# Conditional GAN-Based Two-Stage ISP Tuning Method: A Reconstruction-Enhancement Proxy Framework
## 4 Experiments
### Object Detection Tuning Experiment  
This section evaluates the effectiveness of various parameter generation methods on Infinite-ISP using object detection tasks and assesses the image object detection performance using Fast R-CNN trained on KITTI. The training of Fast R-CNN is based on the KITTI dataset, and the labels in the KITTI dataset are merged into three major categories, namely car, pedestrian, and cyclist. In this section, similar to the previous section, the proposed proxy method will be compared with end-to-end proxy, hand-tuning, random parameters, and default parameters to compare the performance of the parameters generated by the above methods on object detection tasks in Infinite-ISP. The criteria we used to evaluation are Average Precision (AP), True Positive (TP), False Negative (FN) and F1 score, where [EMAIL_ADDRESS]/% are used to evaluate the object detection performance of each method on three types of targets (car, person, bicycle). The [EMAIL_ADDRESS]/% averages the object detection performance of the three types of targets to get the overall object detection performance. TP and FN are used to count correctly detected targets and incorrectly detected targets, respectively, where TP counts the number of prediction boxes with Intersection Over Unions (IOUs) greater than  
Figure 6: Part of the test result of denoising tuning experiment. * The noise input is processed by demosaic and white balance. the threshold (properly predicted), and FN counts targets not properly predicted. The F1 score is used to evaluate the balance performance of Precision and Recall; the calculation formulas are shown as follows:  
\[\left\{ \begin{aligned} Precision&=\frac{TP}{TP+FP}\\ Recall&=\frac{TP}{TP+FN}\\ F1&=\frac{2\cdot Precision\cdot Recall}{Precision+ Recall}\end{aligned} \right. \tag{12}\]  
where False Positive (FP) is the number of prediction boxes with IOUs less than the threshold. In the object detection tuning step, we randomly initialize the hyper-parameters \(\mathcal{P}\). As mentioned above, we use \(\mathcal{L}_{1}\) as loss function. We use ADAM optimizer (\(\beta_{1}=0.9\), \(\beta_{2}=0.999\)) and an LR of 0.000005. After about epoch 35, the \(\mathcal{P}\) tend to convergent. Then, the \(\mathcal{P}\) are used in the Infinite-ISP to get the output images. By calculating all the above-mentioned metrics on object detection task with the ISP-processed images with parameters obtained by various methods, the effects of different tuning methods are obtained to facilitate quantitative comparison. The results are shown in Table 4 below:  
From this table, we can see that firstly, compared to the improvement in tuning effect for denoising tasks, the improvement in tuning effect for object detection is smaller, indicating that CV tasks such as object recognition are less sensitive to ISP parameter adjustments. At the same time, compared to default parameters, the performance degradation of random parameters in object recognition tasks is smaller than that in denoising tasks, which also indicates that the necessity of parameter adjustment varies in various tasks. Secondly, we can see that in the process of using proxy tuning instead of hand-tuning, compared to the performance improvement of denoising tasks on SSIM, the performance improvement of object detection tasks is more significant, while compared to denoising tasks, the performance improvement on PSNR is even less significant. This indicates that the use of the proxy tuning method has a tendency to improve the performance of different tasks, which also confirms the previous conclusion. Furthermore, similar to denoising tasks, there is a clear upper limit to the performance improvement of hand-tuning, and using proxy tuning can break through this upper limit in certain indicators. Although the overall performance improvement is not as good as denoising tasks, the application of proxy tuning in object detection can effectively break through the upper limit of hand-tuning.