because the cars and drinks in VOC 2007 are generally of difference appearances or views compared with the object instances in CARPK and Drink35.

### Criteria.

Since we focus on the detection-based approaches, we adopt the widely-used [@Girshick15; @RenHGS15; @LiuAESRFB16; @RedmonF17] mAP@0.5 as one of the metrics, which measures the mean average precision (mAP) using 0.5 as the interaction-over-union (IoU) threshold.

Following [@LempitskyZ10; @HsiehLH17], we also use the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE) to evaluate the accuracy of the counting results. MAE is defined as $\sum_{i}|n_i - n_i'|/N$, while RMSE as $\sum_{i}\sqrt{(n_i - n_i')^2}/N$, where $n_i$ is the number of objects predicted by the model for the $i$-th testing image, $n_i'$ is the ground-truth number of objects, and $N$ is the total number of testing images.

### Implementation details.

The data augmentation is of great importance for the network learning due to the small training set with incomplete labels. Motivated by the implementation of Yolo [@RedmonF17] and SSD [@LiuAESRFB16], we incorporate the random scaling, random aspect ratio distortion, and color jittering. Random rotation is also implemented for the car counting problem by multiple data samplers (motivated by SSD [@LiuAESRFB16]), so that non-rotated images are 