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Hi sir!
There is no dytox++ configuration for imagenet100 and imagenet1000 in your code,
I appended the following configuration to imagenet_dytox_plus.yaml to pretend that this is imagenet_dytox_plusplus.yaml: # SAM sam_rho: 3.0 sam_adaptive: true # ASAM sam_skip_first: true sam_mode: [tr]
and I got the final result is :
"acc": 70.64, "avg_acc": 78.93
compare with "acc": 72.46, "avg_acc": 80.76 in your Appendix Table 10, my result is much lower.
Can you update the configuration of dytox++ for imagenet100 and imagenet1000?
The text was updated successfully, but these errors were encountered:
You can see updated results for dytox and dytox+ in table 17 of the last version of my paper.
Unfortunatly i've never re-run dytox++ because it's slower with SAM.
Hi sir!
There is no dytox++ configuration for imagenet100 and imagenet1000 in your code,
I appended the following configuration to imagenet_dytox_plus.yaml to pretend that this is imagenet_dytox_plusplus.yaml:
# SAM sam_rho: 3.0 sam_adaptive: true # ASAM sam_skip_first: true sam_mode: [tr]
and I ran the following command (using 2GPUs):
bash train.sh 0,1 --options options/data/imagenet100_10-10.yaml options/data/imagenet100_order1.yaml options/model/imagenet_dytox_plus.yaml --name dytox --data-path MY_PATH_TO_DATASET --output-basedir PATH_TO_SAVE_CHECKPOINTS --memory-size 2000
and I got the final result is :
"acc": 70.64, "avg_acc": 78.93
compare with "acc": 72.46, "avg_acc": 80.76 in your Appendix Table 10, my result is much lower.
Can you update the configuration of dytox++ for imagenet100 and imagenet1000?
The text was updated successfully, but these errors were encountered: