## 5 Experiments
### Analysis of Results  
In our initial experiments, we set the threshold values to 0.01 for YOLOS-Ti and 0.35 for CutLER. To further investigate the impact of different thresholds, we conducted additional \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Our Model (With CutLER)** & **\# Objects** & **E. Sprites** & **E. Backdrops** & **Finding kNN** & **Accuracy (\%)** \\  & & **(sec)** & **(sec)** & **(sec)** & **(sec)** & **80.57** \\ \hline \multirow{3}{*}{ResNet-18} & 3 & **0.00325** & **0.03610** & 0.04508 & **80.57** \\  & 6 & **0.00328** & **0.04564** & 0.05653 & **79.85** \\  & 9 & **0.00322** & **0.05744** & 0.07224 & **78.71** \\ \hline \multirow{3}{*}{VGG-16} & 3 & 0.00860 & 0.04320 & 0.04765 & 74.67 \\  & 6 & 0.00896 & 0.05575 & 0.06140 & 74.50 \\  & 9 & 0.00980 & 0.07135 & 0.07719 & 74.81 \\ \hline \multirow{3}{*}{clip-ViT-B-32} & 3 & 0.03090 & 0.12947 & 0.04445 & 72.57 \\  & 6 & 0.02920 & 0.16371 & 0.05634 & 73.60 \\  & 9 & 0.03044 & 0.20680 & 0.07236 & 75.22 \\ \hline \multirow{3}{*}{google/vit-base-patch16-224-in21k} & 3 & 0.07387 & 0.94674 & 0.05959 & 77.97 \\  & 6 & 0.08900 & 1.13765 & 0.07898 & 77.10 \\  & 9 & 0.08695 & 1.52456 & 0.09990 & 78.19 \\ \hline \multirow{3}{*}{google/vit-base-patch32-224-in21k} & 3 & 0.04069 & 0.42205 & 0.06110 & 73.07 \\  & 6 & 0.03170 & 0.54028 & 0.07988 & 74.87 \\  & 9 & 0.03496 & 0.67661 & 0.09998 & 75.77 \\ \hline \multirow{3}{*}{facebook/dino-vitts8} & 3 & 0.13350 & 1.37116 & **0.03289** & 75.07 \\  & 6 & 0.11385 & 1.79386 & **0.04229** & 70.70 \\ \cline{1-1}  & 9 & 0.11850 & 2.38555 & **0.05328** & 69.26 \\ \hline \multirow{3}{*}{facebook/dino-vitb16} & 3 & 0.10889 & 1.07077 & 0.06116 & 75.03 \\ \cline{1-1}  & 6 & 0.06842 & 1.13841 & 0.07899 & 71.40 \\ \cline{1-1}  & 9 & 0.06783 & 1.42612 & 0.09981 & 70.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of our approaches that utilize both the unsupervised object detection model named CutLER and embedding models based on CNNs or Vision Transformers. tests using a wider range of values: 0.01, 0.03, 0.05, 0.07, 0.09, 0.1, 0.3, 0.5, 0.7, and 0.9. Figure 5 provides a comparative analysis of the accuracy of YOLOS-Ti and CutLER under these different settings, while Figure 6 presents the time elapsed for each algorithm at these thresholds. In subsequent analyses, we employ ResNet-18 as our image-embedding algorithm due to its superior performance in earlier experiments. The dataset we used contains nine objects (sprites) in each input image. Figures 5 and 6 indicate that higher object detection thresholds lead to lower accuracy but faster elapsed times for embedding backdrops. This finding is intuitive, as a higher threshold reduces the number of objects detected, thereby requiring fewer image embeddings. The figures also show that CutLER, being an unsupervised model, is significantly more effective than YOLOS-Ti on our dataset, which contains unseen class data. For CutLER, a moderate threshold suffices to achieve high accuracy, making a threshold of 0.3 suitable for multi-label classification. This closely aligns with the default threshold set in the original CutLER code. For YOLOS-Ti, however, even a low threshold of 0.1 does not yield accuracy higher than 70%. To make YOLOS-Ti applicable for our tasks, a very low threshold, such as 0.01, would be required. To examine the results in greater detail, we present visualizations of object detection outcomes at varying thresholds--0.01, 0.05, 0.10, and 0.50--for both YOLOS-Ti (Figure 7) and CutLER (Figure 8). Figure 7 reveals that at a threshold of 0.50, YOLOS-Ti fails to detect any objects in the image. Even when the threshold is lowered to 0.10, it only accurately identifies four out of the nine objects present. To achieve comprehensive object detection using YOLOS-Ti, the threshold needs to be set as low as 0.01. Conversely, CutLER manages to identify seven out of the nine objects even at the relatively high threshold of 0.50. Figure 5: Comparison of accuracy between YOLOS-Ti and CutLER using ResNet-18 across varying thresholds.