From ae2dcfa808d9f7c6e403ab38c32571023ba5a67f Mon Sep 17 00:00:00 2001
From: nunzip <np.scarh@gmail.com>
Date: Thu, 13 Dec 2018 20:48:34 +0000
Subject: Retry format fix

---
 README.md | 39 ++++++++++++++++++++++++++++++++++++++-
 1 file changed, 38 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index d60a3fc..83797dc 100644
--- a/README.md
+++ b/README.md
@@ -6,81 +6,118 @@ usage: evaluate.py [-h] [-t] [-c] [-k] [-m] [-e] [-r] [-a RERANKA]
                    [-P PCA]
 
 optional arguments:
+
   -h, --help            show this help message and exit
+
   -t, --train           Use train data instead of query and gallery
+
   -c, --conf_mat        Show visual confusion matrix
+
   -k, --kmean_alt       Perform clustering with generalized labels(not actual
                         kmean)
+
   -m, --mahalanobis     Perform Mahalanobis Distance metric
+
   -e, --euclidean       Use standard euclidean distance
+
   -r, --rerank          Use k-reciprocal rernaking
+
   -a RERANKA, --reranka RERANKA
                         Parameter k1 for rerank
+
   -b RERANKB, --rerankb RERANKB
                         Parameter k2 for rerank
+
   -l RERANKL, --rerankl RERANKL
                         Parameter lambda for rerank
+
   -n NEIGHBORS, --neighbors NEIGHBORS
                         Use customized ranklist size NEIGHBORS
+
   -v, --verbose         Use verbose output
+
   -s SHOWRANK, --showrank SHOWRANK
                         Save ranklist pics id in a txt file for first SHOWRANK
                         queries
+
   -1, --normalise       Normalise features
+
   -2, --standardise	Standardise features
+
   -M MULTRANK, --multrank MULTRANK
                         Run for different ranklist sizes equal to MULTRANK
+
   -C, --comparison      Compare baseline and improved metric
+
   --data DATA           Folder containing data
+
   -K KMEAN, --kmean KMEAN
                         Perform Kmean clustering, KMEAN number of clusters
+
   -A, --mAP             Display Mean Average Precision
+
   -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 euclidean distance standardising the feature data for the first 10 values of top n and graph them.
+
 	`evaluate.py -2 -M 10`
 
 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
+
 	for data int the same folder as evaluate.py:
+
 		`evaluate.py --data ./` 
+		
 	or for data in another folder:
+
 		`evaluate.py --data ./foo/bar/`
 
 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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