AutoAugment: Learning Augmentation Policies from Data

24 May 2018Ekin D. CubukBarret ZophDandelion ManeVijay VasudevanQuoc V. Le

Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed... (read more)

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Evaluation results from the paper


Task Dataset Model Metric name Metric value Global rank Compare
Fine-Grained Image Classification Caltech-101 AutoAugment Top-1 Error Rate 13.07% # 1
Fine-Grained Image Classification FGVC Aircraft AutoAugment Top-1 Error Rate 7.33% # 1
Fine-Grained Image Classification Oxford 102 Flowers AutoAugment Top-1 Error Rate 4.64% # 2
Fine-Grained Image Classification Oxford-IIIT Pets AutoAugment Top-1 Error Rate 11.02% # 2
Fine-Grained Image Classification Stanford Cars AutoAugment Top-1 Error Rate 5.19% # 1
Image Classification SVHN AutoAugment Percentage error 1.02 # 1