[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
GitHub Repository
Vision-Language Transformer and Query Generation for Referring Segmentation (ICCV 2021)

Vision-Language Transformer and Query Generation for Referring Segmentation Please consider citing our paper in your publications if the project helps

Henghui Ding 143 Dec 23, 2022
Adversarial Graph Augmentation to Improve Graph Contrastive Learning

ADGCL : Adversarial Graph Augmentation to Improve Graph Contrastive Learning Introduction This repo contains the Pytorch [1] implementation of Adversa

susheel suresh 62 Nov 19, 2022
Visualization toolkit for neural networks in PyTorch! Demo -->

FlashTorch A Python visualization toolkit, built with PyTorch, for neural networks in PyTorch. Neural networks are often described as "black box". The

Misa Ogura 692 Dec 29, 2022
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight)

About Code release for Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (ICLR 2022 Spotlight)

THUML @ Tsinghua University 221 Dec 31, 2022
[ACM MM2021] MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification

Introduction This project is developed based on FastReID, which is an ongoing ReID project. Projects BUC In projects/BUC, we implement AAAI 2019 paper

WuYiming 7 Apr 13, 2022
✨✨✨An awesome open source toolbox for stereo matching.

OpenStereo This is an awesome open source toolbox for stereo matching. Supported Methods: BM SGM(T-PAMI'07) GCNet(ICCV'17) PSMNet(CVPR'18) StereoNet(E

Wang Qingyu 6 Nov 04, 2022
This is a pytorch implementation of the NeurIPS paper GAN Memory with No Forgetting.

GAN Memory for Lifelong learning This is a pytorch implementation of the NeurIPS paper GAN Memory with No Forgetting. Please consider citing our paper

Miaoyun Zhao 43 Dec 27, 2022
DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing

DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing Figure: Joint multi-attribute edits using DyStyle model. Great diversity

74 Dec 03, 2022
Control-Raspberry-Pi-Robot-using-Hand-Gestures - A 4WD Robot car based on Raspberry Pi that controlled by hand gestures(using openCV and mediapipe)

Control-Raspberry-Pi-Robot-using-Hand-Gestures you can see all details about thi

MohammadReza Sharifi 27 Dec 13, 2022
Adversarially Learned Inference

Adversarially Learned Inference Code for the Adversarially Learned Inference paper. Compiling the paper locally From the repo's root directory, $ cd p

Mohamed Ishmael Belghazi 308 Sep 24, 2022
[NeurIPS 2021] Official implementation of paper "Learning to Simulate Self-driven Particles System with Coordinated Policy Optimization".

Code for Coordinated Policy Optimization Webpage | Code | Paper | Talk (English) | Talk (Chinese) Hi there! This is the source code of the paper “Lear

DeciForce: Crossroads of Machine Perception and Autonomy 81 Dec 19, 2022
Software associated to AAAI paper "Planning with Biological Neurons and Synapses"

jBrain Software associated with the AAAI 2022 paper Francesco D'Amore, Daniel Mitropolsky, Pierluigi Crescenzi, Emanuele Natale, Christos H. Papadimit

Pierluigi Crescenzi 1 Apr 10, 2022
Torch code for our CVPR 2018 paper "Residual Dense Network for Image Super-Resolution" (Spotlight)

Residual Dense Network for Image Super-Resolution This repository is for RDN introduced in the following paper Yulun Zhang, Yapeng Tian, Yu Kong, Bine

Yulun Zhang 494 Dec 30, 2022
JudeasRx - graphical app for doing personalized causal medicine using the methods invented by Judea Pearl et al.

JudeasRX Instructions Read the references given in the Theory and Notation section below Fire up the Jupyter Notebook judeas-rx.ipynb The notebook dra

Robert R. Tucci 19 Nov 07, 2022
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models.

Attack-Probabilistic-Models This is the source code for Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. This repository contai

SRI Lab, ETH Zurich 25 Sep 14, 2022
SimDeblur is a simple framework for image and video deblurring, implemented by PyTorch

SimDeblur (Simple Deblurring) is an open source framework for image and video deblurring toolbox based on PyTorch, which contains most deep-learning based state-of-the-art deblurring algorithms. It i

220 Jan 07, 2023
Multiple style transfer via variational autoencoder

ST-VAE Multiple style transfer via variational autoencoder By Zhi-Song Liu, Vicky Kalogeiton and Marie-Paule Cani This repo only provides simple testi

13 Oct 29, 2022
Codes for "Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier"

Deep-RTC [project page] This repository contains the source code accompanying our ECCV 2020 paper. Solving Long-tailed Recognition with Deep Realistic

Gina Wu 16 May 26, 2022
Improving Compound Activity Classification via Deep Transfer and Representation Learning

Improving Compound Activity Classification via Deep Transfer and Representation Learning This repository is the official implementation of Improving C

NingLab 2 Nov 24, 2021
[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation

EPCDepth EPCDepth is a self-supervised monocular depth estimation model, whose supervision is coming from the other image in a stereo pair. Details ar

Rui Peng 110 Dec 23, 2022