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@@ -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`