jerett / Keras-CIFAR10

practice on CIFAR10 with Keras

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Keras for CIFAR10

This project demonstrates some personal examples with Keras on CIFAR10.


Introduction


The CIFAR10 dataset is 32x32 size, 50000 train images and 10000 test images. The dataset is divided into 40000 train images, 10000 validation images, and 10000 images.

Result

All result is tested on 10000 test images.You can view and run in the jupyter environment.

Model Notebook Accuracy
SVM svm 33.36%
Softmax softmax 33.11%
simple_cnn simple_cnn 66.75%
vgg vgg 92.32%
inceptionV1 GoogLeNet 93.08%
ResNet18 resnet18 93.47%
small-ResNet20 small_resnet20 91.25%
small-ResNet32 small_resnet32 92.34%
small-ResNet56 small_resnet56 92.37%

About

practice on CIFAR10 with Keras


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Language:Jupyter Notebook 97.8%Language:Python 2.2%Language:Shell 0.0%