detectors using the cleaned up web data and evaluate them on VOC data. Note in this case, we will not use any VOC training images. We will only use web images to train both the CNN and the subsequent SVMs. 4) Finally, we will show scene classification results to further showcase the usefulness of the trained representation.

All the networks are trained with the Caffe Toolbox [@jia2014caffe]. In total we have 2,240 objects, 89 attributes, and 874 scenes. Two networks are trained on Google: 1) The object-attribute network (GoogleO), where the output dimension is 2,329, and 2) All included network (GoogleA), where the output dimension is 3,203. For the first network, $\sim$1.5 million images are downloaded from Google image search. Combining scene images, $\sim$2.1 million images are used in the second network. We set the batch size as 256 and start with a learning rate of 0.01. With Xavier initialization [@glorot2010understanding], the learning rate is reduced by a factor of 10 after every 150K iterations, and we stop training at 450K iterations. For two-stage training, GoogleO is then fine-tuned with $\sim$1.2 million Flickr images. We tested both with (FlickrG) and without (FlickrF) the relationship graph as regularization. Fine-tuning is performed 