Try bold examples README

1 files changed, 15 insertions, 15 deletions

diff --git a/README.md b/README.md
index 89cec2a..84d9fc9 100644
--- a/README.md
+++ b/README.md
@@ -39,9 +39,9 @@ optional arguments:
 
 ```
 
-EXAMPLES for `evaluate.py`:
+**EXAMPLE 1** for `evaluate.py`:
 
-EXAMPLE 1: Run euclidean distance with top n
+**EXAMPLE 1.1**: Run euclidean distance with top n
 
 `evaluate.py -e -n 10` 
 
@@ -49,43 +49,43 @@ or simply
 
 `evaluate.py -n 10`
 
-EXAMPLE 2: Run euclidean distance for the first 10 values of top n and graph them
+**EXAMPLE 1.2**: 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
+**EXAMPLE 1.3**: 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
+**EXAMPLE 1.4**: 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
+**EXAMPLE 1.5**: 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
+**EXAMPLE 1.6**: 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.
+**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 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 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.
+**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 9: 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 results.
 
 `evaluate.py -A -n 5000`
 
-EXAMPLE 10: Run euclidean distance specifying a different data folder location
+**EXAMPLE 1.11**: Run euclidean distance specifying a different data folder location
 
 for data int the same folder as evaluate.py:
 
@@ -95,12 +95,12 @@ or for data in another folder:
 
 `evaluate.py --data ./foo/bar/`
 
-EXAMPLES for `opt.py`:
+**EXAMPLE 2** for `opt.py`:
 
-EXAMPLE 1: optimize top 1 accuracy for k1, k2, lambda speeding up the process with PCA, top 50 eigenvectors
+**EXAMPLE 2.1**: 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
+**EXAMPLE 2.2**: optimize mAP for k1, k2, lambda speeding up the process with PCA, top 50 eigenvectors
 
 `opt.py -P 50 -A`