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Built-In Pretrained Networks

Load built-in pretrained networks and perform transfer learning

Deep Learning Toolbox™ provides several pretrained networks suitable for transfer learning. Transfer learning is the process of taking a pretrained deep learning network and fine-tuning it to learn a new task. Using transfer learning is usually faster and easier than training a network from scratch. You can quickly transfer learned features to a new task using a smaller amount of data. To explore the available pretrained networks, use Deep Network Designer. For more information, see Pretrained Deep Neural Networks.


Deep Network DesignerDesign and visualize deep learning networks


imagePretrainedNetworkPretrained neural network for images (Seit R2024a)


  • 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.

  • 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.

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