Question about 'RPN' location!

[yuwenxiong]

Question about 'RPN' location!

[YuwenXiong]

Hi @fighting-liu ,
Actually that is my own decision. The original faster rcnn with ResNet cannot insert RPN after res5c because of ROIPooling. But in R-FCN we can do that, and it may look a little bit strange if we make these two parts derive from different layers, so I decided to make them both on top of res5c. And I don't think it will affect final results much, you can try it by yourself :).

[xiaoxiongli]

Dear Orpine,
I am using the res5c too, and I find that if you use res5c, this happed:

Now I am training the 4th R-FCN stage (totally 4 stage, with OHEM, 229 category), and
I met a situation that the highest training accuracy(0.949) happened in Iteration 0 (that means just
use the stage 2' model). And after training several iteration(max 110000 as the author set), the final accuray is still lower than Iteration 0.

I feel confuse that Why this happens..., if this always happens, we even do not need the last training stage..., this problem bother me a lot......, help..

I try 3 kind of base learning rate: 0.01, 0.001, 0.0001. the situation I mentioned just now is almost same. just like this:

------------------------- Iteration 0 -------------------------
Training : # accuracy 0.949, loss (cls 0.124, reg 0.243)
Testing : accuracy 0.926, loss (cls 0.201, reg 0.223)

------------------------- Iteration 2000 -------------------------
Training : accuracy 0.938, loss (cls 0.148, reg 0.0596)
Testing : accuracy 0.927, loss (cls 0.2, reg 0.0525)

------------------------- Iteration 4000 -------------------------
Training : accuracy 0.938, loss (cls 0.147, reg 0.0418)
Testing : accuracy 0.927, loss (cls 0.199, reg 0.0504)

------------------------- Iteration 6000 -------------------------
Training : accuracy 0.94, loss (cls 0.144, reg 0.0399)
Testing : accuracy 0.927, loss (cls 0.198, reg 0.0497)

.....
.....

------------------------- Iteration 98000 -------------------------
Training : accuracy 0.943, loss (cls 0.135, reg 0.0315)
Testing : accuracy 0.929, loss (cls 0.193, reg 0.0431)

------------------------- Iteration 100000 -------------------------
Training : accuracy 0.943, loss (cls 0.137, reg 0.0317)
Testing : accuracy 0.929, loss (cls 0.193, reg 0.0431)

------------------------- Iteration 102000 -------------------------
Training : # accuracy 0.943, loss (cls 0.134, reg 0.0317)
Testing : accuracy 0.929, loss (cls 0.193, reg 0.0429)

....
....

And even worse, when testing the image, the proposals I get with RFCN is bad than faster-rcnn(ZF)...., I do not know why?

[YuwenXiong]

Hi @xiaoxiongli , I see you post this in the original R-FCN repo too, and the reason is that the iteration 0 info is useless. It just run 1 iteration, which means it just use 2 images(ims_per_batch=2 if you didn't change it). So the iteration 0's training log is inaccurate(Note that validation loss is accurate) . That's no problem, we can see that the loss decreased indeed.

And it is possible that stage-2 model achieves better result than stage-4 model, because stage-2 model didn't share weights, the RPN you used to produce rois has different weights with R-FCN, which means you introduce more parameters.

I don't understand your last statement, I think R-FCN doesn't produce region proposals, you mean in 4-step training, RPN with R-FCN(ResNet) gives worse region proposals than RPN with fast-rcnn(ZF)?

[xiaoxiongli]

Dear @orpine :
thank you for your reply.
Yes, in 4 step training, in my experiment, it seems that RPN with R-FCN(ResNet101) gives worse region proposals than RPN with fast-rcnn(ZF). as I mentioned just now, I am using the res5c which means I share the weight from beginning of the ResNet101 to res5c(including res5c).
I've send a email to you.