# FSRFNet: Feature-selective and Spatial Receptive Fields Networks
## 5 Experiments
### Tiny ImageNet Classification
\begin{array}{c}1\times 1,124\\ 3\times 3,24,G-32\\ 1\times 1,2048\end{array} \right]\times 3\) & \(\left[ \begin{array}{c}1\times 1,124\\ 3\times 3,124,G-32\\ 1\times 1,2048\end{array} \right]\times 3\) & \(\left[ \begin{array}{c}1\times 1,124\\ 3\times 3,24,G-32\\ 1\times 1,2048\end{array} \right]\times 1204\) & \(\left[ \begin{array}{c}1\times 1,124\\ 3\times 3,24,G-32\\ 1\times 1,2048\end{array} \right]\times 1204\) & \(\left[ \begin{array}{c}1\times 1,124\\ 3\times 3,24,G-32\\ 1\times 1,2048\end{array} \right]\times 12048\) & \(\left[ \begin{array}{c}1\times 1,124\\ 3\times 3,24,G-32\\ 1\times 1,2048\end{array} \right]\times 12048\) & \(\left[ \begin{array}{c}1\times 1,124\\ 3\times 3,24,G-32\\ 1\times 1,2048\end{array} \right]\times 12048\) & \(\left[ \begin{array}{c}1\times 1,124\\ 3\times 3,24,G-32\\ 1\times 1,2048\end{array} \right]\times 12048\) & \(\left[ \begin{array}{c}1\times 1,124\\ 3\times 3,24,G-32\\ 1\times 1,2048\end{array} \right]\times 12048\) & \(\left[ \begin{array}{c}1\times 1,124\\ 3\times 3,24,G-32\\ 1\times 1,2048\end{array} \right]\times 12048\) & \(\left[ \begin{array}{c}1\times 1,124\\ 3\times 3,24,G-32\\ 1\times 1,2048\end{array} \right]\times 3\) & \(\left[ \begin{array}{c}1\times 1,124\\ 3\times 3,24,G-32\\ 1\times 1,2048\end{array} \right]\times 120 parameters and calculations of FSRFNet-50 are 22% and 27% smaller than ResNeXt-101, respectively, which demonstrates the superiority of the additive effects of feature-selective and spatial attention. We plot the top-1 error rate of the proposed ResNeXt-50, ResNeXt-101, SENet-50, SENet-101 SKNet-50, SKNet-101, FSRFNet-50 and FSRFNet-101 with respect to the number of parameters in the Figure 5. we can also find that FSRFNets utilizes parameters more efficiently than these models. For instance, FSRFNet-50 outperforms ResNeXt-50 by achieving \(\sim\)34.3 top-1 error with similar model complexity. Remarkably, FSRFNet-50 achieves \(\sim\)20.2 top-1 error, although SENet-101 and SKNeXt-101 is 40.0% and 38.6% larger in parameter. Additionally, we choose the representative compact architecture of ShuffleNetV2 [38] and MobileNetV2 [39], which represents one of the strongest lightweight models, to evaluate the generalization capabilities of FSRF blocks. For comparison, SE, SK and FSRF blocks are embedded in ShuffleNetV2 [38] and MobileNetV2 [39]. Similar to [56], the number of channels in each block is scaled to generate networks of different complexities, marked as 0.5\(\times\), 0.75\(\times\), and 1\(\times\). By exploring the different scale models in Tables 3 and 4, we can observe that FSRF blocks improve the accuracy based on ShuffleNetV2 [38] and MobileNetV2 [39] baselines. From Figures 6 \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Models** & **\#P** & **GFLOPs** & **Top-1 Err. (\%)** \\ \hline ResNeXt-50 (our impl.) & 25.7M & 4.36 & 36.12 \\ ResNeXt-101 (our impl.) & 45.3M & 8.07 & 35.54 \\ SENet-50 (our impl.) & 28.6M & 4.51 & 35.31 \\ SENet-101 (our impl.) & 49.7M & 8.13 & 34.87 \\ SKNet-50 (our impl.) & 28.1M & 4.78 & 34.79 \\ SKNet-101 (our impl.) & 49.2M & 8.45 & 34.16 \\ FSRFNet-50 (ours) & 35.5M & 5.86 & 34.27 \\ FSRFNet-101 (ours) & 56.8M & 11.32 & 33.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Single \(224\times 224\) cropped top-1 error rates (%) on the Tiny ImageNet validation set and complexity comparisons. SENet, SKNet, and FSRFNet are all based on the corresponding ResNeXt backbones. The definition of FLOPs follows [56], i.e., the number of floating-point multiplication-adds. and #P denotes the number of parameters. Figure 5: Relationship between the performance of FSRFNet and the number of its parameters, compared with the corresponding ResNeXt backbones. and 7, we can also see that FSRF blocks perform well compared to the ShuffleNetV2 [38] and MobileNetV2 [39] baseline models based on SENet and SKNet, respectively.