# Lightweight RT-DETR with Attentional Up-Downsampling Pyramid Network
## 4 Results
### Ablation Experiments  
Ablation experiments were performed on a randomly selected annotated dataset to evaluate the effectiveness of the lightweight design. The outcomes of these experiments are presented in Table 4, while the optimized precision results are detailed in Table 5.  
Initially, RT-DETR-R18, the most lightweight model in the RT-DETR series, was chosen as the enhanced baseline. The ADown downsampling operator was then incorporated, and the backbone was replaced with the GELAN module, the [EMAIL_ADDRESS] precision improved by 0.3%, while [EMAIL_ADDRESS]-0.95 decreased by 0.4%. The recall rate increased by 0.4%, and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & [EMAIL_ADDRESS] **(\%)** & **Params (M)** & **GFLOPs** & **Weight Size (MB)** & **FPS** \\ \hline RT-DETR R18 & 99 & 19.879464 & 57.0 & 36.8 & 49.5 \\ ADwon+RepNCSPELAN4 & 99.3 & 9.27348 & 27.6 & 18.2 & 49.2 \\ ADwon+RepNCSPELAN4 + & 99.3 & 9.01236 & 26.8 & 17.7 & 49.9 \\ AdaptiveGateDownsample & 99.4 & 9.60372 & 30.4 & 18.9 & 57.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ablation experiments in terms of model lightweight. Figure 6: Training Process Diagram. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]–0.95 (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline RT-DETR R18 & 99 & 86.1 & 99.4 & 98.7 \\ ADwon+RepNCSPELAN4 & 99.3 & 85.7 & 99.3 & 99.1 \\ ADwon+RepNCSPELAN4 + & 99.3 & 86.2 & 99.5 & **99.4** \\ AdaptiveGateDownsample & **99.4** & **86.4** & **99.6** & **99.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of ablation experiments in terms of model-detection accuracy. accuracy decreased by 0.1%. Simultaneously, the parameters, GFLOPS, and weight size were significantly reduced by 53.35%, 51.58%, and 52.85%, respectively, thereby enhancing the model's lightweight characteristics. Subsequently, the self-developed downsampling module based on the attention mechanism, AdaptiveGateDownsample, was added to the neck of the network to replace the original downsampling module. This addition addressed the shortcomings of conventional downsampling in terms of feature selectivity, adaptiveness, and global information retention, making the downsampling process more effective. As a result, precision (P) and recall (R) increased by 0.2% and 0.3%, respectively, while [EMAIL_ADDRESS]-0.95 improved by 0.5%. Compared to previous experiments, the parameters, GFLOPS, and weight size were reduced by 2.82%, 2.9%, and 2.75%. Finally, the self-developed upsampling module based on the attention mechanism, AdaptiveGateUpsample, replaced the upsampling module in the neck of the network. This change also addressed the deficiencies of conventional upsampling in terms of feature selectivity, adaptiveness, and global information retention, resulting in more precise upsampling. Consequently, the new feature fusion network, Attentional Up-Downsampling Pyramid Network (AUDPN), was established, enhancing target-detection capability. This led to improvements in [EMAIL_ADDRESS], [EMAIL_ADDRESS]-0.95, and precision by 0.1%, 0.2%, and 0.1%, respectively, while the model's parameters, GFLOPS, and weight size increased by 6.56%, 13.43%, and 6.78%. The improvements have been completed, and the final Lightweight RT-DETR with AUDPN model demonstrated comprehensive advantages over the baseline model under the experimental conditions of this study. Compared to RT-DETR-R18, its parameters, GFLOPS, and weight size decreased by 51.69%, 46.67%, and 51.04%, while [EMAIL_ADDRESS], [EMAIL_ADDRESS]-0.95, precision, and recall increased by 0.4%, 0.3%, 0.2%, and 0.7%, respectively. A visual comparison of the various parameters from the ablation experiments is illustrated in Figures 7 and 8. In the established training environment for the Lightweight RT-DETR with AUDPN, the FPS reached 57.2, indicating that the model can perform approximately 57.2 inferences per second.