Is AutoAugment and Mixup used for training detection models as well?

[jfern-1084]

Hi,

I have used AutoAugment to train the Cascade R-CNN + ResNeST 50 models on my custom dataset along with the standard detectron2 augmentations of Random Shortest Range (640 - 800) and HFlip. Unfortunately I have not been able to achieve optimal results. I am trying to add mixup to the training process as well, as recommended in the paper. As per my understanding ResNest models were trained with AutoAugment and Mixup while detection and segmentation models were trained with Random Shortest Range and HFlip.

I would like to get some clarity on whether AutoAugment and Mixup were used to train the Cascade R-CNN + ResNeST 50. Kindly let me know if I have misunderstood the augmentation policy for detection and segmentation models.