Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Ar

Calculate average precisions based on evaluation code of PASCAL VOC. This function calculates average precisions from given precisions and re

Calculate precision and recall based on evaluation code of PASCAL VOC. This function calculates precision and recall of predicted bounding bo

Compute the average precision, given the recall and precision curves. Code originally from https://github.com/rbgirshick/py-faster-rcnn. # A

create interaction model that is responsible for interations among feature groups.

create models for each interactive feature group

Constructs module list of layer blocks from module configuration in module_defs

create models, namely feature extractor, aggregator and discriminator, for a region representing a feature group.

create models for all regions that each represents a feature group.

Calculate average precisions based on evaluation code of PASCAL VOC. This function evaluates predicted bounding boxes obtained from a dataset

Return: total_loss: the average loss accuracy: the evaluation map

Return: total_loss: the average loss accuracy: the evaluation map

brief Convert a Matplotlib figure to a 4D numpy array with RGBA channels and return it @param fig: a matplotlib figure @return a nu

convert figure to ndarray

Forward Faster R-CNN and calculate losses. Here are notations used. * :math:`N` is the batch size. * :math:`R` is the number