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```
usage: evaluate.py [-h] [-t] [-c] [-k] [-m] [-e] [-r] [-p RERANKA]
[-q RERANKB] [-l RERANKL] [-n NEIGHBORS] [-v] [-s SHOWRANK]
[-1] [-M MULTRANK] [-C COMPARISON] [--data DATA] [-K KMEAN]
[-P] [-2 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
-p RERANKA, --reranka RERANKA
Parameter k1 for rerank -p '$k1val' -ARGUMENT
REQUIRED, default=9-
-q RERANKB, --rerankb RERANKB
Parameter k2 for rerank -q '$k2val' -ARGUMENT
REQUIRED, default=3-
-l RERANKL, --rerankl RERANKL
Coefficient to combine distances(lambda) -l
'$lambdaval' -ARGUMENT REQUIRED, default=0.3-
-n NEIGHBORS, --neighbors NEIGHBORS
Use customized ranklist size -n 'size' -ARGUMENT
REQUIRED, default=1-
-v, --verbose Use verbose output
-s SHOWRANK, --showrank SHOWRANK
Save ranklist pics id in a txt file. Number of
ranklists saved specified as -s '$number' -ARGUMENT
REQUIRED, default=0-
-1, --normalise Normalise features
-M MULTRANK, --multrank MULTRANK
Run for different ranklist sizes equal to M -ARGUMENT
REQUIRED, default=1-
-C COMPARISON, --comparison COMPARISON
Set to 2 to obtain a comparison of baseline and
improved metric -ARGUMENT REQUIRED, default=1-
--data DATA You can either put the data in a folder called 'data',
or specify the location with --data 'path' -ARGUMENT
REQUIRED, default='data'-
-K KMEAN, --kmean KMEAN
Perform Kmean clustering of size specified through -K
'$size' -ARGUMENT REQUIRED, default=0-
-P, --mAP Display Mean Average Precision for ranklist of size -n
'$size'
-2 PCA, --PCA PCA Use PCA with -2 '$n_components' -ARGUMENT REQUIRED,
default=0-
```
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