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3D convolutional neural network for video classification
| Date | Stars |
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| 2026-07-31 | 277 |
| 2026-08-05 | 277 |
| 2026-08-06 | 277 |
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# 3DCNN Implementation of 3D Convolutional Neural Network for video classification using [Keras](https://keras.io/)(with [tensorflow](https://www.tensorflow.org/) as backend). ## Description This code requires [UCF-101 dataset](http://crcv.ucf.edu/data/UCF101.php). 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: ```sh 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: ```sh 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](https://github.com/rysmarie/MotionRecognition/tree/master/result_cnn_10class), [result\_cnn\_101class](https://github.com/rysmarie/MotionRecognition/tree/master/result_cnn_101class), [result\_3dcnn\_10class](https://github.com/rysmarie/MotionRecognition/tree/master/result_3dcnn_10class), [result\_3dcnn\_101class](https://github.com/rysmarie/MotionRecognition/tree/master/result_3dcnn_101class) , [result\_ensemble](https://github.com/kcct-fujimotolab/3DCNN/tree/master/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 `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.
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:adf1655c70e0bd25, llm:Repository description: '3D convolutional neural network for video classification' (language: Python).
matched fp:adf1655c70e0bd25, llm:Repository description: '3D convolutional neural network for video classification' (language: Python).