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```
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
```
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