Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

是否能提供一下对比实验中ResNet50的结果权重呢? #36

Open
WYHZQ opened this issue Aug 6, 2022 · 2 comments
Open

是否能提供一下对比实验中ResNet50的结果权重呢? #36

WYHZQ opened this issue Aug 6, 2022 · 2 comments

Comments

@WYHZQ
Copy link

WYHZQ commented Aug 6, 2022

作者您好:
您是否方便提供一下论文表1中的ResNet50您训练所得的top-1为77.27的结果权重呢?万分感谢。

@cfzd
Copy link
Owner

cfzd commented Aug 8, 2022

@WYHZQ
抱歉,这个已经没有了。不过训出这个模型的setting很简单,就是标准setting加上label smoothing和cos linearning rate decay。

你也可以很简单的用我们现成的代码训出来,只需要在训练命令上把模型替换掉就行了,把-a fcanet50换成-a resnet50即可。

python -m torch.distributed.launch --nproc_per_node=$NGPUS main.py -a resnet50 --dali_cpu --b 128 --loss-scale 128.0 --opt-level O2 /path/to/your/ImageNet

@WYHZQ
Copy link
Author

WYHZQ commented Oct 24, 2022

@WYHZQ 抱歉,这个已经没有了。不过训出这个模型的setting很简单,就是标准setting加上label smoothing和cos linearning rate decay。

你也可以很简单的用我们现成的代码训出来,只需要在训练命令上把模型替换掉就行了,把- 一个fcanet 50换成- 一个resnet 50即可。

- -nproc_per_node=$NGPUS main.py--a resnet 50--dali_cpu --b 128 --损耗等级128.0 --可选级别O2 /路径/到/您的/ImageNet

好的,谢谢您的回复!

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants