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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