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s3d_g_pytorch

Separable 3D CNN with a spatio-temporal gating mechanism(S3D_G), proposaled in Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification(ECCV2018).
(Finish completely!!!)

About the repo

This repo is to reimplement S3D_G, a powerful neural network for extracting spatial-temporal features from video/continuous frames. It produces very competitive result on several action classification benchmarks. For more detail, please access the link above.

S3D_G and Sep-Inc

Prerequisite

  • python3
  • Pytorch1.0

Quickly Start

Clone this repo

$ git clone https://github.com/BIGJUN777/s3d_g_pytorch.git   

Install dependencies using pip

$ pip install -r requirements.txt

Prepare dataset

  1. Download database: UCF101 or HMDB51.(Now just ucf101 supported!)
  2. Create the folder and put the database into by making symlinks(or you can put databases into directly)
    $ mkdir dataset     		
    $ ln -s .../UCF101/UCF-101 dataset/UCF-101
    
  3. Train the model. It will take some time to process the data in the first time running. Pay attention pls. Add -h to see more optional arguments.
    $ python train.py
    
  4. Visualization:
    $ tensorboard --logdir=log
    

Inference/Demo

$ python inference.py --video path/to/a/video

Reference

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Reimplement the S3D_G neural network in pytorch

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