## 4 Results and Discussion
### Data Generation Configuration
#### 4.2.1 Ablation Study  
In our first experiment, we evaluated different configurations of our data generation pipeline. In each configuration, we generated a synthetic dataset of 3000 images, which were then used to train YOLOv8-Nano. Every model was pre-trained for 20 epochs using the synthetic images with a resolution of \(800\times 800\) pixels. Training was capped at 20 epochs, as we observed no significant improvement in mean Average Precision (mAP) beyond this point. Extending the pre-training to 50 epochs, which doubled the training time, resulted in a 3.6% decrease in AP\({}^{50}\) and a 0.4% decrease in mAP, suggesting overfitting to the dataset's specific characteristics with prolonged training. After pre-training the model on our generated dataset, we performed a brief fine-tuning on 1000 real images to achieve further improvements. All models were evaluated using the validation set provided in the AGAR dataset [4]. Table 2 offers an overview of the ablation studies conducted. Initially, our data generation was limited to synthetic images featuring a single colony per agar plate, without differentiating between colonies of the same class that appeared in different colors due to varying backgrounds. Each step in our ablation study is labeled with consecutive Roman numerals, with the corresponding \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Full Set**} & \multicolumn{2}{c}{**FT-Set**} \\  & **AP\({}^{50}\)** & **mAP\({}^{50}\)** & **AP\({}^{50}\)** & **mAP\({}^{50}\)** \\ \hline _S. aureus_ & 94.9 & 62.0 & 89.2 & 54.6 \\ _B. subtilis_ & 97.9 & 72.1 & 95.0 & 64.5 \\ _P. aeruginosa_ & 96.2 & 67.0 & 93.1 & 59.7 \\ _E. col_ & 97.7 & 74.2 & 94.2 & 65.8 \\ _C. albicans_ & 81.9 & 49.2 & 73.4 & 37.3 \\ \hline Mean & 93.7 & 64.9 & 89.0 & 56.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Baseline evaluation with YOLOv8-Nano [48] on the AGAR dataset [4] using the entire dataset (Full Set) and the fine-tuning subsets of 1000 images (FT-Set). adjustments and findings discussed in detail in the following sections. The final approach, resulting from these studies, is outlined in Section 3. _(1) Raw data:_ First, the detection model was trained using synthetic agar dish images directly generated from cutouts created by the SAM [15] without quality assurance. These cutouts were not filtered, resulting in many of them being poor representations of microbial colonies. For instance, some cutouts included artifacts such as agar dish lettering instead of actual colonies, while others captured colonies that were part of a cluster, causing unnatural edges and harsh lines where the colonies were cut apart, rather than realistic colony boundaries. A direct comparison with our second baseline model, trained exclusively on the fine-tuning set of 1000 real images (see Table 1), demonstrates that even artifact-rich representations of colonies in generated images can enhance detection performance. By pre-training our model on additional Copy-Paste data before fine-tuning on real data, we were able to achieve an mAP of 58.9 and an AP\({}^{50}\) of 91.2, outperforming the baseline by \(+2.5\) and \(+2.2\), respectively. However, the results were significantly below the baseline trained on the full train set of real images, with an AP\({}^{50}\) of 93.7 and an mAP of 64.9. Therefore, further refinement of the augmentation method is needed to improve it. _(II) No overlapping colonies:_ To improve the generated images, a number of cutouts of singular microbial colonies were manually selected. These cutouts featured realistic-looking colonies with minimal artifacts from the agar dish and mostly clear, well-defined edges. Based on these filtered colonies, we generated a synthetic dataset for pre-training. As the generated images lacked overlapping colonies, the model could only learn the concept of colony overlap during the fine-tuning. Pre-training on this dataset, followed by fine-tuning, resulted in an AP\({}^{50}\) of 90.6 and an mAP of 58.4.