Can not achieve similar results For Tiny ImageNet

[MapleLeafKiller]

Thanks for your paper and your code, they are great work and help me a lot.
I did experiments on tiny imagenet dataset following the settings revealed on your paper, howerer i can't achieve similar results, for
long tailed 1:100 tiny imagenet, the top-1 validation error I got is:
ERM SGD: 80.05
LDAM SGD: 72.8
It has a big gap with the results showed in your paper. So I wonder if there is any setting or trick I have missed?
In the you mentioned:<We perform
1 crop test with the validation images.> I wonder how it is done specifically.
For ResNet-18, I use:
backbone = models.resnet18(pretrained=True)
backbone.avgpool = nn.AdaptiveAvgPool2d(1)
num_ftrs = backbone.fc.in_features
if USE_NORM:
backbone.fc = NormedLinear(num_ftrs, 200)
else:
backbone.fc = nn.Linear(num_ftrs, 200)
Is it correct?
Looking forward to your reply, thank you very much!