(0.20)     5.58 (0.20)
                      $5$    $50$     87.90      6.02 (0.05)     8.23 (0.05)
                      $5$    $25$     61.26     49.28 (0.05)    54.45 (0.05)
                      $5$    $10$      4.95     99.39 (0.05)    106.51 (0.05)
                      $5$     $5$      4.58     101.93 (0.10)   108.99 (0.10)
                      $5$    $50$     83.73     11.54 (0.05)    16.76 (0.05)
                      $5$    $25$     80.27     16.52 (0.05)    21.45 (0.05)
                      $5$    $10$     73.46     22.47 (0.05)    27.63 (0.05)
                      $5$     $5$     72.44     23.63 (0.05)    26.36 (0.05)

  : Accuracy on CARPK with different training images. $C_i$: number of training images from the whole training set; $C_a$: number of labeled bounding box annotations for each image; mAP: the higher, the better; MAE/RSME: the lower, the better. The value in parenthesis of MAE/RSME is the threshold to decide if the predicted bounding box is valid []{data-label="tbl:differentc1c2"}

Results on CARPK
----------------

The results are shown in Table \[tbl:differentc1c2\] for different numbers of training images ($C_i$) and different numbers of labeled bounding boxes ($C_a$) in each image. To count the number of objects, we need a threshold to determine if the predicted bounding box should be kept from the detector. Since different settings might favor different thresholds, we select the one with lowest MAE or RSME among $[0.05:0.05:0.7]$. We select the best threshold for MAE and RSME to examine the best performance 