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Pretrained Networks

Use pretrained image networks to quickly learn new tasks

Use transfer learning to take advantage of the knowledge provided by a pretrained network to learn new patterns in new image 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 create models for new tasks without defining and training a new network, having millions of images, or having a powerful GPU. To explore the pretrained networks available, use Deep Network Designer.

Apps

Deep Network Designer

Design, visualize, and train deep learning networks

Functions

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Train Network

`trainingOptions`	Options for training deep learning neural network
`trainNetwork`	Train neural network
`trainnet`	Train deep learning neural network (Since R2023b)
`analyzeNetwork`	Analyze deep learning network architecture

Pretrained Networks

`squeezenet`	SqueezeNet convolutional neural network
`googlenet`	GoogLeNet convolutional neural network
`inceptionv3`	Inception-v3 convolutional neural network
`densenet201`	DenseNet-201 convolutional neural network
`mobilenetv2`	MobileNet-v2 convolutional neural network (Since R2019a)
`resnet18`	ResNet-18 convolutional neural network
`resnet50`	ResNet-50 convolutional neural network
`resnet101`	ResNet-101 convolutional neural network
`xception`	Xception convolutional neural network (Since R2019a)
`inceptionresnetv2`	Pretrained Inception-ResNet-v2 convolutional neural network
`nasnetlarge`	Pretrained NASNet-Large convolutional neural network (Since R2019a)
`nasnetmobile`	Pretrained NASNet-Mobile convolutional neural network (Since R2019a)
`shufflenet`	Pretrained ShuffleNet convolutional neural network (Since R2019a)
`darknet19`	DarkNet-19 convolutional neural network (Since R2020a)
`darknet53`	DarkNet-53 convolutional neural network (Since R2020a)
`efficientnetb0`	EfficientNet-b0 convolutional neural network (Since R2020b)
`alexnet`	AlexNet convolutional neural network
`vgg16`	VGG-16 convolutional neural network
`vgg19`	VGG-19 convolutional neural network

Prediction

`classify`	Classify data using trained deep learning neural network
`predict`	Predict responses using trained deep learning neural network
`activations`	Compute deep learning network layer activations
`confusionchart`	Create confusion matrix chart for classification problem
`sortClasses`	Sort classes of confusion matrix chart

Blocks

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Deep Neural Networks

Predict	Predict responses using a trained deep learning neural network (Since R2020b)
Image Classifier	Classify data using a trained deep learning neural network (Since R2020b)

Topics

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.
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.
Transfer Learning Using Pretrained Network
This example shows how to fine-tune a pretrained GoogLeNet 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.
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.
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

Classify Image Using GoogLeNet

Classify an image using the pretrained deep convolutional neural network GoogLeNet.

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Extract Image Features Using Pretrained Network

Extract learned image features from a pretrained convolutional neural network and use those features to train an image classifier. Feature extraction is the easiest and fastest way to use the representational power of pretrained deep networks. For example, you can train a support vector machine (SVM) using fitcecoc (Statistics and Machine Learning Toolbox™) on the extracted features. Because feature extraction only requires a single pass through the data, it is a good starting point if you do not have a GPU to accelerate network training with.

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Transfer Learning with Deep Network Designer

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

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Convert Classification Network into Regression Network

Convert a trained classification network into a regression network.

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Classify Images in Simulink Using GoogLeNet

Classify an image in Simulink® using the Image Classifier block. The example uses the pretrained deep convolutional neural network GoogLeNet to perform the classification.

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