## 4 Results
### Results of Our Experiments
All three YOLO-NAS models demonstrate superior performance, outperforming the other models in the experiments, as shown by the red line with triangle marks. Detectors utilizing the Swin Transformer or Deformable-DETR (see (b)) had substantially larger model sizes than those of the compared methods (see (a)). Other details are illustrated in Section 4.2. Within the CoDETR family, we employed four distinct types of pretrained checkpoints: Deformable-DETR, Swin-L pretrained on COCO, Swin-L pretrained on Object365, and ResNet50. Surprisingly, the model equipped with a ResNet50 backbone outperformed the others, offering the best trade-off between size and efficiency. Similarly, within the DINO family, three different model configurations were selected: four-scale, five-scale with a ResNet50 backbone, and four-scale with a Swin-L backbone. Although models based on the Swin Transformer had more parameters, they exhibited lower performance. Notably, within the YOLO series, it became evident that larger model sizes did not necessarily translate to superior scores. Remarkably, the YOLO-NAS model demonstrated superior performance across the board. Despite its more compact model size, the YOLO-NAS-S model achieved a score of 68.5, delivering efficient results. Meanwhile, the slightly larger YOLO-NAS-L model delivered the best performance, reaching a score of 70.0. These two models produced the most significant and noteworthy results, securing the top two positions in terms of performance. As shown in Figure 7, the blue line with square marks represents YOLOv8, a model that is relatively insensitive to changes in model size. In contrast, the green line with circles indicates the performance of YOLOv5. Notably, the YOLOv5-large model outperformed all YOLOv8 variants. In Section 4.1, we proposed an index for heuristic masks to establish an 'upper limit' for our experiments. We generated ground-truth boxes for whole frames on the dataset by utilizing the provided annotations. With these boxes, SAM generated heuristic masks that highlighted the target. We observed that SAM achieved favorable results, attaining \(\mathcal{J}\)&\(\mathcal{F}\)-scores of 84.2. In other words, when conducting segmentation using accurate boxes and SAM, we obtained a score of 84.2. Therefore, our experiments cannot surpass this 'upper limit'. For a more specific model comparison, we selected models with similar model sizes. As seen in Figure 7, models around 50M in size are represented by six dots. Each dot corresponds to YOLO-NAS M, YOLOv5-L, YOLOv8 L, Dino-5 scale, and DINO-4 scale. When comparing models with similar sizes, YOLO-NAS achieved the best results on DAVIS 2017 in terms of both the \(\mathcal{J}\)-score and \(\mathcal{F}\)-score. Figure 8 presents the results for the 'horse-jump' and 'judo' sequences from the DAVIS 2017 dataset. Excluding these two sequences resulted in improved performance, as shown in Table 2. DINO-4 scale-swin outperformed DINO-4 scale, and the CoDETR-swin models performed better than YOLOv5-M, L, and YOLOv8-M when \"camel\" and \"goldfish\" were removed. Nevertheless, across all scenarios, the YOLO-NAS and CoDETR-ResNet models achieved the top four rankings in performance. Therefore, for optimal results, it is recommended to use YOLO-NAS and CoDETR-ResNet models, particularly when dealing with challenging images. Figure 8: The provided figure summarizes the exemplary results on the DAVIS 2017 dataset for the “horse-jump” and “judo” sequences. The outcomes are from four different models in the following order: DINO-5 scale, CoDETR-R50, YOLOv8-S, and YOLO-NAS-L.  
Figure 9: An example of the camel (**left**) and goldfish (**right**) sequences in the DAVIS dataset. The common features have similarities between the object and the background.