主要内容
trainssdobjectdetector
训练SSD深度学习对象检测器
句法
描述
训练探测器
trainedDetector= TrainsSdobjectDetector（trainingData那LGRAPH.那选项）
此功能要求您有深入学习工具箱™。建议您还有并行计算工具箱™与CUDA一起使用®-NeLabled nvidia.®GPU。有关支持的计算能力的信息，请参阅金宝appGPU支金宝app持情况（并行计算工具箱）。
[每次迭代还返回有关培训进度的信息，例如培训损失和准确性。trainedDetector那信息) = trainSSDObjectDetector (___）
恢复训练探测器
trainedDetector= TrainsSdobjectDetector（trainingData那检查点那选项）
调整一个检测器
trainedDetector= TrainsSdobjectDetector（trainingData那探测器那选项）
额外的属性
trainedDetector= TrainsSdobjectDetector（___那名称，价值）名称，价值对参数和任何先前的输入。
例子
训练SSD对象检测器
加载车辆检测的训练数据进入工作区。
data = load（“vehicleTrainingData.mat”）;trainingData = data.vehicleTrainingData;
指定存储培训样本的目录。在培训数据中添加文件名的完整路径。
dataDir = fullfile (toolboxdir ('想象'），'VisionData'）;trainingdata.imagefilename = fullfile（datadir，trainingdata.imagefilename）;
使用表中的文件创建一个图像数据存储。
IMDS = imageageAtastore（trainingData.ImageFilename）;
使用表中的标签列创建框标签数据存储。
BLDS = BoxLabeldAtastore（TrainingData（:,2：结束））;
结合数据存储。
DS =联合（IMDS，BLD）;
加载预先突出的SSD对象检测网络。
net = load（'ssdvehicledetectorm.at'）;lgraph = net.lgraph
Lapraph =具有属性的图层图：图层：[132×1 nnet.cnn.layer.layer]连接：[141×2表] InputNames：{'input_1'} OutputNames：{'focal_loss''rcnnboxregression'}
检查SSD网络中的图层及其属性。您还可以通过遵循所提供的步骤来创建SSD网络创建SSD对象检测网络。
lapraph.Layers.
ANS = 132×1层阵列带图层：1'INPUT_1'图像输入224×224×3图像，具有“ZSCORE”归一化2'CONC1'卷积32 3×3×3卷绕与步幅[2 2]并填充'相同'3'BN_CONV1'批量归一化批量归一化与32通道4'CONV1_RELU'夹释放的Relu Cliple Relu与天花板6 5'扩展_CONV_DEPTHWIES'分组32组1 3×3×1卷绕卷曲[1 1]并填充'同样'6'Expanded_conv_depthWise_bn'批量归一化批量归一化与32通道7'扩展_conv_depthwise_relu'剪切refliprecreu与天花板6 8'扩展_conv_project'卷积16 1×1×32卷积与步幅[11]和填充'相同'9'expanded_conv_project_bn'批量归一化具有16个通道10'Lock_1_14的批量归一化卷积96 1×1×16卷绕与步幅[11]和填充'相同的'11'块_1_expand_bn'批量归一化批量归一化与96通道12'块_ xpand_relu'剪辑recurecreure使用天花板6 13'块_1_1_depthise'分组卷积96组1 3×3×1卷绕卷曲[2 2]和填充'相同的'14'块_1_depthWise_bn'批量标准化与96通道15'块_1_depthWise_relu'剪裁释放recure天花板6 16'Block_1_Project'卷积24 1×1×96卷绕与步幅[11]和填充'相同的'17'块_1_Project_Bn'批量归一化批量归一化批量归一化与24通道18'块_展开的卷积144 1×1×24卷积与步幅的卷积[11]和填充'相同的'19'块_2_expand_bn'批量归一化批量归一化与144通道20'block_2_expand_relu'剪切的Relu剪切的recure，其中带有天花板6 21'块_2_depthwher'分组的卷积144组的1 3×3×1卷绕卷积[1 1 1]和填充'相同的'22'块_2_depthWise_bn'批量归一化批量归一化与144通道23'块_2_depthWise_relu'夹释放的recrure relu与天花板6 24'块_project'卷积241×1×144卷绕卷发[11]和填充'相同'25'块_2_project_bn'批量归一化批量归一化与24通道26'block_2_add'添加元素 - 方面添加2个输入27'块_展开'卷积144 1×1×24卷绕卷曲[11]和填充'相同'28'块_3_expand_bn'批量归一化批量归一化与144通道29'block_3_expand_relu'剪切Relu剪切的reflure relu，带有天花板6 30'block_3_depthwhe'分组卷积144组1 3×3×1的组带有步幅的卷曲[2 2]和填充'相同的'31'块_3_depthWise_bn'批量归一化批量标准化，带有144个通道32'块_3_depthWise_relu'剪裁释放reflure，带有天花板6 33'block_3_project'卷积32 1×1×144卷积[1 1] and padding 'same' 34 'block_3_project_BN' Batch Normalization Batch normalization with 32 channels 35 'block_4_expand' Convolution 192 1×1×32 convolutions with stride [1 1] and padding 'same' 36 'block_4_expand_BN' Batch Normalization Batch normalization with 192 channels 37 'block_4_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6 38 'block_4_depthwise' Grouped Convolution 192 groups of 1 3×3×1 convolutions with stride [1 1] and padding 'same' 39 'block_4_depthwise_BN' Batch Normalization Batch normalization with 192 channels 40 'block_4_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6 41 'block_4_project' Convolution 32 1×1×192 convolutions with stride [1 1] and padding 'same' 42 'block_4_project_BN' Batch Normalization Batch normalization with 32 channels 43 'block_4_add' Addition Element-wise addition of 2 inputs 44 'block_5_expand' Convolution 192 1×1×32 convolutions with stride [1 1] and padding 'same' 45 'block_5_expand_BN' Batch Normalization Batch normalization with 192 channels 46 'block_5_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6 47 'block_5_depthwise' Grouped Convolution 192 groups of 1 3×3×1 convolutions with stride [1 1] and padding 'same' 48 'block_5_depthwise_BN' Batch Normalization Batch normalization with 192 channels 49 'block_5_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6 50 'block_5_project' Convolution 32 1×1×192 convolutions with stride [1 1] and padding 'same' 51 'block_5_project_BN' Batch Normalization Batch normalization with 32 channels 52 'block_5_add' Addition Element-wise addition of 2 inputs 53 'block_6_expand' Convolution 192 1×1×32 convolutions with stride [1 1] and padding 'same' 54 'block_6_expand_BN' Batch Normalization Batch normalization with 192 channels 55 'block_6_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6 56 'block_6_depthwise' Grouped Convolution 192 groups of 1 3×3×1 convolutions with stride [2 2] and padding 'same' 57 'block_6_depthwise_BN' Batch Normalization Batch normalization with 192 channels 58 'block_6_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6 59 'block_6_project' Convolution 64 1×1×192 convolutions with stride [1 1] and padding 'same' 60 'block_6_project_BN' Batch Normalization Batch normalization with 64 channels 61 'block_7_expand' Convolution 384 1×1×64 convolutions with stride [1 1] and padding 'same' 62 'block_7_expand_BN' Batch Normalization Batch normalization with 384 channels 63 'block_7_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6 64 'block_7_depthwise' Grouped Convolution 384 groups of 1 3×3×1 convolutions with stride [1 1] and padding 'same' 65 'block_7_depthwise_BN' Batch Normalization Batch normalization with 384 channels 66 'block_7_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6 67 'block_7_project' Convolution 64 1×1×384 convolutions with stride [1 1] and padding 'same' 68 'block_7_project_BN' Batch Normalization Batch normalization with 64 channels 69 'block_7_add' Addition Element-wise addition of 2 inputs 70 'block_8_expand' Convolution 384 1×1×64 convolutions with stride [1 1] and padding 'same' 71 'block_8_expand_BN' Batch Normalization Batch normalization with 384 channels 72 'block_8_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6 73 'block_8_depthwise' Grouped Convolution 384 groups of 1 3×3×1 convolutions with stride [1 1] and padding 'same' 74 'block_8_depthwise_BN' Batch Normalization Batch normalization with 384 channels 75 'block_8_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6 76 'block_8_project' Convolution 64 1×1×384 convolutions with stride [1 1] and padding 'same' 77 'block_8_project_BN' Batch Normalization Batch normalization with 64 channels 78 'block_8_add' Addition Element-wise addition of 2 inputs 79 'block_9_expand' Convolution 384 1×1×64 convolutions with stride [1 1] and padding 'same' 80 'block_9_expand_BN' Batch Normalization Batch normalization with 384 channels 81 'block_9_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6 82 'block_9_depthwise' Grouped Convolution 384 groups of 1 3×3×1 convolutions with stride [1 1] and padding 'same' 83 'block_9_depthwise_BN' Batch Normalization Batch normalization with 384 channels 84 'block_9_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6 85 'block_9_project' Convolution 64 1×1×384 convolutions with stride [1 1] and padding 'same' 86 'block_9_project_BN' Batch Normalization Batch normalization with 64 channels 87 'block_9_add' Addition Element-wise addition of 2 inputs 88 'block_10_expand' Convolution 384 1×1×64 convolutions with stride [1 1] and padding 'same' 89 'block_10_expand_BN' Batch Normalization Batch normalization with 384 channels 90 'block_10_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6 91 'block_10_depthwise' Grouped Convolution 384 groups of 1 3×3×1 convolutions with stride [1 1] and padding 'same' 92 'block_10_depthwise_BN' Batch Normalization Batch normalization with 384 channels 93 'block_10_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6 94 'block_10_project' Convolution 96 1×1×384 convolutions with stride [1 1] and padding 'same' 95 'block_10_project_BN' Batch Normalization Batch normalization with 96 channels 96 'block_11_expand' Convolution 576 1×1×96 convolutions with stride [1 1] and padding 'same' 97 'block_11_expand_BN' Batch Normalization Batch normalization with 576 channels 98 'block_11_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6 99 'block_11_depthwise' Grouped Convolution 576 groups of 1 3×3×1 convolutions with stride [1 1] and padding 'same' 100 'block_11_depthwise_BN' Batch Normalization Batch normalization with 576 channels 101 'block_11_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6 102 'block_11_project' Convolution 96 1×1×576 convolutions with stride [1 1] and padding 'same' 103 'block_11_project_BN' Batch Normalization Batch normalization with 96 channels 104 'block_11_add' Addition Element-wise addition of 2 inputs 105 'block_12_expand' Convolution 576 1×1×96 convolutions with stride [1 1] and padding 'same' 106 'block_12_expand_BN' Batch Normalization Batch normalization with 576 channels 107 'block_12_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6 108 'block_12_depthwise' Grouped Convolution 576 groups of 1 3×3×1 convolutions with stride [1 1] and padding 'same' 109 'block_12_depthwise_BN' Batch Normalization Batch normalization with 576 channels 110 'block_12_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6 111 'block_12_project' Convolution 96 1×1×576 convolutions with stride [1 1] and padding 'same' 112 'block_12_project_BN' Batch Normalization Batch normalization with 96 channels 113 'block_12_add' Addition Element-wise addition of 2 inputs 114 'block_13_expand' Convolution 576 1×1×96 convolutions with stride [1 1] and padding 'same' 115 'block_13_expand_BN' Batch Normalization Batch normalization with 576 channels 116 'block_13_expand_relu' Clipped ReLU Clipped ReLU with ceiling 6 117 'block_13_depthwise' Grouped Convolution 576 groups of 1 3×3×1 convolutions with stride [2 2] and padding 'same' 118 'block_13_depthwise_BN' Batch Normalization Batch normalization with 576 channels 119 'block_13_depthwise_relu' Clipped ReLU Clipped ReLU with ceiling 6 120 'block_13_project' Convolution 160 1×1×576 convolutions with stride [1 1] and padding 'same' 121 'block_13_project_BN' Batch Normalization Batch normalization with 160 channels 122 'block_13_project_BN_anchorbox1' Anchor Box Layer. Anchor Box Layer. 123 'block_13_project_BN_mbox_conf_1' Convolution 10 3×3 convolutions with stride [1 1] and padding [1 1 1 1] 124 'block_13_project_BN_mbox_loc_1' Convolution 20 3×3 convolutions with stride [1 1] and padding [1 1 1 1] 125 'block_10_project_BN_anchorbox2' Anchor Box Layer. Anchor Box Layer. 126 'block_10_project_BN_mbox_conf_1' Convolution 10 3×3 convolutions with stride [1 1] and padding [1 1 1 1] 127 'block_10_project_BN_mbox_loc_1' Convolution 20 3×3 convolutions with stride [1 1] and padding [1 1 1 1] 128 'confmerge' SSD Merge Layer. SSD Merge Layer. 129 'locmerge' SSD Merge Layer. SSD Merge Layer. 130 'anchorBoxSoft' Softmax softmax 131 'focal_loss' Focal Loss Layer. Focal Loss Layer. 132 'rcnnboxRegression' Box Regression Output smooth-l1 loss
配置网络培训选项。
选择= trainingOptions ('sgdm'那......'italllearnrate'，5e-5，......'minibatchsize'16，......'verbose'，真的，......'maxepochs', 50岁,......'洗牌'那'每个时代'那......“VerboseFrequency”10，......“CheckpointPath”，Tempdir）;
培训SSD网络。
[探测器，信息] = TRAINESDOBJECTDETECTOR（DS，LGROPP，选项）;
*************************************************************************训练an SSD Object Detector for the following object classes: * vehicle Training on single CPU. Initializing input data normalization. |=======================================================================================================| | Epoch | Iteration | Time Elapsed | Mini-batch | Mini-batch | Mini-batch | Base Learning | | | | (hh:mm:ss) | Loss | Accuracy | RMSE | Rate | |=======================================================================================================| | 1 | 1 | 00:00:18 | 0.8757 | 48.51% | 1.47 | 5.0000e-05 | | 1 | 10 | 00:01:43 | 0.8386 | 48.35% | 1.43 | 5.0000e-05 | | 2 | 20 | 00:03:15 | 0.7860 | 48.87% | 1.37 | 5.0000e-05 | | 2 | 30 | 00:04:42 | 0.6771 | 48.65% | 1.23 | 5.0000e-05 | | 3 | 40 | 00:06:46 | 0.7129 | 48.43% | 1.28 | 5.0000e-05 | | 3 | 50 | 00:08:37 | 0.5723 | 49.04% | 1.09 | 5.0000e-05 | | 4 | 60 | 00:10:12 | 0.5632 | 48.72% | 1.08 | 5.0000e-05 | | 4 | 70 | 00:11:25 | 0.5438 | 49.11% | 1.06 | 5.0000e-05 | | 5 | 80 | 00:12:35 | 0.5277 | 48.48% | 1.03 | 5.0000e-05 | | 5 | 90 | 00:13:39 | 0.4711 | 48.95% | 0.96 | 5.0000e-05 | | 6 | 100 | 00:14:50 | 0.5063 | 48.72% | 1.00 | 5.0000e-05 | | 7 | 110 | 00:16:11 | 0.4812 | 48.99% | 0.97 | 5.0000e-05 | | 7 | 120 | 00:17:27 | 0.5248 | 48.53% | 1.04 | 5.0000e-05 | | 8 | 130 | 00:18:33 | 0.4245 | 49.32% | 0.90 | 5.0000e-05 | | 8 | 140 | 00:19:37 | 0.4889 | 48.87% | 0.98 | 5.0000e-05 | | 9 | 150 | 00:20:47 | 0.4213 | 49.18% | 0.89 | 5.0000e-05 | | 9 | 160 | 00:22:02 | 0.4753 | 49.45% | 0.97 | 5.0000e-05 | | 10 | 170 | 00:23:13 | 0.4454 | 49.31% | 0.92 | 5.0000e-05 | | 10 | 180 | 00:24:22 | 0.4378 | 49.26% | 0.92 | 5.0000e-05 | | 11 | 190 | 00:25:29 | 0.4278 | 49.13% | 0.90 | 5.0000e-05 | | 12 | 200 | 00:26:39 | 0.4494 | 49.77% | 0.93 | 5.0000e-05 | | 12 | 210 | 00:27:45 | 0.4298 | 49.03% | 0.90 | 5.0000e-05 | | 13 | 220 | 00:28:47 | 0.4296 | 49.86% | 0.90 | 5.0000e-05 | | 13 | 230 | 00:30:05 | 0.3987 | 49.65% | 0.86 | 5.0000e-05 | | 14 | 240 | 00:31:13 | 0.4042 | 49.46% | 0.87 | 5.0000e-05 | | 14 | 250 | 00:32:20 | 0.4244 | 50.16% | 0.90 | 5.0000e-05 | | 15 | 260 | 00:33:31 | 0.4374 | 49.72% | 0.93 | 5.0000e-05 | | 15 | 270 | 00:34:38 | 0.4016 | 48.95% | 0.86 | 5.0000e-05 | | 16 | 280 | 00:35:47 | 0.4289 | 49.44% | 0.91 | 5.0000e-05 | | 17 | 290 | 00:36:58 | 0.3866 | 49.10% | 0.84 | 5.0000e-05 | | 17 | 300 | 00:38:10 | 0.4077 | 49.59% | 0.87 | 5.0000e-05 | | 18 | 310 | 00:39:24 | 0.3943 | 49.74% | 0.86 | 5.0000e-05 | | 18 | 320 | 00:40:48 | 0.4206 | 49.99% | 0.89 | 5.0000e-05 | | 19 | 330 | 00:41:53 | 0.4504 | 49.72% | 0.94 | 5.0000e-05 | | 19 | 340 | 00:42:55 | 0.3449 | 50.38% | 0.78 | 5.0000e-05 | | 20 | 350 | 00:44:01 | 0.3450 | 49.57% | 0.77 | 5.0000e-05 | | 20 | 360 | 00:44:59 | 0.3769 | 50.24% | 0.83 | 5.0000e-05 | | 21 | 370 | 00:46:05 | 0.3336 | 50.40% | 0.76 | 5.0000e-05 | | 22 | 380 | 00:47:01 | 0.3453 | 49.27% | 0.78 | 5.0000e-05 | | 22 | 390 | 00:48:04 | 0.4011 | 49.72% | 0.87 | 5.0000e-05 | | 23 | 400 | 00:49:06 | 0.3307 | 50.32% | 0.75 | 5.0000e-05 | | 23 | 410 | 00:50:03 | 0.3186 | 50.01% | 0.73 | 5.0000e-05 | | 24 | 420 | 00:51:10 | 0.3491 | 50.43% | 0.78 | 5.0000e-05 | | 24 | 430 | 00:52:17 | 0.3299 | 50.31% | 0.76 | 5.0000e-05 | | 25 | 440 | 00:53:35 | 0.3326 | 50.78% | 0.76 | 5.0000e-05 | | 25 | 450 | 00:54:42 | 0.3219 | 50.61% | 0.75 | 5.0000e-05 | | 26 | 460 | 00:55:55 | 0.3090 | 50.59% | 0.71 | 5.0000e-05 | | 27 | 470 | 00:57:08 | 0.3036 | 51.48% | 0.71 | 5.0000e-05 | | 27 | 480 | 00:58:16 | 0.3359 | 50.43% | 0.76 | 5.0000e-05 | | 28 | 490 | 00:59:24 | 0.3182 | 50.35% | 0.73 | 5.0000e-05 | | 28 | 500 | 01:00:36 | 0.3265 | 50.71% | 0.76 | 5.0000e-05 | | 29 | 510 | 01:01:44 | 0.3415 | 50.53% | 0.78 | 5.0000e-05 | | 29 | 520 | 01:02:51 | 0.3126 | 51.15% | 0.73 | 5.0000e-05 | | 30 | 530 | 01:03:59 | 0.3179 | 50.74% | 0.75 | 5.0000e-05 | | 30 | 540 | 01:05:15 | 0.3032 | 50.83% | 0.72 | 5.0000e-05 | | 31 | 550 | 01:06:25 | 0.2868 | 50.69% | 0.68 | 5.0000e-05 | | 32 | 560 | 01:07:42 | 0.2716 | 50.85% | 0.66 | 5.0000e-05 | | 32 | 570 | 01:08:53 | 0.3016 | 51.32% | 0.71 | 5.0000e-05 | | 33 | 580 | 01:10:05 | 0.2624 | 51.35% | 0.63 | 5.0000e-05 | | 33 | 590 | 01:11:12 | 0.3145 | 51.38% | 0.73 | 5.0000e-05 | | 34 | 600 | 01:12:31 | 0.2949 | 51.28% | 0.70 | 5.0000e-05 | | 34 | 610 | 01:13:46 | 0.3070 | 51.22% | 0.73 | 5.0000e-05 | | 35 | 620 | 01:15:01 | 0.3119 | 51.49% | 0.73 | 5.0000e-05 | | 35 | 630 | 01:16:14 | 0.2869 | 51.81% | 0.70 | 5.0000e-05 | | 36 | 640 | 01:17:28 | 0.3401 | 51.28% | 0.78 | 5.0000e-05 | | 37 | 650 | 01:18:40 | 0.3123 | 51.43% | 0.73 | 5.0000e-05 | | 37 | 660 | 01:19:58 | 0.2954 | 51.27% | 0.71 | 5.0000e-05 | | 38 | 670 | 01:21:12 | 0.2792 | 52.17% | 0.68 | 5.0000e-05 | | 38 | 680 | 01:22:29 | 0.3225 | 51.36% | 0.76 | 5.0000e-05 | | 39 | 690 | 01:23:41 | 0.2867 | 52.63% | 0.69 | 5.0000e-05 | | 39 | 700 | 01:24:56 | 0.3067 | 51.52% | 0.73 | 5.0000e-05 | | 40 | 710 | 01:26:13 | 0.2718 | 51.84% | 0.66 | 5.0000e-05 | | 40 | 720 | 01:27:25 | 0.2888 | 52.03% | 0.70 | 5.0000e-05 | | 41 | 730 | 01:28:42 | 0.2854 | 51.96% | 0.69 | 5.0000e-05 | | 42 | 740 | 01:29:57 | 0.2744 | 51.18% | 0.67 | 5.0000e-05 | | 42 | 750 | 01:31:10 | 0.2582 | 51.90% | 0.64 | 5.0000e-05 | | 43 | 760 | 01:32:25 | 0.2586 | 52.48% | 0.64 | 5.0000e-05 | | 43 | 770 | 01:33:35 | 0.2632 | 51.47% | 0.65 | 5.0000e-05 | | 44 | 780 | 01:34:46 | 0.2532 | 51.58% | 0.63 | 5.0000e-05 | | 44 | 790 | 01:36:07 | 0.2889 | 52.19% | 0.69 | 5.0000e-05 | | 45 | 800 | 01:37:20 | 0.2551 | 52.35% | 0.63 | 5.0000e-05 | | 45 | 810 | 01:38:27 | 0.2863 | 51.29% | 0.69 | 5.0000e-05 | | 46 | 820 | 01:39:43 | 0.2700 | 52.58% | 0.67 | 5.0000e-05 | | 47 | 830 | 01:40:54 | 0.3234 | 51.96% | 0.76 | 5.0000e-05 | | 47 | 840 | 01:42:08 | 0.2819 | 52.88% | 0.69 | 5.0000e-05 | | 48 | 850 | 01:43:23 | 0.2743 | 52.80% | 0.67 | 5.0000e-05 | | 48 | 860 | 01:44:38 | 0.2365 | 52.21% | 0.60 | 5.0000e-05 | | 49 | 870 | 01:45:58 | 0.2271 | 52.23% | 0.58 | 5.0000e-05 | | 49 | 880 | 01:47:21 | 0.3006 | 52.23% | 0.72 | 5.0000e-05 | | 50 | 890 | 01:48:35 | 0.2494 | 52.32% | 0.63 | 5.0000e-05 | | 50 | 900 | 01:49:55 | 0.2383 | 53.51% | 0.61 | 5.0000e-05 | |=======================================================================================================| Detector training complete. *************************************************************************
检查探测器的属性。
探测器
Detector =具有属性的SSDObjectDetector：ModelName：'车辆网络：[1×1 dagnetwork] ClassNames：{'车辆'背景'}锚箱：{[5×2双] [5×2双]}
您可以通过检查每个迭代的训练损失来验证训练的准确性。
图绘图（Info.TrainingLoss）网格在包含(的迭代次数) ylabel ('每次迭代的训练损失'）
在测试图像上测试SSD检测器。
img = imread ('ssdtestdetect.png'）;
在图像上运行SSD对象检测器以进行车辆检测。
[bboxes,分数]=检测(探测器,img);
显示检测结果。
如果（〜isempty（bboxes））img = InsertObjectAnnotation（IMG，“矩形”，bboxes，得分）;结尾图imshow（img）
输入参数
输出参数
参考
W. Liu, E. angelov, D. Erhan, C. Szegedy, S. Reed, C. fu, A.C. Berg。“SSD: Single Shot MultiBox Detector”。欧洲计算机视觉会议(ECCV)，Springer Verlag，2016
也可以看看
应用
功能
对象
在R2020A中介绍
你也可以从以下列表中选择一个网站:
