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CUB200 recall@1(0.575) #1

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kebinC opened this issue Sep 7, 2018 · 6 comments
Open

CUB200 recall@1(0.575) #1

kebinC opened this issue Sep 7, 2018 · 6 comments

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@kebinC
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kebinC commented Sep 7, 2018

hi, i run your code using CUB200 dataset, but i can't reappear result of recall@1(0.575). Can you give me more detail about configure of parameters, likely training batch size, labels-per-batch? And how do you train, one stage or two stages? Thanks.

@chenbinghui1
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hi, i run your code using CUB200 dataset, but i can't reappear result of recall@1(0.575). Can you give me more detail about configure of parameters, likely training batch size, labels-per-batch? And how do you train, one stage or two stages? Thanks.

57.5% is ok

@asanakoy
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@kebinC did you solve the issue? What is the highest result you get?

@kebinC
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kebinC commented Mar 30, 2019

@kebinC did you solve the issue? What is the highest result you get?

No, the highest recall@1 i got is about 0.51.

@LeeRock
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LeeRock commented Apr 11, 2019

@kebinC did you solve the issue? What is the highest result you get?

No, the highest recall@1 i got is about 0.51.

hi ,i only get 0.21@1 .How do you adjust the hyper parameter in the original code ?

@LeeRock
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LeeRock commented Apr 11, 2019

hi, i run your code using CUB200 dataset, but i can't reappear result of recall@1(0.575). Can you give me more detail about configure of parameters, likely training batch size, labels-per-batch? And how do you train, one stage or two stages? Thanks.

57.5% is ok

By running the original repo , I only get 0.21@1 .Would you please give me some suggestion?Thank U.

@chenbinghui1
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@kebinC
First, in line424 of train_bier.py, the axis should be 1, not 0.
Have you implemented the baseline results?
If you achieve similar baseline results, then you can try `activation', this will achieve R@1=~56.5.

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