under these two criteria. The threshold is in parentheses of Table \[tbl:differentc1c2\]. Note the criterion of mAP does not depend on the threshold. From the table, we have the following observations and discussions.

### What is the upper bound performance using all the training data?

In the fully supervised setting ($C_i = \text{ALL}$, $C_a = \text{ALL}$), our detector (OD) could achieve 96.67% mAP@0.5, 2.94 MAE and 3.94 RMSE. This significantly outperforms the state-of-the-art method of LPN [@HsiehLH17], whose MAE is 23.80 and RMSE is 36.79 (mAP is not reported). Other baseline approaches are not shown here because the accuracy is lower than LPN. This demonstrates a strong object detector towards the counting problem.

### Do we need hundreds of training images for car counting?

We apply the OD on $C_i=5$ images with all the labeled annotations ($C_a = \text{ALL}$), and the detector can still achieves 91.97 mAP@0.5, 3.00 MAE, and 5.58 RSME without extra background images. Compared with $C_i = \text{ALL}$, $C_a = \text{ALL}$ which uses 989 training images, this result is very encouraging. It clearly indicates that to develop a car counting algorithm, 5 or a few more images might be enough rather than several hundreds. The 5 image 