# Compact Spatial Pyramid Pooling Deep Convolutional Neural Network Based Hand Gestures Decoder
## 7 Results
Additionally, this theory can be proved more in the analysis of the weight and the \(l1\)-normalization ranking of the nodes and filters in all hidden layers. Table 4 has the experimental results to prove the assumptions above. The whole worst scored 20 filters and node weights after can been seen in Table 4. The first hidden layers have different nodes. such as middle nodes as less weight, but the last convolutional layer almost exclusively has low scoring weight outermost filters when compared to the projection of previous convoluted output. Figure 11 is the visual representation pruning justification in the proposed method. Original proposed DCNN's first six convolutional layers filters are shown based on the \(l1\)-normalized values. Half of these nodes have the normalized weight value that falls less than the median. Accordingly, pruning half of the nodes in reality did not affect the total accuracy of the proposed DCNN. Moreover, retraining the pruned model forces the weights to generalized of features detection more than \"memorization\" of the dataset. As a result, the pruned DCNN gained more accuracy than the original model. This practical pruning also proves that the bigger DCNN is often not useful for smaller features/labels dataset classification. Table 5 shows the testing accuracy, precision, recall, and \(f1\) score for the all 3 proposed model. Although the overall accuracy of the original DCNN had 90% over all classes, the individual precision was skewed to some classes and less in some classes. This results in bias classification accuracy in real life. However, pruning and retraining of the model made the DCNN more generalized, and the class-wise precision and recall improved significantly. The overall accuracy also increases due to the newly pruned DCNN. After pruning and retraining for 100 epochs, the general accuracy improved into 92% in the pruned DCNN. Additionally, the introduction of SPP in the pruned DCNN further improves the accuracy of up to 95%. This is around 2% improvement of the previous model, as suggested by [PERSON] et al. [4]. Moreover, SPP makes the final model dimension invariant. As a result, the input image can be fed directly without any pre-processing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Layers**} & **Numbers of Original** & **Numbers of Pruned** & **Percentage of Pruning** & **Worst Scored 20 Filters/Nodes** \\  & **NodeFilters** & **NodeFilters** & **NodeFilters** & **NodeFilters** \\ \hline (Conv2D) 64 & 64 & 32 & 50 & 29.7, 38, 31, 23, 54, 27, 40, 62, 45, 61, 44, 42, 25, 20, 15, 49, 4, 50, 1 \\ (Conv2D) 128 & 128 & 64 & 50 & 70.3, 39,  
The pruning of the DCNN not only improved the accuracy, it also compacted the DCNN with less weight parameter. Table 6 shows the final pruning statistics of the proposed DCNN. The original DCNN had a total of 22,498,973 weight parameters and the pruned DCNN had the final 7,323,869 weight parameters. This pruning makes the network more than 3\(\times\) smaller than the original, with a 67.45% compression rate. Figure 12 displays that some common mislabeling occurs in all proposed DCNN models. In hindsight, this might contradict the high accuracy of pruned model and SPP DCNN, but further examination of the input images shows that the original input image is often too visually obscure to separate distinguishable features with convolution. Furthermore, some of the images are more visually similar to predicted class than ground truth. Table 7 shows the advanced performance of the all 3 proposed DCNN. Each model's performance is based on a 4000 random sample input and initialized 5 times for randomness in each epoch. Accuracy and \(R^{2}\) score for all 3 model both prove the viability of using this proposed DCNN in real-life scenarios. A comparison among 3 models based on the \(R^{2}\)-score makes the SPP-based DCNN the best model to use in real-life applications. This ensures the prediction quality of the proposed SPP-DCNN suitable for a faster reliable hand gesture classifier.