3DCNN

Implementation of 3D Convolutional Neural Network for video classification using Keras(with tensorflow as backend).

Description

This code requires 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.

You can use visualize_input.py to make an input image which will maximize the specific output. This code is able to maximize a layer's output of any classification model. (Only dense layer convolutional layer(2D/3D) and pooling layer(2D/3D) are allowed.)

Requirements

python3
opencv3 (with ffmpeg), keras, numpy, tqdm

Options

Options of 3dcnn.py are as following:
--batch batch size, default is 128
--epoch the number of epochs, default is 100
--videos a name of directory where dataset is stored, default is UCF101
--nclass the number of classes you want to use, default is 101
--output a directory where the results described above will be saved
--color use RGB image or grayscale image, default is False
--skip get frames at interval or continuously, default is True
--depth the number of frames to use, default is 10

Options of 3dcnn_ensemble.py are almost same as those of 3dcnn.py. You can use --nmodel option to set the number of models.

Options of visualize_input.py are as follows:
--model saved json file of a model
--weights saved hd5 file of a model weights
--layernames True to show layer names of a model, default is False
--name the name of a layer which will be maximized
--index the index of a layer output which will be maximized
--iter the number of iteration, default is 20

You can see more information by using --help option

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

You can generate the input image which maximizes 0th output of layer named 'dense_2' like this:

python visualize_input.py -m result_cnn_10class/ucf101cnnmodel.json -w result_cnn_10class/ucf101cnnmodel.hd5 -n 'dense_2' -i 0 --iter 100

When I got the results in result_cnn_10class, result_cnn_101class, result_3dcnn_10class, result_3dcnn_101class , result_ensemble, I set the options like the follows:

	nclass	batch	epoch	color	skip	depth	nmodel	accuracy
2dcnn.py	10	128	100	False	True	-	-	0.844
2dcnn.py	101	128	100	False	True	-	-	0.558
3dcnn.py	10	128	100	False	True	10	-	0.900
3dcnn.py	101	128	100	False	True	10	-	0.692
3dcnn_ensemble.py	101	128	100	False	True	10	10	0.876

Other files