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introduction.md
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## Better Augmentations
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We knew that without a good augmentation strategy we could never get competitive results to a model trained on 400 million images. Therefor we implemented heavy augmentations to make the training more data efficient.
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## Better Training
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## Better Augmentations
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We knew that without a good augmentation strategy we could never get competitive results to a model trained on 400 million images. Therefor we implemented heavy augmentations to make the training more data efficient. They include random affine transformations and perspective changes, as well as occasional equalization and random changes to brightness, contrast, saturation and hue. We made sure to keep hue augmentations limited however to still give the model the ability to learn color definitions.
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While we would have liked to have augmentations for the captions as well after some experimentation we settled with random sampling from the five captions available in MSCOCO and leaving the rest of the captions unmodified.
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## Better Training
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