YOLOv11按目标大小优化，模型更小更快，精度不降
YOLOv11 Optimization for Efficient Resource Utilization
- 为不同尺寸目标定制化剪枝与重构YOLOv11架构
- 在保持原精度前提下，推理速度提升，模型体积减少
- 自动匹配数据集目标大小，推荐最优版本使用
本研究旨在优化第十一版 You Only Look Once (YOLOv11) 模型，通过构建针对不同尺寸目标的改进版本实现资源高效利用。改进方法包括剪除冗余层并重新配置主架构，使每个版本专精于特定尺寸范围的目标检测（从小到大）。为辅助模型选择，提出一个目标尺寸分类程序，可根据数据集特征自动识别最适配的版本。在多个数据集上的实验表明，所提模型在保持原始 YOLOv11 精度的前提下，显著提升计算资源效率，部分版本在检测性能上甚至超越原模型。此外，优化后的模型具备更小的参数量和更快的推理速度。模型权重及目标尺寸分类器代码可在该仓库中获取。
原文摘要 · Abstract (English)
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
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