## 4 Experiments
### Experimental Evaluation Index  
Top-1 accuracy, Top-5 accuracy, pruning model parameters (Params), and floating-point operations (FLOPs) are usually adopted to measure the effectiveness of the model pruning algorithm.  
(1) Top-1 accuracy  
Figure 8: The pre-processed traffic sign image. (**a**) shows the original traffic sign image; (**b**) shows the image with histogram equalization for the V channel in HSV; (**c**) shows the image with histogram equalization for the Y channel in YUV.  
Top-1 accuracy refers to the accuracy of the first category consistent with the actual result, i.e., the predicted label takes the largest one in the last probability vector as the prediction result. If the classification with the greatest probability in the prediction result is correct, the prediction is correct; otherwise, the prediction is wrong.  
Top-5 accuracy refers to the accuracy of the top five categories containing the actual results, i.e., among the top five categories with the largest probability vector, the probability of correct occurrence indicates a correct prediction; otherwise, the prediction is wrong.  
Params refers to the total number of parameters that need to be trained in model training, and the number of parameters in an ordinary convolutional layer is calculated as \((K_{h}\times K_{w}\times C_{i}+1)\times C_{o}\), where \(K_{h}\) is the height of the convolution kernel, \(K_{w}\) is the width of the convolution kernel, \(C_{i}\) is the number of channels of the input, \(C_{o}\) is the number of channels of the output, and \(+1\) is the bias. The formula for calculating the parameter quantity of the fully connected layer of the model is \((C_{i}+1)\times C_{o}\), where \(C_{i}\) is the number of nodes in the input, \(C_{o}\) is the number of nodes in the output, and \(+1\) is the bias.  
FLOPs  
FLOPs represent the number of floating-point operations per second (FLOPs), which can be used to measure the complexity of an algorithm or model. The calculation formula of FLOPs is \([C_{i}\times K_{w}\times K_{h}+1]\times W\times H\times C_{o}\), where \(C_{i}\) is the number of channels in the input; \(C_{o}\) is the number of channels in the output; \(C_{i}\times K_{w}\times K_{h}\) is the calculation amount of a convolution multiplication operation; \(C_{i}\times K_{w}\times K_{h}-1\) is the calculation amount of a convolution addition operation; \(W\) and \(H\) are the length and width of the feature map, respectively; \(K_{h}\) is the height of the convolution kernel; and \(K_{w}\) is the width of the convolution kernel.  
When other pruning methods are taken for comparing experiments, the difference between the accuracy of the pruned model and the accuracy of the initial model (the unpruned model) is used as the main basis for evaluating the effect of the pruning algorithm. Additionally, the compression and acceleration effects of the network model are evaluated by the two indicators of Params and FLOPs. When the pruning network contains fewer parameters, it is indicated that the compression ratio of the network is greater, and the reduction of parameter size leads to a decrease in FLOPs. In this case, the network running speed is improved because fewer FLOPs are required. The decline of the two standards of Params and FLOPs in the post-pruning model is controlled by the pruning rate \(\beta_{i}\), and a better compression effect can be obtained with a larger pruning rate. When a convolutional layer is pruned at the pruning rate \(\beta_{i}\), the parameter size of the layer will be reduced to \((1-\beta_{i})\times(K_{h}\times K_{w}\times C_{i}+1)\times C_{o}\), and the amount of floating-point calculation needed will be reduced to \((1-\beta_{i})\times[C_{i}\times K_{w}\times K_{h}+1]\times W\times H\times C_{o}\).