# boostrapping
bootstrapping = 1 # enable bootstrapping extraction or not
bootstrapping_save = 1 # save bootstrapped samples directly as a dataset
display_bootstrapping = 1 # display bootstrapping extraction
# paths
gt_path = /xml_path/ # path to xml annotations
input_dir = /images_path/ # path of images to bootstrap on
output_dir = ${current_dir}# path to save outputs
# bootstrapping params
gt_pos_matching = .5 # positives have to match at least this much to be considered
gt_neg_matching = .4 # negatives can only match positives this much to be negative
gt_negneg_matching = .5 # negatives can only match each other this much in a same image
gt_min_context = 1.29 # positives must have at least this much context
gt_minvisibility = .3 # positives must be at least this much visible
gt_min_aspect_ratio = .25 # minimum positives aspect ratio to be considered
gt_max_aspect_ratio = .6 # maximum positives aspect ratio to be considered
gt_mindims = 2x2 # minimum dimensions of positive boxes to be considered
gt_minborders =
gt_included = class1,class3# classes to be included for positive extraction
gt_mirror_pos = 1 # extract vertically-mirrored samples
#gt_scales = .775,.6,.466
gt_neg_max = 20 # maximum number of negatives per classifier and image
# negative bootstrapping
bootstrapping_max = 1000 # limit to this number of extracted samples (optional)
gt_extract_pos = 0 # extract positive samples or not
gt_extract_neg = 1 # extract negative samples or not
gt_neg_threshold = .01 # minimum confidence of extracted negative samples
gt_neg_gt_only = 0 # only extract negatives when positives are present in image
input_random = 1 # taking random images is better for bootstrapping
gt_name = bootstrap_neg # name of saved bootstrapping dataset
bbox_scalings = 1x1 # scaling factors of detected boxes
# positive bootstrapping
bootstrapping_max = #10
gt_extract_pos = 1
gt_extract_neg = 0
nthreads = 1
gt_name = bootstrapping_pos