ResNet-18 convolutional neural network
ResNet-18 is a convolutional neural network that is 18 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database [1]. The pretrained 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 ResNet-18 model. Follow the steps of Classify Image Using GoogLeNet and replace GoogLeNet with
ResNet-18.
To retrain the network on a new classification task, follow the steps of Train Deep Learning Network to Classify New Images and load ResNet-18 instead of GoogLeNet.
returns a ResNet-18 network
trained on the ImageNet data set.net
= resnet18
This function requires the Deep Learning Toolbox™ Model for ResNet-18 Network support package. If this support package is not installed, then the function provides a download link.
returns a ResNet-18 network trained on the ImageNet data set. This syntax is equivalent to
net
= resnet18('Weights','imagenet'
)net = resnet18
.
returns the untrained ResNet-18 network architecture. The untrained model does not require
the support package. lgraph
= resnet18('Weights','none'
)
[1] ImageNet. http://www.image-net.org
[2] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Deep residual learning for image recognition." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016.
DAGNetwork
| densenet201
| googlenet
| inceptionresnetv2
| layerGraph
| plot
| resnet101
| resnet50
| squeezenet
| trainNetwork
| vgg16
| vgg19