## 5 Experiments
### Competition Test  
In previous experiments, we have verified that our training-clipping method can maintain detection accuracy in the 8-bit quantization process. In this section, we compare the accuracy and speed of other advanced quantization methods in Faster RCNN, as shown in Table 8 (here TOP-1 ACC means top-1 accuracy).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SVD LR** & **AP(IOU)** & **AP(IOU)** & **AP(IOU)** & **AR(IOU)** & **AR(IOU)** & **AR(IOU)** \\
**Strategies** & **0.5:0.95** & **0.5** & **0.75** & **0.5:0.95** & **0.5:0.95** & **0.5:0.95** \\ \hline LR = 0.005 & 0.5033 & 0.7380 & 0.5540 & 0.4340 & 0.6177 & 0.6228 \\ LR = 0.0005 & 0.5050 & 0.7340 & 0.5590 & 0.4379 & 0.6195 & 0.6263 \\ LR = 0.00005 & 0.5087 & 0.7900 & 0.5610 & 0.4400 & 0.6230 & 0.6280 \\ Baseline & 0.5191 & 0.803 & 0.5756 & 0.4468 & 0.6317 & 0.6389 \\ \hline \hline \end{tabular}
\end{table}
Table 6: SVD performance on learning rate decade strategies.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Strategies** & **AP(IOU)** & **AP(IOU)** & **AP(IOU)** & **AR(IOU)** & **AR(IOU)** & **AR(IOU)** \\
**Strategies** & **0.5:0.95** & **0.5** & **0.75** & **0.5:0.95** & **0.5:0.95** & **0.5:0.95** \\ \hline L & 0.5090 & 0.8000 & 0.5690 & 0.4410 & 0.6250 & 0.6320 \\ L + Lq & 0.5190 & 0.8032 & 0.5754 & 0.4465 & 0.6315 & 0.6378 \\ Baseline & 0.5191 & 0.8030 & 0.5756 & 0.4468 & 0 Table 8 compares the baseline and two outstanding 8-bit quantization methods in terms of accuracy, number of parameters, compression ratio, and CPU inference time. In terms of precision, the training-clipping method has the highest precision, even slightly exceeding baseline. Moreover, the precision also reaches 80% after adding the SVD method, which is 0.9% higher than the method in [11] and 1.6% higher than the method in [28], reflecting the effectiveness of our improved quantization compensation loss function (Lq). In terms of the number of parameters and the compression rate, the basic ratio is kept at 1/4 because they are all 8-bit quantization, and the SVD method is significantly better than the other methods, reaching a compression rate of 18.84%, which greatly reduces the model redundancy and proves the reliability of the SVD method. In terms of inference time, our training-clipping method has an inference time of 79 ms for one image, which is 280% faster compared to the baseline. Compared with the method of [11], we directly clipped out the interval of weights to avoid the search time of weights, which is improved by 13 ms, but in the method of [28], model pruning is added in addition to the quantization strategy, which is 5 ms faster than our method. However, after using SVD, the inference time of our method was shortened to 58 ms, which is better. In general, our method is superior in both inference speed and accuracy.  
To further verify the reliability of our method, experiments were conducted on other widely used datasets and achieved good results. The experimental results are shown in Table 9. On the ImageNet dataset, the training-clipping approach's best accuracy is 69.73%, which is only 0.03% worse than the baseline. Even with the SVD method, the high accuracy of 69.71% is ensured. The accuracy of the training-clipping approach drops by 0.27% on the COCO dataset, which is nine times greater than that of ImageNet. The SVD method's use on the COCO dataset similarly results in an accuracy loss of 0.05%. This indicates that in the hard-to-learn samples with multi-scale bounding boxes, the subtle information loss may lead to some misdetection and omission, but overall, our quantization method still maintains a small accuracy loss and shows reliability on different datasets.