Fine-Grained Recognition without Part Annotations: Supplementary Material
نویسندگان
چکیده
In the main text we showed that large gains from using a VGGNet [5] architecture on the CUB-2011 [6] dataset. We show a similar comparison on the cars-196 [3] dataset in Tab. 1. As before, using a VGGNet architecture leads to large gains. Particularly striking is the gain from fine-tuning a VGGNet on cars-196 – a basic R-CNN goes from 57.4% to 88.4% accuracy only by fine-tuning, much larger than the already sizeable gain from fine-tuning a CaffeNet [2].
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