Built-In Pretrained Networks
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.
Apps
| Deep Network Designer | Design and visualize deep learning networks |
Functions
imagePretrainedNetwork | Pretrained neural network for images (Since R2024a) |
Blocks
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.
- 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.
Related Information
Featured Examples
Classify Image Using GoogLeNet
Classify an image using the pretrained deep convolutional neural network GoogLeNet.
Prepare Network for Transfer Learning Using Deep Network Designer
Interactively fine-tune a pretrained deep learning network to learn a new image classification task.
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.
Build Simple App for Deep Learning Inference Using App Designer
Use App Designer to create an app that can classify images using a deep neural network. You can modify the app for other types of deep learning inference, for example, sequence classification or image regression.
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.
Adapt Pretrained Audio Network for New Data Using Deep Network Designer
Interactively adapt a pretrained network to classify new audio signals using Deep Network Designer.
Convert Classification Network into Regression Network
Convert a trained classification network into a regression network.
Multilabel Image Classification Using Deep Learning
Use transfer learning to train a deep learning model for multilabel image classification.
Classify Videos Using Deep Learning
Create a network for video classification by combining a pretrained image classification model and an LSTM network.
