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arthurdouillard / dytox

Dynamic Token Expansion with Continual Transformers, accepted at CVPR 2022

Home Page:https://arxiv.org/abs/2111.11326

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Confusing about the configurations

haoranD opened this issue · comments

commented

Dear Authors,

Many thanks for such amazing work and I am very interested in your work.

Recently, It tried to run the code on two GPU via:

bash train.sh 0,1 \ --options options/data/cifar100_2-2.yaml options/data/cifar100_order1.yaml options/model/cifar_dytox.yaml \ --name dytox \ --data-path MY_PATH_TO_DATASET \ --output-basedir PATH_TO_SAVE_CHECKPOINTS \ --memory-size 1000

I am happy that I can successfully get the results you reported, which is 64.82 exactly.

WechatIMG10

HOWEVER, I am still a bit confused, how can I get the result as around 70.20 reported in the paper?

Looking forward to your reply, and I would be truly grateful if you could help with this. Thank you.

Best,
Haoran

commented

Those results were obtained with distributed memory while training with many GPUs (more than 2, thus giving more rehearsal than the white line with only 2 GPUs).

Those gray results were the original results in the paper. They make sense as we limit the rehearsal per-process, however for a fair evaluation, I've gray them out, as you should compare with the other newer results instead.