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Can't get 65+ with GOT10k #86

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ISosnovik opened this issue May 16, 2020 · 14 comments
Open

Can't get 65+ with GOT10k #86

ISosnovik opened this issue May 16, 2020 · 14 comments

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@ISosnovik
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Hello

Thanks for your code.
I followed your code and trained ResNet22 on GOT10k.
No modification were done. At the end I got 64.7 on OTB15
How did you get 65.4?

@JudasDie
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Hello

Thanks for your code.
I followed your code and trained ResNet22 on GOT10k.
No modification were done. At the end I got 64.7 on OTB15
How did you get 65.4?

Hi, thanks for your interest.
Have you tuned the parameters (see tuning toolkit) ? In my experience, training on GOT10K is easy to get good results.

@ISosnovik
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I used the ones you've provided.
Do I need to tune them each time I train my model? So for each set of weights I have a different set of hyperparameters?

@JudasDie
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I used the ones you've provided.
Do I need to tune them each time I train my model? So for each set of weights I have a different set of hyperparameters?

I would tune it only if I get a nice result after epoch testing. If you have enough resources, of course you could tune it every time. For example, if your current is 64.7, it should be 65.5-67 in my experience.

@davinca
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davinca commented Jun 8, 2020

@ISosnovik @zhangliliang
Have u trained the siamrpn model successfully with GOT10K?
I got an situation like this:
image
my torch version is: torch.1.2.0

@ISosnovik
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@davinca you can check #38

@davinca
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davinca commented Jun 10, 2020

@ISosnovik thanks!

@davinca
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davinca commented Jun 13, 2020

@ISosnovik @JudasDie I trained ResNet22 on GOT10k and get 47.8 on OTB15,No modification in SiamRPN.ymal. the result is abnormal and confusing.=-=

@JudasDie
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@ISosnovik @JudasDie I trained ResNet22 on GOT10k and get 47.8 on OTB15,No modification in SiamRPN.ymal. the result is abnormal and confusing.=-=

The models provided on readme (got10k) are for "FC" versions (Res22FC and Res22W FC). If you want to use "RPN", please use default configs. If you want to train on GOT10K (as you mentioned), please use FC models (20w pairs each epoch are enough). When you tune the hyper-parameters, I would suggest you start with VOT, since OTB15 containing more frames takes much longer time.

@davinca
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davinca commented Jun 18, 2020

@JudasDie I use FC model to initial the SiamRPN and increase pairs for epoch(40w), but gives me 0.53 on OTB100.

@ISosnovik
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ISosnovik commented Jun 18, 2020

And what is 20w and 40w? @davinca

@davinca
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davinca commented Jun 18, 2020

@ISosnovik it indicates the number of training samples per epoch.

@ISosnovik
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What is w?

@ISosnovik
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Oh. I suppose it is 10 000

@davinca
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davinca commented Jun 18, 2020

yep =-=

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