Round 1: Completed Weight: 35.0

πŸ•΅οΈ Introduction

Solving a Rubik's cube is now too mainstream they say. But what if you have 10,000 pictures of the Rubiks cube and you are asked to stitch it all together? This is not a problem we need, this is a problem we deserve!

For input you will be given a large number of images, for about half of them, we have measured the orientation of the cube. But to be able to stitch all those images together, you have to figure out how to predict the orientation of the Rubik's Cube for the rest of the images.

Understand with code! Here is some code to get you started right away! πŸ˜„ :

πŸ’Ύ Dataset

The training dataset consists of 5000 images of size 512x512 with 3 channels each (for RGB). The associated labels is single continuous variable:

  • xRot : Orientation of the Cube, in degrees, along an arbitrarily chosen axis (a number between 0 and 360). The axis around which this value is measured is consistent across the whole of the training and the test set.

The test dataset consists of 5001 images of size 512x512 with 3 channels each (for RGB). The goal of the task is to predict the xRot value of the Rubik's Cube in these test images.

πŸ“ Files

Following files are available in the resources section:

  • train.tar.gz - (5000 samples) Tar File containing all the training images, and associated labels
  • test-images.tar.gz - (5001 samples) Tar file containing all the test images
  • sample_submission.csv - A sample submission file (with random predictions) to demonstrate the expected file structure by the evaluation setup.

πŸš€ Submission

  • Prepare a CSV containing header as :
    • filename, xRot
    • The values of the filename the column should match the file name of each of the images in the test set.
    • The values of the xRot should be a number between 0 and 360, representing the orientation of the cube (in degrees) along the respective axis.
  • Sample submission format is available in the resources section of the challenge page as sample_submission.csv.

Make your first submission here πŸš€ !!

πŸ–Š Evaluation Criteria

During the evaluation, Mean Squared Error be used to test the overall performance of your solution.

πŸ“± Contact


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