https://github.com/zakizhou/CAPTCHA
implement classification for CAPTCHA in TensorFlow
https://github.com/zakizhou/CAPTCHA
Last synced: 29 days ago
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implement classification for CAPTCHA in TensorFlow
- Host: GitHub
- URL: https://github.com/zakizhou/CAPTCHA
- Owner: zakizhou
- Created: 2016-10-08T12:27:52.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-11-17T11:02:16.000Z (over 7 years ago)
- Last Synced: 2024-11-14T03:34:25.342Z (7 months ago)
- Language: Python
- Size: 133 KB
- Stars: 68
- Watchers: 6
- Forks: 25
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-captcha - zakizhou/CAPTCHA - implementacja klasyfikacji dla CAPTCHA w TensorFlow. (Łamanie / Ogólne)
README
# CAPTCHA
Newbie about Deep Learning and TensorFlow?
Boring with MNIST?
Want a more interesting and complicated application?
This is for you.
This repo contains a cnn model for recognizing
numbers of captcha
## WHAT IS CAPTCHA
CAPTCHA is kind of images that contains chars and digits for people to recognize, it is used
in website log in to test you whether you are a robot or a person. In this repo we will develop
a small convolutional neural network with TensorFlow to recognize it.
For simplicity, images will only contain four digits with noise
**we say a image is classified correctly if and only if four digits inside this image are all classified correctly**
Two sample images are listed below
## requirements
python 2.7 with following packages installed should work fine
1. numpy
2. TensorFlow(verison >= 1.4) (because we will use `tf.data`)
3. captcha(you can install it with `pip install captcha`)
(anaconda environment is strongly recommended for managing these packages)
windows and python 3.X are not tested but should be OK.
GPU is not a must, but without it, training might be very slow.
## SOME FEATURES
* Train and validation images are generated on the fly, doesn't need to download any big datasets.
* Inputs of model is built on top of `tf.data` instead of old queue-based api, so reading
the code combined with the [official document](https://www.tensorflow.org/programmers_guide/datasets) about `tf.data` will help you understand how
to write it yourself.
* Very short code, easy to read.
## USAGE
First clone this repo
```
git clone https://github.com/zakizhou/CAPTCHA
```
Before run training, training and validation images should be
generated, change to the root dir of this repo and run
```
cd CAPTCHA
mkdir -p images/train
mkdir -p images/validation
mkdir -p tfrecords
mkdir -p save
python captcha_producer.py -n 30000 -p images/train
```
This will generate 30000 training images in the images/train/ and also convert infomation about
these images into `tfrecords/train.tfrecords` file.
for validation set:
```
python captcha_producer.py -n 3000 -p "images/validation"
```
Now you can run this model with
```
python captcha_train.py
```
## Result
After 10000 steps (you can manually change num of steps in `captcha_train.py` file) training on single GTX1060, this model achieved
around 70% accuracy, adjusting the scale of parameters or adding dropout
should still improve this performance
## Details about files
* `images/train` and `images/validation` will contains generated train and validation images
* `tfrecords` will contains about generated tfrecords(`train.tfrecords`, `validation.tfrecords`)
* `save` will contains saved model after training
* `captcha_producer.py` is used to generate images and tfrecords
* `captcha_model.py` contains utils functions for defining the model
* `captcha_data.py` is used for build input for model
* `captcha_config.py` contains configs for model
* `captcha_train.py` is used for training model
## TODO
* add multi gpu training code
* add tensorboard code
* add functions for keep on training after shutdown