forked from PaddlePaddle/PaddleOCR
-
Notifications
You must be signed in to change notification settings - Fork 0
/
rec_mv3_none_bilstm_ctc_simple.yml
106 lines (98 loc) · 2.26 KB
/
rec_mv3_none_bilstm_ctc_simple.yml
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
Global:
use_gpu: false
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/mv3_none_bilstm_ctc/
save_epoch_step: 500
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 127
# if pretrained_model is saved in static mode, load_static_weights must set to True
load_static_weights: True
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
max_text_length: 80
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
character_type: 'ch'
use_space_char: False
infer_mode: False
use_tps: False
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
learning_rate:
lr: 0.001
regularizer:
name: 'L2'
factor: 0.00001
Architecture:
type: rec
algorithm: CRNN
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: small
small_stride: [ 1, 2, 2, 2 ]
Neck:
name: SequenceEncoder
encoder_type: fc
hidden_size: 96
Head:
name: CTC
fc_decay: 0.00001
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
TRAIN:
dataset:
name: SimpleDataSet
data_dir: ./rec
file_list:
- ./rec/train.txt # dataset1
ratio_list: [ 0.4,0.6 ]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecAug:
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
batch_size: 256
shuffle: True
drop_last: True
num_workers: 8
EVAL:
dataset:
name: SimpleDataSet
data_dir: ./rec
file_list:
- ./rec/val.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size: 256
num_workers: 8