mxnet.ndarray.contrib.Proposal¶

mxnet.ndarray.contrib.Proposal(cls_prob=None, bbox_pred=None, im_info=None, rpn_pre_nms_top_n=_Null, rpn_post_nms_top_n=_Null, threshold=_Null, rpn_min_size=_Null, scales=_Null, ratios=_Null, feature_stride=_Null, output_score=_Null, iou_loss=_Null, out=None, name=None, **kwargs)¶

Generate region proposals via RPN

Parameters

cls_prob (NDArray) – Score of how likely proposal is object.
bbox_pred (NDArray) – BBox Predicted deltas from anchors for proposals
im_info (NDArray) – Image size and scale.
rpn_pre_nms_top_n (int, optional, default='6000') – Number of top scoring boxes to keep after applying NMS to RPN proposals
rpn_post_nms_top_n (int, optional, default='300') – Overlap threshold used for non-maximumsuppresion(suppress boxes with IoU >= this threshold
threshold (float, optional, default=0.7) – NMS value, below which to suppress.
rpn_min_size (int, optional, default='16') – Minimum height or width in proposal
scales (tuple of <float>, optional, default=[4,8,16,32]) – Used to generate anchor windows by enumerating scales
ratios (tuple of <float>, optional, default=[0.5,1,2]) – Used to generate anchor windows by enumerating ratios
feature_stride (int, optional, default='16') – The size of the receptive field each unit in the convolution layer of the rpn,for example the product of all stride’s prior to this layer.
output_score (boolean, optional, default=0) – Add score to outputs
iou_loss (boolean, optional, default=0) – Usage of IoU Loss
out (NDArray, optional) – The output NDArray to hold the result.

Returns

out – The output of this function.

Return type

NDArray or list of NDArrays