Merge pull request #1482 from alexgorban/master

attention_ocr# Update checkpoint and instructions.

attention_ocr/README.md

@@ -22,16 +22,25 @@ Pull requests:
 
 ## Requirements
 
-1. Installed TensorFlow library ([instructions][TF]).
-2. At least 158Gb of free disk space to download FSNS dataset:
+1. Install the TensorFlow library ([instructions][TF]). For example:
 
 ```
-aria2c -c -j 20 -i ../street/python/fsns_urls.txt
+virtualenv --system-site-packages ~/.tensorflow
+source ~/.tensorflow/bin/activate
+pip install --upgrade pip
+pip install --upgrade tensorflow_gpu
 ```
 
-3. 16Gb of RAM or more, 32Gb is recommended.
-4. The train.py works with in both modes CPU and GPU, using GPU is preferable.
-   The GPU mode was tested with Titan X and GTX980.
+2. At least 158GB of free disk space to download the FSNS dataset:
+
+```
+cd models/attention_ocr/python/datasets
+aria2c -c -j 20 -i ../../../street/python/fsns_urls.txt
+cd ..
+```
+
+3. 16GB of RAM or more; 32GB is recommended.
+4. `train.py` works with both CPU and GPU, though using GPU is preferable. It has been tested with a Titan X and with a GTX980.
 
 [TF]: https://www.tensorflow.org/install/
 [FSNS]: https://github.com/tensorflow/models/tree/master/street
@@ -50,7 +59,8 @@ To train from scratch:
 python train.py
 ```
 
-To train a model using a pre-trained inception weights as initialization:
+To train a model using pre-trained Inception weights as initialization:
+
 ```
 wget http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz
 tar xf inception_v3_2016_08_28.tar.gz
@@ -60,16 +70,17 @@ python train.py --checkpoint_inception=inception_v3.ckpt
 To fine tune the Attention OCR model using a checkpoint:
 
 ```
-wget http://download.tensorflow.org/models/attention_ocr_2017_05_01.tar.gz
-tar xf attention_ocr_2017_05_01.tar.gz
-python train.py --checkpoint=model.ckpt-232572
+wget http://download.tensorflow.org/models/attention_ocr_2017_05_17.tar.gz
+tar xf attention_ocr_2017_05_17.tar.gz
+python train.py --checkpoint=model.ckpt-399731
 ```
 
 ## Disclaimer
 
 This code is a modified version of the internal model we used for our paper.
-Currently it reaches 82.71% full sequence accuracy after 215k steps of training.
+Currently it reaches 83.79% full sequence accuracy after 400k steps of training.
 The main difference between this version and the version used in the paper - for
 the paper we used a distributed training with 50 GPU (K80) workers (asynchronous
-updates), the provided checkpoint was created using this code after ~60 hours of
-training on a single GPU (Titan X).
+updates), the provided checkpoint was created using this code after ~6 days of
+training on a single GPU (Titan X) (it reached 81% after 24 hours of training),
+the coordinate encoding is missing TODO(alexgorban@).