# Application of Deep Neural Networks in Recognition of Selected Types of Objects in Digital Images
## 2 Results of Research
Figure 19 shows GPU and memory consumption during training. GPU consumption peaked when using SGD with LR = \(1\times 10^{-3}\), at 25.24%. Using the Adam optimizer brought GPU consumption down to 23.76%, but at the cost of a higher loss. Dynamically adjusting the LR in Adam reduced GPU consumption to 22.88%, while improving model performance. The most effective GPU utilization was recorded using the LR reduction strategy on Plateau for both the Adam and SGD optimizers, where utilization was 23.20% and 23.80%, respectively, with both strategies achieving significant  
Figure 15: Comparison of GPU and memory consumption during training of GoogLeNet model with different activation functions. (**a**) GPU and memory consumption for ReLU. (**b**) GPU and memory consumption for Leaky ReLU. (**c**) GPU and memory consumption for ELU. Figure 17: Comparison of GPU and memory consumption when training the LeNet-5 model at different learning rates. (**a**) GPU and memory consumption for SGD with LR = \(1\times 10^{-3}\). (**b**) GPU and memory consumption for Adam with LR = \(1\times 10^{-3}\). (**c**) GPU and memory consumption for Adam with dynamically adjustable LR. (**d**) GPU and memory consumption for [PERSON] with LR reduced on Plateau. (**e**) GPU and memory consumption for SGD with LR reduced on Plateau. Figure 18: Comparison of the results of the AlexNet model training process at different learning rates. (**a**) Results for SGD with LR = \(1\times 10^{-3}\). (**b**) Results for [PERSON] with LR = \(1\times 10^{-3}\). (**c**) Results for [PERSON] with dynamically adjusted LR. (**d**) Results for [PERSON] with LR reduced on Plateau. (**e**) Results for SGD with LR reduced on Plateau. Figure 19: Comparison of GPU and memory consumption when training the AlexNet model at different learning rates. (**a**) GPU and memory consumption for SGD with LR = \(1\times 10^{-3}\). (**b**) GPU and memory consumption for [PERSON] with LR = \(1\times 10^{-3}\). (**c**) GPU and memory consumption for Adam with dynamically adjustable LR. (**d**) GPU and memory consumption for Adam with LR reduced on Plateau. (**e**) GPU and memory consumption for SGD with LR reduced on Plateau. ### GoogLeNet  
Figure 20 shows the effect of different learning strategies on the performance of the GoogLeNet model. Using the SGD optimizer with LR = \(1\times 10^{-3}\) provided the highest accuracy of 75.45% with a loss of 2.51, showing the good performance of this method. The Adam optimizer with the same learning rate yielded a slightly lower accuracy (67.41%) and a better loss of 1.31. The use of dynamic LR in Adam resulted in improved performance, achieving an accuracy of 70.27% and a loss of 1.17. The best results were achieved using the Adam optimizer with LR reduction on Plateau, where the accuracy was 71.43% and a loss of 1.20, while using the SGD optimizer with LR reduction on Plateau yielded very similar results to the regular SGD, achieving an accuracy of 74.91% and a loss of 2.48. Figure 21 presents GPU and memory consumption for different GoogLeNet model learning strategies. GPU usage ranged from 53.92% to 57.36%, with the highest usage recorded using the SGD optimizer with LR = \(1\times 10^{-3}\). GPU memory usage remained at 25.72% to 27.8%. RAM occupation ranged from 47.9% to 52.21%, showing efficient management of memory resources. Average CPU utilization oscillated between 14.37% and 18.89%, indicating moderate CPU load during training. The best results in terms of resource efficiency and model performance were obtained using the Adam optimizer with LR reduction on Plateau. Figure 21: _Cont._  
Figure 20: Comparison of the results of the GoogLeNet model training process at different learning rates. (**a**) Results for SGD with LR = \(1\times 10^{-3}\). (**b**) Results for Adam with LR = \(1\times 10^{-3}\). (**c**) Results for [PERSON] with dynamically adjusted LR. (**d**) Results for [PERSON] with LR reduced on Plateau. (**e**) Results for SGD with LR reduced on Plateau.