shufflenet
Syntax
Description
ShuffleNet is a convolutional neural network that is trained on more than a million images from the ImageNet database [1]. The network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The network has an image input size of 224-by-224. For more pretrained networks in MATLAB®, see Pretrained Deep Neural Networks.
You can use classify
to
classify new images using the ShuffleNet model. Follow the steps of Classify Image Using GoogLeNet and replace GoogLeNet
with ShuffleNet.
To retrain the network on a new classification task, follow the steps of Train Deep Learning Network to Classify New Images and load ShuffleNet instead of GoogLeNet.
Examples
Output Arguments
References
[1] ImageNet. http://www.image-net.org
[2] Zhang, Xiangyu, Xinyu Zhou, Mengxiao Lin, and Jian Sun. "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices." arXiv preprint arXiv:1707.01083v2 (2017).
Version History
Introduced in R2019a
See Also
Deep Network Designer | vgg16
| vgg19
| googlenet
| trainNetwork
| layerGraph
| DAGNetwork
| resnet50
| resnet101
| inceptionresnetv2
| squeezenet
| densenet201
| nasnetmobile
| nasnetlarge