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

Last update: Jan 08, 2023

Overview

Semantic Segmentation on PyTorch

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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

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

Related tags

Overview

Semantic Segmentation on PyTorch

Installation

Usage

Train

Evaluation

Demo

Support

Model

Dataset

Result

Overfitting Test

To Do

References

Owner

The Pytorch code of "Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification", CVPR 2022 (Oral).

Kaggle-titanic - A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.

High-Fidelity Pluralistic Image Completion with Transformers (ICCV 2021)

Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion (CVPR 2021)

Distributed Arcface Training in Pytorch

The reference baseline of final exam for XMU machine learning course

LERP : Label-dependent and event-guided interpretable disease risk prediction using EHRs

《Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement》(ECCV 2020) GitHub: [fig9]

Clustering with variational Bayes and population Monte Carlo

Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"

Python implementation of "Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation"

ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

"Exploring Vision Transformers for Fine-grained Classification" at CVPRW FGVC8

Subnet Replacement Attack: Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks

Pytorch implementation of paper "Learning Co-segmentation by Segment Swapping for Retrieval and Discovery"

Semi-supervised Implicit Scene Completion from Sparse LiDAR

A collection of implementations of deep domain adaptation algorithms

A simple pytorch pipeline for semantic segmentation.

Code for Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task