## 5 Experiments
### Experimental Results  
Recall that the baseline approaches do not use object detection models. That is to say, they embed the entire backdrop images, along with their added sprites, into vectors. Table 2 shows the experimental results of these baseline approaches using various CNN-/Vision Transformer-based embedding models. As expected, since the CNN-based model ResNet-18 is the most lightweight among our embedding models, it performed the fastest, but its accuracy values were the lowest. Additionally, the model that showed the best accuracy is the clip-ViT-B-32 model. This result could be attributed to the fact that only this model uses different types of image data for training, instead of ImageNet. The overall results are quite surprising, as they show very low accuracy across all models. This finding seems to contradict the results in [5], where a similar approach \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Model** & **Model Size (MB)** & **Dimension** \\ \hline \multirow{2}{*}{CNN-based embedding} & ResNet-18 & 45 & 512 \\  & VGG-16 & 528 & 512 \\ \hline \multirow{4}{*}{Transformer-based embedding} & clip-ViT-B-32 & 577 & 512 \\  & google/vit-base-patch16-224-in21k & 330 & 768 \\  & google/vit-base-patch32-224-in21k & 336 & 768 \\  & facebook/dino-vit8\(\}\) & 83 & 384 \\  & facebook/dino-vitb16 & 327 & 768 \\ \hline Supervised object detection & YOLOS-Ti & 25 & n/a \\ \hline Unsupervised object detection & CutLER & 548 & n/a \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Models used in our experiments and their corresponding statistics. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Baseline Model (No Object Detection)** & **\# Objects** & \begin{tabular}{c} **E. Sprites** \\ **(sec)** \\ \end{tabular} & \
\begin{tabular}{c} **E. Backdrops** \\ **(sec)** \\ \end{tabular} &
\begin{tabular}{c} **Finding kNN** \\ **(sec)** \\ \end{tabular} & **Accuracy (\%)** \\ \hline \multirow{4}{*}{ResNet-18} & 3 & **0.00392** & 0.01194 & 0.00091 & 9.07 \\  & 6 & **0.00394** & 0.01268 & 0.00105 & 13.38 \\  & 9 & **0.00395** & 0.01278 & 0.00091 & 16.07 \\ \hline \multirow{4}{*}{VGG-16} & 3 & 0.00598 & **0.00751** & 0.00057 & 8.63 \\  & 6 & 0.00616 & **0.00823** & 0.00034 & 12.27 \\  & 9 & 0.00626 & **0.00830** & 0.00033 & 15.52 \\ \hline \multirow{4}{*}{clip-ViT-B-32} & 3 & 0.01899 & 0.02922 & 0.00111 & **23.80** \\  & 6 & 0.01823 & 0.03004 & 0.00125 & **23.48** \\  & 9 & 0.01688 & 0.03070 & 0.00128 & **24.74** \\ \hline \multirow{4}{*}{google/vit-base-patch16-224-in21k} & 3 & 0.12293 & 0.12377 & 0.00052 & 13.03 \\  & 6 & 0.12042 & 0.12344 & 0.00071 & 15.75 \\  & 9 & 0.12167 & 0.12510 & 0.00060 & 19.73 \\ \hline \multirow{4}{*}{google/vit-base-patch32-224-in21k} & 3 & 0.04717 & 0.05188 & 0.00068 & 11.67 \\  & 6 & 0.04744 & 0.05056 & 0.00051 & 15.42 \\  & 9 & 0.04645 & 0.05224 & 0.00079 & 18.86 \\ \hline \multirow{4}{*}{facebook/dino-vit8} & 3 & 0.16925 & 0.17861 & **0.00027** & 15.73 \\  & 6 & 0.16584 & 0.17353 & **0.00033** & 17.62 \\ \cline{1-1}  & 9 & 0.17505 & 0.17824 & **0.00029** & 19.02 \\ \hline \multirow{4}{*}{facebook/dino-vitb16} & 3 & 0.11947 & 0.12290 & 0.00081 & 12.40 \\ \cline{1-1}  & 6 & 0.11988 & 0.12233 & 0.00049 & 15.82 \\ \cline{1-1}  & 9 & 0.11897 & 0.12344 & 0.00076 & 17.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of the baseline approaches that do not use object detection models but use CNN- or Vision Transformer-based embedding models. was effectively used to educate children about machine learning. In that study, children prepared their own images and used models that did not include object detection, yet there were no complaints about the models' performance. These results suggest that our dataset is likely more complex, potentially due to the presence of colorful and distracting objects in the background images. Table 3 shows the experimental results of our approaches based on the supervised object detection model, named YOLOS-Ti.