## 4 Experiments
### Experimental Results and Analysis
#### 4.3.2 Experimental Results and Analysis of SJ_LLE Method Pruning  
In this experiment, the ASFP pruning, HYDRA pruning, FPGM pruning, and SJ-LLE pruning methods proposed in this paper are used to prune the model at different pruning ratios. Since excessive clipping of the model will cause the accuracy of the model to decrease significantly, and the model with excessive performance loss is not conducive to practical applications, the maximum pruning ratio is set to 70% in the experiment. The experiment is conducted on the BelgiumTS and GTSRB datasets, and the accuracy of the model after pruning is analyzed under the same parameters as those in other experimental conditions.  
The SJ-LLE method is a similarity filter pruning algorithm based on JS_divergence proposed in Section 2.5.  
The ASFP [21] and Filter method uses the L2 norm size of the filter as the evaluation criterion and cuts the filter with a small norm value.  
The HYDRA and Filter pruning method [6] regards the pruning target as an empirical risk-minimization problem and then uses SGD to solve the minimization problem. By letting the training target guide the search for connections to be pruned, it proposes a scaled initialization of importance score, which is a key driver behind the high robustness and accuracy of the compression network.  
The FPGM pruning method [24] selects the filter with the largest replaceable contribution. Specifically, the median geometry of the filters within the same layer is calculated. Depending on the characteristics of the median geometric, the filter near it can be represented by the remaining filters.  
The ViT model is pruned by using four pruning methods, namely SJ-LLE, ASFP, HYDRA, Filter, and FPGM, and the accuracy of the post-pruning model is tested on the BelgiumTS and GTSRB datasets, respectively. The results are shown in Tables 3 and 4. Inthese two tables, different pruning ratios are set for the model pruned in the first experiment, and rows 2-5 show the accuracy of the model tested on the dataset after pruning at different ratios by using the four pruning methods, respectively.  
On the BelgiumTS dataset, the ViT model is clipped by using four different pruning methods, and the results are shown in Table 2. It can be seen from this table that the model after pruning with the SJ-LLE method has obvious advantages in experimental accuracy. For example, when the model pruning ratio is 50%, the model accuracy is only lost by 1.90%. Under the same pruning ratio, the test accuracy loss of the model pruned with the ASFP pruning method, the HYDRA method, the Filter method, and the FPGM method is 2.12, 2.07, and 1.96 times that of the SJ-LLE method, respectively. When the model pruning ratio is 70%, the model accuracy loss is only 2.69%, which is 2.08, 1.72, 3.06, and 1.59 times higher than that of the model pruned with the ASFP pruning method, HYDRA method, and FPGM method, respectively.  
Then, the model is pruned by using the SJ-LLE, ASFP, HYDRA, Filter, and FPGM pruning methods on the GTSRB dataset, and the results are shown in Table 3. From the experimental results in Table 3, it can be found that the model performance after pruning with the SJ-LLE method is better than that of other pruning methods under the same cutting ratio. For example, when the pruning ratio is 70%, the experimental accuracy loss of the model after pruning by the SJ-LLE method is 1.99%. Under the same pruning ratio, the test accuracy after pruning the SJ-LLE method is 2.20%, 1.38%, 3.09%, and 0.86% higher than that of the ASFP pruning method, HYDRA method, and FPGM method, respectively.