lcnn/models/line_detect/line_net.yaml

image:
  mean: [109.730, 103.832, 98.681]
  stddev: [22.275, 22.124, 23.229]

batch_size: 4
batch_size_eval: 2

# backbone multi-task parameters
head_size: [[2], [1], [2]]
loss_weight:
jmap: 8.0
lmap: 0.5
joff: 0.25
lpos: 1
lneg: 1
boxes: 1.0

# backbone parameters
backbone: resnet50_fpn
#backbone: resnet18_fpn
#backbone: mobilenet_v3_large_fpn
#  backbone: unet
depth: 4
num_stacks: 1
num_blocks: 1
num_classes: 2

# sampler parameters
## static sampler
n_stc_posl: 300
n_stc_negl: 40

## dynamic sampler
n_dyn_junc: 300
n_dyn_posl: 300
n_dyn_negl: 80
n_dyn_othr: 600

# LOIPool layer parameters
n_pts0: 32
n_pts1: 8

# line verification network parameters
dim_loi: 128
dim_fc: 1024

# maximum junction and line outputs
n_out_junc: 250
n_out_line: 2500

# additional ablation study parameters
use_cood: 0
use_slop: 0
use_conv: 0

# junction threashold for evaluation (See #5)
eval_junc_thres: 0.008