Pretrained DenseNet-201 convolutional neural network
DenseNet-201 is a convolutional neural network that is trained on more than a million images from the ImageNet database [1]. The network is 201 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 DenseNet-201 model. Follow the steps of Classify Image Using GoogLeNet and replace GoogLeNet
with DenseNet-201.
To retrain the network on a new classification task, follow the steps of Train Deep Learning Network to Classify New Images and load DenseNet-201 instead of GoogLeNet.
[1] ImageNet. http://www.image-net.org
[2] Huang, Gao, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger. "Densely Connected Convolutional Networks." In CVPR, vol. 1, no. 2, p. 3. 2017.
DAGNetwork
| alexnet
| googlenet
| inceptionresnetv2
| inceptionv3
| layerGraph
| plot
| resnet101
| resnet18
| resnet50
| squeezenet
| trainNetwork
| vgg16
| vgg19