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The main goal is to build a synthetic data creation tool that can take the glyph of a legend label and create a pattern of it that can be used with background templates to create a synthetic training set. The secondary goal will be to measure the performance of various types of models on this problem and try and create a better performing custom model.
| Milestone | Quality Metric | Date |
|---|---|---|
| Show that a synthetic training set can be used to train the base model that is equal to or better then the original training set. | F-score of model trained on synthetic data is >= the F-score from training on the original dataset | 3-4 weeks after start |
| Show proof that the initial concept holds weight. The scores from the competition were mostly in the <10% range, with the highest being above 30%. | F-score is >50% | 2 months after start |
| F-score is >95% | Project Conclusion |
Project Products
- Research paper describing the tool creating synthetic data
- Research paper contrasting the performance of various models on this problem and my custom one.
- Tool to create synthetic data
- ML Model to train against these glyphs.
- Small talks on research.
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