Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers.

Related tags

Deep Learningtransformersimage-recognitionmlps
Overview

Less is More: Pay Less Attention in Vision Transformers

Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers.

By Zizheng Pan, Bohan Zhuang, Haoyu He, Jing Liu and Jianfei Cai.

In our paper, we present a novel Less attention vIsion Transformer (LIT), building upon the fact that convolutions, fully-connected (FC) layers, and self-attentions have almost equivalent mathematical expressions for processing image patch sequences. LIT uses pure multi-layer perceptrons (MLPs) to encode rich local patterns in the early stages while applying self-attention modules to capture longer dependencies in deeper layers. Moreover, we further propose a learned deformable token merging module to adaptively fuse informative patches in a non-uniform manner.

If you use this code for a paper please cite:

@article{pan2021less,
  title={Less is More: Pay Less Attention in Vision Transformers},
  author={Pan, Zizheng and Zhuang, Bohan and He, Haoyu and Liu, Jing and Cai, Jianfei},
  journal={arXiv preprint arXiv:2105.14217},
  year={2021}
}

Usage

First, clone this repository.

git clone https://github.com/MonashAI/LIT

Next, create a conda virtual environment.

# Make sure you have a NVIDIA GPU.
cd LIT/
bash setup_env.sh [conda_install_path] [env_name]

# For example
bash setup_env.sh /home/anaconda3 lit

Note: We use PyTorch 1.7.1 with CUDA 10.1 for all experiments. The setup_env.sh has illustrated all dependencies we used in our experiments. You may want to edit this file to install a different version of PyTorch or any other packages.

Data Preparation

Download the ImageNet 2012 dataset from here, and prepare the dataset based on this script. The file structure should look like:

imagenet
├── train
│   ├── class1
│   │   ├── img1.jpeg
│   │   ├── img2.jpeg
│   │   └── ...
│   ├── class2
│   │   ├── img3.jpeg
│   │   └── ...
│   └── ...
└── val
    ├── class1
    │   ├── img4.jpeg
    │   ├── img5.jpeg
    │   └── ...
    ├── class2
    │   ├── img6.jpeg
    │   └── ...
    └── ...

Model Zoo

We provide baseline LIT models pretrained on ImageNet 2012.

Name Params (M) FLOPs (G) Top-1 Acc. (%) Model Log
LIT-Ti 19 3.6 81.1 google drive/github log
LIT-S 27 4.1 81.5 google drive/github log
LIT-M 48 8.6 83.0 google drive/github log
LIT-B 86 15.0 83.4 google drive/github log

Training and Evaluation

In our implementation, we have different training strategies for LIT-Ti and other LIT models. Therefore, we provide two codebases.

For LIT-Ti, please refer to code_for_lit_ti.

For LIT-S, LIT-M, LIT-B, please refer to code_for_lit_s_m_b.

License

This repository is released under the Apache 2.0 license as found in the LICENSE file.

Acknowledgement

This repository has adopted codes from DeiT, PVT and Swin, we thank the authors for their open-sourced code.

You might also like...
This is an official implementation of CvT: Introducing Convolutions to Vision Transformers.

Introduction This is an official implementation of CvT: Introducing Convolutions to Vision Transformers. We present a new architecture, named Convolut

Bin Xiao 175 Jan 8, 2023
Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention

cosFormer Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention Update log 2022/2/28 Add core code License This

null 120 Dec 15, 2022
Official implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

CrossViT This repository is the official implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification. ArXiv If

International Business Machines 168 Dec 29, 2022
The official implementation of ELSA: Enhanced Local Self-Attention for Vision Transformer
The official implementation of ELSA: Enhanced Local Self-Attention for Vision Transformer

ELSA: Enhanced Local Self-Attention for Vision Transformer By Jingkai Zhou, Pich

DamoCV 87 Dec 19, 2022
Implementation of SE3-Transformers for Equivariant Self-Attention, in Pytorch.

SE3 Transformer - Pytorch Implementation of SE3-Transformers for Equivariant Self-Attention, in Pytorch. May be needed for replicating Alphafold2 resu

Phil Wang 207 Dec 23, 2022
Pytorch implementation for our ICCV 2021 paper
Pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering".

TRAnsformer Routing Networks (TRAR) This is an official implementation for ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visu

Ren Tianhe 49 Nov 10, 2022
PyTorch Implementation of CvT: Introducing Convolutions to Vision Transformers
PyTorch Implementation of CvT: Introducing Convolutions to Vision Transformers

CvT: Introducing Convolutions to Vision Transformers Pytorch implementation of CvT: Introducing Convolutions to Vision Transformers Usage: img = torch

Rishikesh (ऋषिकेश) 193 Jan 3, 2023
A PyTorch implementation of ViTGAN based on paper ViTGAN: Training GANs with Vision Transformers.
A PyTorch implementation of ViTGAN based on paper ViTGAN: Training GANs with Vision Transformers.

ViTGAN: Training GANs with Vision Transformers A PyTorch implementation of ViTGAN based on paper ViTGAN: Training GANs with Vision Transformers. Refer

Hong-Jia Chen 127 Dec 23, 2022
Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image classification, in Pytorch
Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image classification, in Pytorch

Transformer in Transformer Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image c

Phil Wang 272 Dec 23, 2022
Comments
  • Problem about DCN

    Problem about DCN

    I have some problems about compiling DCN, thus it is hard for me to use the DCN.deform_conv2d_forward and DCN.deform_conv2d_backward functions. Can 'deform_conv2d_naive' be used instead of this part? Or is there other methods for me to accomplish this DCN.deform_conv2d part?

    opened by jarygrace 3
  • How to use LITNet as a beckbone of object detection

    How to use LITNet as a beckbone of object detection

    Could you please release the code of using LITNet as a beckbone of RetinaNet as mentioned in your paper? I wonder how to use it as a beckbone for object detection...

    opened by sunhuisunhui 2
Owner
GitHub Repository
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning (ECCV 2020 Oral and TRO)

Visual Interestingness Refer to the project description for more details. This code based on the following paper. Chen Wang, Yuheng Qiu, Wenshan Wang,

Chen Wang 36 Sep 08, 2022
Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

258 Dec 29, 2022
TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL, and utterance id

TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL

3 Dec 26, 2022
Research into Forex price prediction from price history using Deep Sequence Modeling with Stacked LSTMs.

Forex Data Prediction via Recurrent Neural Network Deep Sequence Modeling Research Paper Our research paper can be viewed here Installation Clone the

Alex Taradachuk 2 Aug 07, 2022
Supervised 3D Pre-training on Large-scale 2D Natural Image Datasets for 3D Medical Image Analysis

Introduction This is an implementation of our paper Supervised 3D Pre-training on Large-scale 2D Natural Image Datasets for 3D Medical Image Analysis.

24 Dec 06, 2022
Source Code For Template-Based Named Entity Recognition Using BART

Template-Based NER Source Code For Template-Based Named Entity Recognition Using BART Training Training train.py Inference inference.py Corpus ATIS (h

174 Dec 19, 2022
The dynamics of representation learning in shallow, non-linear autoencoders

The dynamics of representation learning in shallow, non-linear autoencoders The package is written in python and uses the pytorch implementation to ML

Maria Refinetti 4 Jun 08, 2022
Code and real data for the paper "Counterfactual Temporal Point Processes", available at arXiv.

counterfactual-tpp This is a repository containing code and real data for the paper Counterfactual Temporal Point Processes. Pre-requisites This code

Networks Learning 11 Dec 09, 2022
This repository contains the implementation of the paper: Federated Distillation of Natural Language Understanding with Confident Sinkhorns

Federated Distillation of Natural Language Understanding with Confident Sinkhorns This repository provides an alternative method for ensembled distill

Deep Cognition and Language Research (DeCLaRe) Lab 11 Nov 16, 2022
Source code for Task-Aware Variational Adversarial Active Learning

Contrastive Coding for Active Learning under Class Distribution Mismatch Official PyTorch implementation of ["Contrastive Coding for Active Learning u

27 Nov 23, 2022
Implementation of Uformer, Attention-based Unet, in Pytorch

Uformer - Pytorch Implementation of Uformer, Attention-based Unet, in Pytorch. It will only offer the concat-cross-skip connection. This repository wi

Phil Wang 72 Dec 19, 2022
the official code for ICRA 2021 Paper: "Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation"

G2S This is the official code for ICRA 2021 Paper: Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation by Hemang

NeurAI 4 Jul 27, 2022
Source code related to the article submitted to the International Conference on Computational Science ICCS 2022 in London

POTHER: Patch-Voted Deep Learning-based Chest X-ray Bias Analysis for COVID-19 Detection Source code related to the article submitted to the Internati

Tomasz Szczepański 1 Apr 29, 2022
A Learning-based Camera Calibration Toolbox

Learning-based Camera Calibration A Learning-based Camera Calibration Toolbox Paper The pdf file can be found here. @misc{zhang2022learningbased,

Eason 14 Dec 21, 2022
A light and fast one class detection framework for edge devices. We provide face detector, head detector, pedestrian detector, vehicle detector......

A Light and Fast Face Detector for Edge Devices Big News: LFD, which is a big update of LFFD, now is released (2021.03.09). It is strongly recommended

YonghaoHe 1.3k Dec 25, 2022
"Graph Neural Controlled Differential Equations for Traffic Forecasting", AAAI 2022

Graph Neural Controlled Differential Equations for Traffic Forecasting Setup Python environment for STG-NCDE Install python environment $ conda env cr

Jeongwhan Choi 55 Dec 28, 2022
Official Repository for our ECCV2020 paper: Imbalanced Continual Learning with Partitioning Reservoir Sampling

Imbalanced Continual Learning with Partioning Reservoir Sampling This repository contains the official PyTorch implementation and the dataset for our

Chris Dongjoo Kim 40 Sep 18, 2022
This program uses trial auth token of Azure Cognitive Services to do speech synthesis for you.

🗣️ aspeak A simple text-to-speech client using azure TTS API(trial). 😆 TL;DR: This program uses trial auth token of Azure Cognitive Services to do s

Levi Zim 359 Jan 05, 2023
ReferFormer - Official Implementation of ReferFormer

The official implementation of the paper: Language as Queries for Referring Vide

Jonas Wu 232 Dec 29, 2022
用opencv的dnn模块做yolov5目标检测，包含C++和Python两个版本的程序

yolov5-dnn-cpp-py yolov5s,yolov5l,yolov5m,yolov5x的onnx文件在百度云盘下载， 链接：https://pan.baidu.com/s/1d67LUlOoPFQy0MV39gpJiw 提取码：bayj python版本的主程序是main_yolov5.

365 Jan 04, 2023