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_base_ = ['./ndl.py']
dataset_type = 'CocoDataset'
classes = ('line_main', 'line_none', 'line_inote', 'line_hnote', 'line_caption',
'block_fig', 'block_table', 'block_pillar', 'block_folio', 'block_rubi',
'block_chart', 'block_eqn', 'block_cfm', 'block_eng',
'char', 'void')
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
image_size = 1024
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(image_size, image_size), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(image_size, image_size),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=1,
train=dict(
type=dataset_type,
# explicitly add your class names to the field `classes`
classes=classes,
ann_file='/tmp/generated/dataset_kindai_preprocessed_train.json',
img_prefix='/tmp/dataset_kindai_preprocessed_out',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
# explicitly add your class names to the field `classes`
classes=classes,
ann_file='/tmp/generated/dataset_kindai_preprocessed_test.json',
img_prefix='/tmp/dataset_kindai_preprocessed_out',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
# explicitly add your class names to the field `classes`
classes=classes,
ann_file='/tmp/generated/dataset_kindai_preprocessed_test.json',
img_prefix='/tmp/dataset_kindai_preprocessed_out',
pipeline=test_pipeline))
evaluation = dict(interval=10, metric=['bbox'], classwise=True)
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