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ModelZoo
ResNet50_tensorflow
Commits
b7253ccd
"awq/git@developer.sourcefind.cn:OpenDAS/autoawq.git" did not exist on "2290bdb2fe350985f7e2ef991140087c25a3514c"
Commit
b7253ccd
authored
May 17, 2017
by
Neal Wu
Committed by
GitHub
May 17, 2017
Browse files
Clean up changes for the attention_ocr README
parent
e9211fca
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attention_ocr/README.md
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b7253ccd
...
@@ -22,7 +22,7 @@ Pull requests:
...
@@ -22,7 +22,7 @@ Pull requests:
## Requirements
## Requirements
1.
Installe
d
TensorFlow library (
[
instructions
][
TF
]
).
1.
Install
th
e TensorFlow library (
[
instructions
][
TF
]
).
For example:
```
```
virtualenv --system-site-packages ~/.tensorflow
virtualenv --system-site-packages ~/.tensorflow
...
@@ -31,19 +31,16 @@ pip install --upgrade pip
...
@@ -31,19 +31,16 @@ pip install --upgrade pip
pip install --upgrade tensorflow_gpu
pip install --upgrade tensorflow_gpu
```
```
2.
At least 158Gb of free disk space to download FSNS dataset:
2.
At least 158GB of free disk space to download the FSNS dataset:
Assume your current directory is
`models/attention_ocr/python`
```
```
cd datasets
cd
models/attention_ocr/python/
datasets
aria2c -c -j 20 -i ../../../street/python/fsns_urls.txt
aria2c -c -j 20 -i ../../../street/python/fsns_urls.txt
cd
-
cd
..
```
```
3.
16Gb of RAM or more, 32Gb is recommended.
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.
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.
The GPU mode was tested with Titan X and GTX980.
[
TF
]:
https://www.tensorflow.org/install/
[
TF
]:
https://www.tensorflow.org/install/
[
FSNS
]:
https://github.com/tensorflow/models/tree/master/street
[
FSNS
]:
https://github.com/tensorflow/models/tree/master/street
...
@@ -62,7 +59,8 @@ To train from scratch:
...
@@ -62,7 +59,8 @@ To train from scratch:
python train.py
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
wget http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz
tar xf inception_v3_2016_08_28.tar.gz
tar xf inception_v3_2016_08_28.tar.gz
...
...
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