Clarifai/mmsegmentation

Clarifai/mmsegmentation

Description: OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Language: Python

License: Apache-2.0

Stars: 0

Forks: 0

Open issues: 0

Created: 2021-09-20T18:01:30Z

Pushed: 2024-10-07T23:58:35Z

Default branch: master

Fork: yes

Parent repository: open-mmlab/mmsegmentation

Archived: no

README:

   

Documentation: https://mmsegmentation.readthedocs.io/

English | [简体中文](README_zh-CN.md)

Introduction

MMSegmentation is an open source semantic segmentation toolbox based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch 1.5+.


Major features

• Unified Benchmark

We provide a unified benchmark toolbox for various semantic segmentation methods.

• Modular Design

We decompose the semantic segmentation framework into different components and one can easily construct a customized semantic segmentation framework by combining different modules.

• Support of multiple methods out of box

The toolbox directly supports popular and contemporary semantic segmentation frameworks, *e.g.* PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc.

• High efficiency

The training speed is faster than or comparable to other codebases.

License

This project is released under the [Apache 2.0 license](LICENSE).

Changelog

v0.24.1 was released in 5/1/2022. Please refer to [changelog.md](docs/en/changelog.md) for details and release history.

Benchmark and model zoo

Results and models are available in the [model zoo](docs/en/model_zoo.md).

Supported backbones:

• [x] ResNet (CVPR'2016)
• [x] ResNeXt (CVPR'2017)
• [x] [HRNet (CVPR'2019)](configs/hrnet)
• [x] [ResNeSt (ArXiv'2020)](configs/resnest)
• [x] [MobileNetV2 (CVPR'2018)](configs/mobilenet_v2)
• [x] [MobileNetV3 (ICCV'2019)](configs/mobilenet_v3)
• [x] [Vision Transformer (ICLR'2021)](configs/vit)
• [x] [Swin Transformer (ICCV'2021)](configs/swin)
• [x] [Twins (NeurIPS'2021)](configs/twins)
• [x] [BEiT (ICLR'2022)](configs/beit)
• [x] [ConvNeXt (CVPR'2022)](configs/convnext)
• [x] [MAE (CVPR'2022)](configs/mae)

Supported methods:

• [x] [FCN (CVPR'2015/TPAMI'2017)](configs/fcn)
• [x] [ERFNet (T-ITS'2017)](configs/erfnet)
• [x] [UNet (MICCAI'2016/Nat. Methods'2019)](configs/unet)
• [x] [PSPNet (CVPR'2017)](configs/pspnet)
• [x] [DeepLabV3 (ArXiv'2017)](configs/deeplabv3)
• [x] [BiSeNetV1 (ECCV'2018)](configs/bisenetv1)
• [x] [PSANet (ECCV'2018)](configs/psanet)
• [x] [DeepLabV3+ (CVPR'2018)](configs/deeplabv3plus)
• [x] [UPerNet (ECCV'2018)](configs/upernet)
• [x] [ICNet (ECCV'2018)](configs/icnet)
• [x] [NonLocal Net (CVPR'2018)](configs/nonlocal_net)
• [x] [EncNet (CVPR'2018)](configs/encnet)
• [x] [Semantic FPN (CVPR'2019)](configs/sem_fpn)
• [x] [DANet (CVPR'2019)](configs/danet)
• [x] [APCNet (CVPR'2019)](configs/apcnet)
• [x] [EMANet (ICCV'2019)](configs/emanet)
• [x] [CCNet (ICCV'2019)](configs/ccnet)
• [x] [DMNet (ICCV'2019)](configs/dmnet)
• [x] [ANN (ICCV'2019)](configs/ann)
• [x] [GCNet (ICCVW'2019/TPAMI'2020)](configs/gcnet)
• [x] [FastFCN (ArXiv'2019)](configs/fastfcn)
• [x] [Fast-SCNN (ArXiv'2019)](configs/fastscnn)
• [x] [ISANet (ArXiv'2019/IJCV'2021)](configs/isanet)
• [x] [OCRNet (ECCV'2020)](configs/ocrnet)
• [x] [DNLNet (ECCV'2020)](configs/dnlnet)
• [x] [PointRend (CVPR'2020)](configs/point_rend)
• [x] [CGNet (TIP'2020)](configs/cgnet)
• [x] [BiSeNetV2 (IJCV'2021)](configs/bisenetv2)
• [x] [STDC (CVPR'2021)](configs/stdc)
• [x] [SETR (CVPR'2021)](configs/setr)
• [x] [DPT (ArXiv'2021)](configs/dpt)
• [x] [Segmenter (ICCV'2021)](configs/segmenter)
• [x] [SegFormer (NeurIPS'2021)](configs/segformer)
• [x] [K-Net (NeurIPS'2021)](configs/knet)

Supported datasets:

• [x] Cityscapes
• [x] PASCAL VOC
• [x] ADE20K
• [x] Pascal Context
• [x] COCO-Stuff 10k
• [x] COCO-Stuff 164k
• [x] CHASE_DB1
• [x] DRIVE
• [x] HRF
• [x] STARE
• [x] Dark Zurich
• [x] Nighttime Driving
• [x] LoveDA
• [x] Potsdam
• [x] Vaihingen
• [x] iSAID

Installation

Please refer to [get_started.md](docs/en/get_started.md#installation) for installation and [dataset_prepare.md](docs/en/dataset_prepare.md#prepare-datasets) for dataset preparation.

Get Started

Please see [train.md](docs/en/train.md) and [inference.md](docs/en/inference.md) for the basic usage of MMSegmentation. There are also tutorials for [customizing…