SqueezeBrains SDK 1.13
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Results of the SVL of a level per model. More...
#include <sb.h>
Data Fields | |
float | goodness |
Goodness of the training. More... | |
float | accuracy |
Accuracy. More... | |
float | score |
Level score. More... | |
int | tp |
Number of TRUE POSITIVE samples. More... | |
int | fp |
Number of FALSE POSITIVE samples. More... | |
int | tn |
Number of TRUE NEGATIVE samples. More... | |
int | fn |
Number of FALSE NEGATIVE samples. More... | |
int | op |
Number of OPTIONAL POSITIVE samples. More... | |
int | on |
Number of OPTIONAL NEGATIVE samples. More... | |
int | out_of_roi |
Number of Out Of ROI samples, both optional and required. More... | |
int | mod_disabled |
Number of samples with disabled model, both optional and required. More... | |
int | reset |
1 if the SVL of the model has been resetted or when the user reset the SVL, 0 otherwise. More... | |
int | num_samples_l |
Number of samples of the training dataset used for learning. More... | |
int | num_samples_t |
Number of samples of the training dataset not used for learning. More... | |
int | num_img_l |
Number of images of the training dataset used for learning. | |
sb_t_svl_par_optimization_mode | optimization_mode |
Optimization mode. More... | |
char | classificator [32] |
Classificator choosen by SVL. More... | |
char | features [SB_PAR_FEATURES_NAMES_LEN] |
List of the features choosen by SVL. More... | |
char | features_available [SB_PAR_FEATURES_NAMES_LEN] |
List of the features choosen by user for SVL. More... | |
char * | warning |
Warning in string format occurred during the training. | |
sb_t_svl_res_epochs | epochs |
Results of training of module based on Deep Learning. More... | |
Results of the SVL of a level per model.
For a Retina project you could write that:
While for a Surface project you could write that:
These two formulas are true only if there are no optional out_of_roi samples.
Usually, in Retina project, num_samples_t should be very small if compared to num_samples_l, on the contrary, in Surface projects, num_samples_t is very large because it also counts the background samples, i.e. True Negative samples.
float sb_t_svl_res_level::accuracy |
Accuracy.
char sb_t_svl_res_level::classificator[32] |
sb_t_svl_res_epochs sb_t_svl_res_level::epochs |
Results of training of module based on Deep Learning.
Used only by Deep Surface and Deep Cortex projects.
char sb_t_svl_res_level::features[SB_PAR_FEATURES_NAMES_LEN] |
List of the features choosen by SVL.
The features are separated by the SB_DELIMITER character.
Used only by Retina and Surface projects.
char sb_t_svl_res_level::features_available[SB_PAR_FEATURES_NAMES_LEN] |
List of the features choosen by user for SVL.
The features are separated by the SB_DELIMITER character.
Used only by Retina and Surface projects.
int sb_t_svl_res_level::fn |
Number of FALSE NEGATIVE samples.
int sb_t_svl_res_level::fp |
Number of FALSE POSITIVE samples.
float sb_t_svl_res_level::goodness |
int sb_t_svl_res_level::mod_disabled |
Number of samples with disabled model, both optional and required.
int sb_t_svl_res_level::num_samples_l |
int sb_t_svl_res_level::num_samples_t |
int sb_t_svl_res_level::on |
Number of OPTIONAL NEGATIVE samples.
int sb_t_svl_res_level::op |
Number of OPTIONAL POSITIVE samples.
sb_t_svl_par_optimization_mode sb_t_svl_res_level::optimization_mode |
int sb_t_svl_res_level::out_of_roi |
Number of Out Of ROI samples, both optional and required.
int sb_t_svl_res_level::reset |
1 if the SVL of the model has been resetted or when the user reset the SVL, 0 otherwise.
The value is valid only after sb_svl_run has been called and not after sb_project_load.
float sb_t_svl_res_level::score |
int sb_t_svl_res_level::tn |
Number of TRUE NEGATIVE samples.
int sb_t_svl_res_level::tp |
Number of TRUE POSITIVE samples.