Official PyTorch implementation of Segmenter: Transformer for Semantic Segmentation

Overview

Segmenter: Transformer for Semantic Segmentation

Figure 1 from paper

Segmenter: Transformer for Semantic Segmentation by Robin Strudel*, Ricardo Garcia*, Ivan Laptev and Cordelia Schmid.

*Equal Contribution

Installation

Define os environment variables pointing to your checkpoint and dataset directory, put in your .bashrc:

export DATASET=/path/to/dataset/dir

Install PyTorch 1.9 then pip install . at the root of this repository.

To download ADE20K, use the following command:

python -m segm.scripts.prepare_ade20k $DATASET

Model Zoo

We release models with a Vision Transformer backbone initialized from the improved ViT models.

ADE20K

Segmenter models with ViT backbone:

Name mIoU (SS/MS) # params Resolution FPS Download
Seg-T-Mask/16 38.1 / 38.8 7M 512x512 52.4 model config log
Seg-S-Mask/16 45.3 / 46.9 27M 512x512 34.8 model config log
Seg-B-Mask/16 48.5 / 50.0 106M 512x512 24.1 model config log
Seg-L-Mask/16 51.3 / 53.2 334M 512x512 10.6 model config log
Seg-L-Mask/16 51.8 / 53.6 334M 640x640 - model config log

Segmenter models with DeiT backbone:

Name mIoU (SS/MS) # params Resolution FPS Download
Seg-B/16 47.1 / 48.1 87M 512x512 27.3 model config log
Seg-B-Mask/16 48.7 / 50.1 106M 512x512 24.1 model config log

Pascal Context

Name mIoU (SS/MS) # params Resolution FPS Download
Seg-L-Mask/16 58.1 / 59.0 334M 480x480 - model config log

Inference

Download one checkpoint with its configuration in a common folder, for example seg_tiny_mask.

You can generate segmentation maps from your own data with:

python -m segm.inference --model-path seg_tiny_mask/checkpoint.pth -i images/ -o segmaps/ 

To evaluate on ADE20K, run the command:

# single-scale evaluation:
python -m segm.eval.miou seg_tiny_mask/checkpoint.pth ade20k --singlescale
# multi-scale evaluation:
python -m segm.eval.miou seg_tiny_mask/checkpoint.pth ade20k --multiscale

Train

Train Seg-T-Mask/16 on ADE20K on a single GPU:

python -m segm.train --log-dir seg_tiny_mask --dataset ade20k \
  --backbone vit_tiny_patch16_384 --decoder mask_transformer

To train Seg-B-Mask/16, simply set vit_base_patch16_384 as backbone and launch the above command using a minimum of 4 V100 GPUs (~12 minutes per epoch) and up to 8 V100 GPUs (~7 minutes per epoch). The code uses SLURM environment variables.

Logs

To plot the logs of your experiments, you can use

python -m segm.utils.logs logs.yml

with logs.yml located in utils/ with the path to your experiments logs:

root: /path/to/checkpoints/
logs:
  seg-t: seg_tiny_mask/log.txt
  seg-b: seg_base_mask/log.txt

Video Segmentation

Zero shot video segmentation on DAVIS video dataset with Seg-B-Mask/16 model trained on ADE20K.

BibTex

@article{strudel2021,
  title={Segmenter: Transformer for Semantic Segmentation},
  author={Strudel, Robin and Garcia, Ricardo and Laptev, Ivan and Schmid, Cordelia},
  journal={arXiv preprint arXiv:2105.05633},
  year={2021}
}

Acknowledgements

The Vision Transformer code is based on timm library and the semantic segmentation training and evaluation pipeline is using mmsegmentation.

Owner
PhD student at Ecole Normale Supérieure and INRIA Paris
MVSDF - Learning Signed Distance Field for Multi-view Surface Reconstruction

MVSDF - Learning Signed Distance Field for Multi-view Surface Reconstruction This is the official implementation for the ICCV 2021 paper Learning Sign

110 Dec 20, 2022
Namish Khanna 40 Oct 11, 2022
SpeechNAS Better Trade off between Latency and Accuracy for Large Scale Speaker Verification

SpeechNAS Better Trade off between Latency and Accuracy for Large Scale Speaker Verification

Wentao Zhu 24 May 20, 2022
Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection

Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection Main requirements torch = 1.0 torchvision = 0.2.0 Python 3 Environm

15 Apr 04, 2022
Imaging, analysis, and simulation software for radio interferometry

ehtim (eht-imaging) Python modules for simulating and manipulating VLBI data and producing images with regularized maximum likelihood methods. This ve

Andrew Chael 5.2k Dec 28, 2022
Modelisation on galaxy evolution using PEGASE-HR

model_galaxy Modelisation on galaxy evolution using PEGASE-HR This is a labwork done in internship at IAP directed by Damien Le Borgne (https://github

Adrien Anthore 1 Jan 14, 2022
The PyTorch implementation for paper "Neural Texture Extraction and Distribution for Controllable Person Image Synthesis" (CVPR2022 Oral)

ArXiv | Get Start Neural-Texture-Extraction-Distribution The PyTorch implementation for our paper "Neural Texture Extraction and Distribution for Cont

Ren Yurui 111 Dec 10, 2022
Distributed Deep learning with Keras & Spark

Elephas: Distributed Deep Learning with Keras & Spark Elephas is an extension of Keras, which allows you to run distributed deep learning models at sc

Max Pumperla 1.6k Jan 05, 2023
A Model for Natural Language Attack on Text Classification and Inference

TextFooler A Model for Natural Language Attack on Text Classification and Inference This is the source code for the paper: Jin, Di, et al. "Is BERT Re

Di Jin 418 Dec 16, 2022
A simple python module to generate anchor (aka default/prior) boxes for object detection tasks.

PyBx WIP A simple python module to generate anchor (aka default/prior) boxes for object detection tasks. Calculated anchor boxes are returned as ndarr

thatgeeman 4 Dec 15, 2022
YouRefIt: Embodied Reference Understanding with Language and Gesture

YouRefIt: Embodied Reference Understanding with Language and Gesture YouRefIt: Embodied Reference Understanding with Language and Gesture by Yixin Che

16 Jul 11, 2022
A simple interface for editing natural photos with generative neural networks.

Neural Photo Editor A simple interface for editing natural photos with generative neural networks. This repository contains code for the paper "Neural

Andy Brock 2.1k Dec 29, 2022
Employee-Managment - Company employee registration software in the face recognition system

Employee-Managment Company employee registration software in the face recognitio

Alireza Kiaeipour 7 Jul 10, 2022
This project aim to create multi-label classification annotation tool to boost annotation speed and make it more easier.

This project aim to create multi-label classification annotation tool to boost annotation speed and make it more easier.

4 Aug 02, 2022
Code for visualizing the loss landscape of neural nets

Visualizing the Loss Landscape of Neural Nets This repository contains the PyTorch code for the paper Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer

Tom Goldstein 2.2k Jan 09, 2023
YOLOX_AUDIO is an audio event detection model based on YOLOX

YOLOX_AUDIO is an audio event detection model based on YOLOX, an anchor-free version of YOLO. This repo is an implementated by PyTorch. Main goal of YOLOX_AUDIO is to detect and classify pre-defined

intflow Inc. 77 Dec 19, 2022
(AAAI2022) Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation

SM-PPM This is a Pytorch implementation of our paper "Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Seman

W-zx-Y 10 Dec 07, 2022
Lightweight Face Image Quality Assessment

LightQNet This is a demo code of training and testing [LightQNet] using Tensorflow. Uncertainty Losses: IDQ loss PCNet loss Uncertainty Networks: Mobi

Kaen 5 Nov 18, 2022
FinEAS: Financial Embedding Analysis of Sentiment 📈

FinEAS: Financial Embedding Analysis of Sentiment 📈 (SentenceBERT for Financial News Sentiment Regression) This repository contains the code for gene

LHF Labs 31 Dec 13, 2022
pytorchのスライス代入操作をonnxに変換する際にScatterNDならないようにするサンプル

pytorch_remove_ScatterND pytorchのスライス代入操作をonnxに変換する際にScatterNDならないようにするサンプル。 スライスしたtensorにそのまま代入してしまうとScatterNDになるため、計算結果をcatで新しいtensorにする。 python ver

2 Dec 01, 2022