2012 and report results in Table \[tab:voc\_2012\]. In this case, our networks outperform the ImageNet pretrained network even after fine-tuning (200K iterations, 40K step size). Note that the original R-CNN paper fine-tuned the ImageNet CNN using train data alone and therefore reports lower performance [@girshick2014rich]. For fairness, we fine-tuned both ImageNet network and our networks on combined trainval images (ImageNet-TV). In both VOC 2007 and 2012, our webly supervised CNNs tend to work better for vehicles, probably because we have lots of data for cars and other vehicles ($\sim$500 classes). On the other hand, ImageNet CNN seems to outperform our network on animals [@russakovsky2014imagenet] (cat). This is probably because ImageNet has a lot more data for animals. It also suggests our CNNs can potentially benefit from more animal categories.

[**Does web supervision work because the image search engine is CNN-based?**]{} One possible hypothesis can be that our approach performs comparably to ImageNet-CNN because Google image search itself uses a trained CNN. To test if this hypothesis is true, we trained a separate CNN using NEIL images downloaded from Google before March 2013 (pre-CNN based image search era). Despite the data being noisier and less ($\sim$450 per category), we observe $\sim$1% 