<s>
The	O
CIFAR-10	B-General_Concept
dataset	O
(	O
Canadian	O
Institute	O
For	O
Advanced	O
Research	O
)	O
is	O
a	O
collection	O
of	O
images	O
that	O
are	O
commonly	O
used	O
to	O
train	O
machine	O
learning	O
and	O
computer	B-Application
vision	I-Application
algorithms	O
.	O
</s>
<s>
The	O
CIFAR-10	B-General_Concept
dataset	O
contains	O
60,000	O
32x32	O
color	O
images	O
in	O
10	O
different	O
classes	O
.	O
</s>
<s>
CIFAR-10	B-General_Concept
is	O
a	O
set	O
of	O
images	O
that	O
can	O
be	O
used	O
to	O
teach	O
a	O
computer	O
how	O
to	O
recognize	O
objects	O
.	O
</s>
<s>
Since	O
the	O
images	O
in	O
CIFAR-10	B-General_Concept
are	O
low-resolution	O
(	O
32x32	O
)	O
,	O
this	O
dataset	O
can	O
allow	O
researchers	O
to	O
quickly	O
try	O
different	O
algorithms	O
to	O
see	O
what	O
works	O
.	O
</s>
<s>
CIFAR-10	B-General_Concept
is	O
a	O
labeled	O
subset	O
of	O
the	O
80	B-General_Concept
Million	I-General_Concept
Tiny	I-General_Concept
Images	I-General_Concept
dataset	O
.	O
</s>
<s>
Various	O
kinds	O
of	O
convolutional	B-Architecture
neural	I-Architecture
networks	I-Architecture
tend	O
to	O
be	O
the	O
best	O
at	O
recognizing	O
the	O
images	O
in	O
CIFAR-10	B-General_Concept
.	O
</s>
<s>
This	O
is	O
a	O
table	O
of	O
some	O
of	O
the	O
research	O
papers	O
that	O
claim	O
to	O
have	O
achieved	O
state-of-the-art	O
results	O
on	O
the	O
CIFAR-10	B-General_Concept
dataset	O
.	O
</s>
<s>
CIFAR-10	B-General_Concept
is	O
also	O
used	O
as	O
a	O
performance	O
benchmark	O
for	O
teams	O
competing	O
to	O
run	O
neural	O
networks	O
faster	O
and	O
cheaper	O
.	O
</s>
