README.md

X3D: Progressive Network Expansion for Efficient Video Recognition

Christoph Feichtenhofer,
In CVPR, 2020. [Paper]

Getting started

IMPORTANT The naïve implementation of channelwise 3D convolution (Conv3D operation with group size > 1) in PyTorch is extremely slow. To have fast GPU runtime with X3D models, please patch the following pull request before using X3D: Conv3D pull request

To use X3D models please refer to the configs under configs/Kinetics, or see the MODEL_ZOO.md for pre-trained models. See paper for details. For example, the command

python tools/run_net.py \
  --cfg configs/Kinetics/X3D-XS.yaml \
  DATA.PATH_TO_DATA_DIR path_to_your_dataset \

should train and test an extra small (XS) X3D model on your dataset.

Citing X3D

If you find X3D useful for your research, please consider citing the paper using the following BibTeX entry.

@inproceedings{x3d2020,
  Author    = {Christoph Feichtenhofer},
  Title     = {{X3D}: Progressive Network Expansion for Efficient Video Recognition},
  Booktitle = {{CVPR}},
  Year      = {2020}}