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attention_ocr/README.md

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@@ -76,13 +76,13 @@ tar xf attention_ocr_2017_05_17.tar.gz
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python train.py --checkpoint=model.ckpt-399731
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```
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## How to use your own image data to train the Model?
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## How to use your own image data to train the model
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You need to define a new dataset. There are two options:
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1. Store data in the same format as the FSNS dataset and just reuse the
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[python/datasets/fsns.py](https://github.com/tensorflow/models/blob/master/attention_ocr/python/datasets/fsns.py)
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module. E.g. create a file datasets/newtextdataset.py
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module. E.g., create a file datasets/newtextdataset.py:
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```
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import fsns
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python train.py --dataset_name=newtextdataset
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```
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Please note the eval.py will also require the same flag.
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Please note that eval.py will also require the same flag.
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2. Define a new dataset format. The model needs the following data to train:
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- images: input images, shape [batch_size x H x W x 3];
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- labels: ground truth label ids, shape=[batch_size x seq_length];
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- labels_one_hot: labels in one-hot encoding, shape [batch_size x seq_length x num_char_classes];
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Refer to the [python/data_provider.py](https://github.com/tensorflow/models/blob/master/attention_ocr/python/data_provider.py#L33)
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for more details. You can use the [python/datasets/fsns.py](https://github.com/tensorflow/models/blob/master/attention_ocr/python/datasets/fsns.py)
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Refer to [python/data_provider.py](https://github.com/tensorflow/models/blob/master/attention_ocr/python/data_provider.py#L33)
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for more details. You can use [python/datasets/fsns.py](https://github.com/tensorflow/models/blob/master/attention_ocr/python/datasets/fsns.py)
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as the example.
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## How to use a pre-trained model
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The inference part was not released yet, but it is pretty straightforward to
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implement one in python or C++.
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implement one in Python or C++.
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The recommended way is to use the [Serving infrastructure](https://tensorflow.github.io/serving/serving_basic).
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