# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 4 Experiments
### Experiment Results
#### 4.2.2 SQ-DETR Inference Testing  
Figure 4 presents the object detection results of the SQ-DETR model across various scenes. Overall, the model demonstrates a strong capability to accurately detect and classify a wide range of objects. Notably, in the first image, the model successfully identifies and categorizes a tennis player, with precise bounding boxes around both the person and the tennis racket. Similarly, in the second image, multiple individuals are accurately detected and classified, again with precise bounding boxes. The model also accurately detects and classifies a refrigerator, birds, and cups in the third image, with well-fitted bounding boxes. In the fourth image, the model correctly identifies and classifies a person skiing, with accurate bounding boxes for both the person and their ski boots. The fifth image showcases the model's ability to detect and classify a truck and several people, with accurate bounding boxes for the truck, individuals, and a car. The model once again demonstrates its ability to detect a refrigerator in the sixth image, with a precise bounding box. In the seventh image, the model successfully identifies and classifies a boat, a person, and a handbag, with accurate bounding boxes for each. Finally, in the eighth image, the model accurately detects and classifies a person surfing, with precise bounding boxes for the person and their surfboard.  
In general, the SQ-DETR model exhibits a high level of proficiency in accurately detecting and classifying objects across diverse scenes. The model is capable of detecting a variety of objects, including people, vehicles, furniture, and sports equipment. The bounding boxes generated by the model are typically accurate and effectively represent the location and size of the detected objects.  
The SQ-DETR model consistently demonstrates the ability to detect objects under varying lighting conditions and viewpoints. It also effectively detects objects that are partially obscured and objects of different sizes. These capabilities make the SQ-DETR model primed for effective application in a variety of real-world object detection scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Model (Backbone)** & **Layers** & **Drop Ratio** & **Epoch** & **Dec. FLOPs** & \(AP\) & \(AP_{\textbf{30}}\) & \(AP_{\textbf{75}}\) & \(AP_{\textbf{S}}\) & \(AP_{\textbf{M}}\) & \(AP_{\textbf{L}}\) \\ \hline RT-DETR [31] (R-18) & 3 & - & 72 & 12.8 & **46.2** & **80.1** & 45.9 & **27.7** & 43.6 & **52.2** \\ SQ-DETR (R-18) & 3 & 100, 100, 100 & 72 & 12.6 & **46.2** & **80.1** & 45.9 & **27.7** & 43.7 & 52.0 \\ SQ-DETR (R-18) & 3 & 200, 70, 30 & 72 & **11.8** & **46.2** & **80.1** & **46.0** & **27.7** & **43.8** & 52.1 \\ \hline RT-DETR [31] (R-50) & 6 & - & 72 & 21.1 & 48.1 & **81.8** & 48.4 & 29.8 & 45.6 & 53.9 \\ SQ-DETR (R-50) & 6 & 50, 50, 50, 50, 50, 50 & 72 & **19.7** & **48.2** & **81.8** & **48.4** & **29.8** & **45.6** & **53.9** \\ SQ-DETR (R-50) & 6 & 40, 40, 40, 60, 60, 60 & 72 & 19.8 & 48.1 & 81.7 & **48.4** & **29.8** & **45.6** & 53.8 \\ SQ-DETR (R-50) & 6 & 100, 100, 100, 0, 0, 0 & 62 & 19.8 & 48.0 & 81.7 & 48.3 & **29.8** & **45.6** & 53.8 \\ \hline \end{tabular}
\end{table}
Table 3: Evaluation of SQ-DETR on the CrowdHuman Dataset for assessment of generalizability.