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.

App

Deep Network Designer

Progetta, visualizza e addestra le reti di Deep Learning

Funzioni

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Train Network

`trainingOptions`	Opzioni per l’addestramento della rete neurale di Deep Learning
`trainnet`	Train deep learning neural network (Da R2023b)
`analyzeNetwork`	Analyze deep learning network architecture

Pretrained Networks

imagePretrainedNetwork Pretrained neural network for images (Da R2024a)

Prediction

`predict`	Compute deep learning network output for inference (Da R2019b)
`minibatchpredict`	Mini-batched neural network prediction (Da R2024a)
`scores2label`	Convert prediction scores to labels (Da R2024a)
`confusionchart`	Create confusion matrix chart for classification problem
`sortClasses`	Sort classes of confusion matrix chart

Blocchi

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Deep Neural Networks

Predict	Predict responses using a trained deep learning neural network (Da R2020b)
Image Classifier	Classifica i dati utilizzando una rete neurale addestrata di Deep Learning (Da R2020b)

Argomenti

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.
Retrain Neural Network to Classify New Images
This example shows how to retrain a pretrained SqueezeNet neural network to perform classification on a new collection of images.
Reti neurali profonde preaddestrate
Apprendere come scaricare e utilizzare le reti neurali convoluzionali preaddestrate per la classificazione, il transfer learning e l’estrazione di feature.
Deep Learning in MATLAB
Scoprire le capacità del Deep Learning in MATLAB^® utilizzando le reti neurali convoluzionali per la classificazione e la regressione, incluse le reti preaddestrate e il transfer learning, nonché l’addestramento su GPU, CPU, cluster e cloud.
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.

Esempi in primo piano

Classificazione di immagini utilizzando GoogLeNet

Questo esempio mostra come classificare un’immagine con la rete neurale convoluzionale profonda preaddestrata 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 con Deep Network Designer

Eseguire in modo interattivo il perfezionamento di una rete preaddestrata di Deep Learning per apprendere una nuova attività di classificazione delle immagini.

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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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