MNIST In CSV by Kaggle | Technology and IT

About this Dataset

MNIST In CSV

The MNIST dataset provided in a easy-to-use CSV format

The original dataset is in a format that is difficult for beginners to use. This dataset uses the work of Joseph Redmon to provide the MNIST dataset in a CSV format.

The dataset consists of two files:

mnist_train.csv
mnist_test.csv

The mnist_train.csv file contains the 60,000 training examples and labels. The mnist_test.csv contains 10,000 test examples and labels. Each row consists of 785 values: the first value is the label (a number from 0 to 9) and the remaining 784 values are the pixel values (a number from 0 to 255).

Tables

Mnist Test

@kaggle.oddrationale_mnist_in_csv.mnist_test

3.69 MB
10000 rows
785 columns


CREATE TABLE mnist_test (
  "label" BIGINT,
  "n_1x1" BIGINT,
  "n_1x2" BIGINT,
  "n_1x3" BIGINT,
  "n_1x4" BIGINT,
  "n_1x5" BIGINT,
  "n_1x6" BIGINT,
  "n_1x7" BIGINT,
  "n_1x8" BIGINT,
  "n_1x9" BIGINT,
  "n_1x10" BIGINT,
  "n_1x11" BIGINT,
  "n_1x12" BIGINT,
  "n_1x13" BIGINT,
  "n_1x14" BIGINT,
  "n_1x15" BIGINT,
  "n_1x16" BIGINT,
  "n_1x17" BIGINT,
  "n_1x18" BIGINT,
  "n_1x19" BIGINT,
  "n_1x20" BIGINT,
  "n_1x21" BIGINT,
  "n_1x22" BIGINT,
  "n_1x23" BIGINT,
  "n_1x24" BIGINT,
  "n_1x25" BIGINT,
  "n_1x26" BIGINT,
  "n_1x27" BIGINT,
  "n_1x28" BIGINT,
  "n_2x1" BIGINT,
  "n_2x2" BIGINT,
  "n_2x3" BIGINT,
  "n_2x4" BIGINT,
  "n_2x5" BIGINT,
  "n_2x6" BIGINT,
  "n_2x7" BIGINT,
  "n_2x8" BIGINT,
  "n_2x9" BIGINT,
  "n_2x10" BIGINT,
  "n_2x11" BIGINT,
  "n_2x12" BIGINT,
  "n_2x13" BIGINT,
  "n_2x14" BIGINT,
  "n_2x15" BIGINT,
  "n_2x16" BIGINT,
  "n_2x17" BIGINT,
  "n_2x18" BIGINT,
  "n_2x19" BIGINT,
  "n_2x20" BIGINT,
  "n_2x21" BIGINT,
  "n_2x22" BIGINT,
  "n_2x23" BIGINT,
  "n_2x24" BIGINT,
  "n_2x25" BIGINT,
  "n_2x26" BIGINT,
  "n_2x27" BIGINT,
  "n_2x28" BIGINT,
  "n_3x1" BIGINT,
  "n_3x2" BIGINT,
  "n_3x3" BIGINT,
  "n_3x4" BIGINT,
  "n_3x5" BIGINT,
  "n_3x6" BIGINT,
  "n_3x7" BIGINT,
  "n_3x8" BIGINT,
  "n_3x9" BIGINT,
  "n_3x10" BIGINT,
  "n_3x11" BIGINT,
  "n_3x12" BIGINT,
  "n_3x13" BIGINT,
  "n_3x14" BIGINT,
  "n_3x15" BIGINT,
  "n_3x16" BIGINT,
  "n_3x17" BIGINT,
  "n_3x18" BIGINT,
  "n_3x19" BIGINT,
  "n_3x20" BIGINT,
  "n_3x21" BIGINT,
  "n_3x22" BIGINT,
  "n_3x23" BIGINT,
  "n_3x24" BIGINT,
  "n_3x25" BIGINT,
  "n_3x26" BIGINT,
  "n_3x27" BIGINT,
  "n_3x28" BIGINT,
  "n_4x1" BIGINT,
  "n_4x2" BIGINT,
  "n_4x3" BIGINT,
  "n_4x4" BIGINT,
  "n_4x5" BIGINT,
  "n_4x6" BIGINT,
  "n_4x7" BIGINT,
  "n_4x8" BIGINT,
  "n_4x9" BIGINT,
  "n_4x10" BIGINT,
  "n_4x11" BIGINT,
  "n_4x12" BIGINT,
  "n_4x13" BIGINT,
  "n_4x14" BIGINT,
  "n_4x15" BIGINT
);

Mnist Train

@kaggle.oddrationale_mnist_in_csv.mnist_train

19 MB
60000 rows
785 columns


CREATE TABLE mnist_train (
  "label" BIGINT,
  "n_1x1" BIGINT,
  "n_1x2" BIGINT,
  "n_1x3" BIGINT,
  "n_1x4" BIGINT,
  "n_1x5" BIGINT,
  "n_1x6" BIGINT,
  "n_1x7" BIGINT,
  "n_1x8" BIGINT,
  "n_1x9" BIGINT,
  "n_1x10" BIGINT,
  "n_1x11" BIGINT,
  "n_1x12" BIGINT,
  "n_1x13" BIGINT,
  "n_1x14" BIGINT,
  "n_1x15" BIGINT,
  "n_1x16" BIGINT,
  "n_1x17" BIGINT,
  "n_1x18" BIGINT,
  "n_1x19" BIGINT,
  "n_1x20" BIGINT,
  "n_1x21" BIGINT,
  "n_1x22" BIGINT,
  "n_1x23" BIGINT,
  "n_1x24" BIGINT,
  "n_1x25" BIGINT,
  "n_1x26" BIGINT,
  "n_1x27" BIGINT,
  "n_1x28" BIGINT,
  "n_2x1" BIGINT,
  "n_2x2" BIGINT,
  "n_2x3" BIGINT,
  "n_2x4" BIGINT,
  "n_2x5" BIGINT,
  "n_2x6" BIGINT,
  "n_2x7" BIGINT,
  "n_2x8" BIGINT,
  "n_2x9" BIGINT,
  "n_2x10" BIGINT,
  "n_2x11" BIGINT,
  "n_2x12" BIGINT,
  "n_2x13" BIGINT,
  "n_2x14" BIGINT,
  "n_2x15" BIGINT,
  "n_2x16" BIGINT,
  "n_2x17" BIGINT,
  "n_2x18" BIGINT,
  "n_2x19" BIGINT,
  "n_2x20" BIGINT,
  "n_2x21" BIGINT,
  "n_2x22" BIGINT,
  "n_2x23" BIGINT,
  "n_2x24" BIGINT,
  "n_2x25" BIGINT,
  "n_2x26" BIGINT,
  "n_2x27" BIGINT,
  "n_2x28" BIGINT,
  "n_3x1" BIGINT,
  "n_3x2" BIGINT,
  "n_3x3" BIGINT,
  "n_3x4" BIGINT,
  "n_3x5" BIGINT,
  "n_3x6" BIGINT,
  "n_3x7" BIGINT,
  "n_3x8" BIGINT,
  "n_3x9" BIGINT,
  "n_3x10" BIGINT,
  "n_3x11" BIGINT,
  "n_3x12" BIGINT,
  "n_3x13" BIGINT,
  "n_3x14" BIGINT,
  "n_3x15" BIGINT,
  "n_3x16" BIGINT,
  "n_3x17" BIGINT,
  "n_3x18" BIGINT,
  "n_3x19" BIGINT,
  "n_3x20" BIGINT,
  "n_3x21" BIGINT,
  "n_3x22" BIGINT,
  "n_3x23" BIGINT,
  "n_3x24" BIGINT,
  "n_3x25" BIGINT,
  "n_3x26" BIGINT,
  "n_3x27" BIGINT,
  "n_3x28" BIGINT,
  "n_4x1" BIGINT,
  "n_4x2" BIGINT,
  "n_4x3" BIGINT,
  "n_4x4" BIGINT,
  "n_4x5" BIGINT,
  "n_4x6" BIGINT,
  "n_4x7" BIGINT,
  "n_4x8" BIGINT,
  "n_4x9" BIGINT,
  "n_4x10" BIGINT,
  "n_4x11" BIGINT,
  "n_4x12" BIGINT,
  "n_4x13" BIGINT,
  "n_4x14" BIGINT,
  "n_4x15" BIGINT
);