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zhangrui_wolf 90496b4687
[Feature] Add RepVGG backbone and checkpoints. (#414) 
* Add RepVGG code.

* Add se_module as plugin.

* Add the repvggA0 primitive config

* Change repvggA0.py to fit mmcls

* Add RepVGG configs

* Add repvgg_to_mmcls

* Add tools/deployment/convert_repvggblock_param_to_deploy.py

* Change configs/repvgg/README.md

* Streamlining the number of configuration files.

* Fix lints

* Delete plugins

* Delete code about plugin.

* Modify the code for using se module.

* Modify config to fit repvgg with se.

* Change se_cfg to allow loading of pre-training parameters.

* Reduce the complexity of the configuration file.

* Finsh unitest for repvgg.

* Fix bug about se in repvgg_to_mmcls.

* Rename convert_repvggblock_param_to_deploy.py to reparameterize_repvgg.py, and delete setting about device.

* test commit

* test commit

* test commit command

* Modify repvgg.py to make the code more readable.

* Add value=0 in F.pad()

* Add se_cfg to arch_settings.

* Fix bug.

* modeify some attr name and Update unit tests

* rename stage_0 to stem and branch_identity to branch_norm

* update unit tests

* add m.eval in unit tests

* [Enhance] Enhence SE layer to support custom squeeze channels. (#417)

* add enhenced SE

* Update

* rm basechannel

* fix docstring

* Update se_layer.py

fix docstring

* [Docs] Add algorithm readme and update meta yml (#418)

* Add README.md for models without checkpoints.

* Update model-index.yml

* Update metafile.yml of seresnet

* [Enhance] Add `hparams` argument in `AutoAugment` and `RandAugment` and some other improvement. (#398)

* Add hparams argument in `AutoAugment` and `RandAugment`.

And `pad_val` supports sequence instead of tuple only.

* Add unit tests for `AutoAugment` and `hparams` in `RandAugment`.

* Use smaller test image to speed up uni tests.

* Use hparams to simplify RandAugment config in swin-transformer.

* Rename augment config name from `pipeline` to `pipelines`.

* Add some commnet ad docstring.

* [Feature] Support classwise weight in losses (#388)

* Add classwise weight in losses:CE,BCE,softBCE

* Update unit test

* rm some extra code

* rm some extra code

* fix broadcast

* fix broadcast

* update unit tests

* use new_tensor

* fix lint

* [Enhance] Better result visualization (#419)

* Imporve result visualization to support wait time and change the backend
to matplotlib.

* Add unit test for visualization

* Add adaptive dpi function

* Rename `imshow_cls_result` to `imshow_infos`.

* Support str in `imshow_infos`

* Improve docstring.

* Bump version to v0.15.0 (#426)

* [CI] Add PyTorch 1.9 and Python 3.9 build workflow, and remove some CI. (#422)

* Add PyTorch 1.9 build workflow, and remove some CI.

* Add Python 3.9 CI

* Show Python 3.9 support.

* [Enhance] Rename the option `--options` in some tools to `--cfg-options`. (#425)

* [Docs] Fix sphinx version (#429)

* [Docs] Add `CITATION.cff` (#428)

* Add CITATION.cff

* Fix typo in setup.py

* Change author in setup.py

* modeify some attr name and Update unit tests

* rename stage_0 to stem and branch_identity to branch_norm

* update unit tests

* add m.eval in unit tests

* Update unit tests

* refactor

* refactor

* Alignment inference accuracy

* Update configs, readme and metafile

* Update readme

* return tuple and fix metafile

* fix unit test

* rm regnet and classifiers changes

* update auto_aug

* update metafile & readme

* use delattr

* rename cfgs

* Update checkpoint url

* Update readme

* Rename config files.

* Update readme and metafile

* add comment

* Update mmcls/models/backbones/repvgg.py

Co-authored-by: Ma Zerun <mzr1996@163.com>

* Update docstring

* Improve docstring.

* Update unittest_testblock

Co-authored-by: Ezra-Yu <1105212286@qq.com>
Co-authored-by: Ma Zerun <mzr1996@163.com>
2021-09-29 11:06:23 +08:00
.github
[Docs] Update getting_started.md and install.md and rewrite finetune.md (#466)
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configs
[Feature] Add RepVGG backbone and checkpoints. (#414)
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demo
[Docs] Add Copyright information. (#413)
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docker
Bump version to v0.15.0 (#426)
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docs
[Docs] Update getting_started.md and install.md and rewrite finetune.md (#466)
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docs_zh-CN
[Docs] Update getting_started.md and install.md and rewrite finetune.md (#466)
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mmcls
[Feature] Add RepVGG backbone and checkpoints. (#414)
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requirements
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resources
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tests
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tools
[Feature] Add RepVGG backbone and checkpoints. (#414)
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.gitignore
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.readthedocs.yml
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LICENSE
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MANIFEST.in
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model-index.yml
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README_zh-CN.md
[Docs] Update getting_started.md and install.md and rewrite finetune.md (#466)
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README.md
[Docs] Update getting_started.md and install.md and rewrite finetune.md (#466)
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requirements.txt
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setup.cfg
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setup.py
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README.md

Build Status Documentation Status codecov license

Introduction

English | 简体中文

MMClassification is an open source image classification toolbox based on PyTorch. It is a part of the OpenMMLab project.

Documentation: https://mmclassification.readthedocs.io/en/latest/

demo

Major features

  • Various backbones and pretrained models
  • Bag of training tricks
  • Large-scale training configs
  • High efficiency and extensibility

License

This project is released under the Apache 2.0 license.

Changelog

v0.15.0 was released in 31/8/2021. 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
  • SE-ResNet
  • SE-ResNeXt
  • RegNet
  • ShuffleNetV1
  • ShuffleNetV2
  • MobileNetV2
  • MobileNetV3
  • Swin-Transformer

Installation

Please refer to install.md for installation and dataset preparation.

Getting Started

Please see getting_started.md for the basic usage of MMClassification. There are also tutorials for finetuning models, adding new dataset, designing data pipeline, and adding new modules.

Citation

If you find this project useful in your research, please consider cite:

@misc{2020mmclassification,
    title={OpenMMLab's Image Classification Toolbox and Benchmark},
    author={MMClassification Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmclassification}},
    year={2020}
}

Contributing

We appreciate all contributions to improve MMClassification. Please refer to CONTRUBUTING.md for the contributing guideline.

Acknowledgement

MMClassification is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new classifiers.

Projects in OpenMMLab

  • MMCV: OpenMMLab foundational library for computer vision.
  • MIM: MIM Installs OpenMMLab Packages.
  • 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: OpenMMLab toolbox for text detection, recognition and understanding.
  • MMGeneration: OpenMMlab toolkit for generative models.
Description
OpenMMLab Image Classification Toolbox and Benchmark
deep-learningimage-classificationimagenetmobilenetpytorchregnetresnetresnextsenetshufflenetswin-transformer
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