{
  "fetched_at": "2026-10-19T11:40:30Z",
  "source_url": "https://www.kaggle.com/competitions/computer-vision-lab-practice-cv/data",
  "pages": [
    {
      "name": "rules",
      "content": "It's an open book exam",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "data-description",
      "content": "<p>The data files train.csv and test.csv contain gray-scale images of hand-drawn digits, from zero through nine.</p>\r\n<p>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.</p>\r\n<p>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.</p>\r\n<p>Each pixel column in the training set has a name like pixelx, 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 pixelx is located on row i and column j of a 28 x 28 matrix, (indexing by zero).</p>\r\n<p>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.</p>\r\n<p>Visually, if we omit the \"pixel\" prefix, the pixels make up the image like this:</p>\r\n<pre>000 001 002 003 ... 026 027\r\n028 029 030 031 ... 054 055\r\n056 057 058 059 ... 082 083\r\n |   |   |   |  ...  |   |\r\n728 729 730 731 ... 754 755\r\n756 757 758 759 ... 782 783 \r\n</pre>\r\n<p>The test data set, (test.csv), is the same as the training set, except that it does not contain the \"label\" column.</p>\r\n<p>Your submission file should be in the following format: For each of the 28000 images in the test set, output a single line containing the ImageId and the digit you predict. For example, if you predict that the first image is of a 3, the second image is of a 7, and the third image is of a 8, then your submission file would look like:</p>\r\n<pre>ImageId,Label<br />1,3<br />2,7<br />3,8 \r\n(27997 more lines)</pre>\r\n<p>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.</p>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "<h3>Goal</h3>\nThe goal in this competition is to take an image of a handwritten single digit, and determine what that digit is.<br>For every  in the test set, you should predict the correct label. \n\n<h3>Metric</h3>\n<p>This competition is evaluated on the categorization accuracy of your predictions (the percentage of images you get correct).</p>\n\n<h3>Submission File Format</h3>\n<p>The file should contain a header and have the following format:</p>\n<pre>ImageId,Label<br>1,0<br>2,0<br>3,0<br>etc.</pre>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}