## 4 Results and Discussion
### Data Generation Configuration
#### 4.2.1 Ablation Study
These values were slightly lower than the model trained on raw SAM data. These results suggest that for optimal learning, the synthetic dataset must contain examples of overlapping microbial colonies, as these also occur in large numbers in real images. _(III) Pairs of overlapping colonies:_ To further improve the quality of the synthetic images, we included manually pre-selected pairs (clusters of two overlapping colonies) with only minimal artifacts in the generation process. Larger clusters are addressed in step (VI), but for now, we aim to assess the impact on detection accuracy when only two connected colonies are considered. The model pre-trained on this dataset and subsequently fine-tuned achieved an AP\({}^{50}\) of 91.9 and an mAP of 60.5. This represents an improvement of \(+1.3\) and \(+2.1\), respectively, compared to the fine-tuned model trained on raw SAM data. These results underscore the \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{_S. aureus_} & \multicolumn{2}{c}{_B. subtilis_} & \multicolumn{2}{c}{_P. aeruginosa_} & \multicolumn{2}{c}{_E. coli_} & \multicolumn{2}{c}{_C. albicans_} & \multicolumn{2}{c}{_Mean_} \\  & **AP\({}^{50}\)** & **mAP** & **AP\({}^{50}\)** & **mAP** & **AP\({}^{50}\)** & **mAP** & **AP\({}^{50}\)** & **mAP** & **AP\({}^{50}\)** & **mAP** & **AP\({}^{50}\)** & **mAP** \\ \hline Baseline & 89.2 & 54.6 & 95.0 & 64.5 & 93.1 & 59.7 & 94.2 & 65.8 & 73.4 & 37.3 & 89.0 & 56.4 \\ \hline (I) Raw data & 92.3 & 56.9 & 96.7 & 66.5 & 94.4 & 61.8 & 95.4 & 68.2 & 77.1 & 41.0 & 91.2 & 58.9 \\ (II) No overlapping colonies & 91.6 & 55.7 & 95.5 & 66.1 & 94.5 & 61.7 & 95.2 & 67.9 & 76.3 & 40.8 & 90.6 & 58.4 \\ (III) Pairs of overlapping colonies & 93.1 & 58.9 & 96.7 & 67.2 & 94.7 & 62.8 & 95.9 & 69.6 & 78.9 & **43.9** & 91.9 & 60.5 \\ (IV) Color differentiation & 92.9 & 58.4 & 96.9 & 67.0 & 94.8 & 62.9 & 96.1 & 69.3 & 78.6 & 42.7 & 91.9 & 60.1 \\ (V) Two classes max & 92.3 & 59.0 & 97.1 & 68.2 & 94.9 & 63.1 & **96.2** & 69.3 & **79.2** & 43.3 & **92.1** & **60.6** \\ (VI) Big clusters of colonies & **93.6** & **59.2** & 97.1 & 67.7 & 94.8 & **63.6** & 96.0 & **70.1** & 78.9 & 42.6 & **92.1** & **60.6** \\ (VII) Dish color differentiation & 92.6 & 57.6 & 96.9 & 66.9 & 94.6 & 62.3 & 96.0 & 69.0 & 77.5 & 40.5 & 91.5 & 59.3 \\ (VIII) Opacity 90\% & 93.2 & 57.8 & **97.2** & 67.3 & **95.0** & 62.9 & 96.1 & 69.8 & 78.2 & 41.3 & 91.9 & 59.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation studies for optimizing the Copy-Paste data generation pipeline on the AGAR dataset [4] using YOLOv8-Nano [48]. The best results are highlighted in bold; the second-best results are underlined. importance of incorporating overlapping microbial colonies in the augmentation process, although additional refinements are still needed for optimal performance. _(IV) Color differentiation:_ To enhance the realism of the synthetic images, microbial colonies were categorized based on intensity (_dark, medium, light_) and color (_brown, green, yellow_) to better match the appearance of the agar dish, making the images look more natural. At this stage, we still relied on the basic subdivision of agar plates with _bright_ and _dark_ backgrounds (see Section 4.1) provided by the original AGAR dataset. A more refined subdivision is introduced in step (VII). To assess the impact of this visual refinement, a model was trained using the updated dataset. The new images depicted agar dishes containing either single colonies or colony pairs. These colonies were assigned to the two types of agar dishes according to color and intensity. This ensures that each dish features microbial colonies of a consistent color and intensity, improving visual coherence. The pre-trained and fine-tuned model achieved an AP\({}^{50}\) of 91.9 and an mAP of 60.1, as shown in Table 2. These results indicate that incorporating color differentiation had no significant impact on the model's performance.