x1,y1,x2,y2,x3,y3,x4,y4,text_content

top left point: (x1,y1)
top right point: (x2,y2)
bottom right point: (x3,y3)
bottom left point: (x4,y4)

pip install visdom

python -m visdom.server

CUDA_VISIBLE_DEVICES: the gpu used in training, like "0,1"  
img_size: image size like (H, W)  
backbone_type: "resnet18", "resnet34", "resnet50" or "vgg", specify the backbone of model  
epoch: training epoch  
anchor_batch_size: how many anchors selected from one image  
image_batch_size: how many images send to model every training step  
lr: initial learning rate  
lr_de_rate: lr reduce rate, new_lr = lr_de_rate * old_lr, set value of this parameter if use ReduceLROnPlateau scheduler  
cosine_lr_cycle_times： the number of cycle changes of the learning rate, one cycle represents the change from the initial value to the minimum value and then to the initial value  
minimum_lr: mininum learning rate of lr scheduler
use_cosine_lr_sch: True will use cosine learning rate scheduler, False use ReduceLROnPlateau scheduler
patience: number of epochs with no improvement after which learning rate will be reduced, set value of this parameter if use ReduceLROnPlateau scheduler  
weight_decay: weight decay, parameter weight_decay of pytorch optimizer  
lamda_1: regression loss weight  
lamda_2: side refine loss weight  
print_step: one loss information print every print_step  
anchor_count: anchor count of every point  
train_side_ref: True will training the side refine branch, False will not  
train_img_dir: train image dir  
valid_img_dir: validation image dir  
train_label_dir: train label dir  
valid_label_dir: validation label dir  
negative_anchor_iou_thresh: iou threshold, to define a negative anchor    
num_workers: number of thread for data loading  
data_aug_level:  "h", "m", "l" or None. "h" indicates high data augmentation, "m" indicates medium data augmentation, "l" indicates low data augmentation,None indicates will not use data augmentation  
use_focal_loss: True will use focal loss, False will not  
focal_loss_alpha: parameter of focal loss, slightly modify the strenght of mining of positive hard sample  
focal_loss_gamma: parameter of focal loss, control the threnght of mining of hard sample  

python train.py

labelme_json_file_dir: json label file dir  
txt_file_save_dir: txt file save dir  

python labelme2txt.py

TEXT_PROPOSALS_WIDTH: anchor width, use default 16 pixel, don't modify it  
MAX_HORIZONTAL_GAP: the maxmum x distance that group two text proposals become a pair  
MIN_V_OVERLAPS: mininum y overlap that group two text proposals become a pair proposals  
MIN_SIZE_SIM: minimum height shape similarity of tow text proposal in a pair  
USE_SIDE_REFINE: True will use the side refine when generate the text line bounding box, False will not  

anchor_count: anchor count, should same with anchor_count parameter of training config  
backbone_type: "resnet18","resnet34" or "resnet50", should be same as the backbone_type parameter of train.py  
img_size: image size like (H, W), should be same as img_size parameter of train.py  
use_best_model: True will use the model which has the best performance on validation set, False will use the last epoch model  
cls_score_thresh: score thresh for text or no-text  
nms_iou_thresh: nms iou threshold value  
result_output_dir: predict result save dir  
is_draw_proposals: True will draw fine-scale text proposals on result, False will not  
is_draw_textline_bbox: True will draw text line bounding box, False will not  
image_pth: image path for prediction, a file path or a directory path  

python predict.py