Comments (7)
I rerun it using different seeds. The best one is 97.4%, which is still lower than 97.64%.
After all, the reported 97.64% is the best top1 in DARTS papers so far. I am eager to reproduce such a good result.
By the way, I still cannot reproduce the reported ImageNet result (76.0%) using your code (mine is 75.6%).
I would appreciate if you release the log to help me find out what's wrong.
Thanks!
from ista-nas.
Thanks for your attention. This is the training log of the experiment in our paper.
training log cifar.log
I will check the released code recently.
from ista-nas.
Thanks! This is the log file for another seed 19 (97.4%). The only difference is passing a different seed s=19
test_one_stage_s19.log
Moreover, I would appreciate if you can release the training log on ImageNet (76.0%)
from ista-nas.
One stage Imgnet resume.log
One stage Imgnet.log
One stage C10.log
Hi, the following files are some of our logs of the original evaluation on ImageNet.
I have checked the code but did not find any bug.
from ista-nas.
Thanks!
However, the remaining probability is the random seed. Can you provide more logs (different seeds) about the model searched on CIFAR10? I have run the released training script on CIFAR10 using eight seeds but none of them exceeds 97.5%.
Several classmates of mine face with the same issue.
from ista-nas.
I met the same issue as @cxxgtxy.
I ran the command for evaluating one-stage ISTA-NAS on CIFAR10 following README several times, but the accuracies were lower than 97.5%
from ista-nas.
Thanks for the great work! I tried to reproduce the accuracy that reported in the paper on CIFAR10. But I only obtained around 93-94% accuracy via running
python ./tools/evaluation.py --auxiliary --cutout --onestage --arch ISTA_onestage
Any idea how to recap the significant accuracy gap? Thanks! My experiment setting is A100 server torch 1.13.
from ista-nas.
Related Issues (4)
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