这个数据集的官网:http://www.cs.toronto.edu/~kriz/cifar.html
32x32 彩色图片
10个类别的图片,每个类别6,000张,共计60,000张;其中50,000张训练图片,10,000张验证图片
图片上的标题含义:第一个单词表示真实类别,第二个单词表示预测类别。绿色表示预测正确,红色表示预测错误。
Win 10
PyCharm
Anaconda
Python 3.6.7
TensorFlow 1.10.0
Keras 2.2.0
This project forked from jelly-lemon/cifar10_cnn
用Keras实现CIFAR-10图片分类
这个数据集的官网:http://www.cs.toronto.edu/~kriz/cifar.html
32x32 彩色图片
10个类别的图片,每个类别6,000张,共计60,000张;其中50,000张训练图片,10,000张验证图片
图片上的标题含义:第一个单词表示真实类别,第二个单词表示预测类别。绿色表示预测正确,红色表示预测错误。
Win 10
PyCharm
Anaconda
Python 3.6.7
TensorFlow 1.10.0
Keras 2.2.0
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