EMNIST.rar_IA_image recognition_python

EMNIST =====  Developed by @coopss ##### Description This project was intended to explore the properties of convolution neural networks (CNN) and see how they compare to recurrent convolution neural networks (RCNN). This was inspired by a [paper](http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/2B_004.pdf "Recurrent Convolutional Neural Network for Object Recognition") I read that details the effectiveness of RCNNs in object recognition as they perform or even out perform their CNN counterparts with fewer parameters. Aside from exploring CNN/RCNN effectiveness, I built a simple interface to test the more challenging [EMNIST dataset](https://arxiv.org/abs/1702.05373 "EMNIST: an extension of MNIST to handwritten letters") dataset (as opposed to the [MNIST dataset](http://yann.lecun.com/exdb/mnist/ "THE MNIST DATABASE of handwritten digits")) ##### Current Implementation * Multistack CNN * Web-applet testing environment * Touch screen compatible * Works best when letter takes up a good portion of the canvas * Read in .mat file * Currently training on the [byclass dataset](https://cloudstor.aarnet.edu.au/plus/index.php/s/7YXcasTXp727EqB/download) (*direct download link*) * See [paper](https://arxiv.org/abs/1702.05373 "EMNIST: an extension of MNIST to handwritten letters") for more info ##### Todo * Update gif with new webapp * Train more models * RCNN * Optimize hyperparameters * Add a noise (gaussian or likewise) layer to input in an attempt to boost accuracy * Move webapp to a host service like PythonAnywhere ## Environment #### Anaconda: Python 3.5.3 * Tensorflow or tensorflow-gpu (See [here](https://www.tensorflow.org/install/ "Installing TensorFlow") for more info) * Keras * Flask * Numpy * Scipy Note: All dependencies for current build can be found in dependencies.txt ## Usage #### [training.py](https://github.com/Coopss/EMNIST/blob/master/training.py) A training program for classifying the EMNIST dataset usage: training.py [-h] --file [--width WIDTH] [--height HEIGHT] [--max MAX] [--epochs EPOCHS] [--verbose] ##### Required Arguments: -f FILE, --file FILE Path .mat file data ##### Optional Arguments -h, --help show this help message and exit --width WIDTH Width of the images --height HEIGHT Height of the images --max MAX Max amount of data to use --epochs EPOCHS Number of epochs to train on --verbose Enables verbose printing #### [server.py](https://github.com/Coopss/EMNIST/blob/master/server.py) A webapp for testing models generated from [training.py](https://github.com/Coopss/EMNIST/blob/master/training.py) on the EMNIST dataset usage: server.py [-h] [--bin BIN] [--host HOST] [--port PORT] ##### Optional Arguments: -h, --help show this help message and exit --bin BIN Directory to the bin containing the model yaml and model h5 files --host HOST The host to run the flask server on --port PORT The port to run the flask server on