Train I3D model on ucf101 or hmdb51 by tensorflow

This code also for training your own dataset

Setup

First follow the instructions for install kinetics-i3d
Then, clone this repository using

$git clone https://github.com/LossNAN/I3D-Tensorflow.git

How to use our code?

1.Data_process

1>download UCF101 and HMDB51 dataset by yourself
2>extract RGB and FLOW frames by denseFlow_GPU, such as:

~PATH/UCF-101/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c01/i for all rgb frames
~PATH/UCF-101/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c01/x for all x_flow frames
~PATH/UCF-101/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c01/y for all y_flow frames

3>convert images to list for train and test

cd ./list/ucf_list/
bash ./convert_images_to_list.sh ~path/UCF-101 4

you will get train.list and test.list for your own dataset
such as: ~PATH/UCF-101/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c01 0

2.Train your own dataset(UCF101 as example)

1>if you get path errors, please modify by yourself

cd ./experiments/ucf-101
python train_ucf_rgb.py
python train_ucf_flow.py

2>argues

learning_rate: Initial learning rate
max_steps: Number of steps to run trainer
batch_size: Batch size
num_frame_per_clib: Nummber of frames per clib
crop_size: Crop_size
classics: The num of class

3>models will be stored at ./models, and tensorboard logs will be stored at ./visul_logs

tensorboard --logdir=~path/I3D/experiments/ucf_101/visual_logs/

3.Test your own models

1>if you get path errors, please modify by yourself

cd ./experiments/ucf-101
python test_ucf_rgb.py
python test_ucf_flow.py
python test_ucf_rgb+flow.py

4.Result on my linux

Architecture	Pre_train	ACC/top1
RGB+I3D	Kinetics	86.6
FLOW+I3D	Kinetics	91.8
TWO_STREAM+I3D	Kinetics	95.3
FLOW+I3D	IMAGENET+Kinetics	94.72
RGB+I3D	IMAGENET+Kinetics	95.68
TWO_STREAM+I3D	IMAGENET+Kinetics	97.6