From 628420dbd4183a91cc976bca0102df6d70204333 Mon Sep 17 00:00:00 2001
From: nunzip <np.scarh@gmail.com>
Date: Thu, 13 Dec 2018 16:28:40 +0000
Subject: Add examples to README

---
 README.md | 41 ++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 40 insertions(+), 1 deletion(-)

(limited to 'README.md')

diff --git a/README.md b/README.md
index 029e1a0..8d3f863 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,7 @@ optional arguments:
   -b RERANKB, --rerankb RERANKB
                         Parameter k2 for rerank
   -l RERANKL, --rerankl RERANKL
-                        Parameter lambda fo rerank
+                        Parameter lambda for rerank
   -n NEIGHBORS, --neighbors NEIGHBORS
                         Use customized ranklist size NEIGHBORS
   -v, --verbose         Use verbose output
@@ -36,3 +36,42 @@ optional arguments:
   -P PCA, --PCA PCA     Perform pca with PCA eigenvectors
   ```
 
+EXAMPLES for `evaluate.py`:
+
+	EXAMPLE 1: Run euclidean distance with top n
+		`evaluate.py -e -n 10` or simply `evaluate.py -n 10`
+
+	EXAMPLE 2: Run euclidean distance for the first 10 values of top n and graph them
+		`evaluate.py -M 10`
+
+	EXAMPLE 3: Run comparison between baseline and rerank for the first 5 values of top n and graph them
+		`evaluate.py -M 5 -C`
+
+	EXAMPLE 4: Run for kmeans, 10 clusters
+		`evaluate.py -K 10`
+
+	EXAMPLE 5: Run for mahalanobis, using PCA for top 100 eigenvectors to speed up the calculation
+		`evaluate.py -m -P 100`
+
+	EXAMPLE 6: Run rerank for customized values of RERANKA, RERANKB and RERANKL
+		`evaluate.py -r -a 11 -b 3 -l 0.3`
+
+	EXAMPLE 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 8: 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 9: Display mAP. It is advisable to use high n to obtain an accurate results.
+		`evaluate.py -A -n 5000`
+
+	EXAMPLE 10: Run euclidean distance specifying a different data folder location
+		`evaluate.py --data`
+
+EXAMPLES for `opt.py`:
+
+	EXAMPLE 1: optimize top 1 accuracy for k1, k2, lambda speeding up the process with PCA, top 50 eigenvectors
+		`opt.py -P 50`
+
+	EXAMPLE 2: optimize mAP for k1, k2, lambda speeding up the process with PCA, top 50 eigenvectors
+		`opt.py -P 50 -A`
-- 
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