## 3 Results and Discussion  
In addition to the proposed segmentation model for thin-section rock images, several other models were evaluated, including DeepLabV3+, ConvNext, SegFormer and Mask2 Former. All of these models were implemented using the Python-based mmsegmentation framework [34]. This framework serves as a wrapper built on PyTorch (v.2.6) [35] and facilitates rapid prototyping and a comparison of experimental results. Table 3 presents the network training parameters utilized for training these networks. The parameters listed in Table 3 remained consistent across all models, and training was conducted accordingly. To assess the test inference times of the models, training was carried out on the same computer. The hardware specifications and application environment utilized for training the models are detailed in Table 4.  
To assess the performance of the proposed model in comparison to other models, the loss values of each model during training, along with the accuracy and mIoU values for validation, were computed. Figure 7 presents the results for each model based on these metrics. \begin{table}
\begin{tabular}{l l} \hline
**Feature** & **Value** \\ \hline Processor & Ryzen 7900X 12 Core 24 Thread Processor \\ \hline GPU & NVIDIA RTX 4090 24 GB GPU (NVIDIA, Santa Clara, CA, USA) \\ \hline RAM & G-Skill Trident Neo 32 GB 6000 Mhz CL30 Ram \\ \hline Operating System & Ubuntu 22.04 LTS \\ \hline \end{tabular}
\end{table} 
Table 4: Hardware and software details. Figure 7: _Cont._ \begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Loss & Cross Entropy Loss \\ \hline Learning rate scheduler & PolyLR \\ \hline Learning rate & \(5\times 10^{-6}\) \\ \hline Optimizer & AdamW \\ \hline Batch size & 4 \\ \hline Number of iterations & 8000 \\ \hline \end{tabular}
\end{table} 
Table 3: The parameters of segmentation networks. Based on the graphs depicted in Figure 7, a comparison was conducted with respect to the average validation accuracy and mIoU values. Furthermore, the inference times for the test cases were also evaluated. Table 5 represents the results corresponding to these metrics. Figure 7: Training loss, validation MioU and mAccuracy metrics for SegFormer, ConvNext, DeepLabV3+, Mask2 Former and proposed Mask2 Former+Refining models. (**a**) SegFormer; (**b**) ConvNext; (**c**) DeepLabV3+; (**d**) Mask2 Former; (**e**) Mask2 Former+Refining. Results indicate that Mask2 Former, augmented with our proposed post-refining stage, outperformed other models in terms of both accuracy and mIoU. SegFormer, ConvNext, and DeepLabV3+ models yield similar results while maintaining a comparable number of trainable parameters [28]. These architectures are widely adopted for segmentation tasks and achieve effective outcomes with a limited parameter count. In contrast, Mask2 Former exhibits a significantly higher parameter volume, approximately 218 million trainable parameters, compared to the aforementioned models. This suggests more intricate structural properties and a potentially greater learning capacity inherent in Mask2 Former. A key advantage of Mask2 Former lies in its use of mask-based prediction rather than the conventional pixelwise classification employed by models like SegFormer and DeepLabV3+. This distinction is particularly important in the context of thin-section images, where precise boundary delineation, subtle changes in color gradients, and fine object structures play a critical role in segmentation accuracy. Conventional pixelwise approaches often struggle to capture the nuanced variations in thin-section images, as they rely solely on individual pixel classification without leveraging global object-level features. Figure 8 clearly demonstrates that Mask2 Former's predictions are significantly more coherent and unified, whereas other models exhibit scattered and inconsistent pixel-level predictions. However, despite its high parameter count, Mask2 Former does not suffer the expected slowdown in inference time compared to other models.