Pseudo-3D Residual Networks

This repo implements the network structure of P3D[1] with PyTorch, pre-trained model weights are converted from caffemodel, which is supported from the author's repo

Requirements:

• pytorch
• numpy
• ffmpeg (for extract image frames from videos)

Pretrained weights

1, P3D-199 trained on Kinetics dataset:

Google Drive url

2, P3D-199 trianed on Kinetics Optical Flow (TVL1):

Google Drive url

Prepare Dataset UCF101

First, download the dataset from UCF into the data folder and then extract it.

cd data && wget http://crcv.ucf.edu/data/UCF101/UCF101.rar
unrar e UCF101.rar

Next, make 3 folders train, test and validation:

mkdir train test validation

Finally, run scripts to extract image frames from videos;

python move.py
python makeVideoFolder.py
python extract.py

Run Code

1, For Training from scratch

python main.py /path/data/

2, For Fine-tuning

python main.py /path/data/ --pretrained

3, For Evaluate model

python main.py /path/data/ --resume=checkpoint.pth.tar --evaluate

4, For testing model

python main.py /path/data/ --test

Experiment Result From Us

Dataset	Accuracy
UCF-101	81.6%
MERL Shopping	82.6%

Reference:

[1]Learning Spatio-Temporal Representation with Pseudo-3D Residual,ICCV2017