Comments (3)
Use -na 2
to have the labels written to another file.
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Use
-na 2
to have the labels written to another file.
what`s the format of you training data?I saw in the issues that you said that the label format of the training set is [LABEL]_[NUMBER].[EXT].
Use -na 2
to have the labels written to another file.Can this also be used for the label format of the training set for this project?
from crnn.
You would have to edit the data manager to load the label file, but keep in mind that this project was built for Latin-based languages, and I do not if it will work at all with Tibetan. You would have to change the CHAR_VECTOR
to match your characters.
Also, you could try and use the programmable API instead of pre-generated data for your needs. By editing this line: https://github.com/Belval/CRNN/blob/master/CRNN/data_manager.py#L50 to generate Tibetan data, you could avoid having pre-generating your data.
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Related Issues (20)
- How to compute accuracy both from train and test set HOT 2
- How to save model? HOT 1
- How to predict a picture HOT 1
- how many times your model iterates when you train?
- loss fall slow
- model overfitting HOT 2
- Convert code to use functional TensorFlow
- There is no run.py in the TextRecognitionDataGenerator HOT 1
- The Error rate is always 1.0 HOT 1
- fail to test
- Testing result is poor HOT 2
- CHAR_VECTOR is correct? HOT 1
- Unable to run `python3 run.py -ex ../data/test --test --restore` HOT 2
- Scaling input data between 0 and 1 HOT 2
- Default Model gives poor testing result. HOT 5
- Possible bug in using Batch normalisation HOT 1
- batch_dt = sparse_tuple_from(np.reshape(np.array(raw_batch_la), (-1)))应该去掉reshape HOT 1
- Why the output is the same when using the pretrained model for prediction?
- always training the first 100 batch
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