# Predictive Distillation Method of Anchor-Free Object Detection Model for Continual Learning
## 4 Experiment
### Experimental Results
SDR showed lower mAP and \(F1^{i}\) scores than SID, and the model proposed in this study also showed the best results in this experiment. Relative to the comparison target SID (\(k=2\)), the proposed model exhibited good results of 1.6% p for mAP and 0.9% p for \(F1^{i}\). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**20 Classes** & **20 Classes** & **15 Classes** & **Fine Tuning** & **LwF** [6] & **SID [8] & **SID [8]** & **SID [8]** & **SDR** [15] & **SDR** [15] & **Proposed** \\  & **Classes** & **Classes** & **Tuning** & **[6]** & **k = 2** & **k = 3** & **k = 4** & **KD1** & **KD2** & \\ \hline aeroplane & 73.7 & 74.9 & 26.4 & 9.1 & 59 & 44.3 & 33.7 & 51.9 & 2.3 & **68** \\ bicycle & 79.1 & 81.3 & 0 & 9.1 & 77.3 & 77.5 & 76.4 & 75.7 & 38.4 & **78.9** \\ bird & 70.6 & 70.1 & 9.1 & 9.1 & 53.8 & 54.4 & 54.9 & 52 & 14.3 & **61.7** \\ boat & 62.6 & 60.3 & 4.9 & 25.6 & 51.4 & 55.6 & 53 & 8.5 & 0 & **56.2** \\ bottle & 56.9 & 54.7 & 9.1 & 0 & 41.9 & 48.3 & 44.3 & 15.9 & 0.3 & **49.7** \\ bus & 79.1 & 76.6 & 0 & 6.1 & 63.9 & 55.2 & **70.2** & 41.4 & 7.3 & 54.9 \\ car & 83.7 & 84.1 & 34.8 & 16.2 & 77.6 & 79.1 & 79.4 & 38.8 & 34.8 & **79.8** \\ cat & 81.2 & 79.6 & 9.1 & 0 & 62.1 & 53.7 & 53.7 & 73.4 & 50.4 & **78.6** \\ chair & 58.4 & 56.9 & 9.1 & 0 & 43.7 & 48.9 & **50.9** & 28.7 & 13.4 & 50 \\ cow & 78.8 & 70.5 & 18.4 & 0 & **54.5** & 36.8 & 54.4 & 46.8 & 14 & 51 \\ diningtable & 70.5 & 63.1 & 0 & 0 & 60.3 & 59.3 & 56.2 & 54.1 & 0.3 & **61.2** \\ dog & 79.3 & 76.1 & 7.7 & 0 & **67.8** & 61.7 & 57 & 56.8 & 33.3 & 52.7 \\ horse & 81.6 & 82.9 & 27.2 & 0 & **79** & 78.6 & 77.9 & 73.7 & 41.5 & 75.4 \\ motorbike & 79.3 & 81 & 0 & 9.1 & 68.3 & **75.9** & 75.7 & 59.5 & 23.4 & 71 \\ person & 78.7 & 79.2 & 35.2 & 27 & 74.8 & 76.7 & **77.3** & 72.8 & 48.6 & 77 \\ pottedplant & 47.7 & - & **21.4** & 18.7 & 19.2 & 9.5 & 0.8 & 9.8 & 18.3 & 15.9 \\ sheep & 77.9 & - & **19.2** & 19.1 & 15.5 & 14.9 & 14.3 & 12.6 & 15.3 & 15.8 \\ sofa & 70.1 & - & **38** & 35.5 & 27.3 & 19.4 & 16.1 & 14.9 & 29.1 & 26.7 \\ train & 79.1 & - & 14.5 & **22.8** & 12.9 & 11.2 & 9 & 14.7 & 13.7 & 21.7 \\ tv/monitor & 73.4 & - & **41.3** & 32.5 & 37.1 & 17.1 & 12.4 & 26.4 & 39.9 & 34.5 \\ mAP & 73.1 & 72.8 & 16.3 & 12 & 52.4 & 48.9 & 48.4 & 41.4 & 21.9 & **54** \\
1–15 mAP & - & 72.8 & 12.7 & 7.4 & 62.4 & 60.4 & 61 & 50 & 21.5 & **64.4** \\
16–20 mAP & - & - & **26.9** & 25.7 & 22.4 & 14.4 & 10.5 & 15.7 & 23.3 & 22.9 \\ F1\({}^{i}\) & - & - & 17.3 & 11.5 & 32.9 & 23.3 & 17.9 & 23.9 & 22.3 & **33.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of 15 + 5 Scenario (%). Table 3 lists the experimental results for the 10 + 10 scenario. Overall, when looking at the comparison targets, the proposed model preserved much of the existing knowledge. Given that the results of past learning subjects suffered fatal losses in fine tuning and LwF, if there are newer learning data and new classes, then it is easier for existing information to be forgotten. However, in our proposed model, the results of the new learning and existing learning subjects appeared to be almost equal. It can also be seen that the inter-relation method showed good results in the experiment of SID (\(k\) = 3), which showed the best results among the comparison targets. However, the proposed model showed better results of 0.9% p in mAP and 0.6% p in \(F1^{i}\). The results for each scenario of the proposed model are shown in Figure 3. In scenario 19 + 1, the old labels (person) and new labels (TV/monitor) were detected well as shown in Figure 3a. In scenarios 15 + 5 and 10 + 10, some catastrophic forgetting occurred so that one of the two dogs could not be found properly, and the detection accuracy of the dog was also low as shown in Figure 3b. The motorbike was detected by mistaking it as a car in scenario 10 + 10 as shown in Figure 3c.