alldbi / supermix Goto Github PK

HI

Thanks for your great work. I hope to use your supermix technology in object detection framework like yolov4/v5.
How can i integrated it efficiently in this framework?

Hi,

In the original paper of CutMix, it reports top-1 accuracy as 78.6 (21.4 top-1 error) on the ImageNet, however, in the Table.1 of your SuperMix, the corresponding result of CutMix is only 77.2. Could I know what makes the difference? Thanks.

If supermix is used for image segmentation, should we replace KL with an IOU that evaluates x_hat with k inputs and then maximize it？

Hi,

In the original paper of CutMix, it reports top-1 accuracy as 78.6 (21.4 top-1 error), however, in the Table.1 of your SuperMix, the corresponding result is only 77.2. Could I know what makes the difference? Thanks.

Hi, @alldbi thank you for your interesting work

I'm trying to reproduce the results of table 1. on your paper, but I can't find the training code for it.
Or is it just using the train_teacher.py code with loading the produced SuperMix dataset? (+ by using get_cifar100_dataloaders_sample and class CIFAR100InstanceSample?)

When I was looking at the code, I suddenly noticed that you set the tol parameter to 70 percent by default, which means that if the batch is 8, only 2-3 supermix images will be returned. Is it because the performance of supermix is very good, so it does not need a lot of data？ in order to speed up the training, we set high tol parameters.