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I've read some questions on stackoverflow about attention-ocr, and most of them are about the implementation detail of a specific step. What I wanted to know is the pipeline for us to fine-tune this model on our own dataset.

As far as I know, the steps should be:

0) Should we first download FSNS dataset?? I tried to bypass this step and try running inference on just one image, but it always give me error:"ImportError: No module named 'fsns". So I wonder if this error will go away once I set my own dataset up.

1) Store our data in the same format as FSNS. (Links on this topic: How to create dataset in the same format as the FSNS dataset？, how to create cutomized dataset for google tensorflow attention ocr? )

2) Download the pre-trained checkpoint(http://download.tensorflow.org/models/attention_ocr_2017_08_09.tar.gz)

3) Somehow modify the 'model.py' to fit your own purpose.

4) Somehow modify the 'train.py' to train your own module using tensorflow serving.

I am still on the early stage (creating own dataset) on this project now, and confused on how to do it and what's the next stage.

Sta_Doc
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1 Answers1

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The error was caused by incorrect version of Python. They should be run with Python 2, and you can just change the 'import' sentence to solve this error. Try to change the 'import fsns' to 'from datasets import fsns'.

Bhargav Rao
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Daming N
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