MAERec-Gradio / configs /textdet /psenet /psenet_resnet50_fpnf_600e_ctw1500.py
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_base_ = [
'_base_psenet_resnet50_fpnf.py',
'../_base_/datasets/ctw1500.py',
'../_base_/default_runtime.py',
'../_base_/schedules/schedule_adam_600e.py',
]
# optimizer
optim_wrapper = dict(optimizer=dict(lr=1e-4))
train_cfg = dict(val_interval=40)
param_scheduler = [
dict(type='MultiStepLR', milestones=[200, 400], end=600),
]
# dataset settings
ctw1500_textdet_train = _base_.ctw1500_textdet_train
ctw1500_textdet_test = _base_.ctw1500_textdet_test
test_pipeline_ctw = [
dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
dict(type='Resize', scale=(1280, 1280), keep_ratio=True),
dict(
type='LoadOCRAnnotations',
with_polygon=True,
with_bbox=True,
with_label=True),
dict(
type='PackTextDetInputs',
meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'))
]
# pipeline settings
ctw1500_textdet_train.pipeline = _base_.train_pipeline
ctw1500_textdet_test.pipeline = test_pipeline_ctw
train_dataloader = dict(
batch_size=16,
num_workers=8,
persistent_workers=False,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=ctw1500_textdet_train)
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=ctw1500_textdet_test)
test_dataloader = val_dataloader
auto_scale_lr = dict(base_batch_size=64 * 4)