• set flag random=1 in your .cfg-file - it will increase precision by training Yolo for different resolutions: link

• increase network resolution in your .cfg-file (height=608, width=608 or any value multiple of 32) - it will increase precision

• desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty .txt files) - use as many images of negative samples as there are images with objects

• for training for small objects (smaller than 16x16 after the image is resized to 416x416) - set layers = 23 instead of https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L895 set stride=4 instead of https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L892 and set stride=4 instead of https://github.com/AlexeyAB/darknet/blob/6f718c257815a984253346bba8fb7aa756c55090/cfg/yolov4.cfg#L989

总结

数据集是有问题的。

图片太单一了，背景什么都没啥变化，训练结果真的太差了哇。

今天用helen数据集做了新的数据集，　明天打标再训练试试。

先这样了，期待出成果。