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I don't think the RoiPoolConv layer is performing a max pool but rather an average pool #79
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@trzy, do you successfully run this repository? in the testing phase I am facing a problem, no boxes are created which means no objects are detected and also AP is 1 for all classes. could you pls respond to my query? |
I haven’t tried. I’m writing my own FasterRCNN implementation from scratch using Keras that follows the paper more closely. I think my RPN is working but until I add the classifier layer sometime this week or next, I won’t know for sure. Feel free to reach out in a few weeks and see where I am at.
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On Apr 13, 2021, at 6:29 PM, mehnaz1985 ***@***.***> wrote:
@trzy, do you successfully run this repository? in the testing phase I am facing a problem, no boxes are created which means no objects are detected and also AP is 1 for all classes. could you pls respond to my query?
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Thanks for the reply.
Mehnaz Tabassum
PhD Candidate
School of SEIT
Federation University, Gippsland Campus
Victoria, Australia.
…On Wed, Apr 14, 2021 at 12:33 PM trzy ***@***.***> wrote:
I haven’t tried. I’m writing my own FasterRCNN implementation from scratch
using Keras that follows the paper more closely. I think my RPN is working
but until I add the classifier layer sometime this week or next, I won’t
know for sure. Feel free to reach out in a few weeks and see where I am at.
Sent from my iPhone
> On Apr 13, 2021, at 6:29 PM, mehnaz1985 ***@***.***> wrote:
>
>
> @trzy, do you successfully run this repository? in the testing phase I
am facing a problem, no boxes are created which means no objects are
detected and also AP is 1 for all classes. could you pls respond to my
query?
>
> —
> You are receiving this because you were mentioned.
> Reply to this email directly, view it on GitHub, or unsubscribe.
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Hello. Did you figure your issue out? I have the same problem, no boxes drawn on the testing phase and three of my four classes have AP = 1.0 on each image. I am really lost here. |
Great repo and well written blog! I have one question: RoiPoolConv uses tf.image.resize_images, which performs a bilinear interpolation, not a max of the pixels (unless I am misinterpreting its use here). I notice this was present in the original source repo (although that code also featured a Theano backend version that performed max-pooling correctly). This might be something to fix or at least call out with a comment.
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