Deep Learning Toolbox Model for ResNet-101 Network

Pretrained Resnet-101 network model for image classification

ResNet-101 is a pretrained model that has been trained on a subset of the ImageNet database. The model is trained on more than a million images, has 347 layers in total, corresponding to a 101 layer residual network, and can classify images into 1000 object categories (e.g. keyboard, mouse, pencil, and many animals).

Opening the resnet101.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 resnet101 instead of imagePretrainedNetwork if using a release prior to R2024a.

Usage Example:

% Access the trained model
[net, classes] = imagePretrainedNetwork("resnet101");

% 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-101
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')