```
usage: evaluate.py [-h] [-t] [-c] [-k] [-m] [-e] [-r] [-a RERANKA]
                   [-b RERANKB] [-l RERANKL] [-n NEIGHBORS] [-v] [-s SHOWRANK]
                   [-1] [-M MULTRANK] [-C] [--data DATA] [-K KMEAN] [-A]
                   [-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 fo 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
  -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
  ```