# Predictive Distillation Method of Anchor-Free Object Detection Model for Continual Learning
## 4 Experiment
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & **20** & **19** & **Fine** & **LwF** & **SID [8]** & **SID [8]** & **SID [8]** & **SDR** & \multirow{2}{*}{**Proposed KD1**} \\  & **Classes** & **Classes** & **Tuning** & **[6]** & **k = 2** & **k = 3** & **k = 4** & **KD1** & **KD2** \\ \hline aeroplane & 73.7 & 76.6 & 10.7 & 41.9 & 62.5 & 59.5 & 52.4 & 67.7 & 54.1 & **69.1** \\ bicycle & 79.1 & 81.2 & 11.7 & 33.5 & 66.7 & 68.3 & 62 & 73.7 & 63.5 & **78.2** \\ bird & 70.6 & 72.1 & 26.1 & 34 & 52.1 & 41.1 & 25.9 & 64.5 & 58.1 & **64.6** \\ boat & 62.6 & 60.1 & 11.7 & 26.1 & 36.7 & 27.9 & 14.9 & **49.7** & 35.4 & 49 \\ bottle & 56.9 & 54.5 & 8.5 & 21.5 & 38.4 & 24.7 & 30.4 & 47.9 & 28.3 & **59.1** \\ bus & 79.1 & 77.7 & 12.9 & 38.9 & 64.1 & 61.8 & 52.3 & 66.1 & 34.7 & **72.1** \\ car & 83.7 & 84.3 & 14.4 & 45.5 & 64.3 & **76.2** & 70.8 & 48.7 & 34.1 & 73.6 \\ cat & 81.2 & 80.5 & 49 & 58.4 & 68.7 & 44.8 & 18.2 & **78** & 74.8 & 76.2 \\ chair & 58.4 & 58.6 & 9.1 & 21.4 & 35.4 & 39.6 & 37.1 & 40.5 & 24.4 & **49.8** \\ cow & 78.8 & 79.5 & 30.4 & 60.6 & 69 & 67.1 & 54.9 & 73.3 & 66.9 & **74.6** \\ diningtable & 70.5 & 67.5 & 11.6 & 22.3 & 58.1 & 56.7 & 57.6 & 59.1 & 54 & **65.7** \\ dog & 79.3 & 78.5 & 27.3 & 39.5 & 66.8 & 57.8 & 41.7 & **72.1** & 65.8 & 71.9 \\ horse & 81.6 & 84 & 14.4 & 40.9 & 67.2 & 61.7 & 42.8 & 78.1 & 62.9 & **78.4** \\ motorbike & 79.3 & 81.9 & 0.5 & 10.9 & 59.1 & **73** & 61.1 & 65.3 & 49.4 & 70.2 \\ person & 78.7 & 79.4 & 27.5 & 57.1 & 65.6 & 53 & 38.9 & 73.7 & 68.1 & **77.5** \\ pottedplant & 47.7 & 45.5 & 9.1 & 17.8 & 30.8 & 23.6 & 28.5 & 34.1 & 29.3 & **40.6** \\ sheep & 77.9 & 77.6 & 40.5 & 45 & 68.9 & 60.5 & 48.9 & 70.7 & 69.9 & **77.1** \\ sofa & 70.1 & 69.7 & 19.5 & 33.1 & 58.5 & 40.5 & 24.3 & 58.4 & 51.5 & **63.4** \\ train & 79.1 & 79.5 & 27.2 & 54.3 & 63.5 & 63.9 & 57.6 & 71.7 & 54.4 & **73.7** \\ ttv/monitor & 73.4 & - & **42.4** & 22.6 & 16.4 & 15.9 & 12.5 & 13.9 & 17.2 & 27.7 \\ mAP & 73.1 & 73.1 & 20.2 & 36.3 & 55.6 & 50.9 & 41.6 & 60.4 & 49.8 & **65.2** \\ 1–19 mAP & - & 73.1 & 19 & 37 & 57.7 & 52.7 & 43.2 & 62.8 & 51.5 & **67.1** \\ 20 mAP & - & - & **42.4** & 22.6 & 16.4 & 15.9 & 12.5 & 13.9 & 17.2 & 27.7 \\ \(F1^{i}\) & - & - & 26.3 & 28.1 & 25.5 & 24.4 & 19.3 & 22.8 & 25.8 & **39.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of 19 + 1 Scenario (%). classification model, it is difficult to apply it to the object detection model for the original shape, thus proving the need for a unique method for object detection. In SID, it depended on the number of samples; however, when looking at the \(F1^{i}\) scores, the best result was obtained when using \(k=2\). Forgetfulness of existing object knowledge was resolved to some extent, but new learning remained in the 16.4% mAP range; therefore, this was judged not to be the best method. SDR was not very effective in CenterNet, although it has shown good performance in semantic segmentation models. The mAP of it showed good results, and the catastrophic forgetting seemed to have been resolved to some extent; however, the \(F1^{i}\) and the learning results of the new class were not good, thus making it difficult to use the model in real fields. The proposed model compared to the SID (\(k=2\)), which is the target of comparison, showed good results of 9.6% p based on mAP and 13.7% p based on \(F1^{i}\). Table 2 presents comparative experiments with existing studies according to the \(15+5\) scenario. In this result, the worst method of mAP was the method of LwF. The simple LwF method was not suitable for an anchor-free model such as CenterNet. Rather, the mAP was slightly higher in fine tuning, which appears to be due to the addition of five classes, thus resulting in increased detection accuracy for five classes while learning about new classes in the data. In SID, when considering both mAP and \(F1^{i}\) scores, the best results were shown when \(k\) was 2. Among the newly added classes, the results of 'train' were slightly poor.