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Note: Update in the Evaluator

πŸ›  Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.

πŸ•΅οΈ Introduction

This problem we are levelling up from using images to using videos are input! You’ve successfully identified the board position from an image. But can you predict moves of chess pieces from a video clip?

The input will be a short video of a game of chess, with pieces being moved around. Can you create a model that will identify from which location pieces moves and landed where? This is going to be an exciting challenge!

Understand with code! Here is getting started code for you.πŸ˜„

πŸš€ Explainer Video

πŸ’Ύ Dataset

The given dataset contains videos of chess board with pieces moving around the board. Each frame in video is of size 256 * 256 width & hight. A CSV is also provided containing the VideoID and the previous & new locations of multiples pieces from the video. The format of moves are determined by the THE Cartesian Coordinate System and the space between in each label ( for ex. b2b3 d8f7 ) is describing move from a different piece.

Sample Column & Video

|VideoID|label | 
|-------|---------------------------------------------| 
|0      |b2b3 d8f7 f4f5 c2c1r h6f4 f7d8 f2f3 c1b1 b3b7|

The dataset is divided into train and validation set, each containing a zip file and csv corresponding to it. For evaluation you are provided with the test.zip which contain the videos for which you need to find the previous & new locations of pieces.

πŸ“ Files

Following files are available in the resources section:

  • train.csv - (5000 samples) This csv file contains VideoID column which corresponds to train.zip and labels
  • train.zip - (5000 samples) This zip file contains video corresponding to the first column of train.csv.
  • val.csv - (1000 samples) This csv file contains VideoID column which corresponds to val.zip and labels
  • val.zip - (1000 samples) This zip file contains video corresponding to the first column of val.csv.

  • test.zip - (2000 samples) This zip file contains testing videos that will be used for actual evaluation for the leaderboard score.

πŸš€ Submission

  • Prepare a CSV containing headers as VideoID and label containing predicted moves.
  • Sample submission format available at sample_submission.csv in the resorces section.

Make your first submission here πŸš€ !!

πŸ–Š Evaluation Criteria

During evaluation Word Error Rate will be used to test the efficiency of the model and in python using jiwer. For ex.

from jiwer import wer

ground_truth = 'hello world' hypothesis = 'hello duck'

error = wer(ground_truth, hypothesis)

πŸ”— Links

πŸ“± Contact

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