# ACT-FRCNN: Progress Towards Transformer-Based Object Detection
## 4 Experimental Procedure
### Result Discussion  
The Performance and Accuracy  
The model's Average Precision (AP) is compared to DETR-DC5, with a specific focus on speed-accuracy trade-offs. Additionally, the adaptive clustering approach is compared to the K-means clustering method used in [46] were the number of clusters is denoted by \(C\). When \(L\) and \(C\) are adjusted and the AP is calculated using various computational budgets, the ACT technique achieves significantly higher AP than K-means under an equalized computational budget. FLOPs are reduced from 168.9 GFLOPs to 164.3 GFLOPs compared to DETR-ACT, with only 0.5% loss in AP. MTKD substantially enhances AP, especially for devices with smaller FLOP capacities. ACT with L = 32 performs similarly to DETR-ACT, while ACT with L = 16 achieves a 4.6% increase in AP due to MTKD. The advantages of proposed model over K-means clustering. K-means clustering requires the same number of clusters across all encoder levels. However, selecting an appropriate hyper-parameter can be difficult due to the varying distribution of queries based on the input image and encoder layer's index. Misusing the hyper-parameter \(C\) can result in inaccurate estimates or unnecessary computational costs. Furthermore, K-means may not converge properly in certain situations, and many clusters can empty if \(C\) is relatively large, as it is a hierarchical method. To address these issues, the proposed method employs an adaptive mechanism to dynamically calculate the number of prototypes. **Detailed AP Comparision:** The Bounding Box Average Precisions (BBox APs) for the large (AP\({}_{\text{L}}\)), medium (AP\({}_{\text{M}}\)), and small (AP\({}_{\text{S}}\)) instances are reported. Faster RCNN [48] and Discrete Event Time Series [15] is compared with these metrics. Both DETR and Faster R-CNN use Dilated ResNet-50 as the backbone. The results are presented in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **GFLOPs** & **AP** & **AP\({}_{\text{L}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{S}}\)** \\ \hline Backbone & 110.7 & — & — & — & — \\ (ResNet50-DC5) & & & & & \\ \hline DETR-DC5 [15] & +73.4 & 43.3 & 61.1 & 47.3 & 22.5 \\ \hline FasterRCNN-DC5 [49] & +209.3 & 41.1 & 55.0 & 45.9 & 22.9 \\ \hline DETR-ACT [50] & 168.9 & 43.1 & 61.4 & 47.1 & 22.2 \\ \hline ACT (L = 16) & +47.2 & 41.4 & 60.8 & 43.1 & 17.1 \\ ACT (L = 20) & +51.7 & 40.6 & 61.1 & 46.5 & 18.7 \\ ACT (L = 24) & +55.2 & 39.2 & 61.3 & 47.3 & 20.2 \\ ACT (L = 32) & +59.7 & 44.3 & 62.5 & 48.2 & 23.7 \\ \hline ACT + MTKD (L = 16) & +49.6 & 41.3 & 61.1 & 44.3 & 18.5 \\ ACT + MTKD (L = 20) & +52.5 & 41.9 & 61.9 & 47.1 & 20.2 \\ ACT + MTKD (L = 24) & +56.7 & 40.1 & 61.7 & 48.2 & 21.9 \\ ACT + MTKD (L = 32) & +60.2 & 44.9 & 62.7 & 49.1 & 24.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of the AP of ACT-FRCNN model with DETR-ACT and Faster RCNN. Dilated ResNet-50 serves as the foundation of DETR-ACT and Faster RCNN. The ‘+’ in the GFLOPs column denotes more flops in comparison to the backbone. The BBox APs for large, medium, and small instances are designated AP\({}_{\text{L}}\), AP\({}_{\text{M}}\), and APS\({}_{\text{S}}\), respectively. DETR-ACT performs better than our ACT model, with \(L\) equal to 16 while using fewer FLOPs. However, when it comes to detecting items of a certain size, ACT is much better than DETR-ACT. For large objects, our ACT model can provide a better approximation than FasterRCNN-DC5. The most significant AP losses occur with small- and medium-sized objects. Small- and medium-sized items experience the most AP losses. The most significant AP losses occur with small- and medium-sized objects. We suggest that our clustering attention can be considered as a dropout operation that can help prevent overfitting during the distillation of information. Importantly, we found that the keys grouped using our strategy performed well. We tested query grouping, key grouping, and combining queries and keys.