df37f801b6
* update the HRNet-OCR & add ResNet-101-OCR * revise the script * add the results of resnet-101+ocr * add cascade ocr, aspp ocr * add comparison table * move comparison table * support ocr+decoder * revise the ocrnet_sep_aspp * update the results of ocrnet * update the results of ocrnet * add sep-ocr-variants * add bs2x exp of deeplabv3/v3+ * apply sep-conv in ocr module * update the results * update the results * update the results of OCRNet * update the results of OCRNet * correct the results * verify the release branch * init the release branch * add more results of ocrnet and ocrnetplus * resolve the conflicts * rename OCRNetPlus as OCRNet+ * fix the format * fix the lint issues * fix the lint issues * fix the lint issues * fix the lint isort issues * fix the lint yapf issues * fix the format issues * remove the changes by the master branch * remove the changes by the master branch * remove the changes by the master branch * remove the changes by the master branch * remove the changes by the master branch * add the logs folder to .gitignore * recover .gitignore * update readme * update readme * reset the cudnn_benchmark * revise the README of OCRNet * revise the name * revise the reference of OCRNet * revise the Figure of OCRNet+ * update the results of OCR/OCR+ * update the results of OCR/OCR+ * update the results of OCR/OCR+ * fix the format issue * fix the format issue * remove the ocr+ * update the results * update the results * fix the conflicts * fix the lint issue * fix the lint issue * fix the lint issue * fix the lint issue * fix the inconsistency * add urls to README * clean the code * remove the schedule configs * clean the custom code * clean up * remove ocr.png Co-authored-by: Jiarui XU <xvjiarui0826@gmail.com>
Documentation: https://mmsegmentation.readthedocs.io/
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 to 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.
Changelog
v0.5.1 was released in 11/08/2020. 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
Supported methods:
- FCN
- PSPNet
- DeepLabV3
- PSANet
- DeepLabV3+
- UPerNet
- NonLocal Net
- EncNet
- CCNet
- DANet
- GCNet
- ANN
- OCRNet
- Mixed Precision (FP16) Training
Installation
Please refer to INSTALL.md for installation and dataset preparation.
Get Started
Please see getting_started.md for the basic usage of MMSegmentation. There are also tutorials for adding new dataset, designing data pipeline, and adding new modules.
A Colab tutorial is also provided. You may preview the notebook here or directly run on Colab.
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.