検証 / テスト / 前処理など クラス数 ドメイン CIFAR-10 (C10) 45,000 / 5,000 / 10,000 / 標準化(平均0，分散1にスケール) データ拡張(C10+と表記)， 10 一般画像 分類 SVHN 68,257 / 5,000 / 26,032 / 正規化(255で割る) ベスト構造の学習：68,257 + 531,131 10 表札数字 分類 < Back to Alex Krizhevsky's home page 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. The CIFAR-10 dataset 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. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class. Here are the classes in the dataset, as well as 10 random images from each: airplane automobile bird cat deer dog frog horse "3BOE$*'"3EBUBTFUT IUUQTXXXDTUPSPOUPFEVdLSJ[DJGBSIUNM < Back to Alex Krizhevsky's home page The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million t Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. The CIFAR-10 dataset The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 c 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, e exactly 1000 randomly-selected images from each class. The training random order, but some training batches may contain more images fr training batches contain exactly 5000 images from each class. Here are the classes in the dataset, as well as 10 random images fro airplane automobile bird cat deer dog frog horse ship truck $*'"3BOE$*'"3EBUBTFUT < Back to Alex Krizhevsky's home page 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. The CIFAR-10 dataset 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. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class. Here are the classes in the dataset, as well as 10 random images from each: airplane automobile bird cat deer dog frog horse ship truck "3BOE$*'"3EBUBTFUT IUUQTXXXDTUPSPOUPFEVdLSJ[DJGBSIUNM < Back to Alex Krizhevsky's home page The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million ti Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. The CIFAR-10 dataset The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 c 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, e exactly 1000 randomly-selected images from each class. The training random order, but some training batches may contain more images fr training batches contain exactly 5000 images from each class. Here are the classes in the dataset, as well as 10 random images fro airplane automobile bird cat deer dog frog horse ship truck The classes are completely mutually exclusive. There is no overlap b includes sedans, SUVs, things of that sort. "Truck" includes only big t These are the original, variable-resolution, color house- bounding boxes, as shown in the examples images abov illustration purposes. The bounding box information are directly on the images in the dataset.) Each tar.gz file co together with a digitStruct.mat file, which can be loaded contains a struct called digitStruct with the same length element in digitStruct has the following fields: name wh corresponding image. bbox which is a struct array that c digit bounding box in the image. Eg: digitStruct(300 digit bounding box in the 300th image. Format 2: Cropped Digits: train_32x32.mat, test_32x commercial use only) 5IF4USFFU7JFX)PVTF/VNCFST 47)/ %BUBTFU < Back to Alex Krizhevsky's home page 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. The CIFAR-10 dataset 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. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class. Here are the classes in the dataset, as well as 10 random images from each: airplane automobile bird cat deer dog frog horse ship truck The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort. "Truck" includes only big trucks. Neither includes pickup trucks. CIFAR-10*1 SVHN*2 *1：https://www.cs.toronto.edu/~kriz/cifar.html *2：http://ufldl.stanford.edu/housenumbers/
satuma777
marketing2024
upura
sgk
dmattsun
trafficbrain
yukinobaba
beomi
takase
kentosasaki
masatoto
hsmtta
langstat
samanthasiow
andyhume
bryan
malarkey
bluesmoon
eileencodes
deanohume
wjessup
jrom
sferik
tanoku
lynnandtonic
![参考文献 30 • [Chen et al. 2016] T. Chen, I.](https://files.speakerdeck.com/presentations/9071c0425fcd4fa69b558098a3d133f8/slide_29.jpg){kind=link}