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4digit_training done

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HT
2025-07-11 12:21:59 +08:00
parent cfa52e5f2e
commit c10b0719c7
11 changed files with 5770 additions and 12 deletions
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trainer/saved_models/4digit/best_accuracy.pth Normal file
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trainer/saved_models/4digit/log_dataset.txt Normal file
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--------------------------------------------------------------------------------
dataset_root: all_data
opt.select_data: ['4digit_train']
opt.batch_ratio: ['1']
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
dataset_root: all_data
opt.select_data: ['4digit_train']
opt.batch_ratio: ['1']
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
dataset_root: all_data
opt.select_data: ['4digit_train']
opt.batch_ratio: ['1']
--------------------------------------------------------------------------------
dataset_root: all_data dataset: 4digit_train
sub-directory: /4digit_train num samples: 1000
num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
--------------------------------------------------------------------------------
Total_batch_size: 32 = 32
--------------------------------------------------------------------------------
dataset_root: all_data/4digit_valid dataset: /
sub-directory: /. num samples: 200
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
dataset_root: all_data
opt.select_data: ['4digit_train']
opt.batch_ratio: ['1']
--------------------------------------------------------------------------------
dataset_root: all_data dataset: 4digit_train
sub-directory: /4digit_train num samples: 1000
num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
--------------------------------------------------------------------------------
Total_batch_size: 32 = 32
--------------------------------------------------------------------------------
dataset_root: all_data/4digit_valid dataset: /
sub-directory: /. num samples: 200
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
dataset_root: all_data
opt.select_data: ['4digit_train']
opt.batch_ratio: ['1']
--------------------------------------------------------------------------------
dataset_root: all_data dataset: 4digit_train
sub-directory: /4digit_train num samples: 1000
num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
--------------------------------------------------------------------------------
Total_batch_size: 32 = 32
--------------------------------------------------------------------------------
dataset_root: all_data/4digit_valid dataset: /
sub-directory: /. num samples: 200
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
dataset_root: all_data
opt.select_data: ['4digit_train']
opt.batch_ratio: ['1']
--------------------------------------------------------------------------------
dataset_root: all_data dataset: 4digit_train
sub-directory: /4digit_train num samples: 1000
num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
--------------------------------------------------------------------------------
Total_batch_size: 32 = 32
--------------------------------------------------------------------------------
dataset_root: all_data/4digit_valid dataset: /
sub-directory: /. num samples: 200
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
dataset_root: all_data
opt.select_data: ['4digit_train']
opt.batch_ratio: ['1']
--------------------------------------------------------------------------------
dataset_root: all_data dataset: 4digit_train
sub-directory: /4digit_train num samples: 1000
num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
--------------------------------------------------------------------------------
Total_batch_size: 32 = 32
--------------------------------------------------------------------------------
dataset_root: all_data/4digit_valid dataset: /
sub-directory: /. num samples: 200
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
dataset_root: all_data
opt.select_data: ['4digit_train']
opt.batch_ratio: ['1']
--------------------------------------------------------------------------------
dataset_root: all_data dataset: 4digit_train
sub-directory: /4digit_train num samples: 1000
num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
--------------------------------------------------------------------------------
Total_batch_size: 32 = 32
--------------------------------------------------------------------------------
dataset_root: all_data/4digit_valid dataset: /
sub-directory: /. num samples: 200
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
dataset_root: all_data
opt.select_data: ['4digit_train']
opt.batch_ratio: ['1']
--------------------------------------------------------------------------------
dataset_root: all_data dataset: 4digit_train
sub-directory: /4digit_train num samples: 1000
num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
--------------------------------------------------------------------------------
Total_batch_size: 32 = 32
--------------------------------------------------------------------------------
dataset_root: all_data/4digit_valid dataset: /
sub-directory: /. num samples: 200
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
dataset_root: all_data
opt.select_data: ['4digit_train']
opt.batch_ratio: ['1']
--------------------------------------------------------------------------------
dataset_root: all_data dataset: 4digit_train
sub-directory: /4digit_train num samples: 1000
num total samples of 4digit_train: 1000 x 1.0 (total_data_usage_ratio) = 1000
num samples of 4digit_train per batch: 32 x 1.0 (batch_ratio) = 32
--------------------------------------------------------------------------------
Total_batch_size: 32 = 32
--------------------------------------------------------------------------------
dataset_root: all_data/4digit_valid dataset: /
sub-directory: /. num samples: 200
--------------------------------------------------------------------------------

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trainer/saved_models/4digit/log_train.txt Normal file
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trainer/saved_models/4digit/opt.txt Normal file
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------------ Options -------------
number: 0123456789
experiment_name: 4digit
symbol:
lang_char:
train_data: all_data
valid_data: all_data/4digit_valid
manualSeed: 1111
workers: 6
batch_size: 32
num_iter: 3000
valInterval: 5
saved_model:
FT: False
optim: False
lr: 1.0
beta1: 0.9
rho: 0.95
eps: 1e-08
grad_clip: 5
select_data: ['4digit_train']
batch_ratio: ['1']
total_data_usage_ratio: 1.0
batch_max_length: 34
imgH: 32
imgW: 128
rgb: True
contrast_adjust: 0.0
sensitive: True
PAD: True
data_filtering_off: False
Transformation: TPS
FeatureExtraction: ResNet
SequenceModeling: BiLSTM
Prediction: CTC
num_fiducial: 20
input_channel: 3
output_channel: 256
hidden_size: 256
decode: greedy
new_prediction: False
freeze_FeatureFxtraction: False
freeze_SequenceModeling: False
character: 0123456789
num_class: 11
---------------------------------------
------------ Options -------------
number: 0123456789
experiment_name: 4digit
symbol: None
lang_char: None
train_data: all_data
valid_data: all_data/4digit_valid
manualSeed: 1111
workers: 6
batch_size: 32
num_iter: 3000
valInterval: 5
saved_model:
FT: False
optim: False
lr: 1.0
beta1: 0.9
rho: 0.95
eps: 1e-08
grad_clip: 5
select_data: ['4digit_train']
batch_ratio: ['1']
total_data_usage_ratio: 1.0
batch_max_length: 34
imgH: 32
imgW: 128
rgb: True
contrast_adjust: 0.0
sensitive: True
PAD: True
data_filtering_off: False
Transformation: TPS
FeatureExtraction: ResNet
SequenceModeling: BiLSTM
Prediction: CTC
num_fiducial: 20
input_channel: 3
output_channel: 256
hidden_size: 256
decode: greedy
new_prediction: False
freeze_FeatureFxtraction: False
freeze_SequenceModeling: False
character: 0123456789
num_class: 11
---------------------------------------