sldet1beckerTUD_sift_osx_2.cfg

[GLOBAL]
nn_threads  = 1
det_threads = 1
mode        = detection
setmode     = becker
tmp_dir     = sldet1beckerTUD_sift_tmp_1
res_dir     = sldet1beckerTUD_sift_res_1
train_set   = tudtrain
val_set     = tudbval
test_set    = tudtest

[VOC]
imset_path      = VOCdevkit/VOC2007/ImageSets/Main/%s.txt
image_path      = VOCdevkit/VOC2007/JPEGImages/%s.jpg
annotation_path = VOCdevkit/VOC2007/Annotations/%s.xml
gt_object_path  = None
gt_class_path   = VOCdevkit/VOC2007/SegmentationClass/%s.png
classes         = motorbike

[TRAIN-DESCRIPTOR]
dtype       = DescriptorUint8
cache_dir   = descriptors
detector    = densesampling
descriptor  = sift
ds_spacing  = 8
ds_scales   = 2.67+4.0+5.33
outputFormat = binary

[TEST-DESCRIPTOR]
dtype       = DescriptorUint8
cache_dir   = descriptors
detector    = densesampling
descriptor  = sift
ds_spacing  = 8
ds_scales   = 2.67+4.0+5.33
outputFormat = binary

[NBNN]
behmo       = False
checks      = 80

[TEST]
batch_size      = 50
img_pickle_path = batches/%d.pkl

[DETECTION]
method            = single_link
dist              = overlap
hyp_threshold     = becker
hypothesis_metric = bb_qh
detection_metric  = det_becker
distances_path    = distances/%s_%s.pkl
hypotheses_path   = hypotheses/%s_%s.pkl
exemplar_path     = exemplars/%s.npy
theta_m           = 0.8
theta_p           = 0.0