MMLAB的实例分割算法mmsegmentation

2024-02-05 04:12

本文主要是介绍MMLAB的实例分割算法mmsegmentation,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

        当谈及实例分割时,人们往往只会提到一些早期的经典算法,比如 PSP-Net、DeepLabv3、DeepLabv3+ 和 U-Net。然而,实例分割领域已经在过去的五六年中蓬勃发展,涌现出许多新的算法。今天,让我们一起探索这个算法库,它包含了众多最新的实例分割算法。后面,我将会为大家详细介绍如何使用这个算法库。总的来说,若你关注实例分割领域的最新进展,这个算法库值得你拥有。

1、目前支持的算法:


- [x] [SAN (CVPR'2023)](configs/san/)
- [x] [VPD (ICCV'2023)](configs/vpd)
- [x] [DDRNet (T-ITS'2022)](configs/ddrnet)
- [x] [PIDNet (ArXiv'2022)](configs/pidnet)
- [x] [Mask2Former (CVPR'2022)](configs/mask2former)
- [x] [MaskFormer (NeurIPS'2021)](configs/maskformer)
- [x] [K-Net (NeurIPS'2021)](configs/knet)
- [x] [SegFormer (NeurIPS'2021)](configs/segformer)
- [x] [Segmenter (ICCV'2021)](configs/segmenter)
- [x] [DPT (ArXiv'2021)](configs/dpt)
- [x] [SETR (CVPR'2021)](configs/setr)
- [x] [STDC (CVPR'2021)](configs/stdc)
- [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2)
- [x] [CGNet (TIP'2020)](configs/cgnet)
- [x] [PointRend (CVPR'2020)](configs/point_rend)
- [x] [DNLNet (ECCV'2020)](configs/dnlnet)
- [x] [OCRNet (ECCV'2020)](configs/ocrnet)
- [x] [ISANet (ArXiv'2019/IJCV'2021)](configs/isanet)
- [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn)
- [x] [FastFCN (ArXiv'2019)](configs/fastfcn)
- [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet)
- [x] [ANN (ICCV'2019)](configs/ann)
- [x] [EMANet (ICCV'2019)](configs/emanet)
- [x] [CCNet (ICCV'2019)](configs/ccnet)
- [x] [DMNet (ICCV'2019)](configs/dmnet)
- [x] [Semantic FPN (CVPR'2019)](configs/sem_fpn)
- [x] [DANet (CVPR'2019)](configs/danet)
- [x] [APCNet (CVPR'2019)](configs/apcnet)
- [x] [NonLocal Net (CVPR'2018)](configs/nonlocal_net)
- [x] [EncNet (CVPR'2018)](configs/encnet)
- [x] [DeepLabV3+ (CVPR'2018)](configs/deeplabv3plus)
- [x] [UPerNet (ECCV'2018)](configs/upernet)
- [x] [ICNet (ECCV'2018)](configs/icnet)
- [x] [PSANet (ECCV'2018)](configs/psanet)
- [x] [BiSeNetV1 (ECCV'2018)](configs/bisenetv1)
- [x] [DeepLabV3 (ArXiv'2017)](configs/deeplabv3)
- [x] [PSPNet (CVPR'2017)](configs/pspnet)
- [x] [ERFNet (T-ITS'2017)](configs/erfnet)
- [x] [UNet (MICCAI'2016/Nat. Methods'2019)](configs/unet)
- [x] [FCN (CVPR'2015/TPAMI'2017)](configs/fcn)

方法

时间

题目

dsdl

Standard Description Language for DataSet

san

2013

Side adapter network for open-vocabulary semantic segmentation

unet

2015

U-net: Convolutional networks for biomedical image segmentation

erfnet

2017

Erfnet: Efficient residual factorized convnet for real-time semantic segmentation

fcn

2017

Fully convolutional networks for semantic segmentation

pspnet

2017

Pyramid Scene Parsing Network

bisenetv1_r18-d32

2018

Bisenet: Bilateral segmentation network for real-time semantic segmentation

encnet

2018

Context Encoding for Semantic Segmentation

icnet_r50-d8

2018

Icnet for real-time semantic segmentation on high-resolution images

nonlocal

2018

Non-local neural networks

psanet

2018

Psanet: Point-wise spatial attention network for scene parsing

upernet

2018

Unified perceptual parsing for scene understanding

ann

2019

Asymmetric non-local neural networks for semantic segmentation

apcnet

2019

Adaptive Pyramid Context Network for Semantic Segmentation

ccnet

2019

CCNet: Criss-Cross Attention for Semantic Segmentation

danet

2019

Dual Attention Network for Scene Segmentation

emanet_r50-d8

2019

Expectation-maximization attention networks for semantic segmentation

fastfcn

2019

Fastfcn: Rethinking dilated convolution in the backbone for semantic segmentation

fast_scnn

2019

Fast-scnn: Fast semantic segmentation network

hrnet

2019

Deep High-Resolution Representation Learning for Human Pose Estimation

gcnet

2019

Gcnet: Non-local networks meet squeeze-excitation networks and beyond

sem_fpn

2019

Panoptic feature pyramid networks

cgNet

2020

Cgnet: A light-weight context guided network for semantic segmentation

dnlnet

2020

Disentangled Non-Local Neural Networks

ocrnet

2020

Object-Contextual Representations for Semantic Segmentation

pointrend

2020

Pointrend: Image segmentation as rendering

setr

2020

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

bisenetv2

2021

Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation

dpt

2021

Vision Transformers for Dense Prediction

isanet_r50-d8

2021

OCNet: Object Context for Semantic Segmentation

knet

2021

{K-Net: Towards} Unified Image Segmentation

mae

2021

Masked autoencoders are scalable vision learners

mask2former

2021

Per-Pixel Classification is Not All You Need for Semantic Segmentation

maskformer

2021

Per-pixel classification is not all you need for semantic segmentation

segformer

2021

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

segmenter

2021

Segmenter: Transformer for semantic segmentation

stdc

2021

Rethinking BiSeNet For Real-time Semantic Segmentation

Beit

2022

{BEiT}: {BERT} Pre-Training of Image Transformers

convnext

2022

A ConvNet for the 2020s

ddrnet

2022

Deep Dual-Resolution Networks for Real-Time and Accurate Semantic Segmentation of Traffic Scenes

pidnet

2022

PIDNet: A Real-time Semantic Segmentation Network Inspired from PID Controller

poolformer

2022

Metaformer is actually what you need for vision

segnext

2022

SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

VPD

2023

Unleashing Text-to-Image Diffusion Models for Visual Perception

2、支持的骨干网络:

- [x] ResNet (CVPR'2016)
- [x] ResNeXt (CVPR'2017)
- [x] [HRNet (CVPR'2019)](configs/hrnet)
- [x] [ResNeSt (ArXiv'2020)](configs/resnest)
- [x] [MobileNetV2 (CVPR'2018)](configs/mobilenet_v2)
- [x] [MobileNetV3 (ICCV'2019)](configs/mobilenet_v3)
- [x] [Vision Transformer (ICLR'2021)](configs/vit)
- [x] [Swin Transformer (ICCV'2021)](configs/swin)
- [x] [Twins (NeurIPS'2021)](configs/twins)
- [x] [BEiT (ICLR'2022)](configs/beit)
- [x] [ConvNeXt (CVPR'2022)](configs/convnext)
- [x] [MAE (CVPR'2022)](configs/mae)
- [x] [PoolFormer (CVPR'2022)](configs/poolformer)
- [x] [SegNeXt (NeurIPS'2022)](configs/segnext)

3、支持的数据集:


- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#cityscapes)
- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#pascal-voc)
- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#ade20k)
- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#pascal-context)
- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#coco-stuff-10k)
- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#coco-stuff-164k)
- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#chase-db1)
- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#drive)
- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#hrf)
- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#stare)
- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#dark-zurich)
- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#nighttime-driving)
- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#loveda)
- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#isprs-potsdam)
- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#isprs-vaihingen)
- [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#isaid)
- [x] [Mapillary Vistas](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#mapillary-vistas-datasets)
- [x] [LEVIR-CD](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#levir-cd)
- [x] [BDD100K](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#bdd100K)
- [x] [NYU](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#nyu)

4、自定义个人任务:

当然如果以上无法满足,这里面提供了详细的教程与方便的接口,以供制作自己的数据集和设计自己的算法、主干网络、损失函数等。

5、参考文章:

  1. Welcome to MMSegmentation’s documentation! — MMSegmentation 1.2.2 documentation
  2. open-mmlab/mmsegmentation: OpenMMLab Semantic Segmentation Toolbox and Benchmark. (github.com)

这篇关于MMLAB的实例分割算法mmsegmentation的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/679726

相关文章

Python实现图片分割的多种方法总结

《Python实现图片分割的多种方法总结》图片分割是图像处理中的一个重要任务,它的目标是将图像划分为多个区域或者对象,本文为大家整理了一些常用的分割方法,大家可以根据需求自行选择... 目录1. 基于传统图像处理的分割方法(1) 使用固定阈值分割图片(2) 自适应阈值分割(3) 使用图像边缘检测分割(4)

Vue3组件中getCurrentInstance()获取App实例,但是返回null的解决方案

《Vue3组件中getCurrentInstance()获取App实例,但是返回null的解决方案》:本文主要介绍Vue3组件中getCurrentInstance()获取App实例,但是返回nu... 目录vue3组件中getCurrentInstajavascriptnce()获取App实例,但是返回n

SQL表间关联查询实例详解

《SQL表间关联查询实例详解》本文主要讲解SQL语句中常用的表间关联查询方式,包括:左连接(leftjoin)、右连接(rightjoin)、全连接(fulljoin)、内连接(innerjoin)、... 目录简介样例准备左外连接右外连接全外连接内连接交叉连接自然连接简介本文主要讲解SQL语句中常用的表

Python如何将大TXT文件分割成4KB小文件

《Python如何将大TXT文件分割成4KB小文件》处理大文本文件是程序员经常遇到的挑战,特别是当我们需要把一个几百MB甚至几个GB的TXT文件分割成小块时,下面我们来聊聊如何用Python自动完成这... 目录为什么需要分割TXT文件基础版:按行分割进阶版:精确控制文件大小完美解决方案:支持UTF-8编码

openCV中KNN算法的实现

《openCV中KNN算法的实现》KNN算法是一种简单且常用的分类算法,本文主要介绍了openCV中KNN算法的实现,文中通过示例代码介绍的非常详细,对大家的学习或者工作具有一定的参考学习价值,需要的... 目录KNN算法流程使用OpenCV实现KNNOpenCV 是一个开源的跨平台计算机视觉库,它提供了各

springboot+dubbo实现时间轮算法

《springboot+dubbo实现时间轮算法》时间轮是一种高效利用线程资源进行批量化调度的算法,本文主要介绍了springboot+dubbo实现时间轮算法,文中通过示例代码介绍的非常详细,对大家... 目录前言一、参数说明二、具体实现1、HashedwheelTimer2、createWheel3、n

C# WinForms存储过程操作数据库的实例讲解

《C#WinForms存储过程操作数据库的实例讲解》:本文主要介绍C#WinForms存储过程操作数据库的实例,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,望不吝赐教... 目录一、存储过程基础二、C# 调用流程1. 数据库连接配置2. 执行存储过程(增删改)3. 查询数据三、事务处

springboot security验证码的登录实例

《springbootsecurity验证码的登录实例》:本文主要介绍springbootsecurity验证码的登录实例,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,... 目录前言代码示例引入依赖定义验证码生成器定义获取验证码及认证接口测试获取验证码登录总结前言在spring

SpringBoot实现MD5加盐算法的示例代码

《SpringBoot实现MD5加盐算法的示例代码》加盐算法是一种用于增强密码安全性的技术,本文主要介绍了SpringBoot实现MD5加盐算法的示例代码,文中通过示例代码介绍的非常详细,对大家的学习... 目录一、什么是加盐算法二、如何实现加盐算法2.1 加盐算法代码实现2.2 注册页面中进行密码加盐2.

tomcat多实例部署的项目实践

《tomcat多实例部署的项目实践》Tomcat多实例是指在一台设备上运行多个Tomcat服务,这些Tomcat相互独立,本文主要介绍了tomcat多实例部署的项目实践,具有一定的参考价值,感兴趣的可... 目录1.创建项目目录,测试文China编程件2js.创建实例的安装目录3.准备实例的配置文件4.编辑实例的