multibox_target

multibox_target(anchor=None, label=None, cls_pred=None, overlap_threshold=_Null, ignore_label=_Null, negative_mining_ratio=_Null, negative_mining_thresh=_Null, minimum_negative_samples=_Null, variances=_Null, out=None, name=None, **kwargs)

Compute Multibox training targets

Parameters:

anchor : ndarray

Generated anchor boxes.

label : ndarray

Object detection labels.

cls_pred : ndarray

Class predictions.

overlap_threshold : float, optional, default=0.5

Anchor-GT overlap threshold to be regarded as a positive match.

ignore_label : float, optional, default=-1

Label for ignored anchors.

negative_mining_ratio : float, optional, default=-1

Max negative to positive samples ratio, use -1 to disable mining

negative_mining_thresh : float, optional, default=0.5

Threshold used for negative mining.

minimum_negative_samples : int, optional, default=’0’

Minimum number of negative samples.

variances : tuple of <float>, optional, default=[0.1,0.1,0.2,0.2]

Variances to be encoded in box regression target.

out : ndarray, optional

The output ndarray to hold the result.

Returns:

out : ndarray or list of ndarrays

The output of this function.