## 4 Experiments
### Performance Comparison  
This section compares the performance of the TIN on the clamp dataset and those of the existing standard LTR algorithms.  
Clamp Dataset: Table 3 illustrates the LTR results of each technique, where our minimal baseline outperformed the previously developed single-stage models. By gradually increasing the size of the visual backbone, we found that the performance of the TIN also enjoys an improvement. When ResNet-50 is used as the backbone, the accuracy improves by +3.81%, and when the maximum ViT-B/32 is employed, the accuracy improves by 10.8%, enabling our approach to outperform other one-stage models by a large margin. We plotted Figure 5, which clearly shows the gap between the models. In addition, the loss function and accuracy variation of the training process are shown in Figures 6 and 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Train Set** & \multicolumn{2}{c}{**Test Set**} \\ \hline not rusted & rusty & not rusted & rusty \\
11,614 & 283 & 2566 & 58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of the original clamp dataset.  
Figure 5: Comparison with the state-of-the-art approaches on Clamp dataset. TIN with ResNet-50 visual backbone outperforms models with a more complex structure and longer training epochs by a large margin.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Backbone** & **Epochs** & **Many** & **Few** & **Average** \\ \hline \multirow{8}{*}{\(\tau\)-normalized [44]} & R-50 & 100 & 99.87 & 58.61 & 79.01 \\  & R-101 & 100 & 98.45 & 60.30 & 79.38 \\  & R-152 & 100 & 98.76 & 61.65 & 80.205 \\  & X-50 & 100 & 99.31 & 59.69 & 79.50 \\  & X-101 & 100 & 98.62 & 62.57 & 80.59 \\  & X-152 & 100 & 99.78 & 64.80 & 82.29 \\ \hline \multirow{8}{*}{Focal loss [61]} & R-50 & 100 & 99.96 & 36.21 & 68.09 \\  & R-101 & 100 & 99.51 & 41.38 & 70.45 \\  & R-152 & 100 & 98.81 & 58.62 & 78.72 \\  & X-50 & 100 & 99.26 & 24.14 & 61.70 \\  & X-101 & 100 & 98.89 & 53.45 & 76.17 \\  & X-152 & 100 & 99.14 & 25.86 & 62.50 \\ \hline \multirow{8}{*}{Class balance [62]} & R-50 & 100 & 98.55 & 72.41 & 85.48 \\  & R-101 & 100 & 99.30 & 55.17 & 77.24 \\  & R-152 & 100 & 99.38 & 62.07 & 80.73 \\  & X-50 & 100 & 98.97 & 63.79 & 81.38 \\  & X-101 & 100 & 99.38 & 60.34 & 79.86 \\  & X-152 & 100 & 98.69 & 62.07 & 80.38 \\ \hline \multirow{2}{*}{PaCo [63]} & R-50 & 100 & 97.23 & 70.68 & 83.96 \\  & X-50 & 100 & 98.06 & 71.34 & 84.70 \\ \hline \multirow{2}{*}{TIN (Ours)} & R-50 & 50 & 98.41 & 78.62 & 88.51 (+3.81) \\  & ViT-B/32 & 50 & 96.15 & 86.21 & 91.18 (+10.8) \\ \hline \hline \end{tabular}  
* The accuracy outcomes of several backbones and methodologies are compared using the clamp dataset. “R-” and “X-” stand for the ResNet [11] and ResNet [64] backbones, respectively. Compared to these algorithms, our model has a more significant advantage.  
\end{table}
Table 3: Results obtained using the clamp dataset.  
ImageNet-LT Dataset: We further verified the generalization ability of the proposed model on ImageNet-LT. As shown in Table 4, we chose ViT-B/32 as the backbone of the visual model for comparative experiments. We observes that, with only 50 training epochs, the TIN outperforms existing single-stage methods by 14.6%. Moreover, a 27.2 point improvement was achieved in the few-shot case, and the overall accuracy reached 72.8%, which is a state-of-the-art result for a single-stage model.  
Places-LT Dataset: We performed additional generalizability studies on Place-LT, another large, long-tailed dataset. As shown in the Table 5, our model shows some improvements over other single-stage models, but the improvement was not very significant, probably because the long-tailed distribution is more severe in this dataset than in ImageNet-LT.  
Figure 6: **(a)** Figure shows the accuracy of the unrusted (Many) category; **(b)** Figure shows the accuracy of the rusty (Few) category.  
Figure 7: The picture shows the loss reduction of the training process.