## 4 Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class\({}^{\prime}\)Model**} & \multirow{2}{*}{**R-CNN**} & \multirow{2}{*}{**Fast R-CNN**} & \multirow{2}{*}{**Faster R-CNN**} & \multirow{2}{*}{**Mask R-CNN**} & \multirow{2}{*}{**YOLO**} & \multirow{2}{*}{**YOLO**} & \multirow{2}{*}{**YOLO**} & \multirow{2}{*}{**YOLO**} & \multirow{2}{*}{**Refine**} & \multirow{2}{*}{**Center**} & \multirow{2}{*}{**Center**} & \multirow{2}{*}{**Proposed**} \\  & & & **R-CNN** & & & & **v1** & & **v2** & & **v3** & **v4** & **v5** & **Det** & **Net** & **Net2** & **Model** \\ \hline Aeroplane & 77.2 & 84.2 & 84.1 & 88.9 & 77 & 86.3 & 87.2 & 90.1 & 90.5 & 88.7 & 86.1 & 89.6 & 89.7 \\ \hline Bike & 76.4 & 78.9 & 81.2 & 86 & 67.2 & 84.3 & 88.3 & 87.6 & 89.6 & 87 & 87.2 & 90.8 & 91.4 \\ \hline Bird & 68.8 & 75.2 & 75.4 & 80.2 & 69.2 & 74.8 & 78.9 & 79.5 & 78.5 & 83.2 & 82.4 & 85.4 & 89.1 \\ \hline Boat & 50.3 & 53.6 & 56.3 & 61.1 & 43.4 & 59.2 & 65.4 & 70.2 & 75.4 & 76.5 & 67.1 & 73.2 & 69.4 \\ \hline Bottle & 36.7 & 49.2 & 62.7 & 67.5 & 42.3 & 68.1 & 72.5 & 74.5 & 77.3 & 68 & 60.1 & 56.4 & 75.2 \\ \hline Bus & 75.8 & 77.5 & 79.4 & 84.2 & 68.3 & 79.8 & 83.2 & 86.3 & 84.5 & 88.5 & 83.1 & 87.5 & 84.4 \\ \hline Car & 69.6 & 73.2 & 77.2 & 82 & 68.5 & 76.5 & 83.4 & 88.1 & 92.4 & 88.7 & 82.7 & 85.4 & 91.3 \\ \hline Cat & 87.3 & 85.8 & 84.9 & 89.7 & 81.4 & 90.6 & 94.2 & 95.3 & 98.5 & 89.2 & 87.7 & 88.5 & 92.7 \\ \hline Chair & 42.2 & 45.6 & 57.1 & 61.9 & 53.7 & 64.2 & 68.3 & 70.8 & 71.2 & 66.5 & 61.7 & 64.2 & 68.4 \\ \hline Cow & 70.2 & 77.1 & 78.6 & 83.4 & 60.8 & 78.2 & 81.2 & 83.4 & 81.2 & 87.9 & 82.4 & 89.4 & 83.8 \\ \hline Table & 52.2 & 53.1 & 62.2 & 70.1 & 58.2 & 63.7 & 71.3 & 72.3 & 75.4 & 75 & 68.4 & 78.3 & 74.9 \\ \hline Dog & 85.5 & 86.1 & 85.3 & 90.1 & 77.2 & 89.3 & 94.4 & 96.5 & 97 & 86.8 & 89.4 & 89.9 & 93.1 \\ \hline Horse & 78.5 & 80.4 & 82.1 & 86.9 & 72.3 & 82.6 & 87.6 & 83.5 & 82.4 & 89.2 & 84.9 & 88.5 & 89.4 \\ \hline Motorbike & 78.8 & 78.9 & 83.6 & 88.4 & 71.3 & 83.4 & 88.1 & 89.5 & 91.5 & 87.8 & 86.3 & 89.4 & 91.2 \\ \hline Person & 68.3 & 79.3 & 78.9 & 83.7 & 63.5 & 81.5 & 93.3 & 95.6 & 95.9 & 84.7 & 85.7 & 86.5 & 94.8 \\ \hline Plant & 33.1 & 40.1 & 44.2 & 49 & 48.9 & 52.8 & 71.3 & 75.4 & 76.5 & 56.2 & 62.3 & 60.5 & 74.1 \\ \hline Sheep & 66.3 & 72.6 & 73.4 & 78.2 & 59.4 & 77.6 & 78.3 & 76.4 & 74.3 & 83.2 & 84.3 & 85.6 & 79.3 \\ \hline Sofa & 63.7 & 68.4 & 62.3 & 67.1 & 54.8 & 69.8 & 75.2 & 79.5 & 80.3 & 78.7 & 73.1 & 78.4 & 78.7 \\ \hline Train & 76.2 & 80.3 & 81.2 & 87.2 & 73.9 & 85.1 & 85.4 & 85.9 & 84.7 & 88.1 & 85.4 & 85.8 & 89.4 \\ \hline TV & 62.9 & 60.5 & 73.8 & 78.6 & 56.7 & 72.4 & 86.5 & 89.6 & 85.3 & 82.3 & 73.9 & 80.5 & 89.5 \\ \hline mAP & 66 & 70 & 73.2 & 78.2 & 63.4 & 76.8 & 81.7 & 83.5 & 84.12 & 81.8 & 78.7 & 81.69 & 84.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Class-wise performance of the existing models with the proposed model using PASCAL VOC 2007 dataset. Figure 11: Comparison of Loss vs. Epoch @ Dropout. that the model is being trained better and after 130 epochs, the value of the mAP starts stabilizing; hence, any value between 130-150 epochs can be considered to have a mAP of 84%. The number of epochs is considered as high as possible, and the training is terminated based on the error rates. The learning rate is one of the important hyper-parameters which controls the change in the model with the estimated error whenever the weight is updated. The main challenge is choosing the learning rate, as too small a value may increase the training process and get stuck. In the below graph, for the learning rate of 0.0001, the loss was found to be minimum and the value was 0.25, therefore, in our work, a learning rate of 0.0001 was considered, as shown in Figure 13.  
In Figure 14, the mAP was measured with the PASCAL VOC dataset. The chart shows the results for 256 \(\times\) 256, 416 \(\times\) 416 and 608 \(\times\) 608 resolution of the images. It is seen that a higher resolution of the image shows a better mAP.