reduce_zero_label
and applying label_map
in 1.x (#2517)
This is an almost exact duplicate of #2500 (that was made to the `master` branch) now applied to the `1.x` branch. --- ## Motivation I want to fix a bug through this PR. The bug occurs when two options -- `reduce_zero_label=True`, and custom classes are used. `reduce_zero_label` remaps the GT seg labels by remapping the zero-class to 255 which is ignored. Conceptually, this should occur *before* the `label_map` is applied, which maps *already reduced labels*. However, currently, the `label_map` is applied before the zero label is reduced. ## Modification The modification is simple: - I've just interchanged the order of the two operations by moving a few lines from bottom to top. - I've added a test that passes when the fix is introduced, and fails on the original `master` branch. ## BC-breaking (Optional) I do not anticipate this change braking any backward-compatibility. ## Checklist - [x] Pre-commit or other linting tools are used to fix the potential lint issues. - _I've fixed all linting/pre-commit errors._ - [x] The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. - _I've added a unit test._ - [x] If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. - _I don't think this change affects MMDet or MMDet3D._ - [x] The documentation has been modified accordingly, like docstring or example tutorials. - _This change fixes an existing bug and doesn't require modifying any documentation/docstring._
Documentation: https://mmsegmentation.readthedocs.io/en/1.x/
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Introduction
MMSegmentation is an open source semantic segmentation toolbox based on PyTorch. It is a part of the OpenMMLab project.
The 1.x branch works with PyTorch 1.6+.
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.
What's New
v1.0.0rc3 was released in 31/12/2022. Please refer to changelog.md for details and release history.
Installation
Please refer to get_started.md for installation and dataset_prepare.md for dataset preparation.
Get Started
Please see Overview for the general introduction of MMSegmentation.
Please see user guides for the basic usage of MMSegmentation. There are also advanced tutorials for in-depth understanding of mmseg design and implementation .
A Colab tutorial is also provided. You may preview the notebook here or directly run on Colab.
To migrate from MMSegmentation 1.x, please refer to migration.
Benchmark and model zoo
Results and models are available in the model zoo.
Supported backbones:
- ResNet (CVPR'2016)
- ResNeXt (CVPR'2017)
- HRNet (CVPR'2019)
- ResNeSt (ArXiv'2020)
- MobileNetV2 (CVPR'2018)
- MobileNetV3 (ICCV'2019)
- Vision Transformer (ICLR'2021)
- Swin Transformer (ICCV'2021)
- Twins (NeurIPS'2021)
- BEiT (ICLR'2022)
- ConvNeXt (CVPR'2022)
- MAE (CVPR'2022)
- PoolFormer (CVPR'2022)
Supported methods:
- FCN (CVPR'2015/TPAMI'2017)
- ERFNet (T-ITS'2017)
- UNet (MICCAI'2016/Nat. Methods'2019)
- PSPNet (CVPR'2017)
- DeepLabV3 (ArXiv'2017)
- BiSeNetV1 (ECCV'2018)
- PSANet (ECCV'2018)
- DeepLabV3+ (CVPR'2018)
- UPerNet (ECCV'2018)
- ICNet (ECCV'2018)
- NonLocal Net (CVPR'2018)
- EncNet (CVPR'2018)
- Semantic FPN (CVPR'2019)
- DANet (CVPR'2019)
- APCNet (CVPR'2019)
- EMANet (ICCV'2019)
- CCNet (ICCV'2019)
- DMNet (ICCV'2019)
- ANN (ICCV'2019)
- GCNet (ICCVW'2019/TPAMI'2020)
- FastFCN (ArXiv'2019)
- Fast-SCNN (ArXiv'2019)
- ISANet (ArXiv'2019/IJCV'2021)
- OCRNet (ECCV'2020)
- DNLNet (ECCV'2020)
- PointRend (CVPR'2020)
- CGNet (TIP'2020)
- BiSeNetV2 (IJCV'2021)
- STDC (CVPR'2021)
- SETR (CVPR'2021)
- DPT (ArXiv'2021)
- Segmenter (ICCV'2021)
- SegFormer (NeurIPS'2021)
- K-Net (NeurIPS'2021)
- MaskFormer (NeurIPS'2021)
- Mask2Former (CVPR'2022)
Supported datasets:
- Cityscapes
- PASCAL VOC
- ADE20K
- Pascal Context
- COCO-Stuff 10k
- COCO-Stuff 164k
- CHASE_DB1
- DRIVE
- HRF
- STARE
- Dark Zurich
- Nighttime Driving
- LoveDA
- Potsdam
- Vaihingen
- iSAID
Please refer to FAQ for frequently asked questions.
Contributing
We appreciate all contributions to improve MMSegmentation. Please refer to CONTRIBUTING.md for the contributing guideline.
Acknowledgement
MMSegmentation is an open source project that welcome any contribution and feedback. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible as well as standardized toolkit to reimplement existing methods and develop their own new semantic segmentation methods.
Citation
If you find this project useful in your research, please consider cite:
@misc{mmseg2020,
title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
author={MMSegmentation Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmsegmentation}},
year={2020}
}
License
This project is released under the Apache 2.0 license.
Projects in OpenMMLab
- MMEngine: OpenMMLab foundational library for training deep learning models
- MMCV: OpenMMLab foundational library for computer vision.
- MIM: MIM installs OpenMMLab packages.
- MMEval: A unified evaluation library for multiple machine learning libraries.
- MMClassification: OpenMMLab image classification toolbox and benchmark.
- MMDetection: OpenMMLab detection toolbox and benchmark.
- MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
- MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
- MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
- MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
- MMPose: OpenMMLab pose estimation toolbox and benchmark.
- MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
- MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
- MMRazor: OpenMMLab model compression toolbox and benchmark.
- MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
- MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
- MMTracking: OpenMMLab video perception toolbox and benchmark.
- MMFlow: OpenMMLab optical flow toolbox and benchmark.
- MMEditing: OpenMMLab image and video editing toolbox.
- MMGeneration: OpenMMLab image and video generative models toolbox.
- MMDeploy: OpenMMLab Model Deployment Framework.