Weed Detection Model Weights (YOLOv7 Variants)

best_yolov7.pt
This file contains the trained weights of the YOLOv7 base model for weed and cotton detection using UAV imagery. The model was trained on annotated agricultural field images to classify and localize cotton plants and weeds with high precision. This checkpoint represents the best-performing epoch based on mAP@0.5 evaluation and achieved the highest weed detection accuracy among the tested variants.

best_yolov7w6.pt
This is the trained weight file of the YOLOv7-w6, a larger and deeper model architecture optimized for higher capacity and better generalization in complex agricultural environments. The model was trained on the same UAV dataset and delivered competitive performance, though it performed weaker than the base model in cluttered and shaded regions.

best_yolov7x.pt
This .pt file contains the trained weights for the YOLOv7-x, an advanced version evaluated our study. It was trained on UAV-captured agricultural imagery for cotton and weed detection and showed balanced detection performance across classes, though with slightly lower weed accuracy compared to the base model. This checkpoint is suitable for tasks requiring robustness and overall detection stability.

best_yolov7.pt
该文件为YOLOv7基础模型（YOLOv7 base model）的训练权重文件，用于基于无人机（UAV）影像开展杂草与棉花目标检测任务。该模型在标注后的农田图像上进行训练，可高精度地对棉花植株与杂草进行分类与定位。该检查点基于mAP@0.5评估指标选取为性能最优的训练轮次，在所有测试的模型变体中杂草检测精度最高。

best_yolov7w6.pt
该文件为YOLOv7-w6的训练权重文件，YOLOv7-w6是一种尺寸更大、深度更深的模型架构，针对复杂农业场景下的更高容量与更强泛化能力进行了优化。该模型在相同的无人机数据集上进行训练，可提供具有竞争力的检测性能，但在杂乱区域与阴影区域中的表现弱于基础模型。

best_yolov7x.pt
该.pt文件包含YOLOv7-x的训练权重，YOLOv7-x是本研究评估的进阶版本模型。该模型基于无人机采集的农田影像进行训练，用于棉花与杂草检测，在各目标类别间实现了均衡的检测性能，但相较于基础模型，其杂草检测精度略低。该检查点适用于对鲁棒性与整体检测稳定性有要求的任务。