Comments (5)
I am using the dataset specified by you with images in the format label_index.jpg. However, testing results are very poor.
Dataset being used is: https://node1.belval.org/test_dataset.zip. I stored it in data folder and extracted the images.Command being executed: python3 run.py -ex ./data/ --test --restore -bs 32
Output:
Use standard file APIs to check for files with this prefix.
Loading data
Testing
beignet
iiiii
apocynaceous
iiiii
arshine
iiiiiWhy is the output always "iiiiii"?
I have the same problem. Have you solved it?
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I have the same problem. Have you solved it?
from crnn.
I trained the model from scratch. Seems to be working but still not as satisfactory as CRNN should be. I am thinking of rewriting the library from scratch.
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Indeed there seems to be a problem somewhere as I cannot train the model or get suitable predictions anymore. I'll investigate but since the code is in TF 1 I might just rewrite it in eager mode.
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Did you identify the problem?
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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
- 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?
- Problems with training Tibetan pictures HOT 3
- always training the first 100 batch
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