## 6 Analysis and Discussion
### Analysis on Pruning Methods  
For the results of the sparse training Method B, it can be found that different pruning methods also influence on the mAP obviously.  
Pruning method is the most conservative method and can not reduce the size of the model as much as the latter two methods, but the actual comprehensive result is very outstanding. When we use the Pruning method, even if the pruning ratio is set to 80%, the mAP only decreases by less than 3% compared with the original unpruned model, which means the accuracy of the model is basically maintained, while the model size is reduced to 19% of the original model, the number of parameters is reduced to 16% of the original model, and the running time is less than half of the original model. Therefore, in this research, we set Prune80% model to the model with the highest comprehensive performance after pruning.  
As for the Shortcut method, although there is an error when the prune ratio is set to 80%, after analyzing the results when the prune ratio is 30% and 50%, the Shortcut method has more pruning intensity and higher performance compared to the Prune method. However, the detection accuracy of these two methods is not so much different, and there are compilation errors that are temporarily unexplained. As a result, the Shortcut method is not as stable as the Prune method.  
As for the Slim method, we can find that, with the increase of the pruning ratio, the pruning intensity increases significantly, the model size and the number of parameters decrease obviously, but the model accuracy also drops gradually. In fact, after the fine-tune processing on the software platform, the final detection accuracy of the pruned model returns to the same level as the original model even the slim method is used. However, after deploying the model to FPGA, the detection accuracy decreases significantly.  
The Vitis AI quantizer could reduce the computational complexity without losing prediction accuracy; this is only right in the case of unpruned models. After pruning, the model loses some information, and quantization in this case will cause more information loss, leading to the condition that, when the percentage of pruning is too large, the accuracy of the model will not return to the level of the original model, even after fine-tuning.  
In order to observe the effect of the reduced accuracy on FPGA more clearly, we use the original model and Prune80% model to detect dishes on each of the 68 testing images, then select two representative images for analysis. As shown in Figure 7, (a) and (b) represent the detection results of testing image A, while (c) and (d) represent the detection results of testing image B. In these four pictures, (a) and (c) show the detection results using the original model, while (b) and (d) show the detection results using the Prune80% model. From Figure 7, we can find that, after pruning, most of the dishes can be detected correctly, but there are still some dishes near the corners that can not be detected correctly, such as Chopsticks-cover in the upper right corner of (b), and Paper at the top of (d). In addition, there are cases about repeated detection, such as the results of Chopsticks-two near the bottom of (d). In general, even though the Prune80% model has the highest comprehensive performance after pruning, it still has a little lower detection accuracy than the original model. Therefore, in our future research, we will improve the pruning method, so as to enhance the detection accuracy after pruning.