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name: DBNet
base: ['config/icdar2015.yaml']
arch:
type: Model
backbone:
type: resnet50
pretrained: true
neck:
type: FPN
inner_channels: 256
head:
type: DBHead
out_channels: 2
k: 50
post_processing:
type: SegDetectorRepresenter
args:
thresh: 0.3
box_thresh: 0.7
max_candidates: 1000
unclip_ratio: 1.5 # from paper
metric:
type: QuadMetric
args:
is_output_polygon: false
loss:
type: DBLoss
alpha: 1
beta: 10
ohem_ratio: 3
optimizer:
type: Adam
lr_scheduler:
type: Polynomial
args:
learning_rate: 0.001
warmup_epoch: 3
trainer:
seed: 2
epochs: 1200
log_iter: 10
show_images_iter: 50
resume_checkpoint: ''
finetune_checkpoint: ''
output_dir: output/fp16_o2
visual_dl: false
amp:
scale_loss: 1024
amp_level: O2
custom_white_list: []
custom_black_list: ['exp', 'sigmoid', 'concat']
dataset:
train:
dataset:
args:
data_path:
- ./datasets/train.txt
img_mode: RGB
loader:
batch_size: 16
shuffle: true
num_workers: 6
collate_fn: ''
validate:
dataset:
args:
data_path:
- ./datasets/test.txt
pre_processes:
- type: ResizeShortSize
args:
short_size: 736
resize_text_polys: false
img_mode: RGB
loader:
batch_size: 1
shuffle: true
num_workers: 6
collate_fn: ICDARCollectFN