Do we need augmentation when using YOLOv5 as our benchmark?

[yaoshanliang]

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I have a question that when using YOLOv5 as the benchmark, do we use default hyperparameters or close all augmentations, like hsv, translate, mosaic?

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[glenn-jocher]

@yaoshanliang yes, data augmentation can improve model performance, especially when training on limited or imbalanced datasets. YOLOv5 includes several built-in augmentations, such as HSV color space transformations, scaling, and mosaic data augmentation, to enhance model generalization. For further details on YOLOv5's default augmentations and configuration, please refer to the Ultralytics YOLOv5 documentation. If you have any additional questions, feel free to ask!