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| -rw-r--r-- | README.md | 30 |
1 files changed, 15 insertions, 15 deletions
@@ -39,9 +39,9 @@ optional arguments: ``` -EXAMPLES for `evaluate.py`: +**EXAMPLE 1** for `evaluate.py`: -EXAMPLE 1: Run euclidean distance with top n +**EXAMPLE 1.1**: Run euclidean distance with top n `evaluate.py -e -n 10` @@ -49,43 +49,43 @@ or simply `evaluate.py -n 10` -EXAMPLE 2: Run euclidean distance for the first 10 values of top n and graph them +**EXAMPLE 1.2**: 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 +**EXAMPLE 1.3**: 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 +**EXAMPLE 1.4**: 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 +**EXAMPLE 1.5**: 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 +**EXAMPLE 1.6**: 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. +**EXAMPLE 1.7**: 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. +**EXAMPLE 1.8**: 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. +**EXAMPLE 1.9**: 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. +**EXAMPLE 1.10**: 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 +**EXAMPLE 1.11**: Run euclidean distance specifying a different data folder location for data int the same folder as evaluate.py: @@ -95,12 +95,12 @@ or for data in another folder: `evaluate.py --data ./foo/bar/` -EXAMPLES for `opt.py`: +**EXAMPLE 2** for `opt.py`: -EXAMPLE 1: optimize top 1 accuracy for k1, k2, lambda speeding up the process with PCA, top 50 eigenvectors +**EXAMPLE 2.1**: 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 +**EXAMPLE 2.2**: optimize mAP for k1, k2, lambda speeding up the process with PCA, top 50 eigenvectors `opt.py -P 50 -A` |