## 4 Experiments  
The goal of this experiment was to evaluate the performance of the AYOLO algorithm, a specialized object detection model designed for small datasets. The dataset utilized in this study consisted of 80 images, each depicting poppy plants, which posed a significant challenge for object detection due to the limited number of training samples. To assess the effectiveness of AYOLO, its performance was compared against state-of-the-art object detection algorithms, including YOLOv5, YOLOv8, YOLOX, and DETR.  
The training and evaluation processes were carried out on a system equipped with CUDA v11.0, cuDNN v8.1.1, PyTorch 1.7.1, and an NVIDIA Tesla K40 GPU (Nvidia, Santa Clara, CA, USA). This hardware and software setup provided the necessary computational power and efficiency for both training and testing.  
In this study, all algorithms were trained and evaluated using the same raw dataset without any modifications or augmentations. While data augmentation techniques, such as rotation, scaling, translation, and color adjustments, are frequently used in related works to enhance the representation of small targets within datasets, these strategies were intentionally excluded.  
Figure 8: Information transformer encoder module ITEM.  
The rationale behind this decision was to ensure a fair and objective comparison of the intrinsic capabilities of the algorithms. By employing raw data, the evaluation highlights the true strengths and limitations of each model without the potential influence of additional preprocessing steps that could introduce bias. This approach ensures that the results presented in this study are directly comparable across all algorithms and reflect their performance on unaltered data.  
This methodology aligns with the primary goal of the study which as follows: to provide a robust and transparent evaluation of object detection models on a small and challenging dataset. By focusing on raw data, the study underscores the intrinsic effectiveness of AYOLO and its ability to perform competitively against well-established algorithms in resource-constrained scenarios.  
The model was trained for 300 epochs. The results, including the mean Average Precision (mAP), box loss, class loss, and object loss, were tracked over the training period. Figure 9, which depicts the learning curves, shows how AYOLO performed over the epochs, with mAP and loss values reflecting the model's accuracy and efficiency in detecting objects.  
The purple line in the mean Average Precision (mAP) graph represents the model's performance on the standard mAP@50:95 metric. The AYOLO model achieved a peak mAP@50:95 of approximately 0.25, stabilizing after around 100 epochs. The highest mAP@50 score remained lower, at approximately 0.10, which demonstrates the model's ability to achieve accurate predictions under stricter Intersection over Union (IoU) conditions.  
Box loss: Starting at around 2.4, the box loss reduces to approximately 1.7 by the end of the training process. This shows the model's increasing accuracy in predicting the bounding box locations of poppy plants.  
Figure 9: Training.  
Class loss: Initially fluctuating between 3.7 and 2.6, the class loss demonstrates a significant reduction to approximately 2.4, indicating the model's improved capability to correctly classify the objects.  
Object loss: Starting at around 2.1, the object loss stabilizes near 1.9, illustrating the model's improving confidence in predicting the presence of poppy plants in the images.