Open Suriyapongmax opened 3 months ago
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.
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.
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.
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 ?
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
I am working on an OCR project aimed at accurately reading player numbers and names from sports images. These images feature 10 different custom fonts, predominantly thick and bold, which cater to a sports aesthetic. The primary challenge is the model's ability to distinguish between similar characters, particularly under the constraints of these stylized fonts.
Fonts: 10 custom sports fonts (English A-Z, a-z, 0-9). Training Data: Generated dataset of ~200K images, including mixed cases of 3-10 characters with stroke and non-stroke and numbers (00-99) for each font.
After I train with num_iter: 750000 , loss: 0.00126, Valid loss: 0.15052
Problems Encountered:
Request for Help: I am seeking advice on improving my model’s performance in differentiating similar-looking characters,. Any suggestions on training strategies, network adjustments, or data preprocessing techniques would be greatly appreciated.
here are acutal image I want to predict
here are generated image I used as training set.
![oc556143399804](https://github.com/JaidedAI/EasyOCR/assets/32792068/7f1f75f7-fd6d-4af1-9bd3-f639360f8c36)
here is the config I use batch_size: 32
FT: False optim: False lr: 1 beta1: 0.9 total_data_usage_ratio: 1.0 batch_max_length: 34 imgH: 64 imgW: 600 rgb: False contrast_adjust: 0.0 sensitive: True PAD: True data_filtering_off: False Transformation: None FeatureExtraction: VGG SequenceModeling: BiLSTM Prediction: CTC num_fiducial: 20 input_channel: 1 output_channel: 256 hidden_size: 256 decode: greedy
THANK YOU IN ADVANCE !!