# NextDet: Efficient Sparse-to-Dense Object Detection with Attentive Feature Aggregation
## 4 Experimental Analysis
### Experiment Results  
Ablation studies are crucial for any deep learning research and in this paper, an extensive analysis is performed on a single GPU by removing and/or replacing certain parts from the proposed complex deep neural network in order to better understand the network's performance. Six different versions of the proposed NextDet network have been created, trained, and evaluated on two aforementioned datasets to determine the importance of each module  
Furthermore, to test robustness and obtain a fair comparison result, other PyTorch versions of lightweight networks such as ShuffleNet [43] and MobileNetv3 [28] are attached as backbone with NextDet and benchmarked in Tables 2 and 3. ShuffDet + PAN and Mob3 Det + PAN denotes ShuffleNet and MobileNetv3 CNNs with the proposed FPN and PAN networks, ShuffDet + FPN and Mob3 Det + FPN denotes ShuffleNet and MobileNetv3 CNNs with only FPN in its neck design respectively. Table 4 provides a comparison of image classification performance of the backbones [4,28,43] in terms of floating-pointoperation and number of trainable parameters, and Table 5 provides a description of each version of the object detector used in this experimental analysis utilized in experiment analyses described in Tables 2 and 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Light-Weight CNN** & **FLOPs 1** & **Parameters** & **Top-1 Accuracy 2** \\ \hline CondenseNeXt & 26.8 million & 0.16 million & 92.28\% \\ \hline ShuffleNet & 43.43 million & 0.25 million & 91.48\% \\ \hline MobileNetv3 & 36.34 million & 1.84 million & 88.93\% \\ \hline \hline \end{tabular} \({}^{\dagger}\) FLOPs: The number of floating-point operations to measure computational complexity. 2 Top-1 Accuracy: Indicates correct prediction rate for a class with highest probability.  
\end{table} 
Table 4: Comparison of light-weighted CNN backbone performance with a scaling factor of 0.5 on CIFAR-10 dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Architecture Name** & **Backbone** &
\begin{tabular}{c} **Mean** \\ **Precision** \\ \end{tabular} & **mAP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** \\ \hline NextDet & CondenseNeXt & 61.90\% & 35.10\% & 48.95\% & 38.20\% \\ \hline NextDet + FPN & CondenseNeXt & 58.06\% & 33.88\% & 45.16\% & 36.91\% \\ \hline NextDet-SAM & CondenseNeXt & 59.70\% & 34.49\% & 45.82\% & 37.20\% \\ \hline ShuffleNet + PAN & ShuffleNet & 58.31\% & 30.30\% & 42.21\% & 32.95\% \\ \hline ShuffleNet + FPN & ShuffleNet & 53.95\% & 28.97\% & 40.80\% & 31.54\% \\ \hline Mob3 Det + PAN & MobileNetv3 & 54.80\% & 29.90\% & 41.57\% & 32.53\% \\ \hline Mob3 Det + FPN & MobileNetv3 & 52.97\% & 28.56\% & 40.09\% & 31.05\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A summary of performance comparison benchmarked on the Argoverse dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Architecture Name** & **Backbone** &
\begin{tabular}{c} **Mean** \\ **Precision** \\ \end{tabular} & **mAP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** \\ \hline NextDet & CondenseNeXt & 66.80\% & 58.00\% & 81.63\% & 63.73\% \\ \hline NextDet + FPN & CondenseNeXt & 64.86\% & 56.76\% & 79.65\% & 61.87\% \\ \hline NextDet-SAM & CondenseNeXt & 65.36\% & 57.14\% & 80.41\% & 62.27\% \\ \hline ShuffleNet + PAN & ShuffleNet & 59.40\% & 51.10\% & 72.40\% & 56.33\% \\ \hline ShuffleNet + FPN & ShuffleNet & 57.51\% & 50.78\% & 71.02\% & 55.00\% \\ \hline Mob3 Det + PAN & MobileNetv3 & 54.50\% & 50.60\% & 70.95\% & 54.69\% \\ \hline Mob3 Det + FPN & MobileNetv3 & 53.83\% & 50.12\% & 70.18\% & 54.23\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A summary of performance comparison benchmarked on the Microsoft COCO dataset.