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Global:
use_gpu: true
epoch_num: 100
log_smooth_window: 20
print_batch_step: 20
save_model_dir: ./output/SLANet
save_epoch_step: 400
# evaluation is run every 1000 iterations after the 0th iteration
eval_batch_step: [0, 1000]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir: ./output/SLANet/infer
use_visualdl: False
infer_img: ppstructure/docs/table/table.jpg
# for data or label process
character_dict_path: ppocr/utils/dict/table_structure_dict.txt
character_type: en
max_text_length: &max_text_length 500
box_format: &box_format 'xyxy' # 'xywh', 'xyxy', 'xyxyxyxy'
infer_mode: False
use_sync_bn: True
save_res_path: 'output/infer'
d2s_train_image_shape: [3, -1, -1]
amp_custom_white_list: ['concat', 'elementwise_sub', 'set_value']
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
clip_norm: 5.0
lr:
name: Piecewise
learning_rate: 0.001
decay_epochs : [40, 50]
values : [0.001, 0.0001, 0.00005]
regularizer:
name: 'L2'
factor: 0.00000
Architecture:
model_type: table
algorithm: SLANet
Backbone:
name: PPLCNet
scale: 1.0
pretrained: true
use_ssld: true
Neck:
name: CSPPAN
out_channels: 96
Head:
name: SLAHead
hidden_size: 256
max_text_length: *max_text_length
loc_reg_num: &loc_reg_num 4
Loss:
name: SLALoss
structure_weight: 1.0
loc_weight: 2.0
loc_loss: smooth_l1
PostProcess:
name: TableLabelDecode
merge_no_span_structure: &merge_no_span_structure True
Metric:
name: TableMetric
main_indicator: acc
compute_bbox_metric: False
loc_reg_num: *loc_reg_num
box_format: *box_format
Train:
dataset:
name: PubTabDataSet
data_dir: train_data/table/pubtabnet/train/
label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- TableLabelEncode:
learn_empty_box: False
merge_no_span_structure: *merge_no_span_structure
replace_empty_cell_token: False
loc_reg_num: *loc_reg_num
max_text_length: *max_text_length
- TableBoxEncode:
in_box_format: *box_format
out_box_format: *box_format
- ResizeTableImage:
max_len: 488
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- PaddingTableImage:
size: [488, 488]
- ToCHWImage:
- KeepKeys:
keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
loader:
shuffle: True
batch_size_per_card: 48
drop_last: True
num_workers: 1
Eval:
dataset:
name: PubTabDataSet
data_dir: train_data/table/pubtabnet/val/
label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- TableLabelEncode:
learn_empty_box: False
merge_no_span_structure: *merge_no_span_structure
replace_empty_cell_token: False
loc_reg_num: *loc_reg_num
max_text_length: *max_text_length
- TableBoxEncode:
in_box_format: *box_format
out_box_format: *box_format
- ResizeTableImage:
max_len: 488
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- PaddingTableImage:
size: [488, 488]
- ToCHWImage:
- KeepKeys:
keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
loader:
shuffle: False
drop_last: False
batch_size_per_card: 48
num_workers: 1
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Global:
use_gpu: True
epoch_num: 400
log_smooth_window: 20
print_batch_step: 20
save_model_dir: ./output/SLANet_ch
save_epoch_step: 400
# evaluation is run every 331 iterations after the 0th iteration
eval_batch_step: [0, 331]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir: ./output/SLANet_ch/infer
use_visualdl: False
infer_img: ppstructure/docs/table/table.jpg
# for data or label process
character_dict_path: ppocr/utils/dict/table_structure_dict_ch.txt
character_type: en
max_text_length: &max_text_length 500
box_format: &box_format xyxyxyxy # 'xywh', 'xyxy', 'xyxyxyxy'
infer_mode: False
use_sync_bn: True
save_res_path: output/infer
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
clip_norm: 5.0
lr:
learning_rate: 0.001
regularizer:
name: 'L2'
factor: 0.00000
Architecture:
model_type: table
algorithm: SLANet
Backbone:
name: PPLCNet
scale: 1.0
pretrained: True
use_ssld: True
Neck:
name: CSPPAN
out_channels: 96
Head:
name: SLAHead
hidden_size: 256
max_text_length: *max_text_length
loc_reg_num: &loc_reg_num 8
Loss:
name: SLALoss
structure_weight: 1.0
loc_weight: 2.0
loc_loss: smooth_l1
PostProcess:
name: TableLabelDecode
merge_no_span_structure: &merge_no_span_structure True
Metric:
name: TableMetric
main_indicator: acc
compute_bbox_metric: False
loc_reg_num: *loc_reg_num
box_format: *box_format
del_thead_tbody: True
Train:
dataset:
name: PubTabDataSet
data_dir: train_data/table/train/
label_file_list: [train_data/table/train.txt]
transforms:
- DecodeImage:
img_mode: BGR
channel_first: False
- TableLabelEncode:
learn_empty_box: False
merge_no_span_structure: *merge_no_span_structure
replace_empty_cell_token: False
loc_reg_num: *loc_reg_num
max_text_length: *max_text_length
- TableBoxEncode:
in_box_format: *box_format
out_box_format: *box_format
- ResizeTableImage:
max_len: 488
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- PaddingTableImage:
size: [488, 488]
- ToCHWImage:
- KeepKeys:
keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
loader:
shuffle: True
batch_size_per_card: 48
drop_last: True
num_workers: 1
Eval:
dataset:
name: PubTabDataSet
data_dir: train_data/table/val/
label_file_list: [train_data/table/val.txt]
transforms:
- DecodeImage:
img_mode: BGR
channel_first: False
- TableLabelEncode:
learn_empty_box: False
merge_no_span_structure: *merge_no_span_structure
replace_empty_cell_token: False
loc_reg_num: *loc_reg_num
max_text_length: *max_text_length
- TableBoxEncode:
in_box_format: *box_format
out_box_format: *box_format
- ResizeTableImage:
max_len: 488
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- PaddingTableImage:
size: [488, 488]
- ToCHWImage:
- KeepKeys:
keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
loader:
shuffle: False
drop_last: False
batch_size_per_card: 48
num_workers: 1
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Global:
use_gpu: true
epoch_num: 17
log_smooth_window: 20
print_batch_step: 100
save_model_dir: ./output/table_master/
save_epoch_step: 17
eval_batch_step: [0, 6259]
cal_metric_during_train: true
pretrained_model: null
checkpoints:
save_inference_dir: output/table_master/infer
use_visualdl: false
infer_img: ppstructure/docs/table/table.jpg
save_res_path: ./output/table_master
character_dict_path: ppocr/utils/dict/table_master_structure_dict.txt
infer_mode: false
max_text_length: &max_text_length 500
box_format: &box_format 'xywh' # 'xywh', 'xyxy', 'xyxyxyxy'
d2s_train_image_shape: [3, 480, 480]
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: MultiStepDecay
learning_rate: 0.001
milestones: [12, 15]
gamma: 0.1
warmup_epoch: 0.02
regularizer:
name: L2
factor: 0.0
Architecture:
model_type: table
algorithm: TableMaster
Backbone:
name: TableResNetExtra
gcb_config:
ratio: 0.0625
headers: 1
att_scale: False
fusion_type: channel_add
layers: [False, True, True, True]
layers: [1,2,5,3]
Head:
name: TableMasterHead
hidden_size: 512
headers: 8
dropout: 0
d_ff: 2024
max_text_length: *max_text_length
loc_reg_num: &loc_reg_num 4
Loss:
name: TableMasterLoss
ignore_index: 42 # set to len of dict + 3
PostProcess:
name: TableMasterLabelDecode
box_shape: pad
merge_no_span_structure: &merge_no_span_structure True
Metric:
name: TableMetric
main_indicator: acc
compute_bbox_metric: False
box_format: *box_format
Train:
dataset:
name: PubTabDataSet
data_dir: train_data/table/pubtabnet/train/
label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl]
transforms:
- DecodeImage:
img_mode: BGR
channel_first: False
- TableMasterLabelEncode:
learn_empty_box: False
merge_no_span_structure: *merge_no_span_structure
replace_empty_cell_token: True
loc_reg_num: *loc_reg_num
max_text_length: *max_text_length
- ResizeTableImage:
max_len: 480
resize_bboxes: True
- PaddingTableImage:
size: [480, 480]
- TableBoxEncode:
in_box_format: *box_format
out_box_format: *box_format
- NormalizeImage:
scale: 1./255.
mean: [0.5, 0.5, 0.5]
std: [0.5, 0.5, 0.5]
order: hwc
- ToCHWImage: null
- KeepKeys:
keep_keys: [image, structure, bboxes, bbox_masks, shape]
loader:
shuffle: True
batch_size_per_card: 10
drop_last: True
num_workers: 8
Eval:
dataset:
name: PubTabDataSet
data_dir: train_data/table/pubtabnet/val/
label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl]
transforms:
- DecodeImage:
img_mode: BGR
channel_first: False
- TableMasterLabelEncode:
learn_empty_box: False
merge_no_span_structure: *merge_no_span_structure
replace_empty_cell_token: True
loc_reg_num: *loc_reg_num
max_text_length: *max_text_length
- ResizeTableImage:
max_len: 480
resize_bboxes: True
- PaddingTableImage:
size: [480, 480]
- TableBoxEncode:
in_box_format: *box_format
out_box_format: *box_format
- NormalizeImage:
scale: 1./255.
mean: [0.5, 0.5, 0.5]
std: [0.5, 0.5, 0.5]
order: hwc
- ToCHWImage: null
- KeepKeys:
keep_keys: [image, structure, bboxes, bbox_masks, shape]
loader:
shuffle: False
drop_last: False
batch_size_per_card: 10
num_workers: 8
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Global:
use_gpu: true
epoch_num: 400
log_smooth_window: 20
print_batch_step: 5
save_model_dir: ./output/table_mv3/
save_epoch_step: 400
# evaluation is run every 400 iterations after the 0th iteration
eval_batch_step: [0, 400]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: ppstructure/docs/table/table.jpg
save_res_path: output/table_mv3
# for data or label process
character_dict_path: ppocr/utils/dict/table_structure_dict.txt
character_type: en
max_text_length: &max_text_length 500
box_format: &box_format 'xyxy' # 'xywh', 'xyxy', 'xyxyxyxy'
infer_mode: False
amp_custom_black_list: ['matmul_v2','elementwise_add']
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
clip_norm: 5.0
lr:
learning_rate: 0.001
regularizer:
name: 'L2'
factor: 0.00000
Architecture:
model_type: table
algorithm: TableAttn
Backbone:
name: MobileNetV3
scale: 1.0
model_name: small
disable_se: true
Head:
name: TableAttentionHead
hidden_size: 256
max_text_length: *max_text_length
loc_reg_num: &loc_reg_num 4
Loss:
name: TableAttentionLoss
structure_weight: 100.0
loc_weight: 10000.0
PostProcess:
name: TableLabelDecode
Metric:
name: TableMetric
main_indicator: acc
compute_bbox_metric: false # cost many time, set False for training
Train:
dataset:
name: PubTabDataSet
data_dir: train_data/table/pubtabnet/train/
label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_train.jsonl]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- TableLabelEncode:
learn_empty_box: False
merge_no_span_structure: False
replace_empty_cell_token: False
loc_reg_num: *loc_reg_num
max_text_length: *max_text_length
- TableBoxEncode:
- ResizeTableImage:
max_len: 488
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- PaddingTableImage:
size: [488, 488]
- ToCHWImage:
- KeepKeys:
keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
loader:
shuffle: True
batch_size_per_card: 48
drop_last: True
num_workers: 1
Eval:
dataset:
name: PubTabDataSet
data_dir: train_data/table/pubtabnet/val/
label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- TableLabelEncode:
learn_empty_box: False
merge_no_span_structure: False
replace_empty_cell_token: False
loc_reg_num: *loc_reg_num
max_text_length: *max_text_length
- TableBoxEncode:
- ResizeTableImage:
max_len: 488
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- PaddingTableImage:
size: [488, 488]
- ToCHWImage:
- KeepKeys:
keep_keys: [ 'image', 'structure', 'bboxes', 'bbox_masks', 'shape' ]
loader:
shuffle: False
drop_last: False
batch_size_per_card: 48
num_workers: 1