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
Mememoji - A facial expression classification system that recognizes 6 basic emotions: happy, sad, surprise, fear, anger and neutral.

a project built with deep convolutional neural network and ❤️ Table of Contents Motivation The Database The Model 3.1 Input Layer 3.2 Convolutional La

Jostine Ho 761 Dec 05, 2022
Official implementation of "Refiner: Refining Self-attention for Vision Transformers".

RefinerViT This repo is the official implementation of "Refiner: Refining Self-attention for Vision Transformers". The repo is build on top of timm an

101 Dec 29, 2022
Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis"

StrengthNet Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis" https://arxiv.org/abs/2110

RuiLiu 65 Dec 20, 2022
Source code for Acorn, the precision farming rover by Twisted Fields

Acorn precision farming rover This is the software repository for Acorn, the precision farming rover by Twisted Fields. For more information see twist

Twisted Fields 198 Jan 02, 2023
Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.

Torch-template-for-deep-learning Pytorch implementations of some **classical backbone CNNs, data enhancement, torch loss, attention, visualization and

Li Shengyan 270 Dec 31, 2022
Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering

Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering This repository provides the source code of "Consensus Learning

SeongKu-Kang 6 Apr 29, 2022
Development kit for MIT Scene Parsing Benchmark

Development Kit for MIT Scene Parsing Benchmark [NEW!] Our PyTorch implementation is released in the following repository: https://github.com/hangzhao

MIT CSAIL Computer Vision 424 Dec 01, 2022
Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation. It was introduced in Wright, Logan G. & Onodera, Tatsuhiro et al. (2021)1 to train Physical Neural Networ

McMahon Lab 230 Jan 05, 2023
PyTorch implementation of "LayoutTransformer: Layout Generation and Completion with Self-attention"

PyTorch implementation of "LayoutTransformer: Layout Generation and Completion with Self-attention" to appear in ICCV 2021

Kamal Gupta 75 Dec 23, 2022
A working implementation of the Categorical DQN (Distributional RL).

Categorical DQN. Implementation of the Categorical DQN as described in A distributional Perspective on Reinforcement Learning. Thanks to @tudor-berari

Florin Gogianu 98 Sep 20, 2022
PyTorch implementation of Pointnet2/Pointnet++

Pointnet2/Pointnet++ PyTorch Project Status: Unmaintained. Due to finite time, I have no plans to update this code and I will not be responding to iss

Erik Wijmans 1.2k Dec 29, 2022
PASSL包含 SimCLR，MoCo，BYOL，CLIP等基于对比学习的图像自监督算法以及 Vision-Transformer，Swin-Transformer，BEiT，CVT，T2T，MLP_Mixer等视觉Transformer算法

PASSL Introduction PASSL is a Paddle based vision library for state-of-the-art Self-Supervised Learning research with PaddlePaddle. PASSL aims to acce

186 Dec 29, 2022
Official Code for VideoLT: Large-scale Long-tailed Video Recognition (ICCV 2021)

Pytorch Code for VideoLT [Website][Paper] Updates [10/29/2021] Features uploaded to Google Drive, for access please send us an e-mail: zhangxing18 at

Skye 26 Sep 18, 2022
Pytorch Implementation of "Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation"

CRL_EGPG Pytorch Implementation of Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation We use contrastive loss implemented b

YHR 25 Nov 14, 2022
Example-custom-ml-block-keras - Custom Keras ML block example for Edge Impulse

Custom Keras ML block example for Edge Impulse This repository is an example on

Edge Impulse 8 Nov 02, 2022
A pytorch-version implementation codes of paper: "BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation"

BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation A pytorch-version implementation

11 Oct 08, 2022
Joint Discriminative and Generative Learning for Person Re-identification. CVPR'19 (Oral)

Joint Discriminative and Generative Learning for Person Re-identification [Project] [Paper] [YouTube] [Bilibili] [Poster] [Supp] Joint Discriminative

NVIDIA Research Projects 1.2k Dec 30, 2022
All-in-one Docker container that allows a user to explore Nautobot in a lab environment.

Nautobot Lab This container is not for production use! Nautobot Lab is an all-in-one Docker container that allows a user to quickly get an instance of

Nautobot 29 Sep 16, 2022
Space Time Recurrent Memory Network - Pytorch

Space Time Recurrent Memory Network - Pytorch (wip) Implementation of Space Time Recurrent Memory Network, recurrent network competitive with attentio

Phil Wang 50 Nov 07, 2021
An implementation of quantum convolutional neural network with MindQuantum. Huawei, classifying MNIST dataset

关于实现的一点说明 山东大学 2020级 苏博南 www.subonan.com 文件说明 tools.py 这里面主要有两个函数： resize(a, lenb) 这其实是我找同学写的一个小算法hhh。给出一个$28\times 28$的方阵a，返回一个$lenb\times lenb$的方阵。因

ぼっけなす 2 Aug 29, 2022