csh313

> Hey there! 🙌 > > Multi-scale training can indeed help improve mAP by making your model more robust to various object sizes, especially in a diverse dataset like DOTAv1....

> Hey! 👋 > > For large datasets like yours, it's quite normal for training to take a significant amount of time, especially with multi-scale settings which can add to...

> Hello! 👋 > > It sounds like you're making great strides with your training! Using `split_trainval` with `gap=200` and `rates=[1.0]` effectively varies the input data, which can indeed be...

> > @mzy369 不需要，针对DOTA数据的多尺度都是指切图的多尺度，训练都是1024x1024的固定分辨率 > > 您好，为什么我切分出的是682×682的分辨率，而不是1024×1024 你好，请问你解决了吗

> 还没有，多尺度里面可以指定切割的大小，可是我的代码一直报错 就用官方的代码split_dota就可以吧，再用 from ultralytics.data.split_dota import split_trainval, split_test # split train and val set, with labels. split_trainval( data_root='path/to/DOTAv1.0/', save_dir='path/to/DOTAv1.0-split/', rates=[0.5, 1.0, 1.5], # multiscale gap=500 ) # split test set,...