# Evaluating the FLUX.1 Synthetic Data on YOLOv9 for AI-Powered Poultry Farming
## 3 Materials and Methods
### Experiment Models and Setup
\begin{tabular}{l} \hline _real_dataset_ \(\leftarrow\)_collect_real_dataset_ \\ _real_dataset_chunk_ \(\leftarrow\)_select_sample_from_real_dataset_ \\ _selected_generative_ai_model_ \(\leftarrow\)_Flux1.1[dev]_ \\ \multicolumn{2}{l}{**Model training**} \\ _trigger_word_ \(\leftarrow\)_\"CHICKRAD\"_ \\ _lora_rank_ \(\leftarrow\) 16 \\ _steps_ \(\leftarrow\) 1000 \\ _train_(_selected_generative_ai_model_) \\ \multicolumn{2}{l}{**Generative synthetic data**} \\ _lora_scale_ \(\leftarrow\) 1 \\ _guidance_scale_ \(\leftarrow\) 2.5 \\ _prompt_scale_ \(\leftarrow\) 0.8 \\ \multicolumn{2}{l}{\"A close-up isometric photo capturing a cluster of CHICKRAD nestled in a bed of soft, straw-colored hay. The CHICKRAD, in shades of white, brown, and soft yellow, are scattered across the hay, with some pecking, others resting. The variety in CHICKRAD sizes and their various angles create depth and liveliness in the scene. The hay background enhances the rustic feel, bringing out the delicate textures of their fluffy feathers.\" \\ \multicolumn{2}{l}{_generate_synthetic_data_()} \\ \hline \end{tabular}   
The synthetic dataset was expanded using a structured pipeline, generating three images per model call as shown in Figure 5. The left column presents the input parameters used for the model, while the right column displays the corresponding generated images. These parameters have already been described. After generating these images, a small portion (5%) was manually excluded from the training dataset due to poor quality, lack of realism, or mismatch with the input prompt. The same manual procedure was applied to outliers for real images. As previously mentioned, this research focused on a specific farm with unique conditions (e.g., lighting, surface, and colors). Any additional variations beyond these specific conditions were outside the scope of this study and were not included in the fine-tuning process. YOLOv9 represents a significant advancement in real-time object detection, introducing key innovations to enhance performance and efficiency [57]. One of the central challenges in deep neural networks is the Information Bottleneck Principle, which highlights the potential loss of information as data passes through successive layers [58]. This is mathematically represented as  
\[I(X,X)\geq I(X,f_{\theta}(X))\geq I(X,g_{\phi}(f_{\theta}(X))) \tag{1}\]  
where \(I\) denotes mutual information, and \(f\) and \(g\) are transformation functions with parameters \(\theta\) and \(\phi\), respectively. To address this, YOLOv9 introduces Programmable Gradient Information (PGI), ensuring the preservation of essential data across the network's depth, leading to more reliable gradient generation. PGI contains three key components: the main inference branch, auxiliary reversible branches for reliable gradient generation, and multi-layer auxiliary information for more effective deep supervision [59]. Additionally, the architecture incorporates reversible functions, which can be inverted without information loss, expressed as  
\[X=v_{\xi}(r_{\psi}(X)) \tag{2}\]with \(\psi\) and \(\zeta\) as parameters for the reversible and its inverse functions, respectively. This design choice maintains complete information flow, enabling accurate parameter updates. Also, YOLOv9 uses the Generalized Efficient Layer Aggregation Network (GELAN), enhancing feature extraction and fusion, thereby improving accuracy and efficiency. These innovations collectively position YOLOv9 as one of the most effective AI models for computer vision tasks, such as object detection, instance segmentation, etc. By integrating advanced loss functions and optimization strategies, YOLOv9 balances detection accuracy and speed, making it a good candidate for computer vision tasks. The next section covers evaluation metrics: YOLOv9 loss functions, and mean average precision for assessing model accuracy.