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X3D

Introduction

@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.

Notes:

  1. 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.
  2. 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.