## 4 Results
### Results of Our Experiments  
We conducted experiments using six different one-stage object detectors--namely, DETR, DINO, CoDETR, YOLOv5, YOLOv8, and YOLO-NAS--on the entire DAVIS 2017 validation dataset. A comprehensive summary of the results can be found in Table 1, which provides detailed information on each detector, including the backbone used as a feature extractor and the model size. The model size in the table refers to the number of parameters within the detector only. Our analysis encompasses an overview of the overall performance, comparisons within detector categories, and insights related to model sizes. In our overall analysis, we noted that YOLO-NAS demonstrated outstanding collaboration with SAM, aligning well with the target instances. YOLO-NAS achieved the top three results among all versions and combinations. Notably, YOLO-NAS-L outperformed CoDETR by a margin of 1.6 points. CoDETR, a state-of-the-art model in object detection on the COCO dataset, exhibited slightly lower performance than YOLO-NAS. We also conducted a benchmark of various backbone choices for transformer models, including DINOs and CoDETRs. It is widely recognized that detectors employing a convolutional network as the backbone typically have smaller model sizes than those using a transformer backbone. Detectors utilizing the Swin Transformer or Deformable-DETR had substantially larger model sizes, at least three times larger, as depicted in Figure 6(a),b. Interestingly, in our experimental setup, we observed that using a CNN-based model actually resulted in better performance, challenging the common assumption that larger model sizes are always better. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Detector**} & \multicolumn{3}{c}{**Scores**} \\  & **Backbone** & **Pretrained Dataset** & **Model Size** & **FPS** & \(\boldsymbol{\mathcal{J\&F}}\) & \(\boldsymbol{\mathcal{J\}}\) & \(\boldsymbol{\mathcal{F}}\) \\ \hline DETR [17] + SAM & ResNet50 [15] & COCO & 41.5 M & 30.0 & 59.4 & 57.3 & 61.5 \\ \hline DINO [19]-4 scale + SAM & ResNet50 [15] & & 46.9 M & 15.0 & 61.0 & 59.0 & 63.0 \\ DINO [19]-4 scale + SAM & Swin-L [53] & COCO & 217.2 M & 6.0 & 57.5 & 55.5 & 60.0 \\ DINO [19]-5 scale + SAM & ResNet50 [15] & & 47.3 M & 16.0 & 64.7 & 62.8 & 66.5 \\ \hline CoDETR [21] + SAM & Deformable-DETR [54] & COCO & 235.6 M & 50.0 & 59.9 & 58.0 & 61.9 \\ CoDETR [21] + SAM & Swin-L [53] & Object365 & 235.5 M & 50.0 & 61.9 & 58.7 & 63.2 \\ CoDETR [21] + SAM & Swin-L [53] & COCO & 235.5 M & 50.0 & 62.8 & 60.7 & 65.0 \\ CoDETR [21] + SAM & ResNet50 [15] & COCO & 69.1 M & 50.0 & 66.6 & 64.7 & 68.4 \\ \hline YOLOv5-S [24] + SAM & & & 7.2 M & 833.0 & 61.6 & 59.9 & 63.3 \\ YOLOv5-M [24] + SAM & CSPDarknet53 & COCO & 21.1 M & 546.0 & 64.1 & 62.2 & 66.0 \\ YOLOv5-L [24] + SAM & & & 46.5 M & 418.0 & 65.8 & 63.9 & 67.8 \\ \hline YOLOv8-S [25] + SAM & modified & & 11.2 M & 925.0 & 65.2 & 63.4 & 67.0 \\ YOLOv8-M [25] + SAM & CSP- & COCO & 25.9 M & 540.0 & 65.1 & 63.0 & 66.9 \\ YOLOv8-L [25] + SAM & Darknet32 & & 43.7 M & 390.0 & 65.0 & 63.0 & 67.0 \\ \hline YOLO-NAS-S [26] + SAM & & & 19.1 M & 1000.0 & 68.5 & 66.4 & 70.5 \\ YOLO-NAS-M [26] + SAM & & & 51.2 M & 600.0 & 68.2 & 66.3 & 70.2 \\ YOLO-NAS-L [26] + SAM & & & 67.0 M & 400.0 & **70.0** & **68.1** & **71.9** \\ \hline Heuristic method & - & - & - & 1.0 & 84.2 & 80.3 & 88.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The table below illustrates the main results on the DAVIS 2017 validation dataset with the backbone, model size, and the parameter number of the object detectors. The resulting score of the heuristic method is also illustrated at the bottom of the table. With 18 experiments, YOLO-NAS achieved the best \(\mathcal{J}\)- and \(\mathcal{F}\)-scores. Figure 7: A graph of the results on the DAVIS 2017 dataset, representing the model size of the detector and the \(\mathcal{J}\)- and \(\mathcal{F}\)-scores.