learned without fine-tuning on VOC data. Second, we fine tune the CNN by back-propagating the error end-to-end using PASCAL trainval set. The fine-tuning procedure is performed 100K iteration, with a step size of 20K. In both cases, $fc7$ features are extracted to represent patches, and a SVM is learned to produce the final score.

![image](plots_fp_trendarea_animals){width="0.24\linewidth"} ![image](plots_fp_trendarea_furniture){width="0.24\linewidth"} ![image](plots_fp_trendarea_person){width="0.24\linewidth"} ![image](plots_fp_trendarea_vehicles){width="0.24\linewidth"}

We report numbers for all the CNNs on VOC 2007 data in Table \[tab:voc\_2007\]. Several interesting notes:

-   Despite the search engine bias and the noise in the data, our two-stage CNN with graph regularization is on par with ImageNet-trained CNN.

-   Training a network directly on noisy and hard Flickr images hurt the learning process. For example, FlickrS gives the worst performance and in fact when a CNN is trained using all the images from Google and Flickr it gives a mAP of 40.5, which is substantially lower than our mAP.

-   The proposed two-stage training strategy effectively takes advantage of the more realistic data Flickr provides. Without graph regularization we achieve a mAP of 43.4 (FlickrF). However, adding the graph regularization brings our final FlickrG network on par with ImageNet (mAP = 44.7).

We use the same CNNs for VOC 