This is official implementaion of paper "Token Shift Transformer for Video Classification".

Related tags

Deep Learning TokShift-Transformer
Overview

TokShift-Transformer

This is official implementaion of paper "Token Shift Transformer for Video Classification". We achieve SOTA performance 80.40% on Kinetics-400 val. Paper link

Updates

July 11, 2021

  • Release this V1 version (the version used in paper) to public.
  • we are preparing a V2 version which include the following modifications, will release within 1 week:
  1. Directly decode video mp4 file during training/evaluation
  2. Change to adopt standarlize timm code-base.
  3. Performances are further improved than reported in paper version (average +0.5).

April 22, 2021

  • Add Train/Test guidline and Data perpariation

April 16, 2021

  • Publish TokShift Transformer for video content understanding

Model Zoo and Baselines

architecture backbone pretrain Res & Frames GFLOPs x views top1 config
ViT (Video) Base16 ImgNet21k 224 & 8 134.7 x 30 76.02 link k400_vit_8x32_224.yml
TokShift Base-16 ImgNet21k 224 & 8 134.7 x 30 77.28 link k400_tokshift_div4_8x32_base_224.yml
TokShift (MR) Base16 ImgNet21k 256 & 8 175.8 x 30 77.68 link k400_tokshift_div4_8x32_base_256.yml
TokShift (HR) Base16 ImgNet21k 384 & 8 394.7 x 30 78.14 link k400_tokshift_div4_8x32_base_384.yml
TokShift Base16 ImgNet21k 224 & 16 268.5 x 30 78.18 link k400_tokshift_div4_16x32_base_224.yml
TokShift-Large (HR) Large16 ImgNet21k 384 & 8 1397.6 x 30 79.83 link k400_tokshift_div4_8x32_large_384.yml
TokShift-Large (HR) Large16 ImgNet21k 384 & 12 2096.4 x 30 80.40 link k400_tokshift_div4_12x32_large_384.yml

Below is trainig log, we use 3 views evaluation (instead of 30 views) during validation for time-saving.

Installation

  • PyTorch >= 1.7, torchvision
  • tensorboardx

Quick Start

Train

  1. Download ImageNet-22k pretrained weights from Base16 and Large16.
  2. Prepare Kinetics-400 dataset organized in the following structure, trainValTest
k400
|_ frames331_train
|  |_ [category name 0]
|  |  |_ [video name 0]
|  |  |  |_ img_00001.jpg
|  |  |  |_ img_00002.jpg
|  |  |  |_ ...
|  |  |
|  |  |_ [video name 1]
|  |  |   |_ img_00001.jpg
|  |  |   |_ img_00002.jpg
|  |  |   |_ ...
|  |  |_ ...
|  |
|  |_ [category name 1]
|  |  |_ [video name 0]
|  |  |  |_ img_00001.jpg
|  |  |  |_ img_00002.jpg
|  |  |  |_ ...
|  |  |
|  |  |_ [video name 1]
|  |  |   |_ img_00001.jpg
|  |  |   |_ img_00002.jpg
|  |  |   |_ ...
|  |  |_ ...
|  |_ ...
|
|_ frames331_val
|  |_ [category name 0]
|  |  |_ [video name 0]
|  |  |  |_ img_00001.jpg
|  |  |  |_ img_00002.jpg
|  |  |  |_ ...
|  |  |
|  |  |_ [video name 1]
|  |  |   |_ img_00001.jpg
|  |  |   |_ img_00002.jpg
|  |  |   |_ ...
|  |  |_ ...
|  |
|  |_ [category name 1]
|  |  |_ [video name 0]
|  |  |  |_ img_00001.jpg
|  |  |  |_ img_00002.jpg
|  |  |  |_ ...
|  |  |
|  |  |_ [video name 1]
|  |  |   |_ img_00001.jpg
|  |  |   |_ img_00002.jpg
|  |  |   |_ ...
|  |  |_ ...
|  |_ ...
|
|_ trainValTest
   |_ train.txt
   |_ val.txt
  1. Using train-script (train.sh) to train k400
#!/usr/bin/env python
import os

cmd = "python -u main_ddp_shift_v3.py \
		--multiprocessing-distributed --world-size 1 --rank 0 \
		--dist-ur tcp://127.0.0.1:23677 \
		--tune_from pretrain/ViT-L_16_Img21.npz \
		--cfg config/custom/kinetics400/k400_tokshift_div4_12x32_large_384.yml"
os.system(cmd)

Test

Using test.sh (test.sh) to evaluate k400

#!/usr/bin/env python
import os
cmd = "python -u main_ddp_shift_v3.py \
        --multiprocessing-distributed --world-size 1 --rank 0 \
        --dist-ur tcp://127.0.0.1:23677 \
        --evaluate \
        --resume model_zoo/ViT-B_16_k400_dense_cls400_segs8x32_e18_lr0.1_B21_VAL224/best_vit_B8x32x224_k400.pth \
        --cfg config/custom/kinetics400/k400_vit_8x32_224.yml"
os.system(cmd)

Contributors

VideoNet is written and maintained by Dr. Hao Zhang and Dr. Yanbin Hao.

Citing

If you find TokShift-xfmr is useful in your research, please use the following BibTeX entry for citation.

@article{tokshift2021,
  title={Token Shift Transformer for Video Classification},
  author={Hao Zhang, Yanbin Hao, Chong-Wah Ngo},
  journal={ACM Multimedia 2021},
}

Acknowledgement

Thanks for the following Github projects:

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Comments
  • About fine-tune on small-scale datasets

    About fine-tune on small-scale datasets

    Hi,

    Thanks for sharing this amazing work. I'm interested in how to fine-tune on small-scale datasets. Can you share fine-tuning commands and config files? It will be very helpful for me.

    Thanks.

    opened by sha310139 2
Owner
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