Different results on IJB-C

[KamalM8]

Hi Huang,

I really liked your new Curricular face paper especially the idea of automatic curriculum learning. I tried replicating your results on IJB-C dataset using the IR_101 pretrained model that you have supplied. Firstly, I have aligned the faces using the landmarks provided in the ArcFace repository. I then normalized these faces the same way you did for lfw and other datasets in the evaluate.py code. I used the same ArcFace evaluation code. However, the results I am getting are much lower than what is reported in the paper. Here are my results:

TAR @ FAR: [1:1 verification protocol] 1e-6 --> 6.34 1e-5 --> 15.76 1e-4 --> 55.29 1e-3 --> 75.83 1e-2 --> 84.34 1e-1 --> 91.17

These numbers seem buggy to me. Is the code you used to evaluate IJB-C the same as evaluate.py?