First follow the instructions for install kinetics-i3d
Then, clone this repository using
$git clone https://github.com/LossNAN/I3D-Tensorflow.git
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
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/
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
| 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 |