## 5 Experiments  
### Experimental Setup  
In our approach, various types of pre-training models can be utilized. As shown in Table 1, we selected nine pre-trained models for our experiments:  
* We chose ResNet-18 [18] and VGG-16 [19] for embedding, due to their popularity in CNN-based models. * For Vision Transformer-based embedding models, we selected five pre-trained models that are the most recent and popular embedding models found on Hugging Face ([[https://huggingface.co/](https://huggingface.co/)]([https://huggingface.co/](https://huggingface.co/)) (accessed on 19 September 2023.)). Among them, one was developed by OpenAI; two were developed by Google [29]; and two were developed by Facebook [30]. Here, the model \"clip-ViT-B-32\" was developed for mapping texts and images into the same embedding space. All of the models from Google and Facebook utilize Vision Transformers [20] and ImageNet [27]. * As a supervised object detection model, we used YOLOS-Ti [26], which is the most trending model on Hugging Face at the time of writing. The characteristics of this model include its lightweight design; its model size is only 25 MB, allowing for easy use even on low-performance computers. * For unsupervised object detection, we employed CutLER [28]. Known as the most efficient unsupervised object detection model at the time of writing, it is slower than the lightweight supervised object detection model, YOLOS-Ti, but it is expected to significantly enhance accuracy in object detection. It is important to note that we did not compare our approach with methods that do not use neural networks. For example, we did not compare it with ML-KNN [16], which uses k-nearest-neighbor algorithms but not neural networks. Additionally, we did not report the results obtained with SSIM (structural similarity index measure) [17], as it did not perform well in our experiments. We conducted three types of experiments to demonstrate the effectiveness of our approach:  
* As a baseline approach, we only use embedding techniques without the use of object detection models. Because no objects are detected with this approach, the entire image (backdrops with added sprites) is embedded into vectors using various types of CNN- or Vision Transformer-based models. * In addition to this baseline approach, we also experimented with our approaches that employ either supervised or unsupervised object detection models, as well as either CNN- or Vision Transformer-based embedding models. We do not compare our method with existing approaches for multi-label image classification, because these approaches do not satisfy the requirements defined in the Introduction section. Thus, in our experiments, we established a new baseline and compared our methods with this baseline approach. The hyperparameters were configured as follows: First, we set parameter \"m\" to 69, which allowed us to find the exact k-nearest neighbors for each query; recall that there are 69 sprites in our dataset. Given that the number of samples in our experiments was relatively low, we believe that using exact k-nearest-neighbor search is one of the best options, although there may be room for improvement in terms of computational time with the use of approximate k-nearest-neighbor search. Second, for object detection, we used thresholds of 0.01 for supervised object detection with YOLOS-Ti and 0.35 for unsupervised object detection with CutLER. The threshold for YOLOS-Ti was set particularly low because the sprites in our dataset are not highly relevant to the classes on which the object detection model was originally trained. Therefore, we needed to lower the threshold to maximize the identification of our sprites, as illustrated in Figure 3.  
We performed the experiments using a low-performance laptop computer (11 th Gen Intel(r) CoreTM (Intel, Santa Clara, CA, USA) i7-11800H@ 2.30 GHz, 16 GB RAM, RTX 3080 Laptop GPU). This aligns with Requirement 4, as our aim is to present an efficient and lightweight approach.