yakhyo / deep-text-recognition

📃 | Deep Text Recognition Implementation using PyTorch

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deep-text-recognition's Issues

Train my own dataset (string handwritten digits)

0, 'datasets should not be an empty iterable' # type: ignore[arg-type]
AssertionError: datasets should not be an empty iterable

My config.py :

""" Default CONFIGURATIONS """
exp_name = 'logs' # Where to store logs and models
train_data = '../data_lmdb/training/' # path to training dataset
valid_data = '../data_lmdb/validation/' # path to validation dataset

eval_data = '../data_lmdb/validation/' # path to evaluation dataset
benchmark_all_eval = False # evaluate 10 benchmark evaluation datasets

manualSeed = 1111 # for random seed setting
workers = 4 # number of data loading workers, default=4
batch_size = 768 # input batch size
num_gpu = 1 # number of GPU devices, by default 0
num_iter = 300000 # number of iterations to train for
valInterval = 2000 # Interval between each validation
saved_model = '' # path to model to continue training, if you have no any saved_model to continue left it as ''
FT = False # whether to do fine-tuning
adam = False # Whether to use adam (default is Adadelta)
lr = 1.0 # learning rate, default=1.0 for Adadelta
beta1 = 0.9 # beta1 for adam. default=0.9
rho = 0.95 # decay rate rho for Adadelta. default=0.95'
eps = 1e-8 # eps for Adadelta. default=1e-8'
grad_clip = 5 # gradient clipping value. default=5
baiduCTC = False # for data_filtering_off mode
""" Data processing """
select_data = 'MJ-ST' # select training data (default is MJ-ST, which means MJ and ST used as training data)
batch_ratio = '0.5-0.5' # assign ratio for each selected data in the batch # assign ratio for each selected data in the batch
total_data_usage_ratio = 1.0 # total data usage ratio, this ratio is multiplied to total number of data
batch_max_length = 25 # maximum-label-length
imgH = 32 # the height of the input image
imgW = 100 # the width of the input image
rgb = False # use rgb input
character='0123456789' # character label
sensitive = False # for sensitive character mode
PAD = False # whether to keep ratio then pad for image resize
data_filtering_off = False # for data_filtering_off mode
""" Model Architecture """
Transformation = 'TPS' # Transformation stage. None|TPS
FeatureExtraction = 'ResNet' # FeatureExtraction stage. VGG|RCNN|ResNet
SequenceModeling = 'BiLSTM' # SequenceModeling stage. None|BiLSTM
Prediction = 'Attn' # Prediction stage. CTC|Attn
num_fiducial = 20 # number of fiducial points of TPS-STN
input_channel = 1 # the number of input channel of Feature extractor
output_channel = 512 # the number of output channel of Feature extractor
hidden_size = 256 # the size of the LSTM hidden state

I changed in create_lmdb_dataset.py the line:
env = lmdb.open(outputPath, map_size=1099511627776)
To:
env = lmdb.open(outputPath, map_size=1073741824)

Because I had an error :[lmdb.Error: There is not enough space on the disk]