other domains, we collect a small dataset *Drink35*, which contains 10 images as the training set and 25 images as the testing set. The task is to detect and count all the product instances. One example image is shown in the second row of Fig. \[fig:isl\].

To simulate the incompletely-supervised settings, we randomly select $C_i$ training images, and for each image we randomly select at most $C_a$ annotated cars. All the other unselected images are discarded during training. This training set is denoted as $\text{CARPK}\_C_i\_C_a$ or $\text{Drink35}\_C_i\_C_a$ for CARPK and Drink35 datasets, respectively. For example, CARPK\_5\_5 means the training set with 5 images and each with at most 5 annotated bounding boxes. Similarly, the suffix of $\_C_i\_\text{ALL}$ denotes the training set of $C_i$ images with all the annotated boxes, and $\_\text{ALL}\_C_a$ denotes all training images with at most $C_a$ annotated bounding boxes in each image. The test set is not altered for consistent evaluation.

We use the PASCAL VOC [@EveringhamGWWZ10] 2007 trainval set (5011 images) as the extra background images with all the original bounding box labels removed. Note that the images in PASCAL VOC 2007 contains the object of cars and drinks. We keep these images as negative samples 