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

Related tags

Deep LearningTokShift-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:

Owner
VideoNet
VideoNet
GitHub Repository
The official implementation of the IEEE S&P`22 paper "SoK: How Robust is Deep Neural Network Image Classification Watermarking".

Watermark-Robustness-Toolbox - Official PyTorch Implementation This repository contains the official PyTorch implementation of the following paper to

49 Dec 19, 2022
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

AugMax: Adversarial Composition of Random Augmentations for Robust Training Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, an

VITA 112 Nov 07, 2022
The Python code for the paper A Hybrid Quantum-Classical Algorithm for Robust Fitting

About The Python code for the paper A Hybrid Quantum-Classical Algorithm for Robust Fitting The demo program was only tested under Conda in a standard

Anh-Dzung Doan 5 Nov 28, 2022
Learning Representational Invariances for Data-Efficient Action Recognition

Learning Representational Invariances for Data-Efficient Action Recognition Official PyTorch implementation for Learning Representational Invariances

Virginia Tech Vision and Learning Lab 27 Nov 22, 2022
PyTorch code for our ECCV 2020 paper "Single Image Super-Resolution via a Holistic Attention Network"

HAN PyTorch code for our ECCV 2020 paper "Single Image Super-Resolution via a Holistic Attention Network" This repository is for HAN introduced in the

五维空间 140 Nov 23, 2022
VideoGPT: Video Generation using VQ-VAE and Transformers

VideoGPT: Video Generation using VQ-VAE and Transformers [Paper][Website][Colab][Gradio Demo] We present VideoGPT: a conceptually simple architecture

Wilson Yan 470 Dec 30, 2022
An open-source project for applying deep learning to medical scenarios

Auto Vaidya An open source solution for creating end-end web app for employing the power of deep learning in various clinical scenarios like implant d

Smaranjit Ghose 18 May 29, 2022
C3DPO - Canonical 3D Pose Networks for Non-rigid Structure From Motion.

C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion By: David Novotny, Nikhila Ravi, Benjamin Graham, Natalia Neverova, Andrea Vedal

Meta Research 309 Dec 16, 2022
PyTorch implementation of Progressive Growing of GANs for Improved Quality, Stability, and Variation.

PyTorch implementation of Progressive Growing of GANs for Improved Quality, Stability, and Variation. Warning: the master branch might collapse. To ob

559 Dec 14, 2022
This repository contains all the code and materials distributed in the 2021 Q-Programming Summer of Qode.

Q-Programming Summer of Qode This repository contains all the code and materials distributed in the Q-Programming Summer of Qode. If you want to creat

Sammarth Kumar 11 Jun 11, 2021
Contenido del curso Bases de datos del DCC PUC versión 2021-2

IIC2413 - Bases de Datos Tabla de contenidos Equipo Profesores Ayudantes Contenidos Calendario Evaluaciones Resumen de notas Foro Política de integrid

54 Nov 23, 2022
JupyterNotebook - C/C++, Javascript, HTML, LaTex, Shell scripts in Jupyter Notebook Also run them on remote computer

JupyterNotebook Read, write and execute C, C++, Javascript, Shell scripts, HTML, LaTex in jupyter notebook, And also execute them on remote computer R

1 Jan 09, 2022
Iran Open Source Hackathon

Iran Open Source Hackathon is an open-source hackathon (duh) with the aim of encouraging participation in open-source contribution amongst Iranian dev

OSS Hackathon 121 Dec 25, 2022
Repo for "Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions" https://arxiv.org/abs/2201.12296

Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions This repo contains the dataset and code for the paper Benchmarking Ro

Jiachen Sun 168 Dec 29, 2022
6D Grasping Policy for Point Clouds

GA-DDPG [website, paper] Installation git clone https://github.com/liruiw/GA-DDPG.git --recursive Setup: Ubuntu 16.04 or above, CUDA 10.0 or above, py

Lirui Wang 48 Dec 21, 2022
[CVPR 2022 Oral] MixFormer: End-to-End Tracking with Iterative Mixed Attention

MixFormer The official implementation of the CVPR 2022 paper MixFormer: End-to-End Tracking with Iterative Mixed Attention [Models and Raw results] (G

Multimedia Computing Group, Nanjing University 235 Jan 03, 2023
Waymo motion prediction challenge 2021: 3rd place solution

Waymo motion prediction challenge 2021: 3rd place solution 📜 Technical report 🗨️ Presentation 🎉 Announcement 🛆Motion Prediction Channel Website 🛆

158 Jan 08, 2023
Repository for the paper "Exploring the Sensory Spaces of English Perceptual Verbs in Natural Language Data"

Sensory Spaces of English Perceptual Verbs This repository contains the code and collocational data described in the paper "Exploring the Sensory Spac

David Peng 0 Sep 07, 2021
Official repository for Jia, Raghunathan, Göksel, and Liang, "Certified Robustness to Adversarial Word Substitutions" (EMNLP 2019)

Certified Robustness to Adversarial Word Substitutions This is the official GitHub repository for the following paper: Certified Robustness to Adversa

Robin Jia 38 Oct 16, 2022
Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE TGRS, 2021.

Graph Convolutional Networks for Hyperspectral Image Classification Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot T

Danfeng Hong 154 Dec 13, 2022