Almost all computer vision applications rely on annotated images to train, test, and validate the models that power them. Annotating these images can range in complexity from a simple classification to a sophisticated pixel-by-pixel segmentation; the tools that support these tasks vary in quality and sophistication as well. …

Synthetic Minority Oversampling Technique

Synthetic data can be particularly useful in cases where there are too few examples of the minority class for a model to effectively learn the decision boundary. One way to solve this problem is to oversample the minority class, and simply duplicate examples of it in the training dataset. While…

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