主要内容

万博1manbetx支持的网络，层和类

万博1manbetx支持Pretrained网络

GPU编码器™ supports code generation for series and directed acyclic graph (DAG) convolutional neural networks (CNNs or ConvNets). You can generate code for any trained convolutional neural network whose layers are supported for code generation. See万博1manbetx支撑层. You can train a convolutional neural network on either a CPU, a GPU, or multiple GPUs by using the Deep Learning Toolbox™ or use one of the pretrained networks listed in the table and generate CUDA®code.

Network Name 描述 库丁 TensorRT ARM®有限公司mpute Library for Mali GPU

Alexnet

Alexnetconvolutional neural network. For the pretrained AlexNet model, seeAlexnet(Deep Learning Toolbox).

The syntaxAlexnet('Weights','none')不支持代码生成万博1manbetx。

Yes

Yes

Yes

Caffe Network

Caffe的卷积神经网络模型。有关从Caffe导入预算网络的信息，请参阅importCaffeNetwork(Deep Learning Toolbox).

Yes

Yes

Yes

Darknet-19

Darknet-19 convolutional neural network. For more information, seedarknet19(Deep Learning Toolbox).

The syntaxdarknet19（'weights'，'none'）不支持代码生成万博1manbetx。

Yes

Yes

Yes

Darknet-53

Darknet-53 convolutional neural network. for more information, seedarknet53(Deep Learning Toolbox).

The syntaxdarknet53（'weights'，'none'）不支持代码生成万博1manbetx。

Yes

Yes

Yes

DeepLab V3+

DeepLab V3+卷积神经网络。有关更多信息，请参阅deeplabv3plusLayers(Computer Vision Toolbox).

Yes

Yes

No

DenseNet-201

DenseNet-201 convolutional neural network. For the pretrained DenseNet-201 model, seedensenet201(Deep Learning Toolbox).

The syntaxdensenet201('Weights','none')不支持代码生成万博1manbetx。

Yes

Yes

Yes

有效网络-B0

有效网络-B0convolutional neural network. For the pretrained EfficientNet-b0 model, seeefficientnetb0(Deep Learning Toolbox).

The syntaxefficientnetb0('Weights','none')不支持代码生成万博1manbetx。

 Yes Yes Yes

GoogLeNet

GoogLeNet convolutional neural network. For the pretrained GoogLeNet model, seeGooglenet(Deep Learning Toolbox).

The syntaxGooglenet('Weights','none')不支持代码生成万博1manbetx。

Yes

Yes

Yes

Inception-Resnet-V2

Inception-Resnet-V2convolutional neural network. For the pretrained Inception-ResNet-v2 model, seeinceptionresnetv2(Deep Learning Toolbox).

Yes

Yes

No

Inception-v3

Inception-v3 convolutional neural network. For the pretrained Inception-v3 model, seeinceptionv3(Deep Learning Toolbox).

The syntaxinceptionv3（'weights'，'none'）不支持代码生成万博1manbetx。

Yes

Yes

Yes

Mobilenet-v2

Mobilenet-V2卷积神经网络。对于预审前的Mobilenet-V2模型，请参见MobileNetV2(Deep Learning Toolbox).

The syntaxmobileNetv2（'weights'，'none'）不支持代码生成万博1manbetx。

Yes

Yes

Yes

NASNet-Large

NASNet-Large convolutional neural network. For the pretrained NASNet-Large model, seenasnetlarge(Deep Learning Toolbox).

Yes

Yes

No

NASNET-MOBILE

NASNET-MOBILEconvolutional neural network. For the pretrained NASNet-Mobile model, seenasnetmobile(Deep Learning Toolbox).

Yes

Yes

No

ResNet

ResNet-18, ResNet-50, and ResNet-101 convolutional neural networks. For the pretrained ResNet models, seeRESNET50(Deep Learning Toolbox),resnet18(Deep Learning Toolbox), andresnet101(Deep Learning Toolbox).

The syntaxresnetXX('Weights','none')不支持代码生成万博1manbetx。

Yes

Yes

Yes

SegNet

Multi-class pixelwise segmentation network. For more information, seesegnetLayers(Computer Vision Toolbox).

Yes

Yes

No

SqueezeNet

Small deep neural network. For the pretrained SqueezeNet models, see挤压(Deep Learning Toolbox).

The syntax挤压('Weights','none')不支持代码生成万博1manbetx。

Yes

Yes

Yes

VGG-16

VGG-16 convolutional neural network. For the pretrained VGG-16 model, seeVGG16(Deep Learning Toolbox).

The syntaxVGG16('Weights','none')不支持代码生成万博1manbetx。

Yes

Yes

Yes

VGG-19

VGG-19卷积神经网络。有关验证的VGG-19型号，请参见VGG19(Deep Learning Toolbox).

The syntaxVGG19('Weights','none')不支持代码生成万博1manbetx。

Yes

Yes

Yes

Xception

X Ception卷积神经网络。对于预验证的X受体模型，请参见xception(Deep Learning Toolbox).

The syntaxxception('Weights','none')不支持代码生成万博1manbetx。

Yes

Yes

Yes

Yolo V2

You only look once version 2 convolutional neural network based object detector. For more information, seeyolov2Layers(Computer Vision Toolbox)

Yes

Yes

Yes

万博1manbetx支撑层

GPU Coder为表中指定的目标深度学习库支持以万博1manbetx下层为代码生成。

输入层

Layer Name 描述 库丁 TensorRT ARM Compute Library for Mali GPU

imageInputLayer(Deep Learning Toolbox)

An image input layer inputs 2-D images to a network and applies data normalization.

有限公司de generation does not support'正常化'specified using a function handle.

Yes

Yes

Yes

sequenceInputLayer(Deep Learning Toolbox)

序列输入层将序列数据输入到网络。

Cudnn库支持向量和二维图像序列。万博1manbetxTensorrt库仅支持向量输入序列。万博1manbetx

For vector sequence inputs, the number of features must be a constant during code generation.

For image sequence inputs, the height, width, and the number of channels must be a constant during code generation.

有限公司de generation does not support'正常化'specified using a function handle.

Yes

Yes

No

featureinputlayer(Deep Learning Toolbox)

A feature input layer inputs feature data to a network and applies data normalization.

Yes

Yes

Yes

卷积和完全连接层

Layer Name 描述 库丁 TensorRT ARM Compute Library for Mali GPU

convolution2dLayer(Deep Learning Toolbox)

A 2-D convolutional layer applies sliding convolutional filters to the input.

Yes

Yes

Yes

fullyConnectedLayer(Deep Learning Toolbox)

完全连接的层将输入乘以重量矩阵，然后添加偏置向量。

Yes

Yes

No

groupedConvolution2dLayer(Deep Learning Toolbox)

2D分组的卷积层将输入通道分为组，并应用滑动卷积过滤器。使用分组的卷积层进行通道可分离（也称为深度可分离）卷积。

对于具有2D分组的卷积层，ARM MALI GPU的代码生成不支持万博1manbetxNumGroupsproperty set as'channel-wise'or a value greater than two.

Yes

Yes

Yes

transposedConv2dLayer(Deep Learning Toolbox)

A transposed 2-D convolution layer upsamples feature maps.

Yes

Yes

Yes

Sequence Layers

Layer Name 描述 库丁 TensorRT ARM Compute Library for Mali GPU

bilstmLayer(Deep Learning Toolbox)

A bidirectional LSTM (BiLSTM) layer learns bidirectional long-term dependencies between time steps of time series or sequence data. These dependencies can be useful when you want the network to learn from the complete time series at each time step.

对于代码生成，StateActivationFunctionproperty must be set to'tanh'.

对于代码生成，gateactivationFunctionproperty must be set to'sigmoid'.

Yes

Yes

No

flattenLayer(Deep Learning Toolbox)

A flatten layer collapses the spatial dimensions of the input into the channel dimension.

Yes

No

No

gruLayer(Deep Learning Toolbox)

A GRU layer learns dependencies between time steps in time series and sequence data.

有限公司de generation supports only the'after-multiplication'and“反复偏见 - 复发”重置门模式。

Yes

Yes

No

lstmlayer(Deep Learning Toolbox)

An LSTM layer learns long-term dependencies between time steps in time series and sequence data.

对于代码生成，StateActivationFunctionproperty must be set to'tanh'.

对于代码生成，gateactivationFunctionproperty must be set to'sigmoid'.

Yes

Yes

No

sequenceFoldingLayer(Deep Learning Toolbox)

A sequence folding layer converts a batch of image sequences to a batch of images. Use a sequence folding layer to perform convolution operations on time steps of image sequences independently.

Yes

No

No

sequenceInputLayer(Deep Learning Toolbox)

序列输入层将序列数据输入到网络。

Cudnn库支持向量和二维图像序列。万博1manbetxTensorrt库仅支持向量输入序列。万博1manbetx

For vector sequence inputs, the number of features must be a constant during code generation.

For image sequence inputs, the height, width, and the number of channels must be a constant during code generation.

有限公司de generation does not support'正常化'specified using a function handle.

Yes

Yes

No

sequenceUnfoldingLayer(Deep Learning Toolbox)

A sequence unfolding layer restores the sequence structure of the input data after sequence folding.

Yes

No

No

Wordembeddinglayer(Text Analytics Toolbox)

A word embedding layer maps word indices to vectors.

Yes

Yes

No

Activation Layers

Layer Name 描述 库丁 TensorRT ARM Compute Library for Mali GPU

clippedReluLayer(Deep Learning Toolbox)

A clipped ReLU layer performs a threshold operation, where any input value less than zero is set to zero and any value above theclipping ceilingis set to that clipping ceiling.

Yes

Yes

Yes

eluLayer(Deep Learning Toolbox)

ELU激活层在正输入和负输入上执行指数非线性。

Yes

Yes

No

leakyReluLayer(Deep Learning Toolbox)

A leaky ReLU layer performs a threshold operation, where any input value less than zero is multiplied by a fixed scalar.

Yes

Yes

Yes

reluLayer(Deep Learning Toolbox)

A ReLU layer performs a threshold operation to each element of the input, where any value less than zero is set to zero.

Yes

Yes

Yes

softplusLayer(Reinforcement Learning Toolbox)

A软铂层是一个深层神经网络层，可实现软瓣激活Y= log(1 + eX), which ensures that the output is always positive.

Yes

Yes

No

swishLayer(Deep Learning Toolbox)

Swish激活层在层输入上应用SWISH函数。

Yes

Yes

No

tanhLayer(Deep Learning Toolbox)

A hyperbolic tangent (tanh) activation layer applies the tanh function on the layer inputs.

Yes

Yes

Yes

Normalization, Dropout, and Cropping Layers

Layer Name 描述 库丁 TensorRT ARM Compute Library for Mali GPU

batchNormalizationLayer(Deep Learning Toolbox)

A batch normalization layer normalizes each input channel across a mini-batch.

Yes

Yes

Yes

crop2dLayer(Deep Learning Toolbox)

2-D农作物层适用于输入。

Yes

Yes

Yes

crossChannelNormalizationLayer(Deep Learning Toolbox)

A channel-wise local response (cross-channel) normalization layer carries out channel-wise normalization.

Yes

Yes

Yes

dropoutLayer(Deep Learning Toolbox)

辍学层随机将输入元素随机设置为零，并具有给定的概率。

Yes

Yes

Yes

groupNormalizationLayer(Deep Learning Toolbox)

A group normalization layer normalizes a mini-batch of data across grouped subsets of channels for each observation independently.

Yes

Yes

No

scalingLayer(Reinforcement Learning Toolbox)

演员或评论家网络的缩放层。

For code generation, values for the'Scale'and'Bias'属性必须具有相同的维度。

Yes

Yes

Yes

Pooling and Unpooling Layers

Layer Name 描述 库丁 TensorRT ARM Compute Library for Mali GPU

平均泵2Dlayer(Deep Learning Toolbox)

An average pooling layer performs down-sampling by dividing the input into rectangular pooling regions and computing the average values of each region.

Yes

Yes

Yes

globalAveragePooling2dLayer(Deep Learning Toolbox)

A global average pooling layer performs down-sampling by computing the mean of the height and width dimensions of the input.

Yes

Yes

Yes

globalMaxPooling2dLayer(Deep Learning Toolbox)

A global max pooling layer performs down-sampling by computing the maximum of the height and width dimensions of the input.

Yes

Yes

Yes

maxPooling2dLayer(Deep Learning Toolbox)

A max pooling layer performs down-sampling by dividing the input into rectangular pooling regions, and computing the maximum of each region.

If equal max values exists along the off-diagonal in a kernel window, implementation differences for themaxPooling2dLayermight cause minor numerical mismatch between MATLAB®和生成的代码。此问题还会导致每个合并区域中最大值的指数不匹配。有关更多信息，请参阅maxPooling2dLayer(Deep Learning Toolbox).

Yes

Yes

Yes

maxUnpooling2dLayer(Deep Learning Toolbox)

最大不化层不致密最大池层的输出。

If equal max values exists along the off-diagonal in a kernel window, implementation differences for themaxPooling2dLayer可能会导致MATLAB和生成的代码之间的微小数值不匹配。此问题还会导致每个合并区域中最大值的指数不匹配。有关更多信息，请参阅maxUnpooling2dLayer(Deep Learning Toolbox).

Yes

Yes

No

有限公司mbination Layers

Layer Name 描述 库丁 TensorRT ARM Compute Library for Mali GPU

additionLayer(Deep Learning Toolbox)

An addition layer adds inputs from multiple neural network layers element-wise.

Yes

Yes

Yes

concatenationLayer(Deep Learning Toolbox)

串联层采用输入并沿指定的维度串联。

Yes

Yes

No

depthConcatenationLayer(Deep Learning Toolbox)

A depth concatenation layer takes inputs that have the same height and width and concatenates them along the third dimension (the channel dimension).

Yes

Yes

Yes

Object Detection Layers

Layer Name 描述 库丁 TensorRT ARM Compute Library for Mali GPU

AnchorBoxLayer(Computer Vision Toolbox)

An anchor box layer stores anchor boxes for a feature map used in object detection networks.

Yes

Yes

Yes

DepthTospace2Dlayer（图像处理工具箱）

 2-D深度到空间层将数据从深度维度置于2D空间数据的块中。

Yes

Yes

Yes

focalLossLayer(Computer Vision Toolbox)

 焦点损耗层使用焦点损失预测对象类。

Yes

Yes

Yes

spaceToDepthLayer（图像处理工具箱）

 A space to depth layer permutes the spatial blocks of the input into the depth dimension. Use this layer when you need to combine feature maps of different size without discarding any feature data.

Yes

Yes

Yes

ssdMergeLayer(Computer Vision Toolbox)

An SSD merge layer merges the outputs of feature maps for subsequent regression and classification loss computation.

Yes

Yes

No

rcnnboxRegressionlayer(Computer Vision Toolbox)

 A box regression layer refines bounding box locations by using a smooth L1 loss function. Use this layer to create a Fast or Faster R-CNN object detection network.

Yes

Yes

Yes

rpnClassificationLayer(Computer Vision Toolbox)

 区域建议网络（RPN）分类层将图像区域分类为objectorbackgroundby using a cross entropy loss function. Use this layer to create a Faster R-CNN object detection network.

Yes

Yes

Yes

yolov2outputlayer(Computer Vision Toolbox)

Create output layer for YOLO v2 object detection network.

Yes

Yes

Yes

YOLOv2ReorgLayer(Computer Vision Toolbox)

Create reorganization layer for YOLO v2 object detection network.

Yes

Yes

Yes

yolov2transformlayer(Computer Vision Toolbox)

Create transform layer for YOLO v2 object detection network.

Yes

Yes

Yes

输出层

Layer Name 描述 库丁 TensorRT ARM Compute Library for Mali GPU

classificationLayer(Deep Learning Toolbox)

A classification layer computes the cross entropy loss for multi-class classification problems with mutually exclusive classes.

Yes

Yes

Yes

dicePixelClassificationLayer(Computer Vision Toolbox)

A Dice pixel classification layer provides a categorical label for each image pixel or voxel using generalized Dice loss.

Yes

Yes

Yes

focalLossLayer(Computer Vision Toolbox)

 焦点损耗层使用焦点损失预测对象类。

Yes

Yes

Yes

Output Layer(Deep Learning Toolbox)

All output layers including custom classification or regression output layers created by usingnnet.layer.classificationlayerornnet.layer.RegressionLayer.

有关如何定义自定义分类输出层并指定损失函数的示例，请参见Define Custom Classification Output Layer(Deep Learning Toolbox).

有关如何定义自定义回归输出层并指定损失函数的示例，请参见定义自定义回归输出层(Deep Learning Toolbox).

Yes

Yes

Yes

pixelClassificationLayer(Computer Vision Toolbox)

像素分类层为每个图像像素或体素提供了一个分类标签。

Yes

Yes

Yes

rcnnboxRegressionlayer(Computer Vision Toolbox)

 A box regression layer refines bounding box locations by using a smooth L1 loss function. Use this layer to create a Fast or Faster R-CNN object detection network.

Yes

Yes

Yes

regressionLayer(Deep Learning Toolbox)

回归层为回归问题计算半均值的误差损失。

Yes

Yes

Yes

rpnClassificationLayer(Computer Vision Toolbox)

 区域建议网络（RPN）分类层将图像区域分类为objectorbackgroundby using a cross entropy loss function. Use this layer to create a Faster R-CNN object detection network.

Yes

Yes

Yes

sigmoidLayer(Deep Learning Toolbox)

Sigmoid层将Sigmoid函数应用于输入。

Yes

Yes

Yes

SoftMaxlayer(Deep Learning Toolbox)

A softmax layer applies a softmax function to the input.

Yes

Yes

Yes

Custom Keras Layers

Layer Name 描述 库丁 TensorRT ARM Compute Library for Mali GPU

nnet.keras.layer.cliplayer(Deep Learning Toolbox)

Clips the input between the upper and lower bounds.

Yes

Yes

No

nnet.keras.layer.FlattenCStyleLayer(Deep Learning Toolbox)

Flatten activations into 1-D assuming C-style (row-major) order.

Yes

Yes

Yes

nnet.keras.layer.GlobalAveragePooling2dLayer(Deep Learning Toolbox)

空间数据的全球平均合并层。

Yes

Yes

Yes

nnet.keras.layer.PreluLayer(Deep Learning Toolbox)

Parametric rectified linear unit.

Yes

Yes

No

nnet.keras.layer.SigmoidLayer(Deep Learning Toolbox)

Sigmoid activation layer.

Yes

Yes

Yes

nnet.keras.layer.TanhLayer(Deep Learning Toolbox)

Hyperbolic tangent activation layer.

Yes

Yes

Yes

nnet.keras.layer.TimeDistributedFlattenCStyleLayer(Deep Learning Toolbox)

Flatten a sequence of input image into a sequence of vector, assuming C-style (or row-major) storage ordering of the input layer.

Yes

Yes

No

nnet.keras.layer.ZeroPadding2dLayer(Deep Learning Toolbox)

Zero padding layer for 2-D input.

Yes

Yes

Yes

Custom ONNX Layers

Layer Name 描述 库丁 TensorRT ARM Compute Library for Mali GPU

nnet.onnx.layer.ClipLayer(Deep Learning Toolbox)

Clips the input between the upper and lower bounds.

Yes

Yes

No

nnet.onnx.layer.ElementwiseAffineLayer(Deep Learning Toolbox)

Layer that performs element-wise scaling of the input followed by an addition.

Yes

Yes

Yes

nnet.onnx.layer.FlattenInto2dLayer(Deep Learning Toolbox)

Flattens a MATLAB 2D image batch in the way ONNX does, producing a 2D output array withCBformat.

Yes

Yes

No

nnet.onnx.layer.flattenlayer(Deep Learning Toolbox)

将输入张量的空间尺寸变平至通道尺寸。

Yes

Yes

Yes

nnet.onnx.layer.GlobalAveragePooling2dLayer(Deep Learning Toolbox)

空间数据的全球平均合并层。

Yes

Yes

Yes

nnet.onnx.layer.IdentityLayer(Deep Learning Toolbox)

实现ONNX身份操作员的图层。

Yes

Yes

Yes

nnet.onnx.layer.PreluLayer(Deep Learning Toolbox)

Parametric rectified linear unit.

Yes

Yes

No

nnet.onnx.layer.SigmoidLayer(Deep Learning Toolbox)

Sigmoid activation layer.

Yes

Yes

Yes

nnet.onnx.layer.tanhlayer(Deep Learning Toolbox)

Hyperbolic tangent activation layer.

Yes

Yes

Yes

nnet.onnx.layer.VerifyBatchSizeLayer(Deep Learning Toolbox)

Verify fixed batch size.

Yes

Yes

Yes

Custom Layers

Layer Name 描述 库丁 TensorRT ARM Compute Library for Mali GPU

自定义层

您为问题定义的自定义层，带有或没有可学习的参数。

To learn how to define custom deep learning layers, seeDefine Custom Deep Learning Layers(Deep Learning Toolbox)andDefine Custom Deep Learning Layer for Code Generation(Deep Learning Toolbox).

For an example on how to generate code for a network with custom layers, see有限公司de Generation For Object Detection Using YOLO v3 Deep Learning.

The outputs of the custom layer must be fixed-size arrays.

Using'unified'as themallocmodeincoder.gpuConfigrequires extra memory copies leading to slower performance. For custom layers, it is recommended to use'discrete'mode. For more information on GPU memory allocation, seeDiscrete and Managed Modes

库丁targets support both row-major and column-major code generation for custom layers. TensorRT targets support only column-major code generation.

对于代码生成，自定义层必须包含%#codegenpragma.

有限公司de generation for a sequence network containing custom layer and LSTM or GRU layer is not supported.

You can passdlarrayto custom layers if:

  • The custom layer is indlnetwork.

  • 自定义层位于DAG或系列网络中，要么从nnet.layer.Formattable或没有向后传播。

For unsupporteddlarraymethods, then you must extract the underlying data from thedlarray, perform the computations and reconstruct the data back into thedlarrayfor code generation. For example,

functionZ = predict(layer, X)ifcoder.target('matlab') Z = doPredict(X);elseifisdlarray(X) X1 = extractdata(X); Z1 = doPredict(X1); Z = dlarray(Z1);elsez = dopredict（x）;endendend

Yes

Yes

No

万博1manbetx支持的课程

GPU编码器为表中指定的目标深度学习库支持以下类别的万博1manbetx代码生成。

Name 描述 库丁 TensorRT ARM Compute Library for Mali GPU
DAGNetwork(Deep Learning Toolbox)

Directed acyclic graph (DAG) network for deep learning

  • Only theactivations,predict, and分类支持方法。万博1manbetx

Yes

Yes

Yes
dlnetwork(Deep Learning Toolbox)

深入学习网络定制培训循环

  • 有限公司de generation supports only theInputNamesand输出名称properties.

  • 有限公司de generation does not supportdlnetworkobjects without input layers. TheInitialized属性dlnetworkobject must be set to true.

  • 你可以创erate code fordlnetwork具有向量和图像序列输入。代码生成支持包括：万博1manbetx

    • dlarraycontaining vector sequences that have'CT'or'CBT'data formats.

    • dlarraycontaining image sequences that have'SSCT'or'SSCBT'data formats.

    • Multi-inputdlnetworkwith heterogeneous input layers. For RNN networks, multiple input is not supported.

  • 有限公司de generation supports only thepredictobject function. Thedlarrayinput to thepredictmethod must be asingledatatype.

  • 有限公司de generation supportsdlnetworkfor cuDNN and TensorRT targets. Code generation does not supportdlnetworkfor ARM Mali targets.

  • When targeting TensorRT withINT8precision, the last layer(s) of the network must be aSoftMaxlayerlayer.

  • 有限公司de generation supports MIMOdlnetworks.

  • To create adlnetworkobject for code generation, see加载预告片的网络以生成代码.

Yes

Yes

No

pointPillarsObjectDetector（LIDAR工具箱）

PointPillars network to detect objects in lidar point clouds

  • Only thedetect（LIDAR工具箱）方法的方法pointPillarsObjectDetectoris supported for code generation.

  • Only the临界点,selectstrongest, andMiniBatchSizeName-Value pairs of thedetectmethod are supported.

Yes

Yes

No
SeriesNetwork(Deep Learning Toolbox)

Series network for deep learning

  • Only theactivations,分类,predict,predictAndUpdateState,classifyAndUpDateState, and重置object functions are supported.

Yes

Yes

Yes
ssdObjectDetector(Computer Vision Toolbox)

使用基于SSD的检测器检测对象。

  • Only thedetect(Computer Vision Toolbox)方法的方法ssdObjectDetectoris supported for code generation.

  • Theroiargument to thedetectmethod must be a codegen constant (coder.const()）和1x4矢量。

  • Only the临界点,selectstrongest,最低量,Maxsize, andMiniBatchSizeName-Value pairs are supported. All Name-Value pairs must be compile-time constants.

  • 输入图像的通道和批处理大小必须固定尺寸。

  • Thelabels输出作为分类数组返回。

  • In the generated code, the input is rescaled to the size of the input layer of the network. But the bounding box that thedetect方法返回是指原始输入大小。

  • 边界框可能与仿真结果不匹配。

Yes

Yes

No
yolov2ObjectDetector(Computer Vision Toolbox)

使用Yolo V2对象检测器检测对象

  • Only thedetect(Computer Vision Toolbox)方法的方法yolov2ObjectDetectoris supported for code generation.

  • Theroiargument to thedetectmethod must be a codegen constant (coder.const()）和1x4矢量。

  • Only the临界点,selectstrongest,最低量,Maxsize, andMiniBatchSizeName-Value pairs are supported.

  • The height, width, channel, and batch size of the input image must be fixed size.

  • The minimum batch size value passed to detect method must be fixed size.

Yes

Yes

Yes

yolov3ObjectDetector(Computer Vision Toolbox)

Detect objects using YOLO v3 object detector

  • Only thedetect(Computer Vision Toolbox)方法的方法yolov3ObjectDetectoris supported for code generation.

  • Theroiargument to thedetectmethod must be a codegen constant (coder.const()）和1x4矢量。

  • Only the临界点,selectstrongest,最低量,Maxsize, andMiniBatchSizeName-Value pairs are supported.

  • The height, width, channel, and batch size of the input image must be fixed size.

  • The minimum batch size value passed to detect method must be fixed size.

Yes

Yes

No

yolov4ObjectDetector(Computer Vision Toolbox)

Detect objects using YOLO v4 object detector

  • Only thedetect(Computer Vision Toolbox)方法的方法yolov3ObjectDetectoris supported for code generation.

  • Theroiargument to thedetect方法必须是代码生成常数（coder.const()）和1x4矢量。

  • Only the临界点,selectstrongest,最低量,Maxsize, andMiniBatchSizename-value pairs fordetect得到支持万博1manbetx。

Yes

Yes

No

See Also

Functions

Objects

Related Topics