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Find a Legend

Completed 64 weeks ago
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In the previous two challenges in this series we focused on the axes of old crossplots, for this challenge we ask contestants to look for the legend of the plot. Once the legend is identified the categories in the legend must be extracted.

This challenge is the third in a series of retrieving data from documents.  Xeek will be launching one more challenge later in 2022.  A lot of good data is trapped in old crossplots and graphs; let's release it and put it to work.

Data and Labels

The data for this challenge are images of crossplots with two axes.  The crossplots have a variety of sizes, formats, noise, and orientations.  The targets for extraction are the contents of the legends.  The labels for this challenge are enclosed in brackets. Each legend element is within single quotes and separated by a space. Legend titles are not reported. For graphs without legends, the entry should be blank. An example of an image and the corresponding answer are shown below in Figure 1 and Table 1.  Note that some images may be missing this information.

example-plot-find-a-legend.png

Figure 1: Example crossplot for this challenge.

example-answer-find-a-legend.PNG

Table 1: Example answer for plot in Figure 1.

Evaluation

During the challenge, a quantitative score will be used to populate the Predictive Leaderboard.¬† Contestants will submit a CSV as described in the Starter Notebook, containing the target ‚Äėlegend‚Äô.¬† Submissions will be scored against the test answer key using two approaches.¬† Half of the total score will focus on legend text detection and is based on a ratio of Levenshtein distance and total length.¬† The second half of the score is determined by F1 score.¬† Scores will range from 0 to 10 - a higher score is considered more successful.

Contestants can submit up to 5 CSV predictions per day.

At the end of the challenge, Xeek will request the models from the top 10 submissions for review by a panel of judges.  A submission must contain a Jupyter Notebook, a requirements.txt, and any additional parameters a contestant has generated.  The requirements.txt should describe the environment used to generate the model.  It needs to contain the libraries and their versions, and the Python version (>=3.6 is preferred).  See the Starter Notebook on the Data Tab for an example.

The judges will score the top 10 submissions on accuracy and interpretability.  The accuracy of the submission counts for 90% of the final score.  Accuracy will be determined using the same scoring algorithm described above for the Predictive Leaderboard.  The top 20% of scores will receive maximum points (90).  Other submissions will receive points based on how close they were to the top-performing submissions.

The interpretability metric counts for 10% of the final score.  This qualitative metric focuses on the degree of documentation, clearly stating variables for models and using doc strings and markdown.  Submissions with exceptional interpretability will receive maximum points (10).  Other submissions will receive points based on the level to which they meet the criteria.