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@@ -69,19 +69,19 @@ optional arguments:
`evaluate.py -r -a 11 -b 3 -l 0.3`
-**EXAMPLE 1.7**: Run on the training set with euclidean distance and normalize feature vectors. Draw confusion matrix at the end.
+**EXAMPLE 1.7**: Run on the training set with euclidean distance and normalize feature vectors. Draw confusion matrix at the end
`evaluate.py -t -1 -c`
-**EXAMPLE 1.8**: Run euclidean distance standardising the feature data for the first 10 values of top n and graph them.
+**EXAMPLE 1.8**: Run euclidean distance standardising the feature data for the first 10 values of top n and graph them
`evaluate.py -2 -M 10`
-**EXAMPLE 1.9**: Run for rerank top 10 and save the names of the images that compose the ranklist for the first 5 queries: query.txt, ranklist.txt.
+**EXAMPLE 1.9**: Run for rerank top 10 and save the names of the images that compose the ranklist for the first 5 queries: query.txt, ranklist.txt
`evaluate.py -r -s 5 -n 10`
-**EXAMPLE 1.10**: Display mAP. It is advisable to use high n to obtain an accurate results.
+**EXAMPLE 1.10**: Display mAP. It is advisable to use high n to obtain an accurate result
`evaluate.py -A -n 5000`