jiecaoyu
Accuracy drop using pretrained NIN model.
Hi. Thank you for your uploading. I have downloaded NIN cifar10 pretrained model and load it. Then, I run the inference evaluation. At this time. the accuracy is just only 59.36%. In this evaluation, there is no modification and the model and data are used according to Readme. But, when I run the training using pretrained model, the accuracy is very close to the best accuracy. I think that there seems to be any dependency in train and test in your code. To solve this problem, is there any trick or solution? Thanks.
@analog75 Hello, have you solved your problem? I also obtained a lower accuracy using NIN. Thank you
Hi. I solve this. This code adopts random, which should be modified int pseudo-random.
In addition, batch normalization can make your accuracy lowered, which is very natural.
From: Lanweichao [email protected] Sent: Monday, April 6, 2020 6:25 PM To: jiecaoyu/XNOR-Net-PyTorch [email protected] Cc: analog75 [email protected]; Mention [email protected] Subject: Re: [jiecaoyu/XNOR-Net-PyTorch] Accuracy drop using pretrained NIN model. (#77)
@analog75 https://github.com/analog75 Hello, have you solved your problem? I also obtained a lower accuracy using NIN. Thank you
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Hi. Thank you for your uploading. I have downloaded NIN cifar10 pretrained model and load it. Then, I run the inference evaluation. At this time. the accuracy is just only 59.36%. In this evaluation, there is no modification and the model and data are used according to Readme. But, when I run the training using pretrained model, the accuracy is very close to the best accuracy. I think that there seems to be any dependency in train and test in your code. To solve this problem, is there any trick or solution? Thanks.
I had the same issue with you. Test set: Average loss: 9.3183, Accuracy: 1003/10000 (10.03%) Best Accuracy: 86.28%
How to solve this issue?
