基于YOLO v3深度学习的目标检测

这个示例使用:

这个例子显示了如何训练一个YOLO v3意思对象探测器。

深度学习是一种强大的机器学习技术，可以用来训练健壮的目标探测器。有几种用于物体检测的技术，包括Faster R-CNN、you only look once (YOLO) v2和single shot detector (SSD)。这个例子展示了如何训练YOLO v3对象检测器。YOLO v3在YOLO v2的基础上进行了改进，增加了多尺度的检测，帮助检测更小的对象。此外，将用于训练的损失函数分离为边界盒回归的均方误差和用于目标分类的二叉交叉熵，有助于提高检测精度。

笔记：此示例需要计算机Vision Toolbox™型号为Yolo V3对象检测。您可以从附加资源管理器安装yolo v3对象检测的计算机视觉工具箱模型。有关安装加载项的详细信息，请参阅获取和管理加载项．

下载掠夺网络

使用辅助功能下载佩带的网络downloadproveredyolov3detector.避免等待培训完成。如果您想培训网络，请设置doTraining变量来真的．

dotraining = true;如果~doTraining preTrainedDetector = downloadPretrainedYOLOv3Detector();结束

加载数据

本例使用一个包含295张图像的小标记数据集。这些图像中有许多来自加州理工学院汽车1999年和2001年的数据集，可以在加州理工学院计算视觉中心获得网站，由Pietro Perona创作并获得许可使用。每个图像包含一个或两个已标记的车辆实例。较小的数据集对于研究YOLO v3训练过程很有用，但在实践中，需要更多标记的图像来训练健壮的网络。

解压缩车辆图像并加载车辆地面真实数据。

解压缩vehicleDatasetImages.zipdata =负载('车辆有绳索地面纠址.MAT'）;vehicleDataset = data.vehicleDataset;%将完整路径添加到本地车辆数据文件夹。vehicleDataset。imageFilename = fullfile(pwd，车辆数据集。imageFilename);

笔记：在多个类的情况下，数据也可以组织为三列，其中第一列包含具有路径的图像文件名，第二列包含边界框，第三列必须是包含与每个相对应的标签名称的单元格向量边界盒。有关如何排列边界框和标签的更多信息，请参阅Boxlabeldata商店．

所有的边界框必须在表单中[x y宽度高]．此矢量指定左上角和像素中边界框的大小。

将数据设置为培训集的培训集，以及用于评估网络的测试集。使用60％的训练集数据以及用于测试集的其余部分。

RNG（0）;shuffledindices = randperm（高度（车辆达到））;IDX =楼层（0.6 *长度（Shuffleddices））;TrainingDatatbl =车辆升降机（Shuffledindices（1：Idx），:);testdatatbl =车辆levledataset（Shuffleddindes（Idx + 1：结束），:);

创建用于加载图像的图像数据存储。

imdsTrain = imageDatastore (trainingDataTbl.imageFilename);imdsTest = imageDatastore (testDataTbl.imageFilename);

创建一个数据存储为地面真理边界框。

bldsTrain = boxLabelDatastore(trainingDataTbl(:， 2:end));bldsTest = boxLabelDatastore(testDataTbl(:， 2:end));

组合图像和框标签数据存储。

trainingdata =联合（Imdstrain，Bldstrain）;testdata =组合（IMDSTEST，BLDSTEST）;

使用验证输入数据检测无效图像、边框或标签，例如，

带有无效图像格式或包含nan的示例
包含零/非/输入/空的边界框
缺少/非分类标签。

边界框的值应该是有限的、正的、非分数的、非NaN的，并且应该在具有正高度和正宽度的图像边界内。任何无效样本必须丢弃或修复，以便进行适当的培训。

validateInputData (trainingData);validateInputData (testData);

数据扩充

在训练过程中，通过随机变换原始数据来提高网络的精度。通过使用数据增广，您可以向训练数据添加更多种类，而不必实际增加已标记训练样本的数量。

使用转变功能将自定义数据增强应用于培训数据。的AugmentData.示例最后列出的Helper函数对输入数据应用以下扩展。

HSV空间中的颜色抖动增强
随机水平翻转
随机缩放10%

AugmentedTrainingData =转换（TrainingData，@AugmentData）;

读取相同的图像四次并显示增强训练数据。

％可视化增强图像。augmentedData =细胞(4,1);为k = 1:4 data = read(augmentedTrainingData);augmentedData {k} = insertShape(数据{1 1},“矩形”、数据{1,2});重置(augmentedTrainingData);结束图蒙太奇(augmentedData,“BorderSize”, 10)

定义YOLO V3对象检测器

在该示例中的YOLO V3检测器基于SHREEZENET，并且在末端添加了两个检测头的挤压仪中的特征提取网络。第二检测头是第一检测头的两倍，因此更好地检测小物体。请注意，您可以根据要检测的对象的大小指定任何数量的不同大小的检测头。YOLO V3探测器使用使用训练数据估计的锚盒具有与数据集类型对应的更好的初始前沿，并帮助检测器学习准确地预测盒子。有关锚盒的信息，请参阅用于物体检测的锚盒．

YOLO v3探测器中的YOLO v3网络如下图所示。

您可以使用深层网络设计师（深度学习工具箱）创建图中所示的网络。

指定网络输入大小。选择网络输入大小时，请考虑运行网络本身的最小大小，培训图像的大小以及通过在所选大小处理数据产生的计算成本。可行时，选择接近训练图像大小的网络输入大小，大于网络所需的输入大小。为了降低运行示例的计算成本，请指定[227 227 3]的网络输入大小。

NetworkInputSize = [227 227 3];

首先,使用转变对训练数据进行预处理，以便计算锚盒，因为本例中使用的训练图像大于227 × 227，且大小不同。将锚的数量指定为6，以在锚的数量和平均IoU之间实现良好的权衡。使用extimateanchorboxes.用于估计锚箱的函数。有关估算锚箱的详细信息，请参见从训练数据估算锚盒．在使用预先训练的YOLOv3对象检测器的情况下，需要指定在特定训练数据集上计算的锚盒。注意，评估过程是不确定的。为了防止估计锚盒在调优其他超参数时发生变化，在使用rng进行估计之前设置随机种子。

rng(0) trainingdatafreestimation = transform(trainingData， @(data)preprocessData(data, networkInputSize));numAnchors = 6;[锚，meanIoU] = estimateAnchorBoxes(trainingdatafestimation, numAnchors)

锚=6×242 34 161 130 97 93 143 124 33 24 69 66

平均IOU=0.8423

指定锚箱用于两个探测头。锚箱是[MX1]的单元阵列，其中M表示检测磁头的数量。每个检测头由[NX2]矩阵组成锚，其中n是要使用的锚点的数量。选择锚箱对于每个检测头，基于特征映射大小。使用较大锚规模更小锚在较高规模。为此，对锚使用较大的锚固盒首先并将前三个分配给第一检测头和接下来的三个到第二检测头。

区域=锚(:，1).*锚(:，2);[~， idx] = sort(area，“下”）;锚=锚(idx，:);anchorBoxes ={锚(1:3，:)锚(4:6，:)};

加载在ImageNet数据集上返回的挤压Zenet网络，然后指定类名。您还可以选择加载在Coco数据集上培训的其他佩带网络（如）tiny-yolov3-coco要么darknet53-coco或Imagenet数据集，如MobileNet-v2或ResNet-18。当您使用预先训练过的网络时，YOLO v3的性能更好，训练更快。

baseNetwork=挤压网；classNames=trainingDataTbl.Properties.VariableNames（2:end）；

接下来,创建yolov3objectdetector对象，通过添加检测网络源。选择最优的检测网络源需要反复试验，可以使用analyzeNetwork查找网络内潜在探测网络源的名称。在本例中，使用火9贝壳和火5贝壳层,检测网络源．

yolov3Detector = yolov3ObjectDetector(baseNetwork, classNames, anchorBoxes，'detectionnetworksource', {'fire9-concat','fire5-concat'}）;

或者，代替上面使用Squeezenet创建的网络，使用像MS-Coco这样的较大数据集训练的其他预先训练的yolov3架构可以用于在自定义对象检测任务上传输检测器。通过更改ClassNames和锚盒可以实现转移学习。如果自定义对象检测的类作为在预用网络中培训的类的类别或子类之一，则建议使用转移学习工作流。

预处理培训数据

对增强后的训练数据进行预处理，为训练做准备。的进行预处理方法yolov3objectdetector，对输入数据应用以下预处理操作。

通过维护宽高比将图像的大小调整为网络输入大小。
缩放范围内的图像像素[0 1]．

PreprocessedTrainingData =变换（AugmentedTrainingData，@（数据）预处理（Yolov3Detector，Data））;

读取预处理的训练数据。

data =阅读(preprocessedTrainingData);

显示带有边框的图像。

I=数据{1,1}；bbox=数据{1,2}；annotatedImage=insertShape（I，“矩形”, bbox);annotatedImage = imresize (annotatedImage 2);图imshow (annotatedImage)

重置数据存储。

重置(preprocessedTrainingData);

指定培训选项

指定这些培训选项。

将时代数量设置为80。
将最小批量设置为8.．当使用更高的小批量时，稳定的训练可能具有更高的学习率．不过，这应该根据可用内存进行设置。
将学习率设置为0.001。
将预热时间设置为1000迭代。该参数表示根据公式指数增加学习率的迭代次数 $learningRate \times {(\frac{迭代}{warmupPeriod})}^{4.}$ ．它有助于以更高的学习率稳定梯度。
将L2正则化因子设置为0.0005。
指定惩罚阈值为0.5。与地面真实值重叠小于0.5的检测将被扣分。
初始化梯度的速度［］．这是SGDM用来存储梯度的速度。

numEpochs = 80;miniBatchSize = 8;learningRate = 0.001;warmupPeriod = 1000;l2Regularization = 0.0005;penaltyThreshold = 0.5;速度= [];

火车模型

在GPU上的火车，如果有的话。使用GPU需要并行计算工具箱™和CUDA®的NVIDIA®GPU。有关支持的计算能力的信息，请参阅万博1manbetxGPU支万博1manbetx持情况（并行计算工具箱）．

使用小公子使用支持功能将预处理的训练数据拆分为批次万博1manbetxcreateBatchData.它返回与各自类id相结合的批处理图像和包围框。为了更快地提取训练所需的批处理数据，dispatchInBackground应设置为“true”，以确保使用并行池。

小公子自动检测GPU的可用性。如果您没有GPU，或不想使用GPU进行培训，请设置OutputEnvironment参数到“cpu”．

如果CanUseCallallPool DispartinBackground = True;其他的Disparctinbackground = false;结束mbqTrain = minibatchqueue(preprocesedtrainingdata, 2，)......“MiniBatchSize”，小匹马，......“MiniBatchFcn”，@（图像，框，标签）createbatchdata（图像，框，标签，classnames），......“MiniBatchFormat”, (“SSCB”,""]，......“DispatchInBackground”，disparctinbackground，......“输出广播”, ("",“双倍的”]）;

使用支持功能创建培训进度绘图仪万博1manbetxconfigureTrainingProgressPlotter在培训具有自定义训练循环的侦探对象时看到绘图。

最后，指定自定义训练循环。每一次迭代:

从小公子。如果它没有更多的数据，重置小公子和洗牌。
使用dlfeval.和modelGradients功能。功能modelGradients，作为支持函数列出，返回相对万博1manbetx于中的可学习参数的损失梯度净，相应的迷你批量丢失和当前批次的状态。
将权重衰减因子应用于梯度以进行正则化，以获得更稳健的训练。
确定学习率基于迭代使用Piewiselearnicnratewithwarmup.万博1manbetx支持功能。
使用该探测器参数更新sgdmupdate函数。
更新状态具有移动平均的检测器参数。
为每次迭代显示学习率，总损失和单个损失（盒丢失，对象损耗和丢失）。这些可用于解释各自迭代中各自的损失如何变化。例如，在很少几个迭代后箱体损失的突然飙升意味着预测中有inf或nans。
更新培训进度情节。

如果损失在少数时期达到饱和，培训也可以终止。

如果doTraining%创建学习率和小批量损失的子图。无花果=图;[lossPlotter, learningrateful plotter] = configureTrainingProgressPlotter(fig);迭代= 0;％自定义训练循环。为epoch=1:numEpochs重置（mbqTrain）；洗牌（mbqTrain）；而（hasdata（mbqTrain））迭代=迭代+1[XTrain，YTrain]=下一个（mbqTrain）；％使用dlfeval和dlfeval评估模型渐变和损失% modelGradients函数。[gradients，state，lossInfo]=dlfeval（@modelGradients，yolov3Detector，XTrain，YTrain，penaltyThreshold）；%应用L2正则化。gradient = dlupdate(@(g,w) g + l2Regularization*w, gradient, yolov3Detector.Learnables);%确定当前学习率值。currentLR=带预热的分段学习率（迭代、历元、学习率、预热周期、numEpochs）；%使用SGDM优化器更新检测器可学习参数。[yolov3Detector。= sgdmupdate(yolov3检测器。学习性，梯度，速度，当前tlr);％更新DLNetwork的状态参数。yolov3detector.state = state;%显示进展。displayLossInfo(epoch, iteration, currenttlr, lossInfo);%使用新点更新训练图。updateplot (lossPlotter, learningrateful plotter, iteration, currenttlr, lossInfo.totalLoss);结束结束其他的yolov3Detector = preTrainedDetector;结束

时代：1 |迭代：1 |学习率：1E-15 |总损失：2034.4574 |箱体损失：1.2703 |物体损失：2032.5195 |票价：0.66761时期：1 |迭代：2 |学习率：1.6E-14 |总损失：2040.5183 | Box Loss : 5.8543 | Object Loss : 2033.9915 | Class Loss : 0.67264 Epoch : 1 | Iteration : 3 | Learning Rate : 8.1e-14 | Total Loss : 2039.3861 | Box Loss : 5.0515 | Object Loss : 2033.4391 | Class Loss : 0.89554 Epoch : 1 | Iteration : 4 | Learning Rate : 2.56e-13 | Total Loss : 2045.5454 | Box Loss : 2.5824 | Object Loss : 2042.353 | Class Loss : 0.61002 Epoch : 1 | Iteration : 5 | Learning Rate : 6.25e-13 | Total Loss : 2034.3147 | Box Loss : 4.6497 | Object Loss : 2028.9595 | Class Loss : 0.70542 Epoch : 1 | Iteration : 6 | Learning Rate : 1.296e-12 | Total Loss : 2038.448 | Box Loss : 6.7472 | Object Loss : 2031.0361 | Class Loss : 0.66469 Epoch : 1 | Iteration : 7 | Learning Rate : 2.401e-12 | Total Loss : 2043.5757 | Box Loss : 2.474 | Object Loss : 2040.5457 | Class Loss : 0.55609 Epoch : 1 | Iteration : 8 | Learning Rate : 4.096e-12 | Total Loss : 2044.3937 | Box Loss : 6.5438 | Object Loss : 2037.5107 | Class Loss : 0.33914 Epoch : 1 | Iteration : 9 | Learning Rate : 6.561e-12 | Total Loss : 2029.511 | Box Loss : 2.3748 | Object Loss : 2026.4377 | Class Loss : 0.69853 Epoch : 1 | Iteration : 10 | Learning Rate : 1e-11 | Total Loss : 2026.5184 | Box Loss : 2.2127 | Object Loss : 2023.8136 | Class Loss : 0.49224 Epoch : 1 | Iteration : 11 | Learning Rate : 1.4641e-11 | Total Loss : 2052.4109 | Box Loss : 4.4924 | Object Loss : 2047.2883 | Class Loss : 0.63001 Epoch : 1 | Iteration : 12 | Learning Rate : 2.0736e-11 | Total Loss : 2039.0267 | Box Loss : 4.2858 | Object Loss : 2034.1895 | Class Loss : 0.55147 Epoch : 1 | Iteration : 13 | Learning Rate : 2.8561e-11 | Total Loss : 2053.4932 | Box Loss : 2.1127 | Object Loss : 2050.6885 | Class Loss : 0.69185 Epoch : 1 | Iteration : 14 | Learning Rate : 3.8416e-11 | Total Loss : 2040.917 | Box Loss : 2.8612 | Object Loss : 2037.4712 | Class Loss : 0.58456 Epoch : 1 | Iteration : 15 | Learning Rate : 5.0625e-11 | Total Loss : 2043.094 | Box Loss : 2.8008 | Object Loss : 2039.9056 | Class Loss : 0.3876 Epoch : 1 | Iteration : 16 | Learning Rate : 6.5536e-11 | Total Loss : 2031.8059 | Box Loss : 3.2756 | Object Loss : 2028.0403 | Class Loss : 0.49002 Epoch : 1 | Iteration : 17 | Learning Rate : 8.3521e-11 | Total Loss : 2044.72 | Box Loss : 1.6522 | Object Loss : 2042.4524 | Class Loss : 0.61532 Epoch : 1 | Iteration : 18 | Learning Rate : 1.0498e-10 | Total Loss : 2050.0471 | Box Loss : 4.1119 | Object Loss : 2045.4639 | Class Loss : 0.47138 Epoch : 1 | Iteration : 19 | Learning Rate : 1.3032e-10 | Total Loss : 2033.7067 | Box Loss : 2.8427 | Object Loss : 2030.1394 | Class Loss : 0.72465 Epoch : 1 | Iteration : 20 | Learning Rate : 1.6e-10 | Total Loss : 2036.5347 | Box Loss : 3.1793 | Object Loss : 2032.9583 | Class Loss : 0.39718 Epoch : 1 | Iteration : 21 | Learning Rate : 1.9448e-10 | Total Loss : 2029.7346 | Box Loss : 3.6625 | Object Loss : 2025.3025 | Class Loss : 0.76967 Epoch : 1 | Iteration : 22 | Learning Rate : 2.3426e-10 | Total Loss : 2026.9553 | Box Loss : 2.9675 | Object Loss : 2023.5861 | Class Loss : 0.4017 Epoch : 1 | Iteration : 23 | Learning Rate : 2.7984e-10 | Total Loss : 2019.0176 | Box Loss : 0.84919 | Object Loss : 2017.3557 | Class Loss : 0.81262 Epoch : 2 | Iteration : 24 | Learning Rate : 3.3178e-10 | Total Loss : 2033.5848 | Box Loss : 3.0358 | Object Loss : 2029.8921 | Class Loss : 0.65696 Epoch : 2 | Iteration : 25 | Learning Rate : 3.9063e-10 | Total Loss : 2045.8374 | Box Loss : 3.4959 | Object Loss : 2041.6012 | Class Loss : 0.74038 Epoch : 2 | Iteration : 26 | Learning Rate : 4.5698e-10 | Total Loss : 2036.3354 | Box Loss : 2.1746 | Object Loss : 2033.5701 | Class Loss : 0.59084 Epoch : 2 | Iteration : 27 | Learning Rate : 5.3144e-10 | Total Loss : 2036.7156 | Box Loss : 3.0053 | Object Loss : 2032.9835 | Class Loss : 0.72679 Epoch : 2 | Iteration : 28 | Learning Rate : 6.1466e-10 | Total Loss : 2030.1866 | Box Loss : 3.4409 | Object Loss : 2026.0948 | Class Loss : 0.65091 Epoch : 2 | Iteration : 29 | Learning Rate : 7.0728e-10 | Total Loss : 2026.7745 | Box Loss : 1.0128 | Object Loss : 2025.1301 | Class Loss : 0.63154 Epoch : 2 | Iteration : 30 | Learning Rate : 8.1e-10 | Total Loss : 2039.3251 | Box Loss : 3.1312 | Object Loss : 2035.4562 | Class Loss : 0.73767 Epoch : 2 | Iteration : 31 | Learning Rate : 9.2352e-10 | Total Loss : 2034.1394 | Box Loss : 4.8098 | Object Loss : 2028.5234 | Class Loss : 0.8062 Epoch : 2 | Iteration : 32 | Learning Rate : 1.0486e-09 | Total Loss : 2035.0363 | Box Loss : 4.7082 | Object Loss : 2029.7371 | Class Loss : 0.59096 Epoch : 2 | Iteration : 33 | Learning Rate : 1.1859e-09 | Total Loss : 2053.9387 | Box Loss : 3.5839 | Object Loss : 2049.7886 | Class Loss : 0.56609 Epoch : 2 | Iteration : 34 | Learning Rate : 1.3363e-09 | Total Loss : 2041.5179 | Box Loss : 2.808 | Object Loss : 2038.3765 | Class Loss : 0.33344 Epoch : 2 | Iteration : 35 | Learning Rate : 1.5006e-09 | Total Loss : 2035.2411 | Box Loss : 2.7223 | Object Loss : 2032.0865 | Class Loss : 0.43231 Epoch : 2 | Iteration : 36 | Learning Rate : 1.6796e-09 | Total Loss : 2050.2747 | Box Loss : 5.3999 | Object Loss : 2044.2727 | Class Loss : 0.60193 Epoch : 2 | Iteration : 37 | Learning Rate : 1.8742e-09 | Total Loss : 2043.5969 | Box Loss : 5.5765 | Object Loss : 2037.3926 | Class Loss : 0.6279 Epoch : 2 | Iteration : 38 | Learning Rate : 2.0851e-09 | Total Loss : 2038.2933 | Box Loss : 3.4637 | Object Loss : 2034.1479 | Class Loss : 0.6816 Epoch : 2 | Iteration : 39 | Learning Rate : 2.3134e-09 | Total Loss : 2038.1877 | Box Loss : 2.5275 | Object Loss : 2035.101 | Class Loss : 0.55933 Epoch : 2 | Iteration : 40 | Learning Rate : 2.56e-09 | Total Loss : 2036.8016 | Box Loss : 2.0805 | Object Loss : 2034.0248 | Class Loss : 0.69634 Epoch : 2 | Iteration : 41 | Learning Rate : 2.8258e-09 | Total Loss : 2032.4956 | Box Loss : 2.0393 | Object Loss : 2030.0411 | Class Loss : 0.41518 Epoch : 2 | 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Class Loss : 0.46357 Epoch : 14 | Iteration : 311 | Learning Rate : 9.355e-06 | Total Loss : 22.6707 | Box Loss : 1.0287 | Object Loss : 21.2311 | Class Loss : 0.41088 Epoch : 14 | Iteration : 312 | Learning Rate : 9.4759e-06 | Total Loss : 20.3037 | Box Loss : 0.7972 | Object Loss : 19.2077 | Class Loss : 0.29872 Epoch : 14 | Iteration : 313 | Learning Rate : 9.5979e-06 | Total Loss : 24.9406 | Box Loss : 2.2247 | Object Loss : 22.0054 | Class Loss : 0.7105 Epoch : 14 | Iteration : 314 | Learning Rate : 9.7212e-06 | Total Loss : 21.6491 | Box Loss : 1.3531 | Object Loss : 19.9613 | Class Loss : 0.33469 Epoch : 14 | Iteration : 315 | Learning Rate : 9.8456e-06 | Total Loss : 19.9248 | Box Loss : 0.77617 | Object Loss : 18.7892 | Class Loss : 0.35945 Epoch : 14 | Iteration : 316 | Learning Rate : 9.9712e-06 | Total Loss : 18.9517 | Box Loss : 0.53695 | Object Loss : 18.188 | Class Loss : 0.22678 Epoch : 14 | Iteration : 317 | Learning Rate : 1.0098e-05 | Total Loss : 22.1042 | Box Loss : 1.7879 | Object Loss : 19.8364 | Class Loss : 0.47995 Epoch : 14 | Iteration : 318 | Learning Rate : 1.0226e-05 | Total Loss : 18.7485 | Box Loss : 0.62062 | Object Loss : 17.7331 | Class Loss : 0.39474 Epoch : 14 | Iteration : 319 | Learning Rate : 1.0355e-05 | Total Loss : 20.3975 | Box Loss : 0.87357 | Object Loss : 19.0656 | Class Loss : 0.45827 Epoch : 14 | Iteration : 320 | Learning Rate : 1.0486e-05 | Total Loss : 19.8063 | Box Loss : 1.4676 | Object Loss : 17.9057 | Class Loss : 0.43309 Epoch : 14 | Iteration : 321 | Learning Rate : 1.0617e-05 | Total Loss : 19.4166 | Box Loss : 0.75374 | Object Loss : 18.2976 | Class Loss : 0.36532 Epoch : 14 | Iteration : 322 | Learning Rate : 1.075e-05 | Total Loss : 24.5432 | Box Loss : 1.4762 | Object Loss : 22.4664 | Class Loss : 0.60069 Epoch : 15 | Iteration : 323 | Learning Rate : 1.0885e-05 | Total Loss : 18.4276 | Box Loss : 0.98014 | Object Loss : 17.1027 | Class Loss : 0.34474 Epoch : 15 | Iteration : 324 | Learning Rate : 1.102e-05 | Total Loss : 20.2738 | Box Loss : 1.2267 | Object Loss : 18.6558 | Class Loss : 0.39124 Epoch : 15 | Iteration : 325 | Learning Rate : 1.1157e-05 | Total Loss : 17.5261 | Box Loss : 0.7839 | Object Loss : 16.4204 | Class Loss : 0.32173 Epoch : 15 | Iteration : 326 | Learning Rate : 1.1295e-05 | Total Loss : 20.0305 | Box Loss : 1.0768 | Object Loss : 18.4377 | Class Loss : 0.51609 Epoch : 15 | Iteration : 327 | Learning Rate : 1.1434e-05 | Total Loss : 18.1276 | Box Loss : 1.084 | Object Loss : 16.5841 | Class Loss : 0.45952 Epoch : 15 | Iteration : 328 | Learning Rate : 1.1574e-05 | Total Loss : 16.9899 | Box Loss : 0.52159 | Object Loss : 16.25 | Class Loss : 0.21836 Epoch : 15 | Iteration : 329 | Learning Rate : 1.1716e-05 | Total Loss : 17.2788 | Box Loss : 0.79391 | Object Loss : 16.2221 | Class Loss : 0.26281 Epoch : 15 | Iteration : 330 | Learning Rate : 1.1859e-05 | Total Loss : 16.7677 | Box Loss : 0.59361 | Object Loss : 15.8916 | Class Loss : 0.28244 Epoch : 15 | Iteration : 331 | Learning Rate : 1.2004e-05 | Total Loss : 17.8247 | Box Loss : 0.71991 | Object Loss : 16.5339 | Class Loss : 0.57095 Epoch : 15 | Iteration : 332 | Learning Rate : 1.2149e-05 | Total Loss : 17.9062 | Box Loss : 0.95895 | Object Loss : 16.52 | Class Loss : 0.42724 Epoch : 15 | Iteration : 333 | Learning Rate : 1.2296e-05 | Total Loss : 17.3879 | Box Loss : 0.81765 | Object Loss : 16.1917 | Class Loss : 0.37862 Epoch : 15 | Iteration : 334 | Learning Rate : 1.2445e-05 | Total Loss : 15.6942 | Box Loss : 0.55038 | Object Loss : 14.9074 | Class Loss : 0.23641 Epoch : 15 | Iteration : 335 | Learning Rate : 1.2594e-05 | Total Loss : 15.7281 | Box Loss : 0.60656 | Object Loss : 14.9006 | Class Loss : 0.22098 Epoch : 15 | Iteration : 336 | Learning Rate : 1.2746e-05 | Total Loss : 16.8435 | Box Loss : 0.81891 | Object Loss : 15.6955 | Class Loss : 0.32907 Epoch : 15 | Iteration : 337 | Learning Rate : 1.2898e-05 | Total Loss : 20.7239 | Box Loss : 1.6438 | Object Loss : 18.4175 | Class Loss : 0.66266 Epoch : 15 | Iteration : 338 | Learning Rate : 1.3052e-05 | Total Loss : 15.5238 | Box Loss : 0.49358 | Object Loss : 14.7686 | Class Loss : 0.2616 Epoch : 15 | Iteration : 339 | Learning Rate : 1.3207e-05 | Total Loss : 16.3684 | Box Loss : 0.87811 | Object Loss : 15.1227 | Class Loss : 0.36764 Epoch : 15 | Iteration : 340 | Learning Rate : 1.3363e-05 | Total Loss : 15.784 | Box Loss : 0.90518 | Object Loss : 14.5701 | Class Loss : 0.30873 Epoch : 15 | Iteration : 341 | Learning Rate : 1.3521e-05 | Total Loss : 18.1031 | Box Loss : 1.9281 | Object Loss : 15.805 | Class Loss : 0.36997 Epoch : 15 | Iteration : 342 | Learning Rate : 1.3681e-05 | Total Loss : 17.0571 | Box Loss : 1.5809 | Object Loss : 15.1142 | Class Loss : 0.36199 Epoch : 15 | Iteration : 343 | Learning Rate : 1.3841e-05 | Total Loss : 16.2661 | Box Loss : 0.96523 | Object Loss : 14.9404 | Class Loss : 0.36045 Epoch : 15 | Iteration : 344 | Learning Rate : 1.4003e-05 | Total Loss : 15.5492 | Box Loss : 0.8949 | Object Loss : 14.3923 | Class Loss : 0.26203 Epoch : 15 | Iteration : 345 | Learning Rate : 1.4167e-05 | Total Loss : 20.312 | Box Loss : 1.527 | Object Loss : 18.1565 | Class Loss : 0.62849 Epoch : 16 | Iteration : 346 | Learning Rate : 1.4332e-05 | Total Loss : 17.2633 | Box Loss : 1.634 | Object Loss : 15.2527 | Class Loss : 0.37669 Epoch : 16 | Iteration : 347 | Learning Rate : 1.4498e-05 | Total Loss : 14.8747 | Box Loss : 0.7295 | Object Loss : 13.7303 | Class Loss : 0.41487 Epoch : 16 | Iteration : 348 | Learning Rate : 1.4666e-05 | Total Loss : 14.2806 | Box Loss : 0.53956 | Object Loss : 13.4684 | Class Loss : 0.27264 Epoch : 16 | Iteration : 349 | Learning Rate : 1.4835e-05 | Total Loss : 16.0981 | Box Loss : 1.0712 | Object Loss : 14.523 | Class Loss : 0.50388 Epoch : 16 | Iteration : 350 | Learning Rate : 1.5006e-05 | Total Loss : 13.002 | Box Loss : 0.43276 | Object Loss : 12.3231 | Class Loss : 0.24609 Epoch : 16 | Iteration : 351 | Learning Rate : 1.5178e-05 | Total Loss : 14.86 | Box Loss : 1.3465 | Object Loss : 13.2465 | Class Loss : 0.26695 Epoch : 16 | Iteration : 352 | Learning Rate : 1.5352e-05 | Total Loss : 13.5714 | Box Loss : 0.93982 | Object Loss : 12.4149 | Class Loss : 0.21666 Epoch : 16 | Iteration : 353 | Learning Rate : 1.5527e-05 | Total Loss : 14.0531 | Box Loss : 0.75712 | Object Loss : 12.9685 | Class Loss : 0.32748 Epoch : 16 | Iteration : 354 | Learning Rate : 1.5704e-05 | Total Loss : 12.627 | Box Loss : 0.40763 | Object Loss : 12.0022 | Class Loss : 0.21726 Epoch : 16 | Iteration : 355 | Learning Rate : 1.5882e-05 | Total Loss : 13.1038 | Box Loss : 0.32031 | Object Loss : 12.563 | Class Loss : 0.2205 Epoch : 16 | Iteration : 356 | Learning Rate : 1.6062e-05 | Total Loss : 15.1351 | Box Loss : 1.3416 | Object Loss : 13.4434 | Class Loss : 0.35006 Epoch : 16 | Iteration : 357 | Learning Rate : 1.6243e-05 | Total Loss : 13.5998 | Box Loss : 0.86842 | Object Loss : 12.5723 | Class Loss : 0.15913 Epoch : 16 | Iteration : 358 | Learning Rate : 1.6426e-05 | Total Loss : 17.9834 | Box Loss : 2.2705 | Object Loss : 15.0911 | Class Loss : 0.62189 Epoch : 16 | Iteration : 359 | Learning Rate : 1.661e-05 | Total Loss : 13.2651 | Box Loss : 0.77707 | Object Loss : 12.1661 | Class Loss : 0.32198 Epoch : 16 | Iteration : 360 | Learning Rate : 1.6796e-05 | Total Loss : 12.363 | Box Loss : 0.56516 | Object Loss : 11.603 | Class Loss : 0.1949 Epoch : 16 | Iteration : 361 | Learning Rate : 1.6984e-05 | Total Loss : 16.1314 | Box Loss : 1.521 | Object Loss : 14.0555 | Class Loss : 0.55489 Epoch : 16 | Iteration : 362 | Learning Rate : 1.7173e-05 | Total Loss : 12.5865 | Box Loss : 0.60568 | Object Loss : 11.7713 | Class Loss : 0.20958 Epoch : 16 | Iteration : 363 | Learning Rate : 1.7363e-05 | Total Loss : 15.2939 | Box Loss : 0.98848 | Object Loss : 13.8377 | Class Loss : 0.46774 Epoch : 16 | Iteration : 364 | Learning Rate : 1.7555e-05 | Total Loss : 14.0186 | Box Loss : 1.3655 | Object Loss : 12.2908 | Class Loss : 0.3623 Epoch : 16 | Iteration : 365 | Learning Rate : 1.7749e-05 | Total Loss : 11.411 | Box Loss : 0.53581 | Object Loss : 10.6625 | Class Loss : 0.21268 Epoch : 16 | Iteration : 366 | Learning Rate : 1.7944e-05 | Total Loss : 16.9061 | Box Loss : 2.0613 | Object Loss : 14.1356 | Class Loss : 0.70913 Epoch : 16 | Iteration : 367 | Learning Rate : 1.8141e-05 | Total Loss : 12.6763 | Box Loss : 0.65758 | Object Loss : 11.7863 | Class Loss : 0.23245 Epoch : 16 | Iteration : 368 | Learning Rate : 1.834e-05 | Total Loss : 10.0711 | Box Loss : 0.1887 | Object Loss : 9.6617 | Class Loss : 0.22069 Epoch : 17 | Iteration : 369 | Learning Rate : 1.854e-05 | Total Loss : 12.3867 | Box Loss : 0.68877 | Object Loss : 11.4522 | Class Loss : 0.24571 Epoch : 17 | Iteration : 370 | Learning Rate : 1.8742e-05 | Total Loss : 15.6693 | Box Loss : 1.4483 | Object Loss : 13.7836 | Class Loss : 0.43749 Epoch : 17 | Iteration : 371 | Learning Rate : 1.8945e-05 | Total Loss : 11.2525 | Box Loss : 0.38393 | Object Loss : 10.5911 | Class Loss : 0.27744 Epoch : 17 | Iteration : 372 | Learning Rate : 1.915e-05 | Total Loss : 12.624 | Box Loss : 1.0968 | Object Loss : 11.2567 | Class Loss : 0.2705 Epoch : 17 | Iteration : 373 | Learning Rate : 1.9357e-05 | Total Loss : 14.1601 | Box Loss : 1.9424 | Object Loss : 11.6739 | Class Loss : 0.54376 Epoch : 17 | Iteration : 374 | Learning Rate : 1.9565e-05 | Total Loss : 13.8942 | Box Loss : 1.4335 | Object Loss : 12.0368 | Class Loss : 0.42392 Epoch : 17 | Iteration : 375 | Learning Rate : 1.9775e-05 | Total Loss : 11.7365 | Box Loss : 0.51757 | Object Loss : 10.9588 | Class Loss : 0.2601 Epoch : 17 | Iteration : 376 | Learning Rate : 1.9987e-05 | Total Loss : 13.3418 | Box Loss : 1.1792 | Object Loss : 11.7613 | Class Loss : 0.40132 Epoch : 17 | Iteration : 377 | Learning Rate : 2.0201e-05 | Total Loss : 12.5316 | Box Loss : 0.5521 | Object Loss : 11.662 | Class Loss : 0.31745 Epoch : 17 | Iteration : 378 | Learning Rate : 2.0416e-05 | Total Loss : 11.6554 | Box Loss : 0.71192 | Object Loss : 10.7293 | Class Loss : 0.21421 Epoch : 17 | Iteration : 379 | Learning Rate : 2.0633e-05 | Total Loss : 10.5197 | Box Loss : 0.45873 | Object Loss : 9.8577 | Class Loss : 0.20335 Epoch : 17 | Iteration : 380 | Learning Rate : 2.0851e-05 | Total Loss : 10.8727 | Box Loss : 0.6144 | Object Loss : 10.0022 | Class Loss : 0.25605 Epoch : 17 | Iteration : 381 | Learning Rate : 2.1072e-05 | Total Loss : 12.827 | Box Loss : 1.5994 | Object Loss : 10.7265 | Class Loss : 0.50119 Epoch : 17 | Iteration : 382 | Learning Rate : 2.1294e-05 | Total Loss : 13.5798 | Box Loss : 1.0056 | Object Loss : 12.0783 | Class Loss : 0.49589 Epoch : 17 | Iteration : 383 | Learning Rate : 2.1518e-05 | Total Loss : 10.2467 | Box Loss : 0.38574 | Object Loss : 9.653 | Class Loss : 0.20797 Epoch : 17 | Iteration : 384 | Learning Rate : 2.1743e-05 | Total Loss : 9.5454 | Box Loss : 0.31727 | Object Loss : 9.0364 | Class Loss : 0.19168 Epoch : 17 | Iteration : 385 | Learning Rate : 2.1971e-05 | Total Loss : 10.7994 | Box Loss : 1.0524 | Object Loss : 9.5408 | Class Loss : 0.20626 Epoch : 17 | Iteration : 386 | Learning Rate : 2.22e-05 | Total Loss : 13.8463 | Box Loss : 1.1893 | Object Loss : 12.236 | Class Loss : 0.42102 Epoch : 17 | Iteration : 387 | Learning Rate : 2.2431e-05 | Total Loss : 9.5131 | Box Loss : 0.33435 | Object Loss : 8.9877 | Class Loss : 0.19104 Epoch : 17 | Iteration : 388 | Learning Rate : 2.2663e-05 | Total Loss : 11.7341 | Box Loss : 0.82945 | Object Loss : 10.6175 | Class Loss : 0.28709 Epoch : 17 | Iteration : 389 | Learning Rate : 2.2898e-05 | Total Loss : 12.5049 | Box Loss : 1.1493 | Object Loss : 10.9918 | Class Loss : 0.3639 Epoch : 17 | Iteration : 390 | Learning Rate : 2.3134e-05 | Total Loss : 10.0721 | Box Loss : 0.78613 | Object Loss : 9.0417 | Class Loss : 0.24434 Epoch : 17 | Iteration : 391 | Learning Rate : 2.3373e-05 | Total Loss : 12.3075 | Box Loss : 1.3411 | Object Loss : 10.7143 | Class Loss : 0.25203 Epoch : 18 | Iteration : 392 | Learning Rate : 2.3613e-05 | Total Loss : 9.7277 | Box Loss : 0.45647 | Object Loss : 8.966 | Class Loss : 0.30527 Epoch : 18 | Iteration : 393 | Learning Rate : 2.3854e-05 | Total Loss : 13.1086 | Box Loss : 1.666 | Object Loss : 11.1439 | Class Loss : 0.2987 Epoch : 18 | Iteration : 394 | Learning Rate : 2.4098e-05 | Total Loss : 12.9978 | Box Loss : 0.88292 | Object Loss : 11.6079 | Class Loss : 0.50705 Epoch : 18 | Iteration : 395 | Learning Rate : 2.4344e-05 | Total Loss : 10.7919 | Box Loss : 0.53646 | Object Loss : 9.9745 | Class Loss : 0.28095 Epoch : 18 | Iteration : 396 | Learning Rate : 2.4591e-05 | Total Loss : 9.7264 | Box Loss : 0.7848 | Object Loss : 8.6938 | Class Loss : 0.24779 Epoch : 18 | Iteration : 397 | Learning Rate : 2.4841e-05 | Total Loss : 10.4778 | Box Loss : 1.0495 | Object Loss : 9.2081 | Class Loss : 0.22026 Epoch : 18 | Iteration : 398 | Learning Rate : 2.5092e-05 | Total Loss : 9.169 | Box Loss : 0.57491 | Object Loss : 8.3962 | Class Loss : 0.1979 Epoch : 18 | Iteration : 399 | Learning Rate : 2.5345e-05 | Total Loss : 11.1855 | Box Loss : 1.0002 | Object Loss : 9.9185 | Class Loss : 0.26684 Epoch : 18 | Iteration : 400 | Learning Rate : 2.56e-05 | Total Loss : 10.7894 | Box Loss : 1.1357 | Object Loss : 9.1997 | Class Loss : 0.45402 Epoch : 18 | Iteration : 401 | Learning Rate : 2.5857e-05 | Total Loss : 9.2411 | Box Loss : 0.46813 | Object Loss : 8.5093 | Class Loss : 0.2637 Epoch : 18 | Iteration : 402 | Learning Rate : 2.6116e-05 | Total Loss : 10.2135 | Box Loss : 0.60753 | Object Loss : 9.2861 | Class Loss : 0.31993 Epoch : 18 | Iteration : 403 | Learning Rate : 2.6377e-05 | Total Loss : 11.366 | Box Loss : 0.78865 | Object Loss : 10.2008 | Class Loss : 0.37651 Epoch : 18 | Iteration : 404 | Learning Rate : 2.6639e-05 | Total Loss : 8.2499 | Box Loss : 0.39101 | Object Loss : 7.6322 | Class Loss : 0.22674 Epoch : 18 | Iteration : 405 | Learning ...

评估模型

计算机视觉系统工具箱™提供对象检测器评估功能，以测量常见的指标，如平均精度(evaluateDetectionPrecision）和日志平均小姐率（evaluateDetectionMissRate）.在本例中，使用的是平均精度度量。平均精度提供了一个单一的数字，该数字包括探测器进行正确分类的能力(精度)和探测器找到所有相关对象的能力(回忆)。

结果=检测(yolov3Detector testData,'minibatchsize'8);％使用平均精度度量评估对象检测器。[据美联社、召回、精密]= evaluateDetectionPrecision(结果,testData);

精确召回(PR)曲线显示了探测器在不同召回级别上的精确程度。理想情况下，所有召回级别的精度都是1。

％绘图精度召回曲线。图表（召回率、精度）xlabel('记起'）ylabel（“精确性”网格)在头衔（斯普林特）('平均精度= %.2f'（美联社）

使用YOLO v3检测对象

使用探测器进行对象检测。

％读取数据存储。data =阅读(testData);％获取图像。i =数据{1};[Bboxes，Scores，标签] =检测（Yolov3Detector，i）;%在图像上显示检测结果。I=插入对象注释（I，“矩形”bboxes,分数);图imshow(我)

万博1manbetx支持功能

模型梯度函数

功能modelGradients将yolov3objectdetector对象，一小批输入数据XTrain.与相应的地面真相盒YTrain,指定的惩罚阈值作为输入参数，并返回关于所学习参数的丢失渐变yolov3objectdetector，相应的小批量丢失信息，以及当前批次的状态。

模型梯度函数通过执行这些操作来计算总损失和梯度。

生成预测从输入的一批图像使用向前方法。
收集CPU上的预测以进行后处理。
将YOLO v3网格单元坐标的预测转换为边界框坐标，以方便与地面真实数据进行比较anchorBoxGenerator方法yolov3objectdetector．
利用转换后的预测和地面真实数据生成损失计算目标。这些目标是针对边界框位置(x, y，宽度，高度)，对象可信度和类别概率生成的。见支持函数万博1manbetxgenerateTargets．
计算预测边界框的平均平方误差与目标框坐标。见支持函数万博1manbetxbboxOffsetLoss．
确定预测对象置信度与目标对象置信度的二元交叉熵。见支持函数万博1manbetx目标损失．
确定预测类目标与目标的二元交叉熵。见支持函数万博1manbetx课堂失信．
以所有损失的总和计算总损失。
计算可学习项相对于总损失的梯度。

函数[渐变，状态，信息] = MapeStrients（探测器，XTrain，Ytrain，罚款）InputImagesize =尺寸（XTrain，1：2）;％在CPU中收集地面真相进行后处理YTrain =收集(extractdata (YTrain));%从检测器中提取预测。[gatheredPredictions, YPredCell, state] = forward(检测器，XTrain);%从地面真实数据生成预测目标。[boxTarget、objectnessTarget、classTarget、objectMaskTarget、boxErrorScale]=generateTargets（gatheredPredictions，......YTrain，InputImagesize，Detector.anchorboxes，罚款）;%计算损失。boxLoss=bboxOffsetLoss（YPredCell（：，[2 3 7 8]），boxTarget，objectMaskTarget，boxErrorScale）；objLoss=objectnessLoss（YPredCell（：，1），objectnessTarget，objectMaskTarget）；clsLoss=classConfidenceLoss（YPredCell（：，6），classTarget，objectMaskTarget）；totaloss=boxLoss+objLoss+clsLoss；info.boxLoss=boxLoss；info.objLoss=objLoss；info.clsLoss=clsLoss；info.totaloss=totaloss；%计算可学习物品相对于损失的梯度。gradient = dlgradient(totalLoss, detector.Learnables);结束函数boxLoss=bboxOffsetLoss（boxPredCell、BoxDeltTarget、boxMaskTarget、boxErrorScaleTarget）百分比平方误差用于边界框位置。lossX=sum（cellfun（@（a，b，c，d）mse（a.*c.*d，b.*c.*d），boxPredCell（：，1），BoxDeltTarget（：，1），boxMaskTarget（：，1），boxErrorScaleTarget））；有损=总和（cellfun（@（a，b，c，d）mse（a.*c.*d，b.*c.*d），boxPredCell（：，2），boxDeltaTarget（：，2），boxMaskTarget（：，1），boxErrorScaleTarget））；lossW=sum（cellfun（@（a，b，c，d）mse（a.*c.*d，b.*c.*d），boxPredCell（：，3），boxDeltTarget（：，3），boxMaskTarget（：，1），boxErrorScaleTarget））；lossH=sum（cellfun（@（a，b，c，d）mse（a.*c.*d，b.*c.*d），boxPredCell（：，4），BoxDeltTarget（：，4），boxMaskTarget（：，1），boxErrorScaleTarget））；boxLoss=lossX+lossY+lossW+lossH；结束函数objLoss = objectnessLoss(objectnessPredCell, objectnessDeltaTarget, boxMaskTarget)对象分数的％二进制交叉熵损失。objloss = sum（cellfun（@（a，b，c）联语（a。* c，b。* c，'target类别',“独立”)、objectnessPredCell objectnessDeltaTarget boxMaskTarget (:, 2)));结束函数clsLoss = classconfenceloss (classPredCell, classTarget, boxMaskTarget)类置信度的二元交叉熵损失。clsLoss = sum(cellfun(@(a,b,c)) crossentropy(a.*c,b.*c，))'target类别',“独立”)、classPredCell classTarget boxMaskTarget (:, 3)));结束

增强和数据处理功能

函数data = augmentData (A)%应用随机水平翻转和随机X/Y缩放。得到的盒子如果重叠高于0.25，则缩小界限的％缩小。还，抖动图像颜色。data =细胞(大小(A));为ii = 1:size(A,1) I = A{ii,1};bboxes = {ii, 2};标签= {ii, 3};深圳=大小(I);如果numel（sz）== 3 && sz（3）== 3 i = jittercolorhsv（i，......“对比”,0.0,......“颜色”,0.1,......'饱和',0.2,......'亮度', 0.2);结束%随机翻转图像。tform=随机仿射2d('Xreflection'，真的，“规模”，[1 1.1]）;rut = AffineOutputView（SZ，TForm，“BoundsStyle”,“centerOutput”); I=imwarp（I，t形式，“OutputView”，溃败）;％匹配到框相同的变换。[bboxes，index]=bboxwarp（bboxes，tform，rout，'重叠阈值', 0.25);标签=标签(指标);%仅当通过扭曲移除所有框时，才返回原始数据。如果isempty(indices) data(ii，:) = A(ii，:);其他的data(ii，:) = {I, bboxes, labels};结束结束结束函数数据=预处理数据（数据，targetSize）%调整图像大小并将像素缩放到0到1之间。同时缩放％相应的边界框。为II = 1：大小（数据，1）i =数据{II，1};imgsize =尺寸（i）;将单通道输入图像转换为3通道。如果nummel (imgSize) < 3 I = repmat(I,1,1,3);结束bboxes = {ii, 2}数据;我= im2single (imresize(我targetSize (1:2)));规模= targetSize(1:2)。/ imgSize (1:2);bboxes = bboxresize (bboxes、规模);data(ii, 1:2) = {I, bboxes};结束结束函数[XTrain, YTrain] = createBatchData(data, groundTruthBoxes, groundTruthClasses, classNames)%返回沿XTrain和中的批处理维度组合的图像在YTrain中用classid连接的归一化包围盒％沿批处理尺寸连接图像。XTrain = CAT（4，数据{：，1}）;%从类名获取类id。classNames = repmat({classical (classNames')}， size(groundTruthClasses));[~， classIndices] = cellfun(@(a,b))ismember(a,b)， groundTruthClasses, classNames，'统一输出'、假);%添加标签索引和训练图像大小到缩放的边界框％并创建单个单元格响应数组。@(bbox, classid)[bbox, classid]， groundTruthBoxes, classIndices，'统一输出'，假）；len=最大值（cellfun（@（x）大小（x，1），组合响应）；paddedboxes=cellfun（@（v）paddarray（v，[len size（v，1），0]，0，“职位”)、combinedResponses'统一输出'，错误的）;YTrain = Cat（4，PaddedBoxes {：，1}）;结束

学习率Schedule命令功能

函数CurrentLr = PropewiselearningrateWithwarmup（迭代，时代，学会，Marmupperiod，NumPOCH）% piecewiselearningrateful withwarmup函数计算当前%基于迭代次数的学习率。持久的温花豪;如果迭代<=预热%在预热阶段增加迭代次数的学习率。currentLR = learningRate * (((iteration/warmupPeriod)^4); / /循环周期warmUpEpoch =时代;eleesif.迭代>= warmupPeriod && epoch < warmUpEpoch+floor(0.6*(numEpochs-warmUpEpoch))%在热身期结束后，如果剩余纪元数小于60%，则保持学习率不变。currentLR=学习率；eleesif.epoch> = warmupepoch +地板（0.6 *（numepochs-warmupepoch））&& epoch 如果剩余的时期数量超过60％，但较少％超过90％将学习率乘以0.1。currentlr =学习* 0.1;其他的如果剩余的时代是90%以上乘以学问%率0.01。currentlr =学习* 0.01;结束结束

实用功能

函数[lossPlotter, learningrateful plotter] = configureTrainingProgressPlotter(f)％创建子点以显示丢失和学习率。图(f);clf次要情节(2,1,1);ylabel ('学习率'); xlabel(“迭代”）;learningRatePlotter = animatedline;次要情节(2,1,2);ylabel (“全损”); xlabel(“迭代”）;lossPlotter = animatedline;结束函数displaylossinfo（时代，迭代，currentlr，lossinfo）％显示每次迭代的损耗信息。disp (“时代：”+时代+“|迭代：”+迭代+|学习率:+ currentlr +.......|全损:+ double（收集（提取数据（loctInfo.totalloss））+......“|箱损:”+ double（收集（提取数据（lockInfo.boxloss））+......“|物体丢失:”+双(收集(extractdata (lossInfo.objLoss))) +......|等级损失:+双(收集(extractdata (lossInfo.clsLoss))));结束函数更新平面（导光器，学习速率，迭代，CurrentLR，Totalloss）%更新损失和学习率图。addpoints（导光器，迭代，double（提取数据（收集（totalloss））））））;Addpoints（LearningRatePlotter，迭代，CurrentLr）;drawn结束函数探测器= downloadPretrainedYOLOv3Detector ()下载一个经过训练的yolov3探测器。如果〜存在（“yolov3SqueezeNetVehicleExample_21aSPKG.mat”,'文件')如果〜存在（'YOLOV3SQUEEZENETVEHICEEXAMPLE_21ASPKG.ZIP','文件') disp (“下载预训练检测器…”）;pretrowsurl ='https://ssd.mathworks.com/万博1manbetxsupportfiles/vision/data/yolov3squeezenetvehiceExample_21AspKg.zip';WebSave（'YOLOV3SQUEEZENETVEHICEEXAMPLE_21ASPKG.ZIP', pretrainedURL);结束解压缩（'YOLOV3SQUEEZENETVEHICEEXAMPLE_21ASPKG.ZIP'）;结束pretrained =负载(“yolov3SqueezeNetVehicleExample_21aSPKG.mat”); 检测器=预训练检测器；结束

参考文献

[1] Redmon，Joseph和Ali Farhadi。“yolov3：增量改善。”预印刷品，2018年4月8日提交。https://arxiv.org/abs/1804.02767。

另见