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Deep Learning Image Classification

Use pretrained deep networks to quickly learn new tasks, perform transfer learning and fine-tune a network, or perform feature extraction

Transfer learning is the task of using the knowledge provided by a pretrained network to learn new patterns in new data. Fine-tuning a pretrained network with transfer learning is typically much faster and easier than training from scratch. Using pretrained deep networks enables you to quickly learn new tasks without defining and training a new network, having millions of images, or long training times. Transfer learning is suitable when you have small amounts of training data (for example, less than 1000 images). The advantage of transfer learning is that the pretrained network has already learned a rich set of features that can be applied to a wide range of other similar tasks.

You can either fine-tune a pretrained network using new data, or extract learned features from the pretrained network. If you extract learned features, you can then use those features to train a classifier, for example, a support vector machine on new data (Using a classifier requires Statistics and Machine Learning Toolbox™).

If you choose to fine-tune a pretrained network, you can train it on either a CPU, a GPU, or multiple GPUs and/or in parallel. Training on a GPU or in parallel requires the Parallel Computing Toolbox™. Using a GPU requires a CUDA® enabled NVIDIA® GPU with compute capability 3.0 or higher. Specify the training parameters including the execution environment using the trainingOptions function.


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alexnetPretrained AlexNet convolutional neural network
vgg16Pretrained VGG-16 convolutional neural network
vgg19Pretrained VGG-19 convolutional neural network
squeezenetPretrained SqueezeNet convolutional neural network
googlenetPretrained GoogLeNet convolutional neural network
inceptionv3Pretrained Inception-v3 convolutional neural network
resnet50Pretrained ResNet-50 convolutional neural network
resnet101Pretrained ResNet-101 convolutional neural network
inceptionresnetv2Pretrained Inception-ResNet-v2 convolutional neural network
importCaffeNetworkImport pretrained convolutional neural network models from Caffe
importKerasNetworkImport a pretrained Keras network and weights
exportONNXNetworkExport network to ONNX model format
trainingOptionsOptions for training deep learning neural network
trainNetworkTrain neural network for deep learning
imageDataAugmenterConfigure image data augmentation
augmentedImageDatastoreTransform batches to augment image data


Classify Image Using GoogLeNet

This example shows how to classify an image using the pretrained deep convolutional neural network GoogLeNet.

Get Started with Transfer Learning

This example shows how to use transfer learning to retrain AlexNet, a pretrained convolutional neural network, to classify a new set of images.

Transfer Learning Using AlexNet

This example shows how to fine-tune a pretrained AlexNet convolutional neural network to perform classification on a new collection of images.

Transfer Learning Using GoogLeNet

This example shows how to use transfer learning to retrain GoogLeNet, a pretrained convolutional neural network, to classify a new set of images.

Feature Extraction Using AlexNet

This example shows how to extract learned image features from a pretrained convolutional neural network, and use those features to train an image classifier.

Pretrained Convolutional Neural Networks

Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction.

Preprocess Images for Deep Learning

Learn how to resize images for training, prediction and classification, and how to preprocess images using data augmentation and mini-batch datastores.

Featured Examples

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