Official implementation of "SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers"

Overview

NVIDIA Source Code License Python 3.8

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Figure 1: Performance of SegFormer-B0 to SegFormer-B5.

Project page | Paper | Demo (Youtube) | Demo (Bilibili)

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers.
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, and Ping Luo.
Technical Report 2021.

This repository contains the PyTorch training/evaluation code and the pretrained models for SegFormer.

SegFormer is a simple, efficient and powerful semantic segmentation method, as shown in Figure 1.

We use MMSegmentation v0.13.0 as the codebase.

Installation

For install and data preparation, please refer to the guidelines in MMSegmentation v0.13.0.

Other requirements: pip install timm==0.3.2

Evaluation

Download trained weights.

Example: evaluate SegFormer-B1 on ADE20K:

# Single-gpu testing
python tools/test.py local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py /path/to/checkpoint_file

# Multi-gpu testing
./tools/dist_test.sh local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py /path/to/checkpoint_file <GPU_NUM>

# Multi-gpu, multi-scale testing
tools/dist_test.sh local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py /path/to/checkpoint_file <GPU_NUM> --aug-test

Training

Download weights pretrained on ImageNet-1K, and put them in a folder pretrained/.

Example: train SegFormer-B1 on ADE20K:

# Single-gpu training
python tools/train.py local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py 

# Multi-gpu training
./tools/dist_train.sh local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py <GPU_NUM>

License

Please check the LICENSE file. SegFormer may be used non-commercially, meaning for research or evaluation purposes only. For business inquiries, please contact [email protected].

Citation

@article{xie2021segformer,
  title={SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers},
  author={Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M and Luo, Ping},
  journal={arXiv preprint arXiv:2105.15203},
  year={2021}
}
The code of “Similarity Reasoning and Filtration for Image-Text Matching” [AAAI2021]

SGRAF PyTorch implementation for AAAI2021 paper of “Similarity Reasoning and Filtration for Image-Text Matching”. It is built on top of the SCAN and C

Ronnie_IIAU 149 Dec 22, 2022
Nonnegative spatial factorization for multivariate count data

Nonnegative spatial factorization for multivariate count data This repository contains supporting code to facilitate reproducible analysis. For detail

Will Townes 24 Dec 19, 2022
Tensorflow 2 implementation of our high quality frame interpolation neural network

FILM: Frame Interpolation for Large Scene Motion Project | Paper | YouTube | Benchmark Scores Tensorflow 2 implementation of our high quality frame in

Google Research 1.6k Dec 28, 2022
The official re-implementation of the Neurips 2021 paper, "Targeted Neural Dynamical Modeling".

Targeted Neural Dynamical Modeling Note: This is a re-implementation (in Tensorflow2) of the original TNDM model. We do not plan to further update the

6 Oct 05, 2022
An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi

MetaICL: Learning to Learn In Context This includes an original implementation of "MetaICL: Learning to Learn In Context" by Sewon Min, Mike Lewis, Lu

Meta Research 141 Jan 07, 2023
This is an official source code for implementation on Extensive Deep Temporal Point Process

Extensive Deep Temporal Point Process This is an official source code for implementation on Extensive Deep Temporal Point Process, which is composed o

Haitao Lin 8 Aug 15, 2022
Adaout is a practical and flexible regularization method with high generalization and interpretability

Adaout Adaout is a practical and flexible regularization method with high generalization and interpretability. Requirements python 3.6 (Anaconda versi

lambett 1 Feb 09, 2022
MRQy is a quality assurance and checking tool for quantitative assessment of magnetic resonance imaging (MRI) data.

Front-end View Backend View Table of Contents Description Prerequisites Running Basic Information Measurements User Interface Feedback and usage Descr

Center for Computational Imaging and Personalized Diagnostics 58 Dec 02, 2022
Official code of CVPR 2021's PLOP: Learning without Forgetting for Continual Semantic Segmentation

PLOP: Learning without Forgetting for Continual Semantic Segmentation This repository contains all of our code. It is a modified version of Cermelli e

Arthur Douillard 116 Dec 14, 2022
AI Based Smart Exam Proctoring Package

AI Based Smart Exam Proctoring Package It takes image (base64) as input: Provide Output as: Detection of Mobile phone. Detection of More than 1 person

NARENDER KESWANI 3 Sep 09, 2022
Implementation for paper LadderNet: Multi-path networks based on U-Net for medical image segmentation

Implementation for paper LadderNet: Multi-path networks based on U-Net for medical image segmentation This implementation is based on orobix implement

Juntang Zhuang 116 Sep 06, 2022
Flexible time series feature extraction & processing

tsflex is a toolkit for flexible time series processing & feature extraction, that is efficient and makes few assumptions about sequence data. Useful

PreDiCT.IDLab 206 Dec 28, 2022
QICK: Quantum Instrumentation Control Kit

QICK: Quantum Instrumentation Control Kit The QICK is a kit of firmware and software to use the Xilinx RFSoC to control quantum systems. It consists o

81 Dec 15, 2022
ICLR 2021: Pre-Training for Context Representation in Conversational Semantic Parsing

SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing This repository contains code for the ICLR 2021 paper "SCoRE: Pre-Tr

Microsoft 28 Oct 02, 2022
Using deep actor-critic model to learn best strategies in pair trading

Deep-Reinforcement-Learning-in-Stock-Trading Using deep actor-critic model to learn best strategies in pair trading Abstract Partially observed Markov

281 Dec 09, 2022
Crowd-sourced Annotation of Human Motion.

Motion Annotation Tool Live: https://motion-annotation.humanoids.kit.edu Paper: The KIT Motion-Language Dataset Installation Start by installing all P

Matthias Plappert 4 May 25, 2020
Code for the paper “The Peril of Popular Deep Learning Uncertainty Estimation Methods”

Uncertainty Estimation Methods Code for the paper “The Peril of Popular Deep Learning Uncertainty Estimation Methods” Reference If you use this code,

EPFL Machine Learning and Optimization Laboratory 4 Apr 05, 2022
Contour-guided image completion with perceptual grouping (BMVC 2021 publication)

Contour-guided Image Completion with Perceptual Grouping Authors Morteza Rezanejad*, Sidharth Gupta*, Chandra Gummaluru, Ryan Marten, John Wilder, Mic

Sid Gupta 6 Dec 27, 2022
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021.

Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification, IGARSS, 2021. Bobo Xi, Jiaojiao Li, Yunsong Li and Qian Du. Code f

Bobo Xi 7 Nov 03, 2022
Run object detection model on the Raspberry Pi

Using TensorFlow Lite with Python is great for embedded devices based on Linux, such as Raspberry Pi.

Dimitri Yanovsky 6 Oct 08, 2022