I did it using Keras

Datasets - Keras Documentation

https://keras.io/datasets/

Hello,

I have developed this code in Matlab to extract 10000 images from cifar data.

create a directory named base in your work space.

clear all

clc

load('data_batch_1.mat')

im=zeros(32,32,3);

for cpt=1:10000

R=data(cpt,1:1024);

G=data(cpt,1025:2048);

B=data(cpt,2049:3072);

k=1;

for x=1:32

for i=1:32

im(x,i,1)=R(k);

im(x,i,2)=G(k);

im(x,i,3)=B(k);

k=k+1;

end

end

im=uint8(im);

imwrite(im,strcat('..\base\',int2str(cpt),'.png'),'png');

end

Try this code:

if true

clc;

clear all;

load('meta.mat');

%Create folders

for i=1:length(fine_label_names)

mkdir('CIFAR-100\TEST\',fine_label_names{i});

mkdir('CIFAR-100\TRAIN\',fine_label_names{i});

end

%%Training images

load('train.mat');

im=zeros(32,32,3);

for cpt=1:50000

R=data(cpt,1:1024);

G=data(cpt,1025:2048);

B=data(cpt,2049:3072);

k=1;

for x=1:32

for i=1:32

im(x,i,1)=R(k);

im(x,i,2)=G(k);

im(x,i,3)=B(k);

k=k+1;

end

end

im=uint8(im);

pathdest = strcat('CIFAR-100\TRAIN\',fine_label_names{fine_labels(cpt)+1},'\',filenames{cpt});

imwrite(im,pathdest,'png');

end

%%Test images

load('test.mat');

im=zeros(32,32,3);

for cpt=1:10000

R=data(cpt,1:1024);

G=data(cpt,1025:2048);

B=data(cpt,2049:3072);

k=1;

for x=1:32

for i=1:32

im(x,i,1)=R(k);

im(x,i,2)=G(k);

im(x,i,3)=B(k);

k=k+1;

end

end

im=uint8(im);

pathdest = strcat('CIFAR-100\TEST\',fine_label_names{fine_labels(cpt)+1},'\',filenames{cpt});

imwrite(im,pathdest,'png');

end

end