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.
|Deep Network Designer||Design, visualize, and train deep learning networks|
|Options for training deep learning neural network|
|Train deep learning neural network|
|Analyze deep learning network architecture|
|SqueezeNet convolutional neural network|
|GoogLeNet convolutional neural network|
|Inception-v3 convolutional neural network|
|DenseNet-201 convolutional neural network|
|MobileNet-v2 convolutional neural network|
|ResNet-18 convolutional neural network|
|ResNet-50 convolutional neural network|
|ResNet-101 convolutional neural network|
|Xception convolutional neural network|
|Pretrained Inception-ResNet-v2 convolutional neural network|
|Pretrained NASNet-Large convolutional neural network|
|Pretrained NASNet-Mobile convolutional neural network|
|Pretrained ShuffleNet convolutional neural network|
|DarkNet-19 convolutional neural network|
|DarkNet-53 convolutional neural network|
|EfficientNet-b0 convolutional neural network|
|AlexNet convolutional neural network|
|VGG-16 convolutional neural network|
|VGG-19 convolutional neural network|
|Classify data using trained deep learning neural network|
|Predict responses using trained deep learning neural network|
|Compute deep learning network layer activations|
|Create confusion matrix chart for classification problem|
|Sort classes of confusion matrix chart|
Deep Neural Networks
|Predict||Predict responses using a trained deep learning neural network|
|Image Classifier||Classify data using a trained deep learning neural network|
- 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.