{
  "fetched_at": "2026-10-24T15:38:58Z",
  "source_url": "https://www.kaggle.com/competitions/assignmenthw2/data",
  "pages": [
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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    {
      "name": "data-description",
      "content": "1) **images_training**: JPG images of 61578 galaxies. Files are named according to their GalaxyId.\n\n2) **solutions_training**: Probability distributions for the classifications for each of the training images.\n\n3) **images_test**: JPG images of 79975 galaxies. Files are name according to their GalaxyId. You will provide probabilities for each   of these images. \n\n4) **all_ones_benchmark**: Sample submission file corresponding to the All Ones Benchmark\n\n5) **all_zeros_benchmark**: Sample submission file corresponding to the All Zeros Benchmark\n\n6) **central_pixel_benchmark**: Simple benchmark that clusters training galaxies according to the color in the center of the image and then assigns the associated probability values to like-colored images in the test set.\n\nThe first column in each solution is labeled GalaxyID; this is a randomly-generated ID that only allows you to match the probability distributions with the images. The next 37 columns are all floating point numbers between 0 and 1 inclusive. These represent the morphology (or shape) of the galaxy in 37 different categories as identified by crowdsourced volunteer classifications as part of the Galaxy Zoo 2 project. These morphologies are related to probabilities for each category; a high number (close to 1) indicates that many users identified this morphology category for the galaxy with a high level of confidence. Low numbers for a category (close to 0) indicate the feature is likely not present. ",
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    {
      "name": "Description",
      "content": "<p>In this competition you will analyze images of galaxies to determine the probability that it belongs to particular class.</p>\n\nYour model must have at least the following:\n\n•  At least two Convolutional Layers followed by normalization and pooling layers.\n\n•  Activation function ReLU.\n\n•  Optimizer:  Gradient Descent.\n\n•  At least one fully connected layer followed by softmax transformation.\n\nThere are several techniques to improve the performance and generalization of the Convolutional Neural Network.  The objective is to try different methods to tweak the performance of your model and observe how the performance changes. You may use CNN's/ResNet/ODENet, etc.\n",
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    {
      "name": "Evaluation",
      "content": "This competition uses Root Mean Squared Error as the evaluation metric.\n\nFor each student in the competition,  submission  Your submission file must have a header and should be structured in the each of the galaxy IDs and the probability it belongs to each class.\n",
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