A simple image classifier using the CIFAR-10 labeled image dataset. CIFAR-10 images are 32 by 32 pixels and are labelled with one of ten classes. Currently includes a simple k-nearest-neighbor classifier that achieves about 34% accuracy on the test set. Future plans include implementing more accurate SVM, softmax, or neural network classification techniques.
To configure and run the classifier using the Swing GUI, run src/main/gui/RunGUIClassifier.main(). The classifier can also be run non-interactively using RunClassifier.main().
CIFAR-10 dataset collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. http://www.cs.toronto.edu/~kriz/cifar.html
Technical report on dataset: Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009.
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This product includes software developed at the Apache Software Foundation (http://www.apache.org/).