qsdet_beckerTUD_sift_das.cfg

[GLOBAL]
nn_threads  = 4
det_threads = 8
mode        = detection
setmode     = becker
train_sel   = segment
randbg      = 300
tmp_dir     = /local/vdvelden/qsdet4beckerTUD_rsift_tmp_t120_07
res_dir     = scratchdisk/qsdet4beckerTUD_rsift_res_t120_07
train_set   = tudtrain7
val_set     = tudbval
test_set    = tudtest
bg_train_set= tudtrain7


[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      = 1000

[TEST]
k               = 4
batch_size      = 100
img_pickle_path = batches/%d.pkl

[DETECTION]
method            = quickshift
dist              = overlap
hyp_threshold     = nearest
ignore_threshold  = False
hypothesis_metric = bb_descr_qh
detection_metric  = det_becker
distances_path    = distances/%s_%s.pkl
hypotheses_path   = hypotheses/%s_%s.pkl
exemplar_path     = exemplars/%s.npy
quickshift_tree_path = quickshift/%s_%s.pkl
tau               = 1.20