lib.sedna.algorithms.hard_example_mining.hard_example_mining

Hard Example Mining Algorithms

Module Contents

Classes

ThresholdFilter

Object detection Hard samples discovery methods named Threshold

CrossEntropyFilter

Object detection Hard samples discovery methods named CrossEntropy

IBTFilter

Object detection Hard samples discovery methods named IBT
class lib.sedna.algorithms.hard_example_mining.hard_example_mining.ThresholdFilter(threshold: float = 0.5, **kwargs)[source]

Bases: BaseFilter, abc.ABC

Object detection Hard samples discovery methods named Threshold

Parameters

threshold (float) – hard coefficient threshold score to filter img, default to 0.5.

__call__(self, infer_result=None) → bool[source]

predict function, judge the sample is hard or not.

Parameters

infer_result (array_like) – prediction result

Returns

is_hard_sample – True means hard sample, False means not.

Return type

bool

class lib.sedna.algorithms.hard_example_mining.hard_example_mining.CrossEntropyFilter(threshold_cross_entropy=0.5, **kwargs)[source]

Bases: BaseFilter, abc.ABC

Object detection Hard samples discovery methods named CrossEntropy

Parameters

threshold_cross_entropy (float) – hard coefficient threshold score to filter img, default to 0.5.

__call__(self, infer_result=None) → bool[source]

judge the img is hard sample or not.

Parameters

infer_result (array_like) – prediction classes list, such as [class1-score, class2-score, class2-score,….], where class-score is the score corresponding to the class, class-score value is in [0,1], who will be ignored if its value not in [0,1].

Returns

is hard sample – True means hard sample, False means not.

Return type

bool

class lib.sedna.algorithms.hard_example_mining.hard_example_mining.IBTFilter(threshold_img=0.5, threshold_box=0.5, **kwargs)[source]

Bases: BaseFilter, abc.ABC

Object detection Hard samples discovery methods named IBT

Parameters

  • threshold_img (float) – hard coefficient threshold score to filter img, default to 0.5.

  • threshold_box (float) – threshold_box to calculate hard coefficient, formula is hard coefficient = number(prediction_boxes less than threshold_box) / number(prediction_boxes)

__call__(self, infer_result=None) → bool[source]

Judge the img is hard sample or not.

Parameters

infer_result (array_like) – prediction boxes list, such as [bbox1, bbox2, bbox3,….], where bbox = [xmin, ymin, xmax, ymax, score, label] score should be in [0,1], who will be ignored if its value not in [0,1].

Returns

is hard sample – True means hard sample, False means not.

Return type

bool