#3DCNN This is an Inplementation of 3D Convolutional Neural Network for video classification using Keras(with tensorflow as backend).

##Description This code uses UCF-101 dataset. This code generates graphs of accuracy and loss, plot of model, result and class names as txt file and model as hd5 and json.

##Options --batch batch size, default is 128
--epoch the number of epochs, default is 100
--videos the name of directory where dataset is stored, default is UCF101
--nclass the number of classes you want to use, default is 101
--output directory where the results described above will be saved
--color use RGB image or grayscale image, default is False
--skip get frame at interval or contenuously, default is True
--depth the number of frames to use, default is 10

##Demo You can execute like the following.

python 3dcnn.py --batch 32 --epoch 50 --videos dataset/ --nclass 10 --output 3dcnnresult/ --color True --skip False --depth 15

##Other files 2dcnn.py 2DCNN model
display.py get example images from the dataset.
videoto3d.py get frames from a video, extract a class name from filename of a video in UCF101.