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
Deep Two-View Structure-from-Motion Revisited

Deep Two-View Structure-from-Motion Revisited This repository provides the code for our CVPR 2021 paper Deep Two-View Structure-from-Motion Revisited.

Jianyuan Wang 145 Jan 06, 2023
Code examples and benchmarks from the paper "Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective"

Code For the Paper "Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective" Author: Robert Bamler Date: 22 D

4 Nov 02, 2022
This repository is all about spending some time the with the original problem posed by Minsky and Papert

This repository is all about spending some time the with the original problem posed by Minsky and Papert. Working through this problem is a great way to begin learning computer vision.

Jaissruti Nanthakumar 1 Jan 23, 2022
Code for KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs

KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs Check out the paper on arXiv: https://arxiv.org/abs/2103.13744 This repo cont

Christian Reiser 373 Dec 20, 2022
Disagreement-Regularized Imitation Learning

Due to a normalization bug the expert trajectories have lower performance than the rl_baseline_zoo reported experts. Please see the following link in

Kianté Brantley 25 Apr 28, 2022
Bottleneck Transformers for Visual Recognition

Bottleneck Transformers for Visual Recognition Experiments Model Params (M) Acc (%) ResNet50 baseline (ref) 23.5M 93.62 BoTNet-50 18.8M 95.11% BoTNet-

Myeongjun Kim 236 Jan 03, 2023
AI4Good project for detecting waste in the environment

Detect waste AI4Good project for detecting waste in environment. www.detectwaste.ml. Our latest results were published in Waste Management journal in

108 Dec 25, 2022
Open source person re-identification library in python

Open-ReID Open-ReID is a lightweight library of person re-identification for research purpose. It aims to provide a uniform interface for different da

Tong Xiao 1.3k Jan 01, 2023
Generative Modelling of BRDF Textures from Flash Images [SIGGRAPH Asia, 2021]

Neural Material Official code repository for the paper: Generative Modelling of BRDF Textures from Flash Images [SIGGRAPH Asia, 2021] Henzler, Deschai

Philipp Henzler 80 Dec 20, 2022
Code of PVTv2 is released! PVTv2 largely improves PVTv1 and works better than Swin Transformer with ImageNet-1K pre-training.

Updates (2020/06/21) Code of PVTv2 is released! PVTv2 largely improves PVTv1 and works better than Swin Transformer with ImageNet-1K pre-training. Pyr

1.3k Jan 04, 2023
CTF challenges and write-ups for MicroCTF 2021.

MicroCTF 2021 Qualifications About This repository contains CTF challenges and official write-ups for MicroCTF 2021 Qualifications. License Distribute

Shellmates 12 Dec 27, 2022
Adaptive Pyramid Context Network for Semantic Segmentation (APCNet CVPR'2019)

Adaptive Pyramid Context Network for Semantic Segmentation (APCNet CVPR'2019) Introduction Official implementation of Adaptive Pyramid Context Network

21 Nov 09, 2022
Weight estimation in CT by multi atlas techniques

maweight A Python package for multi-atlas based weight estimation for CT images, including segmentation by registration, feature extraction and model

György Kovács 0 Dec 24, 2021
통일된 DataScience 폴더 구조 제공 및 가상환경 작업의 부담감 해소

Lucas coded by linux shell 목차 Mac버전 CookieCutter (autoenv) 1.How to Install autoenv 2.폴더 진입 시, activate 구현하기 3.폴더 탈출 시, deactivate 구현하기 4.Alias 설정하기 5

ello 3 Feb 21, 2022
Logistic Bandit experiments. Official code for the paper "Jointly Efficient and Optimal Algorithms for Logistic Bandits".

Code for the paper Jointly Efficient and Optimal Algorithms for Logistic Bandits, by Louis Faury, Marc Abeille, Clément Calauzènes and Kwang-Sun Jun.

Faury Louis 1 Jan 22, 2022
Learning Synthetic Environments and Reward Networks for Reinforcement Learning

Learning Synthetic Environments and Reward Networks for Reinforcement Learning We explore meta-learning agent-agnostic neural Synthetic Environments (

AutoML-Freiburg-Hannover 16 Sep 02, 2022
Official Keras Implementation for UNet++ in IEEE Transactions on Medical Imaging and DLMIA 2018

UNet++: A Nested U-Net Architecture for Medical Image Segmentation UNet++ is a new general purpose image segmentation architecture for more accurate i

Zongwei Zhou 1.8k Jan 07, 2023
Aws-machine-learning-university-accelerated-tab - Machine Learning University: Accelerated Tabular Data Class

Machine Learning University: Accelerated Tabular Data Class This repository contains slides, notebooks, and datasets for the Machine Learning Universi

AWS Samples 916 Dec 23, 2022
A Python Package for Convex Regression and Frontier Estimation

pyStoNED pyStoNED is a Python package that provides functions for estimating multivariate convex regression, convex quantile regression, convex expect

Sheng Dai 17 Jan 08, 2023
NUANCED is a user-centric conversational recommendation dataset that contains 5.1k annotated dialogues and 26k high-quality user turns.

NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions Overview NUANCED is a user-centric conversational recommen

Facebook Research 18 Dec 28, 2021