【算法介紹】

基于YOLOv11的阿爾茲海默癥嚴(yán)重程度檢測系統(tǒng)是一種創(chuàng)新的醫(yī)療輔助工具，旨在通過先進(jìn)的計(jì)算機(jī)視覺技術(shù)提高阿爾茲海默癥的早期診斷和病情監(jiān)測效率。阿爾茲海默癥是一種漸進(jìn)性的神經(jīng)退行性疾病，通常表現(xiàn)為認(rèn)知障礙、記憶喪失和語言障礙等癥狀，早期診斷對于控制疾病發(fā)展至關(guān)重要。

該系統(tǒng)利用YOLOv11模型，這是一種在目標(biāo)檢測領(lǐng)域具有卓越性能的深度學(xué)習(xí)模型。通過對醫(yī)學(xué)影像（如MRI或CT掃描）的分析，YOLOv11能夠準(zhǔn)確提取出與阿爾茲海默癥相關(guān)的有價(jià)值特征。這些特征不僅可以幫助醫(yī)生快速識別阿爾茲海默癥的早期病變，還能夠追蹤病變區(qū)域的變化，從而監(jiān)測病情的進(jìn)展。

此外，基于YOLOv11的系統(tǒng)還能夠提供個(gè)性化的病情分析。由于阿爾茲海默癥患者的病變特征和進(jìn)展速度可能因人而異，該系統(tǒng)能夠?yàn)槊课换颊咛峁┆?dú)特的影像特征分析，進(jìn)而輔助醫(yī)生制定更加精準(zhǔn)和有效的治療方案。

總之，基于YOLOv11的阿爾茲海默癥嚴(yán)重程度檢測系統(tǒng)為醫(yī)生提供了一種高效、準(zhǔn)確的輔助診斷工具，有望改善阿爾茲海默癥患者的診斷體驗(yàn)和治療效果。

【效果展示】

?

【測試環(huán)境】

windows10
anaconda3+python3.8
torch==1.9.0+cu111
ultralytics==8.3.70
onnxruntime==1.16.3

【模型可以檢測出類別】

ModerateDemented
MildDemented
SevereDemented
NonDemented
VeryMildDemented

【訓(xùn)練數(shù)據(jù)集介紹】

超聲波圖像阿爾茲海默癥嚴(yán)重程度檢測數(shù)據(jù)集VOC+YOLO格式3288張5類別-CSDN博客

【訓(xùn)練信息】

參數(shù)	值
訓(xùn)練集圖片數(shù)	2959
驗(yàn)證集圖片數(shù)	329
訓(xùn)練map	99.4%
訓(xùn)練精度(Precision)	97.6%
訓(xùn)練召回率(Recall)	98.1%

?驗(yàn)證集測試精度信息

Class	Images	Instances	P	R	mAP50	mAP50-95
all	329	329	0.976	0.981	0.994	0.994
ModerateDemented	86	86	1	0.987	0.995	0.995
MildDemented	77	77	0.93	1	0.992	0.992
SevereDemented	52	52	0.989	0.962	0.994	0.994
NonDemented	56	56	1	0.959	0.993	0.993
VeryMildDemented	58	58	0.96	1	0.995	0.995

?【部分實(shí)現(xiàn)源碼】

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.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", 0.25)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()

【訓(xùn)練步驟】

使用YOLO11訓(xùn)練自己的數(shù)據(jù)集需要遵循一些基本的步驟。YOLO11是YOLO系列模型的一個(gè)版本，它在前代基礎(chǔ)上做了許多改進(jìn)，包括但不限于更高效的訓(xùn)練流程和更高的精度。以下是訓(xùn)練自己YOLO格式數(shù)據(jù)集的詳細(xì)步驟：

一、 準(zhǔn)備環(huán)境

1. 安裝必要的軟件：確保你的計(jì)算機(jī)上安裝了Python（推薦3.6或更高版本），以及CUDA和cuDNN（如果你打算使用GPU進(jìn)行加速）。

2. 安裝YOLO11庫：你可以通過GitHub克隆YOLOv8的倉庫或者直接通過pip安裝YOLO11。例如：
? ?pip?install ultralytics

二、數(shù)據(jù)準(zhǔn)備

3. 組織數(shù)據(jù)結(jié)構(gòu)：按照YOLO的要求組織你的數(shù)據(jù)文件夾。通常，你需要一個(gè)包含圖像和標(biāo)簽文件的目錄結(jié)構(gòu)，如：

? ?dataset/
? ?├── images/
? ?│ ? ├── train/
? ?│ ? └── val/
? ?├── labels/
? ?│ ? ├── train/
? ?│ ? └── val/

? ?其中，train和val分別代表訓(xùn)練集和驗(yàn)證集。且images文件夾和labels文件夾名字不能隨便改寫或者寫錯，否則會在訓(xùn)練時(shí)候找不到數(shù)據(jù)集。

4. 標(biāo)注數(shù)據(jù)：使用合適的工具對圖像進(jìn)行標(biāo)注，生成YOLO格式的標(biāo)簽文件。每個(gè)標(biāo)簽文件應(yīng)該是一個(gè).txt文件，每行表示一個(gè)邊界框，格式為：

? ?<類別ID> <中心點(diǎn)x> <中心點(diǎn)y> <寬度> <高度>

? ?這些值都是相對于圖像尺寸的歸一化值。

5. 創(chuàng)建數(shù)據(jù)配置文件：創(chuàng)建一個(gè).yaml文件來定義你的數(shù)據(jù)集，包括路徑、類別列表等信息。例如：
yaml
? ?# dataset.yaml
? ?path: ./dataset ?# 數(shù)據(jù)集根目錄
? ?train: images/train ?# 訓(xùn)練圖片相對路徑
? ?val: images/val ?# 驗(yàn)證圖片相對路徑
? ?
? ?nc: 2 ?# 類別數(shù)
? ?names: ['class1', 'class2'] ?# 類別名稱

三、模型訓(xùn)練

6. 加載預(yù)訓(xùn)練模型：可以使用官方提供的預(yù)訓(xùn)練模型作為起點(diǎn)，以加快訓(xùn)練速度并提高性能。

7. 配置訓(xùn)練參數(shù)：根據(jù)需要調(diào)整訓(xùn)練參數(shù)，如批量大小、學(xué)習(xí)率、訓(xùn)練輪次等。這通常可以通過命令行參數(shù)或配置文件完成。

8. 開始訓(xùn)練：使用YOLO11提供的命令行接口開始訓(xùn)練過程。例如：

? ?yolo train data=dataset.yaml model=yolo11n.yaml epochs=100 imgsz=640

更多參數(shù)如下：

參數(shù)	默認(rèn)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.

? ?這里，data參數(shù)指向你的數(shù)據(jù)配置文件，model參數(shù)指定使用的模型架構(gòu)，epochs設(shè)置訓(xùn)練輪次，imgsz設(shè)置輸入圖像的大小。

四、監(jiān)控與評估

9. 監(jiān)控訓(xùn)練過程：觀察損失函數(shù)的變化，確保模型能夠正常學(xué)習(xí)。

10. 評估模型：訓(xùn)練完成后，在驗(yàn)證集上評估模型的性能，查看mAP（平均精確度均值）等指標(biāo)。

11. 調(diào)整超參數(shù)：如果模型的表現(xiàn)不佳，可能需要調(diào)整超參數(shù)，比如增加訓(xùn)練輪次、改變學(xué)習(xí)率等，并重新訓(xùn)練模型。

五、使用模型

12. 導(dǎo)出模型：訓(xùn)練完成后，可以將模型導(dǎo)出為ONNX或其他格式，以便于部署到不同的平臺。比如將pytorch轉(zhuǎn)成onnx模型可以輸入指令
yolo export model=best.pt format=onnx
這樣就會在pt模塊同目錄下面多一個(gè)同名的onnx模型best.onnx

下表詳細(xì)說明了可用于將YOLO模型導(dǎo)出為不同格式的配置和選項(xiàng)。這些設(shè)置對于優(yōu)化導(dǎo)出模型的性能、大小和跨各種平臺和環(huán)境的兼容性至關(guān)重要。正確的配置可確保模型已準(zhǔn)備好以最佳效率部署在預(yù)期的應(yīng)用程序中。

參數(shù)	類型	默認(rèn)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).

調(diào)整這些參數(shù)可以定制導(dǎo)出過程，以滿足特定要求，如部署環(huán)境、硬件約束和性能目標(biāo)。選擇適當(dāng)?shù)母袷胶驮O(shè)置對于實(shí)現(xiàn)模型大小、速度和精度之間的最佳平衡至關(guān)重要。

導(dǎo)出格式：

可用的YOLO11導(dǎo)出格式如下表所示。您可以使用format參數(shù)導(dǎo)出為任何格式，即format='onnx'或format='engine'。您可以直接在導(dǎo)出的模型上進(jìn)行預(yù)測或驗(yàn)證，即yolo predict model=yolo11n.onnx。導(dǎo)出完成后，將顯示您的模型的使用示例。

導(dǎo)出格式	格式參數(shù)	模型	屬性	參數(shù)
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. 測試模型：在新的數(shù)據(jù)上測試模型，確保其泛化能力良好。

以上就是使用YOLO11訓(xùn)練自己數(shù)據(jù)集的基本步驟。請根據(jù)實(shí)際情況調(diào)整這些步驟中的具體細(xì)節(jié)。希望這些信息對你有所幫助！

【常用評估參數(shù)介紹】

在目標(biāo)檢測任務(wù)中，評估模型的性能是至關(guān)重要的。你提到的幾個(gè)術(shù)語是評估模型性能的常用指標(biāo)。下面是對這些術(shù)語的詳細(xì)解釋：

1. Class：
   • 這通常指的是模型被設(shè)計(jì)用來檢測的目標(biāo)類別。例如，一個(gè)模型可能被訓(xùn)練來檢測車輛、行人或動物等不同類別的對象。
2. Images：
   • 表示驗(yàn)證集中的圖片數(shù)量。驗(yàn)證集是用來評估模型性能的數(shù)據(jù)集，與訓(xùn)練集分開，以確保評估結(jié)果的公正性。
3. Instances：
   • 在所有圖片中目標(biāo)對象的總數(shù)。這包括了所有類別對象的總和，例如，如果驗(yàn)證集包含100張圖片，每張圖片平均有5個(gè)目標(biāo)對象，則Instances為500。
4. P（精確度Precision）：
   • 精確度是模型預(yù)測為正樣本的實(shí)例中，真正為正樣本的比例。計(jì)算公式為：Precision = TP / (TP + FP)，其中TP表示真正例（True Positives），FP表示假正例（False Positives）。
5. R（召回率Recall）：
   • 召回率是所有真正的正樣本中被模型正確預(yù)測為正樣本的比例。計(jì)算公式為：Recall = TP / (TP + FN)，其中FN表示假負(fù)例（False Negatives）。
6. mAP50：
   • 表示在IoU（交并比）閾值為0.5時(shí)的平均精度（mean Average Precision）。IoU是衡量預(yù)測框和真實(shí)框重疊程度的指標(biāo)。mAP是一個(gè)綜合指標(biāo)，考慮了精確度和召回率，用于評估模型在不同召回率水平上的性能。在IoU=0.5時(shí)，如果預(yù)測框與真實(shí)框的重疊程度達(dá)到或超過50%，則認(rèn)為該預(yù)測是正確的。
7. mAP50-95：
   • 表示在IoU從0.5到0.95（間隔0.05）的范圍內(nèi)，模型的平均精度。這是一個(gè)更嚴(yán)格的評估標(biāo)準(zhǔn)，要求預(yù)測框與真實(shí)框的重疊程度更高。在目標(biāo)檢測任務(wù)中，更高的IoU閾值意味著模型需要更準(zhǔn)確地定位目標(biāo)對象。mAP50-95的計(jì)算考慮了從寬松到嚴(yán)格的多個(gè)IoU閾值，因此能夠更全面地評估模型的性能。

這些指標(biāo)共同構(gòu)成了評估目標(biāo)檢測模型性能的重要框架。通過比較不同模型在這些指標(biāo)上的表現(xiàn)，可以判斷哪個(gè)模型在實(shí)際應(yīng)用中可能更有效。

【使用步驟】

使用步驟：
（1）首先根據(jù)官方框架ultralytics安裝教程安裝好yolov11環(huán)境，并安裝好pyqt5
（2）切換到自己安裝的yolo11環(huán)境后，并切換到源碼目錄，執(zhí)行python main.py即可運(yùn)行啟動界面，進(jìn)行相應(yīng)的操作即可

【提供文件】

python源碼
yolo11n.onnx模型（不提供pytorch模型）
訓(xùn)練的map,P,R曲線圖(在weights\results.png)
測試圖片（在test_img文件夾下面）