Reference

3D-DenseNet with TensorFlow

Expand the `Densely Connected Convolutional Networks DenseNets to 3D-DenseNet for action recognition (video classification):

3D-DenseNet - without bottleneck layers
3D-DenseNet-BC - with bottleneck layers

Each model can be tested on such datasets:

KTH
MERL

A number of layers, blocks, growth rate, video normalization and other training params may be changed trough shell or inside the source code.

There are also many other implementations, they may be useful also.

Pre-request libraries

python2
tensorflow 1.0
opencv2 for python2

Step 1: Data preparation (UCF dataset example)

Download the UCF101 (Action Recognition Data Set).
Extract the UCF101.rar file and you will get ../UCF101/<action_name>/<video_name.avi> folder structure.
Use the ./data_prepare/convert_video_to_images.sh script to decode the UCF101 video files to image files.
- run ./data_prepare/convert_video_to_images.sh ../UCF101 25 (number 25 means the fps rate)

Use the ./data_prepare/convert_images_to_list.sh script to create/update the {train,test}.list according to the new UCF101 image folder structure generated from last step (from images to files).

run ./data_prepare/convert_images_to_list.sh .../UCF101 4, this will update the test.list and train.list files (number 4 means the ratio of test and train data is 1/4)

train.list example:

ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c01 0
ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c02 0
ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c03 0
ApplyLipstick/v_ApplyLipstick_g01_c01 1
ApplyLipstick/v_ApplyLipstick_g01_c02 1
ApplyLipstick/v_ApplyLipstick_g01_c03 1
Archery/v_Archery_g01_c01 2
Archery/v_Archery_g01_c02 2
Archery/v_Archery_g01_c03 2
Archery/v_Archery_g01_c04 2
BabyCrawling/v_BabyCrawling_g01_c01 3
BabyCrawling/v_BabyCrawling_g01_c02 3
BabyCrawling/v_BabyCrawling_g01_c03 3
BabyCrawling/v_BabyCrawling_g01_c04 3
BalanceBeam/v_BalanceBeam_g01_c01 4
BalanceBeam/v_BalanceBeam_g01_c02 4
BalanceBeam/v_BalanceBeam_g01_c03 4
BalanceBeam/v_BalanceBeam_g01_c04 4
...

Copy/Cut the test.list and train.list files to the root of video folder (../UCF101).

Step 2: Train or Test the model

Check the trainig help message

python run_dense_net_3d.py -h
Train and test the program

python run_dense_net_3d.py --train --test -ds path/to/video_folder
// Notices that all the logs message will be written in log.txt file in the root folder

Options

run_dense_net_3d.py -> train_params settings

'num_classes': 5,               # The number of the classes that this dataset had
'batch_size': 10,               # Batch Size When we trian the model
'n_epochs': 100,                # The total number of epoch we run the model
'crop_size': (64,64),           # The (width, height) of images that we used to trian the model
'sequence_length': 16,          # The length of the video clip
'overlap_length': 8,            # The overlap of the images when we extract the video clips,
                                  this should be less than sequence_length
'initial_learning_rate': 0.1,
'reduce_lr_epoch_1': 50,        # epochs * 0.5
'reduce_lr_epoch_2': 75,        # epochs * 0.75
'validation_set': True,         # Whether used validation set or not
'validation_split': None,       # None or float
'queue_size': 300,              # The data queue size when we extract the data from dataset,
                                  should be set according to your memory size
'normalization': 'std',         # None, divide_256, divide_255, std

Result

Test results on MERL shopping dataset. Video normalization per channels was used.

Approximate training time for models on GeForce GTX TITAN X (12 GB memory):

3D-DenseNet(k = 12, d = 20) - 25 hrs

ChelovekHe/3d-DenseNet