# Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation Source: [Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:42:15.448Z Authors: Misgana Negassi, Diane Wagner, Alexander Reiterer Published: 2021-10-31T13:04:45.000Z ## Abstract Data augmentation methods enrich datasets with augmented data to improve the performance of neural networks. Recently, automated data augmentation methods have emerged, which automatically design augmentation strategies. Existing work focuses on image classification and object detection, whereas we provide the first study on semantic image segmentation and introduce two new approaches: \\textit{SmartAugment} and \\textit{SmartSamplingAugment}. SmartAugment uses Bayesian Optimization to search over a rich space of augmentation strategies and achieves a new state-of-the-art performance in all semantic segmentation tasks we consider. SmartSamplingAugment, a simple parameter-free approach with a fixed augmentation strategy competes in performance with the existing resource-intensive approaches and outperforms cheap state-of-the-art data augmentation methods. Further, we analyze the impact, interaction, and importance of data augmentation hyperparameters and perform ablation studies, which confirm our design choices behind SmartAugment and SmartSamplingAugment. Lastly, we will provide our source code for reproducibility and to facilitate further research. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()