Train my own dataset on handwritten digits

[bilalltf]

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]

[cherry247]

Is this issue resolved?

[bilalltf]

@cherry247 This is an issue with your dataset you need to make sure that your gt.txt file is as follows:

 {imagepath}\t{label}\n 

Also if the gt.txt file is out of the images folder you need to set the {imagepath} is as follows:

 ├── gt.txt
 └── test
     ├── image_name

 {test/img_nam}\t{label}

[wjbmattingly]

My gt.txt file is structured correctly and I am receiving the same error.

[bulatnv]

Change in create_lmdb_dataset.py: env = lmdb.open(outputPath, map_size=1099511627776) to env = lmdb.open(outputPath, map_size=1073741824) worked for me.