2022年6月28日 · Cifar-10由60000张32*32的RGB彩色图片构成，共10个分类。50000张训练，10000张测试（交叉验证）。这个数据集最大的特点在于将识别迁移到了普适物体，而且应用于多分类

The CIFAR-10 and CIFAR-100 datasets are labeled subsets of the 80 million tiny images dataset. CIFAR-10 and CIFAR-100 were created by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. The CIFAR-10 dataset The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class.

The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. 作者介绍CIFAR-10本质是从一个叫做【the 80 million tiny images dataset】（“8000万张小图”数据集）中精炼剥离出来的一部分，是该数据集的子集。

2019年6月2日 · cifar-10数据集是一个常用的图像分类基准数据集，包含10个类别的60,000张32x32彩色图像。本项目旨在利用深度学习技术，特别是卷积神经网络（cnn）对cifar-10数据集进行图像分类，并通过可视化技术直观地展示分类结果和模型学习过程中的关键信息。二、项目目标。

The CIFAR-100 dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. There are 600 images per class.

2025年4月2日 · 实现cifar-10多分类 数据集介绍. cifar-10数据集由10个类的60000个32×32彩色图像组成，每个类有6000个图像。有50000个训练图像和10000个测试图像。 在这里插入图片描述. 1 引入头文件

The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research.

2020年6月6日 · CIFAR数据集由 Alex Krizhevsky，Vinod Nair 和 Geoffrey Hinton 收集整理自8000万张微型图像数据集，其中CIFAR数据集又根据所涉及的分类对象数量，可分为CIFAR-10和CIFAR-100。该数据集主要用于深度学习的图像分类，目前已被广泛应用。

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images.

2021年11月24日 · The CIFAR-10 dataset was developed for evaluation of deep generative models in 2009 and has subsequently been widely adopted as a machine learning benchmark for image classification/object recognition. It is a subset of the original Tiny Images Dataset (from MIT and NYU), with 10 classes and more reliable labels.