S 框数 = 1860
4C 框数 = 1983
4D 框数 = 1977
4H 框数 = 1957
4S 框数 = 1767
5C 框数 = 2020
5D 框数 = 1934
5H 框数 = 1737
5S 框数 = 1910
6C 框数 = 1768
6D 框数 = 1850
6H 框数 = 1743
6S 框数 = 1876
7C 框数 = 1796
7D 框数 = 1813
7H 框数 = 1911
7S 框数 = 1932
8C 框数 = 1786
8D 框数 = 1895
8H 框数 = 1892
8S 框数 = 1788
9C 框数 = 1774
9D 框数 = 1910
9H 框数 = 1893
9S 框数 = 1775
10C 框数 = 1847
10D 框数 = 1810
10H 框数 = 1897
10S 框数 = 1937
AC 框数 = 1903
AD 框数 = 1934
AH 框数 = 1805
AS 框数 = 1908
JC 框数 = 1965
JD 框数 = 1706
JH 框数 = 1720
JS 框数 = 1810
KC 框数 = 1892
KD 框数 = 1916
KH 框数 = 1876
KS 框数 = 1902
QC 框数 = 1906
QD 框数 = 1940
QH 框数 = 1891
QS 框数 = 2009
总框数：96909
使用标注工具：labelImg
标注规则：对类别进行画矩形框
重要说明：数据集有增强图片
特别声明：本数据集不对训练的模型或者权重文件精度作任何保证，数据集只提供准确且合理标注
图片预览：
标注例子：
【训练信息】
参数
值
训练集图片数
16963
验证集图片数
4847
训练map
99.5%
训练精度(Precision)
99.9%
训练召回率(Recall)
99.9%
验证集测试精度信息
类别
MAP50
all
99
10C
100
10D
100
3H
100
3S
100
4C
100
4D
100
4H
100
4S
100
5C
100
5D
100
5H
100
5S
100
10H
100
6C
100
6D
100
6H
100
6S
100
7C
100
7D
100
7H
100
7S
100
8C
100
8D
100
10S
100
8H
100
8S
100
9C
100
9D
100
9H
100
9S
100
AC
100
AD
100
AH
100
AS
100
2C
99
JC
100
JD
100
JH
100
JS
100
KC
100
KD
100
KH
100
KS
100
QC
99
QD
100
2D
100
QH
100
QS
100
2H
99
2S
100
3C
99
3D
100
【界面设计】
代码语言：javascript代码运行次数：0运行复制import os
import sys
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtCore import QThread, pyqtSignal
from PyQt5.QtGui import QImage, QPixmap
from PyQt5.QtWidgets import QFileDialog, QLabel, QApplication
import image_rc
import threading
import cv2
import numpy as np
import time
from Yolo11Detector import *
class Ui_MainWindow(QtWidgets.QMainWindow):
signal = QtCore.pyqtSignal(str, str)
def setupUi(self):
self.setObjectName("MainWindow")
self.resize(1280, 728)
self.centralwidget = QtWidgets.QWidget(self)
self.centralwidget.setObjectName("centralwidget")
self.detector=None
self.weights_dir = './weights'
self.picture = QtWidgets.QLabel(self.centralwidget)
self.picture.setGeometry(QtCore.QRect(260, 10, 1010, 630))
self.picture.setStyleSheet("background:black")
self.picture.setObjectName("picture")
self.picture.setScaledContents(True)
self.label_2 = QtWidgets.QLabel(self.centralwidget)
self.label_2.setGeometry(QtCore.QRect(10, 10, 81, 21))
self.label_2.setObjectName("label_2")
self.cb_weights = QtWidgets.QComboBox(self.centralwidget)
self.cb_weights.setGeometry(QtCore.QRect(10, 40, 241, 21))
self.cb_weights.setObjectName("cb_weights")
self.cb_weights.currentIndexChanged.connect(self.cb_weights_changed)
self.label_3 = QtWidgets.QLabel(self.centralwidget)
self.label_3.setGeometry(QtCore.QRect(10, 70, 72, 21))
self.label_3.setObjectName("label_3")
self.hs_conf = QtWidgets.QSlider(self.centralwidget)
self.hs_conf.setGeometry(QtCore.QRect(10, 100, 181, 22))
self.hs_conf.setProperty("value", 25)
self.hs_conf.setOrientation(QtCore.Qt.Horizontal)
self.hs_conf.setObjectName("hs_conf")
self.hs_conf.valueChanged.connect(self.conf_change)
self.dsb_conf = QtWidgets.QDoubleSpinBox(self.centralwidget)
self.dsb_conf.setGeometry(QtCore.QRect(200, 100, 51, 22))
self.dsb_conf.setMaximum(1.0)
self.dsb_conf.setSingleStep(0.01)
self.dsb_conf.setProperty("value", 0.3)
self.dsb_conf.setObjectName("dsb_conf")
self.dsb_conf.valueChanged.connect(self.dsb_conf_change)
self.dsb_iou = QtWidgets.QDoubleSpinBox(self.centralwidget)
self.dsb_iou.setGeometry(QtCore.QRect(200, 160, 51, 22))
self.dsb_iou.setMaximum(1.0)
self.dsb_iou.setSingleStep(0.01)
self.dsb_iou.setProperty("value", 0.45)
self.dsb_iou.setObjectName("dsb_iou")
self.dsb_iou.valueChanged.connect(self.dsb_iou_change)
self.hs_iou = QtWidgets.QSlider(self.centralwidget)
self.hs_iou.setGeometry(QtCore.QRect(10, 160, 181, 22))
self.hs_iou.setProperty("value", 45)
self.hs_iou.setOrientation(QtCore.Qt.Horizontal)
self.hs_iou.setObjectName("hs_iou")
self.hs_iou.valueChanged.connect(self.iou_change)
self.label_4 = QtWidgets.QLabel(self.centralwidget)
self.label_4.setGeometry(QtCore.QRect(10, 130, 72, 21))
self.label_4.setObjectName("label_4")
self.label_5 = QtWidgets.QLabel(self.centralwidget)
self.label_5.setGeometry(QtCore.QRect(10, 210, 72, 21))
self.label_5.setObjectName("label_5")
self.le_res = QtWidgets.QTextEdit(self.centralwidget)
self.le_res.setGeometry(QtCore.QRect(10, 240, 241, 400))
self.le_res.setObjectName("le_res")
self.setCentralWidget(self.centralwidget)
self.menubar = QtWidgets.QMenuBar(self)
self.menubar.setGeometry(QtCore.QRect(0, 0, 1110, 30))
self.menubar.setObjectName("menubar")
self.setMenuBar(self.menubar)
self.statusbar = QtWidgets.QStatusBar(self)
self.statusbar.setObjectName("statusbar")
self.setStatusBar(self.statusbar)
self.toolBar = QtWidgets.QToolBar(self)
self.toolBar.setToolButtonStyle(QtCore.Qt.ToolButtonTextBesideIcon)
self.toolBar.setObjectName("toolBar")
self.addToolBar(QtCore.Qt.TopToolBarArea, self.toolBar)
self.actionopenpic = QtWidgets.QAction(self)
icon = QtGui.QIcon()
icon.addPixmap(QtGui.QPixmap(":/images/1.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
self.actionopenpic.setIcon(icon)
self.actionopenpic.setObjectName("actionopenpic")
self.actionopenpic.triggered.connect(self.open_image)
self.action = QtWidgets.QAction(self)
icon1 = QtGui.QIcon()
icon1.addPixmap(QtGui.QPixmap(":/images/2.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
self.action.setIcon(icon1)
self.action.setObjectName("action")
self.action.triggered.connect(self.open_video)
self.action_2 = QtWidgets.QAction(self)
icon2 = QtGui.QIcon()
icon2.addPixmap(QtGui.QPixmap(":/images/3.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
self.action_2.setIcon(icon2)
self.action_2.setObjectName("action_2")
self.action_2.triggered.connect(self.open_camera)
self.actionexit = QtWidgets.QAction(self)
icon3 = QtGui.QIcon()
icon3.addPixmap(QtGui.QPixmap(":/images/4.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
self.actionexit.setIcon(icon3)
self.actionexit.setObjectName("actionexit")
self.actionexit.triggered.connect(self.exit)
self.toolBar.addAction(self.actionopenpic)
self.toolBar.addAction(self.action)
self.toolBar.addAction(self.action_2)
self.toolBar.addAction(self.actionexit)
self.retranslateUi()
QtCore.QMetaObject.connectSlotsByName(self)
self.init_all()【训练步骤】
使用YOLO11训练自己的数据集需要遵循一些基本的步骤。YOLO11是YOLO系列模型的一个版本，它在前代基础上做了许多改进，包括但不限于更高效的训练流程和更高的精度。以下是训练自己YOLO格式数据集的详细步骤：
一、 准备环境
1. 安装必要的软件：确保你的计算机上安装了Python（推荐3.6或更高版本），以及CUDA和cuDNN（如果你打算使用GPU进行加速）。
2. 安装YOLO11库：你可以通过GitHub克隆YOLOv8的仓库或者直接通过pip安装YOLO11。例如：
pip install ultralytics
二、数据准备
3. 组织数据结构：按照YOLO的要求组织你的数据文件夹。通常，你需要一个包含图像和标签文件的目录结构，如：
dataset/
├── images/
│ ├── train/
│ └── val/
├── labels/
│ ├── train/
│ └── val/
其中，train和val分别代表训练集和验证集。且images文件夹和labels文件夹名字不能随便改写或者写错，否则会在训练时候找不到数据集。
4. 标注数据：使用合适的工具对图像进行标注，生成YOLO格式的标签文件。每个标签文件应该是一个.txt文件，每行表示一个边界框，格式为：
<类别ID> <中心点x> <中心点y> <宽度> <高度>
这些值都是相对于图像尺寸的归一化值。
5. 创建数据配置文件：创建一个.yaml文件来定义你的数据集，包括路径、类别列表等信息。例如：
yaml
# dataset.yaml
path: ./dataset # 数据集根目录
train: images/train # 训练图片相对路径
val: images/val # 验证图片相对路径
nc: 2 # 类别数
names: ['class1', 'class2'] # 类别名称
三、模型训练
6. 加载预训练模型：可以使用官方提供的预训练模型作为起点，以加快训练速度并提高性能。
7. 配置训练参数：根据需要调整训练参数，如批量大小、学习率、训练轮次等。这通常可以通过命令行参数或配置文件完成。
8. 开始训练：使用YOLO11提供的命令行接口开始训练过程。例如：
yolo train data=dataset.yaml model=yolo11n.yaml epochs=100 imgsz=640
更多参数如下：
参数
默认值
描述
model
None
Specifies the model file for training. Accepts a path to either a .pt pretrained model or a .yaml configuration file. Essential for defining the model structure or initializing weights.
data
None
Path to the dataset configuration file (e.g., coco8.yaml). This file contains dataset-specific parameters, including paths to training and validation data , class names, and number of classes.
epochs
100
Total number of training epochs. Each epoch represents a full pass over the entire dataset. Adjusting this value can affect training duration and model performance.
time
None
Maximum training time in hours. If set, this overrides the epochs argument, allowing training to automatically stop after the specified duration. Useful for time-constrained training scenarios.
patience
100
Number of epochs to wait without improvement in validation metrics before early stopping the training. Helps prevent overfitting by stopping training when performance plateaus.
batch
16
Batch size, with three modes: set as an integer (e.g., batch=16), auto mode for 60% GPU memory utilization (batch=-1), or auto mode with specified utilization fraction (batch=0.70).
imgsz
640
Target image size for training. All images are resized to this dimension before being fed into the model. Affects model accuracy and computational complexity.
save
True
Enables saving of training checkpoints and final model weights. Useful for resuming training ormodel deployment.
save_period
-1
Frequency of saving model checkpoints, specified in epochs. A value of -1 disables this feature. Useful for saving interim models during long training sessions.
cache
False
Enables caching of dataset images in memory (True/ram), on disk (disk), or disables it (False). Improves training speed by reducing disk I/O at the cost of increased memory usage.
device
None
Specifies the computational device(s) for training: a single GPU (device=0), multiple GPUs (device=0,1), CPU (device=cpu), or MPS for Apple silicon (device=mps).
workers
8
Number of worker threads for data loading (per RANK if Multi-GPU training). Influences the speed of data preprocessing and feeding into the model, especially useful in multi-GPU setups.
project
None
Name of the project directory where training outputs are saved. Allows for organized storage of different experiments.
name
None
Name of the training run. Used for creating a subdirectory within the project folder, where training logs and outputs are stored.
exist_ok
False
If True, allows overwriting of an existing project/name directory. Useful for iterative experimentation without needing to manually clear previous outputs.
pretrained
True
Determines whether to start training from a pretrained model. Can be a boolean value or a string path to a specific model from which to load weights. Enhances training efficiency and model performance.
optimizer
'auto'
Choice of optimizer for training. Options include SGD, Adam, AdamW, NAdam, RAdam, RMSProp etc., or auto for automatic selection based on model configuration. Affects convergence speed and stability.
verbose
False
Enables verbose output during training, providing detailed logs and progress updates. Useful for debugging and closely monitoring the training process.
seed
0
Sets the random seed for training, ensuring reproducibility of results across runs with the same configurations.
deterministic
True
Forces deterministic algorithm use, ensuring reproducibility but may affect performance and speed due to the restriction on non-deterministic algorithms.
single_cls
False
Treats all classes in multi-class datasets as a single class during training. Useful for binary classification tasks or when focusing on object presence rather than classification.
rect
False
Enables rectangular training, optimizing batch composition for minimal padding. Can improve efficiency and speed but may affect model accuracy.
cos_lr
False
Utilizes a cosine learning rate scheduler, adjusting the learning rate following a cosine curve over epochs. Helps in managing learning rate for better convergence.
close_mosaic
10
Disables mosaic data augmentation in the last N epochs to stabilize training before completion. Setting to 0 disables this feature.
resume
False
Resumes training from the last saved checkpoint. Automatically loads model weights, optimizer state, and epoch count, continuing training seamlessly.
amp
True
Enables AutomaticMixed Precision (AMP) training, reducing memory usage and possibly speeding up training with minimal impact on accuracy.
fraction
1.0
Specifies the fraction of the dataset to use for training. Allows for training on a subset of the full dataset, useful for experiments or when resources are limited.
profile
False
Enables profiling of ONNX and TensorRT speeds during training, useful for optimizing model deployment.
freeze
None
Freezes the first N layers of the model or specified layers by index, reducing the number of trainable parameters. Useful for fine-tuning or transfer learning .
lr0
0.01
Initial learning rate (i.e. SGD=1E-2, Adam=1E-3) . Adjusting this value is crucial for the optimization process, influencing how rapidly model weights are updated.
lrf
0.01
Final learning rate as a fraction of the initial rate = (lr0 * lrf), used in conjunction with schedulers to adjust the learning rate over time.
momentum
0.937
Momentum factor for SGD or beta1 for Adam optimizers, influencing the incorporation of past gradients in the current update.
weight_decay
0.0005
L2 regularization term, penalizing large weights to prevent overfitting.
warmup_epochs
3.0
Number of epochs for learning rate warmup, gradually increasing the learning rate from a low value to the initial learning rate to stabilize training early on.
warmup_momentum
0.8
Initial momentum for warmup phase, gradually adjusting to the set momentum over the warmup period.
warmup_bias_lr
0.1
Learning rate for bias parameters during the warmup phase, helping stabilize model training in the initial epochs.
box
7.5
Weight of the box loss component in the loss_function, influencing how much emphasis is placed on accurately predicting bouding box coordinates.
cls
0.5
Weight of the classification loss in the total loss function, affecting the importance of correct class prediction relative to other components.
dfl
1.5
Weight of the distribution focal loss, used in certain YOLO versions for fine-grained classification.
pose
12.0
Weight of the pose loss in models trained for pose estimation, influencing the emphasis on accurately predicting pose keypoints.
kobj
2.0
Weight of the keypoint objectness loss in pose estimation models, balancing detection confidence with pose accuracy.
label_smoothing
0.0
Applies label smoothing, softening hard labels to a mix of the target label and a uniform distribution over labels, can improve generalization.
nbs
64
Nominal batch size for normalization of loss.
overlap_mask
True
Determines whether object masks should be merged into a single mask for training, or kept separate for each object. In case of overlap, the smaller mask is overlayed on top of the larger mask during merge.
mask_ratio
4
Downsample ratio for segmentation masks, affecting the resolution of masks used during training.
dropout
0.0
Dropout rate for regularization in classification tasks, preventing overfitting by randomly omitting units during training.
val
True
Enables validation during training, allowing for periodic evaluation of model performance on a separate dataset.
plots
False
Generates and saves plots of training and validation metrics, as well as prediction examples, providing visual insights into model performance and learning progression.
这里，data参数指向你的数据配置文件，model参数指定使用的模型架构，epochs设置训练轮次，imgsz设置输入图像的大小。
四、监控与评估
9. 监控训练过程：观察损失函数的变化，确保模型能够正常学习。
10. 评估模型：训练完成后，在验证集上评估模型的性能，查看mAP（平均精确度均值）等指标。
11. 调整超参数：如果模型的表现不佳，可能需要调整超参数，比如增加训练轮次、改变学习率等，并重新训练模型。
五、使用模型
12. 导出模型：训练完成后，可以将模型导出为ONNX或其他格式，以便于部署到不同的平台。比如将pytorch转成onnx模型可以输入指令
yolo export model=best.pt format=onnx
这样就会在pt模块同目录下面多一个同名的onnx模型best.onnx
下表详细说明了可用于将YOLO模型导出为不同格式的配置和选项。这些设置对于优化导出模型的性能、大小和跨各种平台和环境的兼容性至关重要。正确的配置可确保模型已准备好以最佳效率部署在预期的应用程序中。
参数
类型
默认值
描述
format
str
'torchscript'
Target format for the exported model, such as 'onnx', 'torchscript', 'tensorflow', or others, defining compatibility with various deployment environments.
imgsz
int or tuple
640
Desired image size for the model input. Can be an integer for square images or a tuple (height, width) for specific dimensions.
keras
bool
False
Enables export to Keras format for Tensorflow SavedModel, providing compatibility with TensorFlow serving and APIs.
optimize
bool
False
Applies optimization for mobile devices when exporting to TorchScript, potentially reducing model size and improving performance.
half
bool
False
Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware.
int8
bool
False
Activates INT8 quantization, further compressing the model and speeding up inference with minimal accuracy loss, primarily for edge devices.
dynamic
bool
False
Allows dynamic input sizes for ONNX, TensorRT and OpenVINO exports, enhancing flexibility in handling varying image dimensions.
simplify
bool
True
Simplifies the model graph for ONNX exports with onnxslim, potentially improving performance and compatibility.
opset
int
None
Specifies the ONNX opset version for compatibility with different ONNX parsers and runtimes. If not set, uses the latest supported version.
workspace
float
4.0
Sets the maximum workspace size in GiB for TensorRT optimizations, balancing memory usage and performance.
nms
bool
False
Adds Non-Maximum Suppression (NMS) to the CoreML export, essential for accurate and efficient detection post-processing.
batch
int
1
Specifies export model batch inference size or the max number of images the exported model will process concurrently in predict mode.
device
str
None
Specifies the device for exporting: GPU (device=0), CPU (device=cpu), MPS for Apple silicon (device=mps) or DLA for NVIDIA Jetson (device=dla:0 or device=dla:1).
调整这些参数可以定制导出过程，以满足特定要求，如部署环境、硬件约束和性能目标。选择适当的格式和设置对于实现模型大小、速度和精度之间的最佳平衡至关重要。
导出格式：
可用的YOLO11导出格式如下表所示。您可以使用format参数导出为任何格式，即format='onnx'或format='engine'。您可以直接在导出的模型上进行预测或验证，即yolo predict model=yolo11n.onnx。导出完成后，将显示您的模型的使用示例。
导出格式
格式参数
模型
属性
参数
pytorch
-
yolo11n.pt
✅
-
torchscript
torchscript
yolo11n.torchscript
✅
imgsz, optimize, batch
onnx
onnx
yolo11n.onnx
✅
imgsz, half, dynamic, simplify, opset, batch
openvino
openvino
yolo11n_openvino_model/
✅
imgsz, half, int8, batch
tensorrt
engine
yolo11n.engine
✅
imgsz, half, dynamic, simplify, workspace, int8, batch
CoreML
coreml
yolo11n.mlpackage
✅
imgsz, half, int8, nms, batch
TF SaveModel
saved_model
yolo11n_saved_model/
✅
imgsz, keras, int8, batch
TF GraphDef
pb
yolo11n.pb
❌
imgsz, batch
TF Lite
tflite
yolo11n.tflite
✅
imgsz, half, int8, batch
TF Edge TPU
edgetpu
yolo11n_edgetpu.tflite
✅
imgsz
TF.js
tfjs
yolo11n_web_model/
✅
imgsz, half, int8, batch
PaddlePaddle
paddle
yolo11n_paddle_model/
✅
imgsz, batch
MNN
mnn
yolo11n.mnn
✅
imgsz, batch, int8, half
NCNN
ncnn
yolo11n_ncnn_model/
✅
imgsz, half, batch
13. 测试模型：在新的数据上测试模型，确保其泛化能力良好。
以上就是使用YOLO11训练自己数据集的基本步骤。请根据实际情况调整这些步骤中的具体细节。希望这些信息对你有所帮助！
【常用评估参数介绍】
在目标检测任务中，评估模型的性能是至关重要的。你提到的几个术语是评估模型性能的常用指标。下面是对这些术语的详细解释：
Class： 这通常指的是模型被设计用来检测的目标类别。例如，一个模型可能被训练来检测车辆、行人或动物等不同类别的对象。Images： 表示验证集中的图片数量。验证集是用来评估模型性能的数据集，与训练集分开，以确保评估结果的公正性。Instances： 在所有图片中目标对象的总数。这包括了所有类别对象的总和，例如，如果验证集包含100张图片，每张图片平均有5个目标对象，则Instances为500。P（精确度Precision）： 精确度是模型预测为正样本的实例中，真正为正样本的比例。计算公式为：Precision = TP / (TP + FP)，其中TP表示真正例（True Positives），FP表示假正例（False Positives）。R（召回率Recall）： 召回率是所有真正的正样本中被模型正确预测为正样本的比例。计算公式为：Recall = TP / (TP + FN)，其中FN表示假负例（False Negatives）。mAP50： 表示在IoU（交并比）阈值为0.5时的平均精度（mean Average Precision）。IoU是衡量预测框和真实框重叠程度的指标。mAP是一个综合指标，考虑了精确度和召回率，用于评估模型在不同召回率水平上的性能。在IoU=0.5时，如果预测框与真实框的重叠程度达到或超过50%，则认为该预测是正确的。mAP50-95： 表示在IoU从0.5到0.95（间隔0.05）的范围内，模型的平均精度。这是一个更严格的评估标准，要求预测框与真实框的重叠程度更高。在目标检测任务中，更高的IoU阈值意味着模型需要更准确地定位目标对象。mAP50-95的计算考虑了从宽松到严格的多个IoU阈值，因此能够更全面地评估模型的性能。这些指标共同构成了评估目标检测模型性能的重要框架。通过比较不同模型在这些指标上的表现，可以判断哪个模型在实际应用中可能更有效。
【使用步骤】
使用步骤：
（1）首先根据官方框架ultralytics安装教程安装好yolov11环境，并安装好pyqt5
（2）切换到自己安装的yolo11环境后，并切换到源码目录，执行python main.py即可运行启动界面，进行相应的操作即可
【提供文件】
python源码
yolo11n.onnx模型（不提供pytorch模型）
训练的map,P,R曲线图(在weights\results.png)
测试图片（在test_img文件夹下面）
注意不提供数据集