of PFOD to propagate the labels. In each stage of PFOD, we train the network with 10K iterations. The learning rate is 0.0001 for the first 100 iterations, 0.001 for the next 4900 iterations, 0.0001 for 4000 iterations and 0.00001 for the last 1000 iterations[^1]. The batch size is set to 64, and the weight decay is 0.0005. The last detector training shares the same parameters. The training takes 1.25 hours on 4 NVidia P100 GPUs to finish the 10K iterations. The implementation is based on Caffe [@jia2014caffe] under the environment of CUDA 8.0 and CUDNN 5.1.

We also report the accuracy without any label propagation, and directly train the object detector on the provided label set. This straightforward approach is denoted as OD as a naive baseline, and all the data augmentation and learning strategy parameters are the same with a single stage of PFOD.

In the incomplete annotation settings, we enable the extra background samples by replacing 16 images in each batch of 64 with the extra background images.

       Method        $C_i$   $C_a$    mAP(%)         MAE            RSME
  ----------------- ------- ------- ---------- --------------- ---------------
   LPN[@HsiehLH17]    989     ALL    NotAvail       23.80           36.79
                      989     ALL     96.67      2.94 (0.15)     3.94 (0.10)
                      $5$     ALL     91.97      4.38 