# Predictive Distillation Method of Anchor-Free Object Detection Model for Continual Learning
## 4 Experiment
### Experimental Results  
In the case of the PascalVOC 2007 and 2012 data used for learning, 'trainval', a material provided from the official site, was downloaded, and the 'trainval.txt' data contained in the downloaded folder was used for training, whereas the PascalVOC 2007 test data was used for testing. To create a CL scenario, a set of usage data for each scenario was defined based on the text data for each class located in the Main folder inside the ImageSets folder. First, the text data corresponding to the first learning class was read in alphabetical order and combined into one, except for overlapping image names, to be used as the first learning data. If there was an object corresponding to the second learning class inside the image, then its label was not used. The data for the second learning scenario read and used the remaining text data file, excluding the overlapping image names. In this process, two conditions needed to be satisfied: first, the image could not be used for primary learning; second, even if it was a new image, the label could not be used if the class corresponding to primary learning existed in the image. We proposed anchor-free CL as part of a knowledge distillation modification scheme in a limited environment that does not reuse data and avoids increasing parameters or layers. We used the t-model as a tool for labeling; its result, t-pred, was combined with the GT of the new learning process. Since GT only has new labels, it causes catastrophic forgetting of existing labels. However, when combined with t-pred, information from existing labels is added, thus allowing it to play a role in learning, such as general learning. The backbone used in CenterNet for our proposed model was ResNet-50. During the experiment, among the options provided by the official code of CenterNet, 'nms' and 'flip_test' were used. All the experiments in this section used the same option as the verification data: \(\lambda_{size}\) = 0.1, \(\lambda_{off}\) = 1, \(\lambda_{size}^{\prime}\) = 0.1, \(\lambda_{off}^{\prime}\) = 1, \(\lambda_{neudet}\) = 1, and \(\lambda_{kdH}\), \(\lambda_{kHWH}\), and \(\lambda_{k_{doff}}\) are 0.3. The result was the mean average decision (mAP), which was rounded to the third decimal place. In the proposed method, the t-model prediction value of the t-model predictive distillation was fixed at 0.2, and the IoU was fixed at 0.8; these were fixed at the same values in all experiments below. For CenterNet used in this study, ResNet-50, which has relatively low complexity and low accuracy, was used as a backbone to compare the algorithm results for the CL approach; therefore, we could expect to achieve an increase in basic accuracy by changing the backbone model or applying another type of anchor-free model. Table 1 lists the experimental results of the 19 + 1 scenario. \(k\) of SID [10] was the number of samples for the inter-relation proposed by SID [8], and SDR [15] was divided into KD1 for distilling only heatmaps according to the model of CenterNet [7] and KD2 for distilling both heatmaps and the WH output layer. The worst result of mAP was obtained using the fine-tuning method, but the result of secondary learning (related to the 'tv/monitor' class) was the best. This is because catastrophic forgetting was not considered at all in the fine-tuning method. In the case of LwF, \(F1^{i}\) showed the best results among the comparison targets; however, when looking at mAPs of 1-19, it appears that there was substantial forgetting of the existing classes. This finding shows that, even with a CL approach that yields good results in the