PaddleOCR/configs/det/det_r50_vd_db.yml

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Global:
algorithm: DB
use_gpu: true
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/det_db/
save_epoch_step: 200
eval_batch_step: [5000, 5000]
train_batch_size_per_card: 8
test_batch_size_per_card: 16
image_shape: [3, 640, 640]
reader_yml: ./configs/det/det_db_icdar15_reader.yml
pretrain_weights: ./pretrain_models/ResNet50_vd_ssld_pretrained/
save_res_path: ./output/det_db/predicts_db.txt
checkpoints:
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.det_model,DetModel
Backbone:
function: ppocr.modeling.backbones.det_resnet_vd,ResNet
layers: 50
Head:
function: ppocr.modeling.heads.det_db_head,DBHead
model_name: large
k: 50
inner_channels: 256
out_channels: 2
Loss:
function: ppocr.modeling.losses.det_db_loss,DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
function: ppocr.optimizer,AdamDecay
base_lr: 0.001
beta1: 0.9
beta2: 0.999
PostProcess:
function: ppocr.postprocess.db_postprocess,DBPostProcess
thresh: 0.3
box_thresh: 0.7
max_candidates: 1000
unclip_ratio: 1.5