Deep Learning with Images

Train convolutional neural networks from scratch or use pretrained networks to quickly learn new tasks

Create new deep networks for image classification and regression tasks by defining the network architecture and training the network from scratch. You can also use transfer learning to take advantage of the knowledge provided by a pretrained network to learn new patterns in new data. Fine-tuning a pretrained image classification network with transfer learning is typically much faster and easier than training from scratch. Using pretrained deep networks enables you to quickly learn new tasks without defining and training a new network, having millions of images, or having a powerful GPU.

After defining the network architecture, you must define training parameters using the trainingOptions function. You can then train the network using trainNetwork. Use the trained network to predict class labels or numeric responses.

You can train a convolutional neural network on a CPU, a GPU, multiple CPUs or GPUs, or in parallel on a cluster or in the cloud. Training on a GPU or in parallel requires Parallel Computing Toolbox™. Using a GPU requires a CUDA® enabled NVIDIA® GPU with compute capability 3.0 or higher. Specify the execution environment using the trainingOptions function.


Deep Network DesignerDesign, visualize, and train deep learning networks


expand all

trainingOptionsOptions for training deep learning neural network
trainNetworkTrain neural network for deep learning
analyzeNetworkAnalyze deep learning network architecture
squeezenetSqueezeNet convolutional neural network
googlenetGoogLeNet convolutional neural network
inceptionv3Inception-v3 convolutional neural network
densenet201DenseNet-201 convolutional neural network
mobilenetv2MobileNet-v2 convolutional neural network
resnet18ResNet-18 convolutional neural network
resnet50ResNet-50 convolutional neural network
resnet101ResNet-101 convolutional neural network
xceptionXception convolutional neural network
inceptionresnetv2Pretrained Inception-ResNet-v2 convolutional neural network
nasnetlargePretrained NASNet-Large convolutional neural network
nasnetmobilePretrained NASNet-Mobile convolutional neural network
shufflenetPretrained ShuffleNet convolutional neural network
darknet19DarkNet-19 convolutional neural network
darknet53DarkNet-53 convolutional neural network
alexnetAlexNet convolutional neural network
vgg16VGG-16 convolutional neural network
vgg19VGG-19 convolutional neural network

Input Layers

imageInputLayerImage input layer
image3dInputLayer3-D image input layer

Convolution and Fully Connected Layers

convolution2dLayer2-D convolutional layer
convolution3dLayer3-D convolutional layer
groupedConvolution2dLayer2-D grouped convolutional layer
transposedConv2dLayerTransposed 2-D convolution layer
transposedConv3dLayerTransposed 3-D convolution layer
fullyConnectedLayerFully connected layer

Activation Layers

reluLayerRectified Linear Unit (ReLU) layer
leakyReluLayerLeaky Rectified Linear Unit (ReLU) layer
clippedReluLayerClipped Rectified Linear Unit (ReLU) layer
eluLayerExponential linear unit (ELU) layer
tanhLayerHyperbolic tangent (tanh) layer

Normalization, Dropout, and Cropping Layers

batchNormalizationLayerBatch normalization layer
crossChannelNormalizationLayer Channel-wise local response normalization layer
dropoutLayerDropout layer
crop2dLayer2-D crop layer
crop3dLayer3-D crop layer

Pooling and Unpooling Layers

averagePooling2dLayerAverage pooling layer
averagePooling3dLayer3-D average pooling layer
globalAveragePooling2dLayerGlobal average pooling layer
globalAveragePooling3dLayer3-D global average pooling layer
globalMaxPooling2dLayerGlobal max pooling layer
globalMaxPooling3dLayer3-D global max pooling layer
maxPooling2dLayerMax pooling layer
maxPooling3dLayer3-D max pooling layer
maxUnpooling2dLayerMax unpooling layer

Combination Layers

additionLayerAddition layer
concatenationLayerConcatenation layer
depthConcatenationLayerDepth concatenation layer

Output Layers

softmaxLayerSoftmax layer
classificationLayerClassification output layer
regressionLayerCreate a regression output layer
augmentedImageDatastoreTransform batches to augment image data
imageDataAugmenterConfigure image data augmentation
augmentApply identical random transformations to multiple images
layerGraphGraph of network layers for deep learning
plotPlot neural network layer graph
addLayersAdd layers to layer graph
removeLayersRemove layers from layer graph
replaceLayerReplace layer in layer graph
connectLayersConnect layers in layer graph
disconnectLayersDisconnect layers in layer graph
DAGNetworkDirected acyclic graph (DAG) network for deep learning
classifyClassify data using a trained deep learning neural network
activationsCompute deep learning network layer activations
predictPredict responses using a trained deep learning neural network
confusionchartCreate confusion matrix chart for classification problem
sortClassesSort classes of confusion matrix chart


ConfusionMatrixChart PropertiesConfusion matrix chart appearance and behavior

Examples and How To

Use Pretrained Networks

Classify Image Using GoogLeNet

This example shows how to classify an image using the pretrained deep convolutional neural network GoogLeNet.

Classify Webcam Images Using Deep Learning

This example shows how to classify images from a webcam in real time using the pretrained deep convolutional neural network GoogLeNet.

Transfer Learning with Deep Network Designer

Interactively fine-tune a pretrained deep learning network to learn a new image classification task.

Train Deep Learning Network to Classify New Images

This example shows how to use transfer learning to retrain a convolutional neural network to classify a new set of images.

Extract Image Features Using Pretrained Network

This example shows how to extract learned image features from a pretrained convolutional neural network and use those features to train an image classifier.

Transfer Learning Using AlexNet

This example shows how to fine-tune a pretrained AlexNet convolutional neural network to perform classification on a new collection of images.

Pretrained Deep Neural Networks

Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction.

Create New Deep Network

Create Simple Deep Learning Network for Classification

This example shows how to create and train a simple convolutional neural network for deep learning classification.

Build Networks with Deep Network Designer

Interactively build and edit deep learning networks.

Train Convolutional Neural Network for Regression

This example shows how to fit a regression model using convolutional neural networks to predict the angles of rotation of handwritten digits.

List of Deep Learning Layers

Discover all the deep learning layers in MATLAB®.

Specify Layers of Convolutional Neural Network

Learn about the layers of a convolutional neural network (ConvNet), and the order they appear in a ConvNet.

Generate MATLAB Code from Deep Network Designer

Generate MATLAB code to recreate designing and training a network in Deep Network Designer.

Train Residual Network for Image Classification

This example shows how to create a deep learning neural network with residual connections and train it on CIFAR-10 data.

Train Generative Adversarial Network (GAN)

This example shows how to train a generative adversarial network (GAN) to generate images.

Train Conditional Generative Adversarial Network (CGAN)

This example shows how to train a conditional generative adversarial network (CGAN) to generate images.

Image Captioning Using Attention

This example shows how to train a deep learning model for image captioning using attention.

Train Network Using Custom Training Loop

This example shows how to train a network that classifies handwritten digits with a custom learning rate schedule.

Train Network with Multiple Outputs

This example shows how to train a deep learning network with multiple outputs that predict both labels and angles of rotations of handwritten digits.

Train a Siamese Network to Compare Images

This example shows how to train a Siamese network to identify similar images of handwritten characters.


Deep Learning in MATLAB

Discover deep learning capabilities in MATLAB using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.

Set Up Parameters and Train Convolutional Neural Network

Learn how to set up training parameters for a convolutional neural network.

Preprocess Images for Deep Learning

Learn how to resize images for training, prediction, and classification, and how to preprocess images using data augmentation, transformations, and specialized datastores.

Preprocess Volumes for Deep Learning

Read and preprocess volumetric image and label data for 3-D deep learning.

Datastores for Deep Learning

Learn how to use datastores in deep learning applications.

Convert Classification Network into Regression Network

This example shows how to convert a trained classification network into a regression network.

Deep Learning Tips and Tricks

Learn how to improve the accuracy of deep learning networks.

Data Sets for Deep Learning

Discover data sets for various deep learning tasks.

Featured Examples