Git Product home page Git Product logo

mtp's Introduction

ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond

Updates | Introduction | Statement |

Current applications

Image Classification: Please see ViTAE-Transformer for image classification;

Object Detection: Please see ViTAE-Transformer for object detection;

Sementic Segmentation: Please see ViTAE-Transformer for semantic segmentation;

Animal Pose Estimation: Please see ViTAE-Transformer for animal pose estimation;

Matting: Please see ViTAE-Transformer for matting;

Remote Sensing: Please see ViTAE-Transformer for Remote Sensing;

Updates

09/04/2021

24/03/2021

  • The pretrained models for both ViTAE and ViTAEv2 are released. The code for downstream tasks are also provided for reference.

07/12/2021

  • The code is released!

19/10/2021

  • The paper is accepted by Neurips'2021! The code will be released soon!

06/08/2021

  • The paper is post on arxiv! The code will be made public available once cleaned up.

Introduction

This repository contains the code, models, test results for the paper ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias. It contains several reduction cells and normal cells to introduce scale-invariance and locality into vision transformers. In ViTAEv2, we explore the usage of window attentions without shift operations to obtain a better balance between memory footprint, speed, and performance. We also stack the proposed RC and NC in a multi-stage manner to faciliate the learning on other vision tasks including detection, segmentation, and pose.

Fig.1 - The details of RC and NC design in ViTAE.

Fig.2 - The multi-stage design of ViTAEv2.

Statement

This project is for research purpose only. For any other questions please contact yufei.xu at outlook.com qmzhangzz at hotmail.com .

Citing ViTAE and ViTAEv2

@article{xu2021vitae,
  title={Vitae: Vision transformer advanced by exploring intrinsic inductive bias},
  author={Xu, Yufei and Zhang, Qiming and Zhang, Jing and Tao, Dacheng},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}
@article{zhang2022vitaev2,
  title={ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond},
  author={Zhang, Qiming and Xu, Yufei and Zhang, Jing and Tao, Dacheng},
  journal={arXiv preprint arXiv:2202.10108},
  year={2022}
}

Other Links

Image Classification: See ViTAE for Image Classification

Object Detection: See ViTAE for Object Detection.

Semantic Segmentation: See ViTAE for Semantic Segmentation.

Animal Pose Estimation: See ViTAE for Animal Pose Estimation.

Matting: See ViTAE for Matting.

Remote Sensing: See ViTAE for Remote Sensing.

mtp's People

Contributors

dotwang avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar

mtp's Issues

Installing the repo and using MMSeg

Dear authors, thank you for your work and for open-sourcing it. I have a question about using this code.

I am not familiar with MMSeg, so this is probably a stupid question. I have installed it using pip but this does not install the tools folder that is needed to run the commands in the README. Should I have instead used the MMSeg repository instead of the pip package ? If yes, how do I use your repo with respect to the MMSeg repo ? It seems that MMSeg does not work like the libraries I am used to, so I am a bit confused.

Thank you in advance and good luck with your future works.

测试

请问应该如何用你训练好的模型做测试呢,没有找到tool/test.py,在windows系统下可以跑吗

How to inference on custom dataset?

Hello, thanks for your amazing job.
I want to use Semantic Segmentation model finetuned on LoveDA, but it seems that there is no script for inference on custom dataset provided here.
How should I do it?

运用

你好,我想请问一下我可以直接用你们已发布的模型,来估计自己数据集图像中目标数量吗,怎样做更加方便呢?谢谢!

Multichannel images

Hello,

It seems that the examples focus on 3 channel images.

Is semantic segmentation expected to work on multichannel images, and is there an example on that?

I know that mmsegmentation has LoadSingleRSImageFromFile, but I wonder if there would be any specific limitation associated with MTP? How to handle pre-trained weights?

Thanks!

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.