Transfer learning with fewer ImageNet classes
Though much previous work tried to uncover the best practices for transfer learning, much is left unexplored. Our preliminary work explores the effect of removing a portion of the ImageNet classes with low per-class validation accuracy on the accuracy of the remaining classes. Furthermore, we explore if models trained with a reduced number of classes are suitable for transfer learning.
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CIFAR-10
CIFAR-100
Oxford 102 Flower
DTD
Food-101
Caltech-101
FGVC-Aircraft
Caltech-256
Birdsnap
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