& 14.78% & 1.46 px & 3.33 px & 98.63%\
$L_t$, $t=0.2$ &6.17% & 13.51% & 1.37 px & 3.10 px & 99.15%\
$L_t$, $t=0.3$ &**4.95%** & **11.89%** & **1.10 px** & **2.60 px** & 99.34%\
$L_t$, $t=0.4$ &[5.18%]{} & [12.10%]{} & 1.25 px & 3.14 px & **99.41%**\
$L_g$, $g=0.2$ & 5.92% & 13.17% & 1.41 px & 3.37 px & 99.15%\
$L_g$, $g=0.4$ & 5.89% & 13.23% & 1.41 px & 3.36 px & 99.31%\
$L_g$, $g=0.6$ & 6.37% & 13.74% & 1.51 px & 3.40 px & 99.08%\
Hard Mining x2 [@simo2015discriminative] & 6.03% & 13.34% & 1.35 px & 2.99 px & 99.07%\
DrLIM [@hadsell2006dimensionality] & 5.36 % & 12.40% & [1.18 px]{} & [2.79 px]{} & 99.15%\
CENT. [@gadot2015patchbatch] & 6.32% & 13.90% & 1.46 px & 3.37 px & 98.72%\
SIFTFlow [@liu2008sift] & 11.52% & 19.79% & 1.99 px & 4.33 px & 97.31%\
SIFTFlow\*[@liu2008sift] & 5.85% & 12.90% & 1.52 px & 3.56 px & 97.31%\

#### Multi-resolution network training

We examine three variants of training our multi-resolution network (green boxes in Figure \[ffscales\]): training it on $100\%$, $50\%$ and $25\%$ resolution although it is only used for $50\%$ and $25\%$ resolution, at 