From c79764987c72be52546e743f60a2d37da3b57a92 Mon Sep 17 00:00:00 2001 From: nunzip Date: Thu, 13 Dec 2018 14:30:00 +0000 Subject: Accuracy values fix --- report/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/report/paper.md b/report/paper.md index a961be0..960fd21 100755 --- a/report/paper.md +++ b/report/paper.md @@ -50,7 +50,7 @@ be used as an alternative to euclidiean distance. To evaluate improvements brought by alternative distance learning metrics a baseline is established through nearest neighbour identification as previously described. Identification accuracies at top1, top5 and top10 are respectively 47%, 67% and 75% -(figure \ref{fig:baselineacc}). The mAP is 47%. +(figure \ref{fig:baselineacc}). The mAP is 47.2%. \begin{figure} \begin{center} @@ -211,7 +211,7 @@ It is also necessary to estimate how precise the ranklist generated is. For this reason an additional method of evaluation is introduced: mAP. See reference @mAP. It is possible to see in figure \ref{fig:ranklist2} how the ranklist generated for the same five queries of figure \ref{fig:eucrank} -has improved for the fifth query. The mAP improves from 47% to 61.7%. +has improved for the fifth query. The mAP improves from 47.2% to 61.7%. \begin{figure} \begin{center} -- cgit v1.2.3-70-g09d2