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convnextv2_tiny_cam_tps_on.yml
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Global:
device: gpu
epoch_num: 20
log_smooth_window: 20
print_batch_step: 10
output_dir: ./output/rec/u14m_filter/convnextv2_tiny_cam_tps_on
eval_epoch_step: [0, 1]
eval_batch_step: [0, 500]
cal_metric_during_train: False
pretrained_model:
checkpoints:
use_tensorboard: false
infer_img:
# for data or label process
character_dict_path: ./tools/utils/EN_symbol_dict.txt
max_text_length: &max_text_length 25
use_space_char: False
save_res_path: ./output/rec/u14m_filter/predicts_convnextv2_cam_tps_on.txt
use_amp: True
Optimizer:
name: AdamW
lr: 0.0008 # for 4gpus bs256/gpu
weight_decay: 0.05
filter_bias_and_bn: True
eps: 1.e-8
LRScheduler:
name: OneCycleLR
warmup_epoch: 1.5 # pct_start 0.075*20 : 1.5ep
cycle_momentum: False
Architecture:
model_type: rec
algorithm: CAM
Transform:
name: Aster_TPS
tps_inputsize: [32, 64]
tps_outputsize: &img_shape [32, 128]
Encoder:
name: CAMEncoder
encoder_config:
name: ConvNeXtV2
depths: [3, 3, 9, 3]
dims: [96, 192, 384, 768]
strides: [[4,4], [2,1], [2,1], [1,1]]
drop_path_rate: 0.2
feat2d: True
nb_classes: 97
strides: [[4,4], [2,1], [2,1], [1,1]]
deform_stride: 2
stage_idx: 2
use_depthwise_unet: True
use_more_unet: False
binary_loss_type: BanlanceMultiClassCrossEntropyLoss
mid_size: False
d_embedding: 512
Decoder:
name: CAMDecoder
num_encoder_layers: -1
beam_size: 0
num_decoder_layers: 2
nhead: 8
max_len: *max_text_length
Loss:
name: CAMLoss
loss_weight_binary: 1.5
label_smoothing: 0.
Metric:
name: RecMetric
main_indicator: acc
is_filter: True
PostProcess:
name: ARLabelDecode
Train:
dataset:
name: LMDBDataSet
data_dir: ../Union14M-L-LMDB-Filtered
transforms:
- DecodeImagePIL: # load image
img_mode: RGB
- PARSeqAugPIL:
- CAMLabelEncode: # Class handling label
font_path: ./arial.ttf
image_shape: *img_shape
- RecTVResize:
image_shape: [64, 256]
padding: False
- KeepKeys:
keep_keys: ['image', 'label', 'length', 'binary_mask'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 256
drop_last: True
num_workers: 4
Eval:
dataset:
name: LMDBDataSet
data_dir: ../evaluation
transforms:
- DecodeImagePIL: # load image
img_mode: RGB
- ARLabelEncode: # Class handling label
- RecTVResize:
image_shape: [64, 256]
padding: False
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 2