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@@ -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` |