Image Classification for Non-Data Scientists

It provides an image classification sample-based pre-trained deep neural network for non-data scientists. You can test the image classificat
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Updated 12 Jun 2023

MATLAB Image Classification for Non-Data Scientists

It provides an image classification sample-based pre-trained deep neural network for non-data scientists. You can test the image classification by just copying images to a folder.

Requirement

It requires Deep Learning Toolbox. Pleae check Deep Learning Toolbox

It also requires to install app of pre-trained network when you use a new network.

Usage

Run demo_image_classification.

img_dir = 'images'; % specify the image folder

imds_train = load_imds( [img_dir,'/train/'] );
imds_test = load_imds( [img_dir,'/test/'] );

imcl = ImageClassifier('resnet18'); % specify the name of pre-trained netowrk.
imcl = imcl.fit( imds_train, 'num_iter', 10000, 'rho', 0.001, 'reg',1E-8, 'smooth', [0.50, 0.75] ); % parameters
[pred, proba] = imcl.pred( imds_test ); % test with test images
[results, acc] = result_table( pred, proba, imds_test ); % generate result table

Available Pre-trained feature extractor

googlenet, inceptionv3, densenet201, mobilenetv2, resnet18, resnet50, resnet101, xception, inceptionresnetv2, shufflenet, nasnetmobile, nasnetlarge, efficientnetb0, alexnet, vgg16, vgg19

Dataset

It includes four models images.

Cite As

Masayuki Tanaka (2024). Image Classification for Non-Data Scientists (https://github.com/mastnk/ImageClassificationForNonDataScientists/releases/tag/0.1.0), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2023a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes
0.1.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.