Comments (3)
You should see a huge difference between training with and without using --weights
, especially in the time it consumes to get the first meaningful results.
However, since the last layer of the neural net is not 'copied' fully (possible changes of the alphabet) and your lines are not fully similar to the training data set of the pretrainined model, the pretraining yields also 'garbage' for the first few iterations.
However, as said, training using a pretrained model should converge way faster.
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@ChWick
thank you very much
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@ChWick
Dear,
Please, if I want to execute Calamari with only one layer LSTM , is the following command correct :
calamari-train --files DatasetPath –network lstm=200 --checkpoint_frequency 1000 --output_dir modelsLSTM200
Thank you
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Related Issues (20)
- calamari-train: warmstart not working without also giving network spec
- featreq: when warmstart-training, init weights of new chars from existing ones HOT 2
- calamari-eval: skip missing pairs HOT 3
- calamari-eval: unknown arguments HOT 6
- calamari-eval: confusion table miscalculates relative frequency HOT 3
- Error when convert old trained model to latest version model HOT 1
- Got exception during training HOT 4
- calamari-ocr 2.2.2 on ubuntu 22.04 partial success, difficulty with GPU software
- Prediction from calamari trained .pb model HOT 5
- Issue while using the model and json HOT 8
- setup.py on Ubuntu20.04: tensorflow is wrong version HOT 7
- Model very sensitive on PNG input HOT 3
- calamari/1.0: hold Tensorflow and Protobuf dependencies HOT 6
- What is the accuracy on Chinese/Japanese text? HOT 2
- Attention layer
- "No training configuration" for code that should not have one HOT 5
- Downgrading of models is not supported (5 to 2). Please upgrade your Calamari instance (currently installed: 1.0.6) HOT 4
- UnknownArgumentError HOT 7
- Release confusion HOT 4
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