Deep Learning Toolbox Model for ResNet-50 Network

Pretrained ResNet-50 network model for image classification

ResNet-50 is a pretrained model that has been trained on a subset of the ImageNet database and that won the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition in 2015. The model is trained on more than a million images, has 177 layers in total, corresponding to a 50 layer residual network, and can classify images into 1000 object categories (e.g. keyboard, mouse, pencil, and many animals).

Opening the resnet50.mlpkginstall file from your operating system or from within MATLAB will initiate the installation process for the release you have.

This mlpkginstall file is functional for R2017b and beyond. Use resnet50 instead of imagePretrainedNetwork if using a release prior to R2024a.

Usage Example:

% Access the trained model
[net, classes] = imagePretrainedNetwork("resnet50");

% See details of the architecture
net.Layers

% Read the image to classify
I = imread('peppers.png');

% Adjust size of the image
sz = net.Layers(1).InputSize
I = I(1:sz(1),1:sz(2),1:sz(3));

% Classify the image using ResNet-50
scores = predict(net, single(I));
label = scores2label(scores, classes)

% Show the image and the classification results
figure
imshow(I)
text(10,20,char(label),'Color','white')