From c79764987c72be52546e743f60a2d37da3b57a92 Mon Sep 17 00:00:00 2001
From: nunzip <np.scarh@gmail.com>
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}
-- 
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