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PyVideoResearch

List of implemented methods:

List of supported datasets:

List of supported tasks:

Contributor: Gunnar Atli Sigurdsson

@inproceedings{sigurdsson2018pyvideoresearch,
author = {Gunnar A. Sigurdsson and Abhinav Gupta},
title = {PyVideoResearch},
year={2018},
code = {https://github.com/gsig/PyVideoResearch},
}

and remember to cite the papers for the datasets/methods you use.

Installation Instructions

Requirements:

Python packages:

See external libraries under external/ for requirements if using their corresponding baselines.

Run the following to get both this repository and the remote repositories under external/

git clone git@github.com:gsig/PyVideoResearch.git
git submodule update --init --recursive

Steps to train your own network:

  1. Download the corresponding dataset
  2. Duplicate and edit one of the experiment files under exp/ with appropriate parameters. For additional parameters, see opts.py
  3. Run an experiment by calling python exp/rgbnet.py where rgbnet.py is your experiment file. See baseline_exp/ for a variety of baselines.
  4. The checkpoints/logfiles/outputs are stored in your specified cache directory.
  5. Build of the code, cite our papers, and say hi to us at CVPR.

Good luck!

Pretrained networks:

We are in the process of preparing and releasing the pre-trained models. If anything is missing, please let us know. The names correspond to experiments under "baseline_exp". While we standardize the names, please be aware that some of the model may have names listed after "original name" in the experiment file. We also provide the generated log.txt file for each experiment as name.txt

The models are stored here: https://www.dropbox.com/sh/duodxydolzz5qfl/AAC0i70lv8ssVRWg4ux5Vv9pa?dl=0

Infrequently Asked Questions