Added notes that the object detector validation methods also output MAP values.

examples/object_detector_advanced_ex.cpp

@@ -247,12 +247,13 @@ int main()
 
         object_detector<image_scanner_type> detector = trainer.train(images, object_locations);
 
-        // We can easily test the new detector against our training data.  This print statement will indicate that it
-        // has perfect precision and recall on this simple task.
-        cout << "Test detector (precision,recall): " << test_object_detection_function(detector, images, object_locations) << endl;
+        // We can easily test the new detector against our training data.  This print
+        // statement will indicate that it has perfect precision and recall on this simple
+        // task.  It will also print the mean average precision (MAP).
+        cout << "Test detector (precision,recall,MAP): " << test_object_detection_function(detector, images, object_locations) << endl;
 
         // The cross validation should also indicate perfect precision and recall.
-        cout << "3-fold cross validation (precision,recall): "
+        cout << "3-fold cross validation (precision,recall,MAP): "
              << cross_validate_object_detection_trainer(trainer, images, object_locations, 3) << endl;
 
 

examples/object_detector_ex.cpp

@@ -207,11 +207,11 @@ int main()
         object_detector<image_scanner_type> detector = trainer.train(images, object_locations);
 
         // We can easily test the new detector against our training data.  This print statement will indicate that it
-        // has perfect precision and recall on this simple task.
-        cout << "Test detector (precision,recall): " << test_object_detection_function(detector, images, object_locations) << endl;
+        // has perfect precision and recall on this simple task.  It will also print the mean average precision (MAP).
+        cout << "Test detector (precision,recall,MAP): " << test_object_detection_function(detector, images, object_locations) << endl;
 
         // The cross validation should also indicate perfect precision and recall.
-        cout << "3-fold cross validation (precision,recall): "
+        cout << "3-fold cross validation (precision,recall,MAP): "
              << cross_validate_object_detection_trainer(trainer, images, object_locations, 3) << endl;
 
 

examples/train_object_detector.cpp

@@ -189,7 +189,7 @@ int main(int argc, char** argv)
                 fout.close();
 
                 cout << "Testing detector on training data..." << endl;
-                cout << "Test detector (precision,recall): " << test_object_detection_function(detector, images, object_locations) << endl;
+                cout << "Test detector (precision,recall,MAP): " << test_object_detection_function(detector, images, object_locations) << endl;
             }
             else
             {
@@ -197,7 +197,7 @@ int main(int argc, char** argv)
                 randomize_samples(images, object_locations);
 
                 // The cross validation should also indicate perfect precision and recall.
-                cout << num_folds << "-fold cross validation (precision,recall): "
+                cout << num_folds << "-fold cross validation (precision,recall,MAP): "
                      << cross_validate_object_detection_trainer(trainer, images, object_locations, num_folds) << endl;
             }
 
@@ -252,7 +252,7 @@ int main(int argc, char** argv)
             if (parser.option("test"))
             {
                 cout << "Testing detector on data..." << endl;
-                cout << "Results (precision,recall): " << test_object_detection_function(detector, images, object_locations) << endl;
+                cout << "Results (precision,recall,MAP): " << test_object_detection_function(detector, images, object_locations) << endl;
                 return EXIT_SUCCESS;
             }
         }