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* vit backbone * fix lint * add docstrings and fix pretrained pos_embed dim not match prob * add unittest for vit * fix lint * add vit based fcn configs * fix import error * support multiple resolution input images * upsample pos_embed at init_weights * support resize pos_embed at evaluation * fix training errors * add more unitest code for vit backbone * unitest for uncovered code * add norm_eval unittest * refactor _pos_embeding * minor change * change var name * rafactor init_weight * load weights after resize * ignore 'module' in pretrain checkpoint * add with_cp * add with_cp Co-authored-by: Jiarui XU <xvjiarui0826@gmail.com> |
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README.md
Documentation: https://mmsegmentation.readthedocs.io/
English | 简体中文
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 1.3+.
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.
Changelog
v0.12.0 was released in 04/03/2021. Please refer to changelog.md for details and release history.
Benchmark and model zoo
Results and models are available in the model zoo.
Supported backbones:
- ResNet
- ResNeXt
- HRNet
- ResNeSt
- MobileNetV2
- MobileNetV3
Supported methods:
- FCN
- PSPNet
- DeepLabV3
- PSANet
- DeepLabV3+
- UPerNet
- NonLocal Net
- EncNet
- CCNet
- DANet
- APCNet
- GCNet
- DMNet
- ANN
- OCRNet
- Fast-SCNN
- Semantic FPN
- PointRend
- EMANet
- DNLNet
- CGNet
- Mixed Precision (FP16) Training
Installation
Please refer to get_started.md for installation and dataset preparation.
Get Started
Please see train.md and inference.md for the basic usage of MMSegmentation. There are also tutorials for customizing dataset, designing data pipeline, customizing modules, and customizing runtime. We also provide many training tricks.
A Colab tutorial is also provided. You may preview the notebook here or directly run on Colab.
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}
}
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.
Projects in OpenMMLab
- MMCV: OpenMMLab foundational library for computer vision.
- MMClassification: OpenMMLab image classification toolbox and benchmark.
- MMDetection: OpenMMLab detection toolbox and benchmark.
- MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
- MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
- MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
- MMTracking: OpenMMLab video perception toolbox and benchmark.
- MMPose: OpenMMLab pose estimation toolbox and benchmark.
- MMEditing: OpenMMLab image and video editing toolbox.
- MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding.