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| author | nunzip <np.scarh@gmail.com> | 2018-12-13 20:43:39 +0000 |
|---|---|---|
| committer | nunzip <np.scarh@gmail.com> | 2018-12-13 20:43:39 +0000 |
| commit | ceb4a157b77b0740a31d764cbd6222fae767f095 (patch) | |
| tree | 15b6e85c8288bf70446eab30f2c389e7b2df2167 | |
| parent | 95f25f18f6af9a2faa08ea288c91bb7d21a4eb5b (diff) | |
| download | vz215_np1915-ceb4a157b77b0740a31d764cbd6222fae767f095.tar.gz vz215_np1915-ceb4a157b77b0740a31d764cbd6222fae767f095.tar.bz2 vz215_np1915-ceb4a157b77b0740a31d764cbd6222fae767f095.zip | |
Include link to manual, Fix README layout
| -rw-r--r-- | README.md | 60 | ||||
| -rwxr-xr-x | report/metadata.yaml | 2 |
2 files changed, 32 insertions, 30 deletions
@@ -1,4 +1,5 @@ ``` + usage: evaluate.py [-h] [-t] [-c] [-k] [-m] [-e] [-r] [-a RERANKA] [-b RERANKB] [-l RERANKL] [-n NEIGHBORS] [-v] [-s SHOWRANK] [-1] [-2] [-M MULTRANK] [-C] [--data DATA] [-K KMEAN] [-A] @@ -35,50 +36,51 @@ optional arguments: 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 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 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 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 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 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 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 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 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 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 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/` +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 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` +EXAMPLE 2: optimize mAP for k1, k2, lambda speeding up the process with PCA, top 50 eigenvectors + `opt.py -P 50 -A` diff --git a/report/metadata.yaml b/report/metadata.yaml index 5f9f737..e4d4470 100755 --- a/report/metadata.yaml +++ b/report/metadata.yaml @@ -3,7 +3,7 @@ title: 'EE4-68 Pattern Recognition (2018-2019) CW2' author: - name: Vasil Zlatanov (01120518), Nunzio Pucci (01113180) email: vz215@ic.ac.uk, np1915@ic.ac.uk - link: 'Sources: < [git](https://git.skozl.com/e4-pattern/) - [tar](https://git.skozl.com/e4-pattern/snapshot/vz215_np1915-master.tar.gz) - [zip](https://git.skozl.com/e4-pattern/snapshot/vz215_np1915-master.zip) >' + link: 'Sources: < [git](https://git.skozl.com/e4-pattern/) - [tar](https://git.skozl.com/e4-pattern/snapshot/vz215_np1915-master.tar.gz) - [zip](https://git.skozl.com/e4-pattern/snapshot/vz215_np1915-master.zip) - [manual](https://git.skozl.com/e4-pattern/about/) >' numbersections: yes lang: en babel-lang: english |