Deep Learning Toolbox Model for Inception-ResNet-v2 Network

Pretrained Inception-ResNet-v2 network model for image classification

Inception-ResNet-v2 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 825 layers in total, and can classify images into 1000 object categories (e.g. keyboard, mouse, pencil, and many animals).

Opening the inceptionresnetv2.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 R2017a and beyond. Use inceptionresnetv2 instead of imagePretrainedNetwork if using a release prior to R2024a.

Usage Example:

% Access the trained model
[net, classes] = imagePretrainedNetwork("inceptionresnetv2");

% 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 Inception-ResNet-v2
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')