mdz/pytorch/PolyLaneNet/1_scripts/config.yaml

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YAML

# Training settings
seed: 0
# exps_dir: 'experiments'
exps_dir: '../weights'
iter_log_interval: 1
iter_time_window: 100
model_save_interval: 1
backup:
model:
name: PolyRegression
parameters:
num_outputs: 35 # (5 lanes) * (1 conf + 2 (upper & lower) + 4 poly coeffs)
pretrained: true
backbone: 'resnet34'
pred_category: false
curriculum_steps: [0, 0, 0, 0]
loss_parameters:
conf_weight: 1
lower_weight: 1
upper_weight: 1
cls_weight: 0
poly_weight: 300
batch_size: 1
epochs: 2695
optimizer:
name: Adam
parameters:
lr: 3.0e-4
lr_scheduler:
name: CosineAnnealingLR
parameters:
T_max: 385
# Testing settings
test_parameters:
conf_threshold: 0.5
# Dataset settings
datasets:
train:
type: LaneDataset
parameters:
dataset: tusimple
split: train
img_size: [360, 640]
normalize: true
aug_chance: 0.9090909090909091 # 10/11
augmentations:
- name: Affine
parameters:
rotate: !!python/tuple [-10, 10]
- name: HorizontalFlip
parameters:
p: 0.5
- name: CropToFixedSize
parameters:
width: 1152
height: 648
root: "datasets/tusimple"
test: &test
type: LaneDataset
parameters:
dataset: tusimple
split: test
max_lanes: 5
img_size: [360, 640]
root: "datasets/tusimple"
normalize: true
augmentations: []
# val = test
val:
<<: *test