X3D

Abstract

This paper presents X3D, a family of efficient video networks that progressively expand a tiny 2D image classification architecture along multiple network axes, in space, time, width and depth. Inspired by feature selection methods in machine learning, a simple stepwise network expansion approach is employed that expands a single axis in each step, such that good accuracy to complexity trade-off is achieved. To expand X3D to a specific target complexity, we perform progressive forward expansion followed by backward contraction. X3D achieves state-of-the-art performance while requiring 4.8x and 5.5x fewer multiply-adds and parameters for similar accuracy as previous work. Our most surprising finding is that networks with high spatiotemporal resolution can perform well, while being extremely light in terms of network width and parameters. We report competitive accuracy at unprecedented efficiency on video classification and detection benchmarks.

Citation

@misc{feichtenhofer2020x3d,
      title={X3D: Expanding Architectures for Efficient Video Recognition},
      author={Christoph Feichtenhofer},
      year={2020},
      eprint={2004.04730},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Model Zoo

Kinetics-400

config	resolution	backbone	top1 10-view	top1 30-view	reference top1 10-view	reference top1 30-view	ckpt
x3d_s_13x6x1_facebook_kinetics400_rgb	short-side 320	X3D_S	72.7	73.2	73.1 [SlowFast]	73.5 [SlowFast]	ckpt[1]
x3d_m_16x5x1_facebook_kinetics400_rgb	short-side 320	X3D_M	75.0	75.6	75.1 [SlowFast]	76.2 [SlowFast]	ckpt[1]

[1] The models are ported from the repo SlowFast and tested on our data. Currently, we only support the testing of X3D models, training will be available soon.

:::{note}

The values in columns named after "reference" are the results got by testing the checkpoint released on the original repo and codes, using the same dataset with ours.
The validation set of Kinetics400 we used consists of 19796 videos. These videos are available at Kinetics400-Validation. The corresponding data list (each line is of the format 'video_id, num_frames, label_index') and the label map are also available.

:::

For more details on data preparation, you can refer to Kinetics400 in Data Preparation.

Test

You can use the following command to test a model.

python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]

Example: test X3D model on Kinetics-400 dataset and dump the result to a json file.

python tools/test.py configs/recognition/x3d/x3d_s_13x6x1_facebook_kinetics400_rgb.py \
    checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \
    --out result.json --average-clips prob

For more details, you can refer to Test a dataset part in getting_started.