## 5 Results and Discussion  
The datasets used for this study are problem specific and a neural network is designed for this specific purpose that does not rely on the use of other pre-trained networks and transfer learning. So network training was done from scratch. Network training was performed on NVIDIA(r) AGX Xavier Developer Kit for 50 epochs. The accuracies for mini-batch, validation and the corresponding losses are summarized in Table 2 for some selected epochs. Using sequential gradient descent (SGD) with momentum (0.9) and a piece wise adjustment to the learning rate schedule, training was run on a 512-core Volta GPU with Tensor Cores. For GPUs with less memory, it may be necessary to reduce the batch size. The choice of SGD optimization has advantages for small datasets. Based on the SGD optimization scheme, ten classes of indoor objects were used for training the model. The validation precision of the architecture to predict an object which was not contained in training data was 76.5%. Furthermore, the performance of the network was improved by fine tuning the weights (parameters) and learning rate. More importantly, the performance of the network improved by about 10.5% using data augmentation and dropout techniques. As we observe from our experimental results, training and validation precision are separated by a large boundary in the case where data augmentation was not implemented. The model in this case attained only around 68.5% precision on the validation dataset. Shown in Figure 10, the training precision improves linearly over time, but the validation precision stalls around 68.5% in the learning process. Moreover, the difference in precision between training and validation losses is clearly observed which is an indication of overfitting. When there is a small number of training datasets, the model sometimes learns from unwanted details from the training dataset that negatively impact the performance of the model on new datasets. This means that the model will not be able to generalize on a new dataset. To reduce the problem of overfitting, we implemented image augmentation and dropout techniques. Image augmentation produces more training images from our existing image by augmenting them using random transformations that yield believable-looking images. This helps expose the model to more aspects of the image and generalize better. Image augmentation as depicted in Figure 11 improved the model performance to well above 74.5%. At the same time, the loss during model training is much reduced, indicating a better model performance. In the dropout scheme, we randomly decreased the number of output units in the dense layer in a range of 10% to 40%. Despite the fact that there was not much noticeable improvement as a result of dropout in training and validation accuracy, there was a progressive improvement of the validation and loss functions as shown in Figure 12a-d. Increasing the number of dropouts from the output units again is not a guarantee for better model performance as it may degrade model performance. In this study, the best model performance is achieved for both validation precision and validation loss at 30% dropout of the output units. The performance of the trained model network is evaluated using validation datasets. Validation data are not used to train the network. Our experimental results reveal that the model classified the data in the training dataset with a success rate of 94.5% and validated results with a success rate of 76.5%. As shown in Figure 12, the training loss reveals how well the model is fitting the training data, while the validation loss reveals how well the