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det_r50_vd_dcn_fce_ctw.yml
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det_r50_vd_dcn_fce_ctw.yml
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Global:
use_gpu: true
epoch_num: 1500
log_smooth_window: 20
print_batch_step: 20
save_model_dir: ./output/det_r50_dcn_fce_ctw/
save_epoch_step: 100
# evaluation is run every 835 iterations
eval_batch_step: [0, 835]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_fce/predicts_fce.txt
Architecture:
model_type: det
algorithm: FCE
Transform:
Backbone:
name: ResNet_vd
layers: 50
dcn_stage: [False, True, True, True]
out_indices: [1,2,3]
Neck:
name: FCEFPN
out_channels: 256
has_extra_convs: False
extra_stage: 0
Head:
name: FCEHead
fourier_degree: 5
Loss:
name: FCELoss
fourier_degree: 5
num_sample: 50
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
learning_rate: 0.0001
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: FCEPostProcess
scales: [8, 16, 32]
alpha: 1.0
beta: 1.0
fourier_degree: 5
box_type: 'poly'
Metric:
name: DetFCEMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/ctw1500/imgs/
label_file_list:
- ./train_data/ctw1500/imgs/training.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
ignore_orientation: True
- DetLabelEncode: # Class handling label
- ColorJitter:
brightness: 0.142
saturation: 0.5
contrast: 0.5
- RandomScaling:
- RandomCropFlip:
crop_ratio: 0.5
- RandomCropPolyInstances:
crop_ratio: 0.8
min_side_ratio: 0.3
- RandomRotatePolyInstances:
rotate_ratio: 0.5
max_angle: 30
pad_with_fixed_color: False
- SquareResizePad:
target_size: 800
pad_ratio: 0.6
- IaaAugment:
augmenter_args:
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
- FCENetTargets:
fourier_degree: 5
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'p3_maps', 'p4_maps', 'p5_maps'] # dataloader will return list in this order
loader:
shuffle: True
drop_last: False
batch_size_per_card: 6
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/ctw1500/imgs/
label_file_list:
- ./train_data/ctw1500/imgs/test.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
ignore_orientation: True
- DetLabelEncode: # Class handling label
- DetResizeForTest:
limit_type: 'min'
limit_side_len: 736
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- Pad:
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 2