The objective of this research is to optimize the eleventh iteration of You Only Look Once (YOLOv11) by developing size-specific modified versions of the architecture. These modifications involve pruning unnecessary layers and reconfiguring the main architecture of YOLOv11. Each proposed version is tailored to detect objects of specific size ranges, from small to large. To ensure proper model selection based on dataset characteristics, we introduced an object classifier program. This program identifies the most suitable modified version for a given dataset. The proposed models were evaluated on various datasets and compared with the original YOLOv11 and YOLOv8 models. The experimental results highlight significant improvements in computational resource efficiency, with the proposed models maintaining the accuracy of the original YOLOv11. In some cases, the modified versions outperformed the original model regarding detection performance. Furthermore, the proposed models demonstrated reduced model sizes and faster inference times. Models weights and the object size classifier can be found in this repository

翻译：本研究旨在通过开发特定尺寸的改进架构版本，对YOLOv11（You Only Look Once）进行优化。这些改进涉及剪枝冗余层并重新配置YOLOv11的主干架构。每个提出的版本都针对特定尺寸范围（从小型到大型）的目标检测任务进行定制。为确保基于数据集特性选择合适模型，我们引入了目标分类器程序。该程序能够为给定数据集识别最适配的改进版本。所提模型在多个数据集上进行评估，并与原始YOLOv11及YOLOv8模型进行对比。实验结果表明，改进模型在保持原始YOLOv11精度的同时，显著提升了计算资源效率。在某些情况下，改进版本在检测性能方面甚至超越了原始模型。此外，所提模型展现出更小的模型尺寸和更快的推理速度。模型权重与目标尺寸分类器可通过此代码库获取。