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Data






数据集 描述
CIFAR 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 CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs).
ImageNet ImageNet is a dataset of over 15 million labeled high-resolution images belonging to roughly 22,000 categories. The images were collected from the web and labeled by human labelers using Amazon’s Mechanical Turk crowd-sourcing tool.
LabelMe
MNIST The MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.
NUS-WIDE NUS-WIDE dataset includes: (1) 269,648 images and the associated tags from Flickr, with a total number of 5,018 unique tags; (2) six types of low-level features extracted from these images, including 64-D color histogram, 144-D color correlogram, 73-D edge direction histogram, 128-D wavelet texture, 225-D block-wise color moments and 500-D bag of words based on SIFT descriptions; and (3) ground-truth for 81 concepts that can be used for evaluation.
SUN-397
Tiny-Image Tiny Images dataset consists of 79,302,017 images, each being a 32x32 color image. This data is stored in the form of large binary files which can be accesed by a Matlab toolbox that we have written.
Wiki Wiki is generated from Wikipedia featured articles and consists of 2,866 image-text pairs. In each pair, the text is an article describing some events or people and the image is closely related to the content of the article.



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