# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 4 Experiments
### Data Preprocessing  
During the data preprocessing stage, we performed filtering and cleaning on the val2017 dataset. Initially, a preliminary inspection revealed that 48 of the 5000 images in the original dataset were unlabeled. To ensure data integrity and eliminate potential sources of bias, we removed these images, retaining a final set of 4952 images.  
Next, for each image, we selected the annotated category that occupies the largest area of pixels as the target class. This decision was guided by several key considerations. Attempting to interpret all small objects within an image could lead to unreliable and less meaningful results. For instance, when a category occupies only a minimal pixel area, the region contains insufficient features, making it difficult for the model to learn effective discriminative information. Consequently, explanations for such regions are more susceptible to local noise, image compression artifacts, or annotation errors, which could cause discrepancies between the explanation and the actual predictive features. Furthermore, when the model explanation slightly expands or contracts such a region, evaluation metrics may exhibit nonlinear variations due to changes in pixel count, making it challenging to consistently assess the effectiveness of the explanation method. This could reduce the reliability of the metric values and potentially lead to misleading conclusions.  
Finally, to ensure the reliability of the statistical analysis, we retained only categories with more than 40 samples. In this process, we removed 44 categories, accounting for 541 images, to prevent statistical bias due to insufficient sample sizes and to avoid extreme fluctuations in evaluation results that could compromise the trustworthiness of the overall experiment. After these filtering steps, the final dataset consisted of 4411 images. The distribution of target categories and their corresponding counts is summarized in Table 2.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Model** & **Backbone** & **Crop Size** & **Lr schd** & **mIoU** \\ \hline DeepLabV3 & R-50-D8 & 512 \(\times\) 512 & 160,000 & 41.09 \\ PSPNet & R-50-D8 & 512 \(\times\) 512 & 160,000 & 39.64 \\ BiSeNetV1 & R-50-D32 & 512 \(\times\) 512 & 160,000 & 34.88 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental configurations and performance comparison of the models [36].  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Category** & **Counts** & **Category** & **Counts** & **Category** & **Counts** \\ \hline Person & 1580 & Cat & 77 & Chair & 62 \\ Car & 109 & Dog & 62 & Couch & 83 \\ Motorcycle & 89 & Horse & 98 & Bed & 105 \\ Airplane & 83 & Sheep & 46 & Dining Table & 249 \\ Bus & 134 & Cow & 73 & Toilet & 102 \\ Train & 148 & Elephant & 83 & TV & 73 \\ Truck & 98 & Bear & 46 & Laptop & 54 \\ Boat & 83 & Zebra & 77 & Oven & 50 \\ Traffic Light & 45 & Giraffe & 91 & Sink & 59 \\ Fire & 45 & Umbrella & 47 & Refrigerator & 58 \\ Hydrant & 86 & Bowl & 59 & Clock & 92 \\ Bird & 61 & Pizza & 54 & Teddy Bear & 50 \\ \hline \end{tabular}
\end{table}
Table 2: Target category distribution statistics of the dataset.