```
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-
```