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Added epsilon to the python object detection training API.

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This commit is contained in:
Davis King 2014-03-09 12:10:15 -04:00
parent 4b6087f748
commit 312157ab18
3 changed files with 20 additions and 1 deletions
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9
tools/python/src/object_detection.cpp
View File

@ -261,6 +261,15 @@ this parameter experimentally."
this parameter experimentally.
!*/
)
.add_property("epsilon", &simple_object_detector_training_options::epsilon,
&simple_object_detector_training_options::epsilon,
"epsilon is the stopping epsilon. Smaller values make the trainer's \n\
solver more accurate but might take longer to train."
/*!
epsilon is the stopping epsilon. Smaller values make the trainer's
solver more accurate but might take longer to train.
!*/
)
.add_property("num_threads", &simple_object_detector_training_options::num_threads,
&simple_object_detector_training_options::num_threads,
"train_simple_object_detector() will use this many threads of \n\

8
tools/python/src/simple_object_detector.h
View File

@ -31,6 +31,7 @@ namespace dlib
num_threads = 4;
detection_window_size = 80*80;
C = 1;
epsilon = 0.01;
}
bool be_verbose;
@ -38,6 +39,7 @@ namespace dlib
unsigned long num_threads;
unsigned long detection_window_size;
double C;
double epsilon;
};
// ----------------------------------------------------------------------------------------
@ -124,6 +126,8 @@ namespace dlib
{
if (options.C <= 0)
throw error("Invalid C value given to train_simple_object_detector(), C must be > 0.");
if (options.epsilon <= 0)
throw error("Invalid epsilon value given to train_simple_object_detector(), epsilon must be > 0.");
dlib::array<array2d<rgb_pixel> > images;
std::vector<std::vector<rectangle> > boxes, ignore;
@ -140,10 +144,11 @@ namespace dlib
structural_object_detection_trainer<image_scanner_type> trainer(scanner);
trainer.set_num_threads(options.num_threads);
trainer.set_c(options.C);
trainer.set_epsilon(0.01);
trainer.set_epsilon(options.epsilon);
if (options.be_verbose)
{
std::cout << "Training with C: " << options.C << std::endl;
std::cout << "Training with epsilon: " << options.epsilon << std::endl;
std::cout << "Training using " << options.num_threads << " threads."<< std::endl;
std::cout << "Training with sliding window " << width << " pixels wide by " << height << " pixels tall." << std::endl;
if (options.add_left_right_image_flips)
@ -195,6 +200,7 @@ namespace dlib
{
std::cout << "Training complete, saved detector to file " << detector_output_filename << std::endl;
std::cout << "Trained with C: " << options.C << std::endl;
std::cout << "Training with epsilon: " << options.epsilon << std::endl;
std::cout << "Trained using " << options.num_threads << " threads."<< std::endl;
std::cout << "Trained with sliding window " << width << " pixels wide by " << height << " pixels tall." << std::endl;
if (upsample_amount != 0)

4
tools/python/src/simple_object_detector_abstract.h
View File

@ -37,6 +37,8 @@ namespace dlib
will encourage the trainer to fit the data better but might lead to
overfitting. Therefore, you must determine the proper setting of
this parameter experimentally.
- epsilon is the stopping epsilon. Smaller values make the trainer's
solver more accurate but might take longer to train.
!*/
fhog_training_options()
@ -46,6 +48,7 @@ namespace dlib
num_threads = 4;
detection_window_size = 80*80;
C = 1;
epsilon = 0.01;
}
bool be_verbose;
@ -53,6 +56,7 @@ namespace dlib
unsigned long num_threads;
unsigned long detection_window_size;
double C;
double epsilon;
};
// ----------------------------------------------------------------------------------------