# Kannada MNIST — Data

来源：https://www.kaggle.com/competitions/Kannada-MNIST/data

采集时间：2026-10-19T11:34:24Z

The data files `train.csv` and `test.csv` contain gray-scale images of hand-drawn digits, from zero through nine, in the Kannada script.

Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive.

The training data set, `train.csv`, has 785 columns. The first column, called `label`, is the digit that was drawn by the user. The rest of the columns contain the pixel-values of the associated image.

Each pixel column in the training set has a name like `pixel{x}`, where `x` is an integer between 0 and 783, inclusive. To locate this pixel on the image, suppose that we have decomposed `x` as `x = i * 28 + j,` where `i` and `j` are integers between 0 and 27, inclusive. Then `pixel{x}` is located on row `i` and column `j` of a 28 x 28 matrix, (indexing by zero).

For example, `pixel31` indicates the pixel that is in the fourth column from the left, and the second row from the top, as in the ascii-diagram below.

Visually, if we omit the "pixel" prefix, the pixels make up the image like this:
<pre>000 001 002 003 ... 026 027
028 029 030 031 ... 054 055
056 057 058 059 ... 082 083
 |   |   |   |  ...  |   |
728 729 730 731 ... 754 755
756 757 758 759 ... 782 783 
</pre>

The test data set, `test.csv`, is the same as the training set, except that it does not contain the `label` column.

The evaluation metric for this contest is the categorization accuracy, or the proportion of test images that are correctly classified. For example, a categorization accuracy of 0.97 indicates that you have correctly classified all but 3% of the images.

## Files

*   **train.csv** - the training set
*   **test.csv** - the test set
*   **sample_submission.csv** - a sample submission file in the correct format
*   **Dig-MNIST.csv** - an additional labeled set of characters that can be used to validate or test model results before submitting to the leaderboard