Pretrained ResNet-50 convolutional neural network
ResNet-50 is a convolutional neural network that is trained on more than a million images from the ImageNet database [1]. The network is 50 layers deep and 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-50 model. Follow the steps of Classify Image Using GoogLeNet and replace
GoogLeNet with ResNet-50.
To retrain the 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.
[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
| alexnet
| densenet201
| googlenet
| inceptionresnetv2
| layerGraph
| plot
| resnet101
| resnet18
| squeezenet
| trainNetwork
| vgg16
| vgg19