From 03c1daed20cc19cc34a084e3e1de13640f55a830 Mon Sep 17 00:00:00 2001 From: Evan Shelhamer Date: Fri, 9 Sep 2016 21:30:44 -0700 Subject: [PATCH] FCN-GoogLeNet is coming soon, promise --- README.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/README.md b/README.md index 5baa0ca..0c2af07 100644 --- a/README.md +++ b/README.md @@ -59,3 +59,16 @@ The evaluation of the geometric classes is fine. * [FCN-32s PASCAL-Context](pascalcontext-fcn32s): single stream, 32 pixel prediction stride net * [FCN-16s PASCAL-Context](pascalcontext-fcn16s): two stream, 16 pixel prediction stride net * [FCN-8s PASCAL-Context](pascalcontext-fcn8s): three stream, 8 pixel prediction stride net + +## Freqeuntly Asked Questions + +**Is learning the interpolation necessary?** In our original experiments the interpolation layers were initialized to bilinear kernels and then learned. +In follow-up experiments, and this reference implementation, the bilinear kernels are fixed. +There is no significant difference in accuracy in our experiments, and fixing these parameters gives a slight speed-up. +Note that in our networks there is only one interpolation kernel per output class, and results may differ for higher-dimensional and non-linear interpolation, for which learning may help further. + +**Why pad the input?**: The 100 pixel input padding guarantees that the network output can be aligned to the input for any input size in the given datasets, for instance PASCAL VOC. +The alignment is handled automatically by net specification and the crop layer. +It is possible, though less convenient, to calculate the exact offsets necessary and do away with this padding. + +**What about FCN-GoogLeNet?**: a reference FCN-GoogLeNet for PASCAL VOC is coming soon. -- GitLab