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Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 57 |
1 files changed, 20 insertions, 37 deletions
@@ -6,56 +6,35 @@ 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 ``` @@ -64,60 +43,64 @@ EXAMPLES for `evaluate.py`: EXAMPLE 1: Run euclidean distance with top n - `evaluate.py -e -n 10` or simply `evaluate.py -n 10` +`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` +`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` +`evaluate.py -M 5 -C` EXAMPLE 4: Run for kmeans, 10 clusters - `evaluate.py -K 10` +`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` +`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` +`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` +`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` +`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` +`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` +`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: +for data int the same folder as evaluate.py: - `evaluate.py --data ./` +`evaluate.py --data ./` - or for data in another folder: +or for data in another folder: - `evaluate.py --data ./foo/bar/` +`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` +`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` +`opt.py -P 50 -A` |