This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.

Related tags

Deep Learningpytorchsemantic-segmentation
Overview

Semantic Segmentation on PyTorch

English | 简体中文

python-image pytorch-image lic-image

This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.

Installation

# semantic-segmentation-pytorch dependencies
pip install ninja tqdm

# follow PyTorch installation in https://pytorch.org/get-started/locally/
conda install pytorch torchvision -c pytorch

# install PyTorch Segmentation
git clone https://github.com/Tramac/awesome-semantic-segmentation-pytorch.git

Usage

Train

  • Single GPU training
# for example, train fcn32_vgg16_pascal_voc:
python train.py --model fcn32s --backbone vgg16 --dataset pascal_voc --lr 0.0001 --epochs 50
  • Multi-GPU training
# for example, train fcn32_vgg16_pascal_voc with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py --model fcn32s --backbone vgg16 --dataset pascal_voc --lr 0.0001 --epochs 50

Evaluation

  • Single GPU evaluating
# for example, evaluate fcn32_vgg16_pascal_voc
python eval.py --model fcn32s --backbone vgg16 --dataset pascal_voc
  • Multi-GPU evaluating
# for example, evaluate fcn32_vgg16_pascal_voc with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS eval.py --model fcn32s --backbone vgg16 --dataset pascal_voc

Demo

cd ./scripts
#for new users:
python demo.py --model fcn32s_vgg16_voc --input-pic ../tests/test_img.jpg
#you should add 'test.jpg' by yourself
python demo.py --model fcn32s_vgg16_voc --input-pic ../datasets/test.jpg

.{SEG_ROOT}
├── scripts
│   ├── demo.py
│   ├── eval.py
│   └── train.py

Support

Model

DETAILS for model & backbone.

.{SEG_ROOT}
├── core
│   ├── models
│   │   ├── bisenet.py
│   │   ├── danet.py
│   │   ├── deeplabv3.py
│   │   ├── deeplabv3+.py
│   │   ├── denseaspp.py
│   │   ├── dunet.py
│   │   ├── encnet.py
│   │   ├── fcn.py
│   │   ├── pspnet.py
│   │   ├── icnet.py
│   │   ├── enet.py
│   │   ├── ocnet.py
│   │   ├── psanet.py
│   │   ├── cgnet.py
│   │   ├── espnet.py
│   │   ├── lednet.py
│   │   ├── dfanet.py
│   │   ├── ......

Dataset

You can run script to download dataset, such as:

cd ./core/data/downloader
python ade20k.py --download-dir ../datasets/ade
Dataset training set validation set testing set
VOC2012 1464 1449 ✘
VOCAug 11355 2857 ✘
ADK20K 20210 2000 ✘
Cityscapes 2975 500 ✘
COCO
SBU-shadow 4085 638 ✘
LIP(Look into Person) 30462 10000 10000 
.{SEG_ROOT}
├── core
│   ├── data
│   │   ├── dataloader
│   │   │   ├── ade.py
│   │   │   ├── cityscapes.py
│   │   │   ├── mscoco.py
│   │   │   ├── pascal_aug.py
│   │   │   ├── pascal_voc.py
│   │   │   ├── sbu_shadow.py
│   │   └── downloader
│   │       ├── ade20k.py
│   │       ├── cityscapes.py
│   │       ├── mscoco.py
│   │       ├── pascal_voc.py
│   │       └── sbu_shadow.py

Result

  • PASCAL VOC 2012
Methods Backbone TrainSet EvalSet crops_size epochs JPU Mean IoU pixAcc
FCN32s vgg16 train val 480 60 ✘ 47.50 85.39
FCN16s vgg16 train val 480 60 ✘ 49.16 85.98
FCN8s vgg16 train val 480 60 ✘ 48.87 85.02
FCN32s resnet50 train val 480 50 ✘ 54.60 88.57
PSPNet resnet50 train val 480 60 ✘ 63.44 89.78
DeepLabv3 resnet50 train val 480 60 ✘ 60.15 88.36

Note: lr=1e-4, batch_size=4, epochs=80.

Overfitting Test

See TEST for details.

.{SEG_ROOT}
├── tests
│   └── test_model.py

To Do

  • add train script
  • remove syncbn
  • train & evaluate
  • test distributed training
  • fix syncbn (Why SyncBN?)
  • add distributed (How DIST?)

References

Owner
Data&Model&Loss
GitHub Repository
Article Reranking by Memory-enhanced Key Sentence Matching for Detecting Previously Fact-checked Claims.

MTM This is the official repository of the paper: Article Reranking by Memory-enhanced Key Sentence Matching for Detecting Previously Fact-checked Cla

ICTMCG 13 Sep 17, 2022
Parallel and High-Fidelity Text-to-Lip Generation; AAAI 2022 ; Official code

Parallel and High-Fidelity Text-to-Lip Generation This repository is the official PyTorch implementation of our AAAI-2022 paper, in which we propose P

Zhying 77 Dec 21, 2022
A bare-bones Python library for quality diversity optimization.

pyribs Website Source PyPI Conda CI/CD Docs Docs Status Twitter pyribs.org GitHub docs.pyribs.org A bare-bones Python library for quality diversity op

ICAROS 127 Jan 06, 2023
Video Instance Segmentation with a Propose-Reduce Paradigm (ICCV 2021)

Propose-Reduce VIS This repo contains the official implementation for the paper: Video Instance Segmentation with a Propose-Reduce Paradigm Huaijia Li

DV Lab 39 Nov 23, 2022
A learning-based data collection tool for human segmentation

FullBodyFilter A Learning-Based Data Collection Tool For Human Segmentation Contents Documentation Source Code and Scripts Overview of Project Usage O

Robert Jiang 4 Jun 24, 2022
NeuralCompression is a Python repository dedicated to research of neural networks that compress data

NeuralCompression is a Python repository dedicated to research of neural networks that compress data. The repository includes tools such as JAX-based entropy coders, image compression models, video c

Facebook Research 297 Jan 06, 2023
Spectralformer: Rethinking hyperspectral image classification with transformers

The code in this toolbox implements the "Spectralformer: Rethinking hyperspectral image classification with transformers". More specifically, it is detailed as follow.

Danfeng Hong 104 Jan 04, 2023
A pytorch implementation of Reading Wikipedia to Answer Open-Domain Questions.

DrQA A pytorch implementation of the ACL 2017 paper Reading Wikipedia to Answer Open-Domain Questions (DrQA). Reading comprehension is a task to produ

Runqi Yang 394 Nov 08, 2022
Distributing reference energies for SMIRNOFF implementations

Warning: This code is currently experimental and under active development. Is it not yet suitable for distribution or use as reference implementation.

Open Force Field Initiative 1 Dec 07, 2021
This tool uses Deep Learning to help you draw and write with your hand and webcam.

This tool uses Deep Learning to help you draw and write with your hand and webcam. A Deep Learning model is used to try to predict whether you want to have 'pencil up' or 'pencil down'.

lmagne 169 Dec 10, 2022
A large-scale face dataset for face parsing, recognition, generation and editing.

CelebAMask-HQ [Paper] [Demo] CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA da

switchnorm 1.7k Dec 26, 2022
SPRING is a seq2seq model for Text-to-AMR and AMR-to-Text (AAAI2021).

SPRING This is the repo for SPRING (Symmetric ParsIng aNd Generation), a novel approach to semantic parsing and generation, presented at AAAI 2021. Wi

Sapienza NLP group 98 Dec 21, 2022
Tensorflow implementation and notebooks for Implicit Maximum Likelihood Estimation

tf-imle Tensorflow 2 and PyTorch implementation and Jupyter notebooks for Implicit Maximum Likelihood Estimation (I-MLE) proposed in the NeurIPS 2021

NEC Laboratories Europe 69 Dec 13, 2022
Data and code for ICCV 2021 paper Distant Supervision for Scene Graph Generation.

Distant Supervision for Scene Graph Generation Data and code for ICCV 2021 paper Distant Supervision for Scene Graph Generation. Introduction The pape

THUNLP 23 Dec 31, 2022
A Pytorch implement of paper "Anomaly detection in dynamic graphs via transformer" (TADDY).

TADDY: Anomaly detection in dynamic graphs via transformer This repo covers an reference implementation for the paper "Anomaly detection in dynamic gr

Yue Tan 21 Nov 24, 2022
For storing the complete exploration of Visual Question Answering for our B.Tech Project

Multi-Image vqa @authors: Akhilesh, Janhavi, Harsh Paper summary, Ideas tried and their corresponding results: on wiki Other discussions: on discussio

Harsh Raj 3 Jun 16, 2022
Rank 3 : Source code for OPPO 6G Data Generation Challenge

OPPO 6G Data Generation with an E2E Framework Homepage of OPPO 6G Data Generation Challenge Datasets H1_32T4R.mat H2_32T4R.mat Please put the original

Sen Pei 97 Jan 07, 2023
The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"

Deep High-Resolution Representation Learning for Human Pose Estimation (CVPR 2019) News [2020/07/05] A very nice blog from Towards Data Science introd

Leo Xiao 3.9k Jan 05, 2023
Final report with code for KAIST Course KSE 801.

Orthogonal collocation is a method for the numerical solution of partial differential equations

Chuanbo HUA 4 Apr 06, 2022
Learning cell communication from spatial graphs of cells

ncem Features Repository for the manuscript Fischer, D. S., Schaar, A. C. and Theis, F. Learning cell communication from spatial graphs of cells. 2021

Theis Lab 77 Dec 30, 2022