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Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 41 |
1 files changed, 40 insertions, 1 deletions
@@ -18,7 +18,7 @@ optional arguments: -b RERANKB, --rerankb RERANKB Parameter k2 for rerank -l RERANKL, --rerankl RERANKL - Parameter lambda fo rerank + Parameter lambda for rerank -n NEIGHBORS, --neighbors NEIGHBORS Use customized ranklist size NEIGHBORS -v, --verbose Use verbose output @@ -36,3 +36,42 @@ optional arguments: -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 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 + `evaluate.py --data` + +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` |