Training Model Struggling with Character Recognition in Custom Sports Fonts

[Suriyapongmax]

Training Model Struggling with Character Recognition in Custom Sports Fonts

[Suriyapongmax]

Hi, I want to ask you how long it took you to fine-tune the model with this number of iterations and dataset size, as I only used 3000 iterations and 20K images, and it is taking a very long time.

200K images with 40,000 Iteratios about 1 hour.

[Suriyapongmax]

Hi, I want to ask you how long it took you to fine-tune the model with this number of iterations and dataset size, as I only used 3000 iterations and 20K images, and it is taking a very long time.

200K images with 40,000 Iteratios about 1 hour.

Thanks for the reply. Also can you please tell me the gpu specification where I have 3050 12G and it still going more than 4 hours.

I use 4060Ti 16GB. How much learning rate & Batch size you choose ?

[alikhalil98771]

Hi, I want to ask you how long it took you to fine-tune the model with this number of iterations and dataset size, as I only used 3000 iterations and 20K images, and it is taking a very long time.

[alikhalil98771]

Hi, I want to ask you how long it took you to fine-tune the model with this number of iterations and dataset size, as I only used 3000 iterations and 20K images, and it is taking a very long time.

200K images with 40,000 Iteratios about 1 hour.

Thanks for the reply. Also can you please tell me the gpu specification where I have 3050 12G and it still going more than 4 hours.

[alikhalil98771]

Hi, I want to ask you how long it took you to fine-tune the model with this number of iterations and dataset size, as I only used 3000 iterations and 20K images, and it is taking a very long time.

200K images with 40,000 Iteratios about 1 hour.

Thanks for the reply. Also can you please tell me the gpu specification where I have 3050 12G and it still going more than 4 hours.

I use 4060Ti 16GB. How much learning rate & Batch size you choose ?

I am using the following config:

manualSeed: 1111
workers: 4
batch_size: 128 #32
num_iter: 3000
valInterval: 200
FT: False
optim: False # default is Adadelta
lr: 1.
beta1: 0.9
rho: 0.95
eps: 0.00000001
grad_clip: 5
#Data processing
select_data: 'e' # this is dataset folder in train_data
batch_ratio: '1' 
total_data_usage_ratio: 1.0
batch_max_length: 35 
imgH: 64
imgW: 600
rgb: False
contrast_adjust: False
sensitive: True
PAD: True
contrast_adjust: 0.0
data_filtering_off: False
# Model Architecture
Transformation: 'None'
FeatureExtraction: 'ResNet'
SequenceModeling: 'BiLSTM'
Prediction: 'CTC'
num_fiducial: 20
input_channel: 1
output_channel: 512
hidden_size: 512
decode: 'greedy'
new_prediction: False
freeze_FeatureFxtraction: False
freeze_SequenceModeling: False