resnet50
ResNet-50 convolutional neural network
Description
ResNet-50 is a convolutional neural network that is 50 layers deep. You can load a pretrained version of the neural network trained on more than a million images from the ImageNet database [1]. The pretrained neural network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the neural network has learned rich feature representations for a wide range of images. The neural network has an image input size of 224-by-224. For more pretrained neural networks in MATLAB®, see Pretrained Deep Neural Networks.
You can use classify
to
classify new images using the ResNet-50 model. Follow the steps of Classify Image Using GoogLeNet and replace GoogLeNet
with ResNet-50.
To retrain the neural network on a new classification task, follow the steps of Train Deep Learning Network to Classify New Images and load ResNet-50 instead of GoogLeNet.
Tip
To create an untrained residual neural network suitable for image classification
tasks, use resnetLayers
.
returns a ResNet-50
neural network trained on the ImageNet data set.net
= resnet50
This function requires the Deep Learning Toolbox™ Model for ResNet-50 Network support package. If this support package is not installed, then the function provides a download link.
returns a ResNet-50 neural network trained on the ImageNet data set. This syntax
is equivalent to net
= resnet50('Weights','imagenet'
)net = resnet50
.
returns the untrained ResNet-50 neural network architecture. The untrained model
does not require the support package. lgraph
= resnet50('Weights','none'
)
Examples
Output Arguments
References
[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.
Extended Capabilities
Version History
Introduced in R2017b
See Also
Deep Network
Designer | resnetLayers
| vgg16
| vgg19
| googlenet
| trainNetwork
| layerGraph
| DAGNetwork
| resnet18
| resnet101
| densenet201
| inceptionresnetv2
| squeezenet