[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Related tags

Deep LearningSETR
Overview

SEgmentation TRansformers -- SETR

image

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers,
Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, Li Zhang,
CVPR 2021

Installation

Our project is developed based on mmsegmentation. Please follow the official mmsegmentation INSTALL.md and getting_started.md for installation and dataset preparation.

Main results

Cityscapes

Method Crop Size Batch size iteration set mIoU
SETR-Naive 768x768 8 40k val 77.37 model config
SETR-Naive 768x768 8 80k val 77.90 model config
SETR-MLA 768x768 8 40k val 76.65 model config
SETR-MLA 768x768 8 80k val 77.24 model config
SETR-PUP 768x768 8 40k val 78.39 model config
SETR-PUP 768x768 8 80k val 79.34 model config
SETR-Naive-DeiT 768x768 8 40k val 77.85 model config
SETR-Naive-DeiT 768x768 8 80k val 78.66 model config
SETR-MLA-DeiT 768x768 8 40k val 78.04 model config
SETR-MLA-DeiT 768x768 8 80k val 78.98 model config
SETR-PUP-DeiT 768x768 8 40k val 78.79 model config
SETR-PUP-DeiT 768x768 8 80k val 79.45 model config

ADE20K

Method Crop Size Batch size iteration set mIoU mIoU(ms+flip)
SETR-Naive 512x512 16 160k Val 48.06 48.80 model config
SETR-MLA 512x512 8 160k val 48.27 50.03 model config
SETR-MLA 512x512 16 160k val 48.64 50.28 model config
SETR-PUP 512x512 16 160k val 48.58 50.09 model config

Pascal Context

Method Crop Size Batch size iteration set mIoU mIoU(ms+flip)
SETR-Naive 480x480 16 80k val 52.89 53.61 model config
SETR-MLA 480x480 8 80k val 54.39 55.39 model config
SETR-MLA 480x480 16 80k val 54.87 55.83 model config
SETR-PUP 480x480 16 80k val 54.40 55.27 model config

Get Started

Train

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} 
# For example, train a SETR-PUP on Cityscapes dataset with 8 GPUs
./tools/dist_train.sh configs/SETR/SETR_PUP_768x768_40k_cityscapes_bs_8.py 8

Single-scale testing

./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM}  [--eval ${EVAL_METRICS}]
# For example, test a SETR-PUP on Cityscapes dataset with 8 GPUs
./tools/dist_test.sh configs/SETR/SETR_PUP_768x768_40k_cityscapes_bs_8.py \
work_dirs/SETR_PUP_768x768_40k_cityscapes_bs_8/iter_40000.pth \
8 --eval mIoU

Multi-scale testing

Use the config file ending in _MS.py in configs/SETR.

./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM}  [--eval ${EVAL_METRICS}]
# For example, test a SETR-PUP on Cityscapes dataset with 8 GPUs
./tools/dist_test.sh configs/SETR/SETR_PUP_768x768_40k_cityscapes_bs_8_MS.py \
work_dirs/SETR_PUP_768x768_40k_cityscapes_bs_8/iter_40000.pth \
8 --eval mIoU

Please see getting_started.md for the more basic usage of training and testing.

Reference

@inproceedings{SETR,
    title={Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers}, 
    author={Zheng, Sixiao and Lu, Jiachen and Zhao, Hengshuang and Zhu, Xiatian and Luo, Zekun and Wang, Yabiao and Fu, Yanwei and Feng, Jianfeng and Xiang, Tao and Torr, Philip H.S. and Zhang, Li},
    booktitle={CVPR},
    year={2021}
}

License

MIT

Acknowledgement

Thanks to previous open-sourced repo:
mmsegmentation
pytorch-image-models

Owner
Fudan Zhang Vision Group
Zhang Vision Group at the School of Data Science of the Fudan University, led by Professor Li Zhang
Fudan Zhang Vision Group
Language Used: Python . Made in Jupyter(Anaconda) notebook.

FACE-DETECTION-ATTENDENCE-SYSTEM Made in Jupyter(Anaconda) notebook. Language Used: Python Steps to perform before running the program : Install Anaco

1 Jan 12, 2022
AttGAN: Facial Attribute Editing by Only Changing What You Want (IEEE TIP 2019)

News 11 Jan 2020: We clean up the code to make it more readable! The old version is here: v1. AttGAN TIP Nov. 2019, arXiv Nov. 2017 TensorFlow impleme

Zhenliang He 568 Dec 14, 2022
Establishing Strong Baselines for TripClick Health Retrieval; ECIR 2022

TripClick Baselines with Improved Training Data Welcome 🙌 to the hub-repo of our paper: Establishing Strong Baselines for TripClick Health Retrieval

Sebastian Hofstätter 3 Nov 03, 2022
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts (ICLR 2022)

MetaShift: A Dataset of Datasets for Evaluating Distribution Shifts and Training Conflicts This repo provides the PyTorch source code of our paper: Me

88 Jan 04, 2023
Effect of Different Encodings and Distance Functions on Quantum Instance-based Classifiers

Effect of Different Encodings and Distance Functions on Quantum Instance-based Classifiers The repository contains the code to reproduce the experimen

Alessandro Berti 4 Aug 24, 2022
A series of Jupyter notebooks with Chinese comment that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

Hands-on-Machine-Learning 目的 这份笔记旨在帮助中文学习者以一种较快较系统的方式入门机器学习, 是在学习Hands-on Machine Learning with Scikit-Learn and TensorFlow这本书的 时候做的个人笔记: 此项目的可取之处 原书的

Baymax 1.5k Dec 21, 2022
Social Distancing Detector

Computer vision has opened up a lot of opportunities to explore into AI domain that were earlier highly limited. Here is an application of haarcascade classifier and OpenCV to develop a social distan

Ashish Pandey 2 Jul 18, 2022
social humanoid robots with GPGPU and IoT

Social humanoid robots with GPGPU and IoT Social humanoid robots with GPGPU and IoT Paper Authors Mohsen Jafarzadeh, Stephen Brooks, Shimeng Yu, Balak

0 Jan 07, 2022
PyTea: PyTorch Tensor shape error analyzer

PyTea: PyTorch Tensor Shape Error Analyzer paper project page Requirements node.js = 12.x python = 3.8 z3-solver = 4.8 How to install and use # ins

ROPAS Lab. 240 Jan 02, 2023
This repo is about to create the Streamlit application for given ML model.

HR-Attritiion-using-Streamlit This repo is about to create the Streamlit application for given ML model. Problem Statement: Managing peoples at workpl

Pavan Giri 0 Dec 10, 2021
A Broader Picture of Random-walk Based Graph Embedding

Random-walk Embedding Framework This repository is a reference implementation of the random-walk embedding framework as described in the paper: A Broa

Zexi Huang 23 Dec 13, 2022
A new test set for ImageNet

ImageNetV2 The ImageNetV2 dataset contains new test data for the ImageNet benchmark. This repository provides associated code for assembling and worki

186 Dec 18, 2022
Vertex AI: Serverless framework for MLOPs (ESP / ENG)

Vertex AI: Serverless framework for MLOPs (ESP / ENG) Español Qué es esto? Este repo contiene un pipeline end to end diseñado usando el SDK de Kubeflo

Hernán Escudero 2 Apr 28, 2022
This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural tree born form a large search space

SeBoW: Self-Born Wiring for neural trees(PaddlePaddle version) This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural

HollyLee 13 Dec 08, 2022
Out-of-Town Recommendation with Travel Intention Modeling (AAAI2021)

TrainOR_AAAI21 This is the official implementation of our AAAI'21 paper: Haoran Xin, Xinjiang Lu, Tong Xu, Hao Liu, Jingjing Gu, Dejing Dou, Hui Xiong

Jack Xin 13 Oct 19, 2022
[NeurIPS 2021] Introspective Distillation for Robust Question Answering

Introspective Distillation (IntroD) This repository is the Pytorch implementation of our paper "Introspective Distillation for Robust Question Answeri

Yulei Niu 13 Jul 26, 2022
The code for our paper CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention.

CrossFormer This repository is the code for our paper CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention. Introduction Existin

cheerss 238 Jan 06, 2023
《Fst Lerning of Temporl Action Proposl vi Dense Boundry Genertor》(AAAI 2020)

Update 2020.03.13: Release tensorflow-version and pytorch-version DBG complete code. 2019.11.12: Release tensorflow-version DBG inference code. 2019.1

Tencent 338 Dec 16, 2022
A Python library that provides a simplified alternative to DBAPI 2

A Python library that provides a simplified alternative to DBAPI 2. It provides a facade in front of DBAPI 2 drivers.

Tony Locke 44 Nov 17, 2021
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data Au

14 Nov 28, 2022