添加mmclassification-0.24.1代码，删除mmclassification-speed-benchmark

openmmlab_test/mmclassification-0.24.1/.gitattributes

+docs/** linguist-documentation
+docs_zh-CN/** linguist-documentation

openmmlab_test/mmclassification-0.24.1/.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+**/*.pyc
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Auto generate documentation
+docs/en/_build/
+docs/en/_model_zoo.rst
+docs/en/modelzoo_statistics.md
+docs/en/papers/
+docs/en/api/generated/
+docs/zh_CN/_build/
+docs/zh_CN/_model_zoo.rst
+docs/zh_CN/modelzoo_statistics.md
+docs/zh_CN/papers/
+docs/zh_CN/api/generated/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# custom
+/data
+.vscode
+.idea
+*.pkl
+*.pkl.json
+*.log.json
+/work_dirs
+/mmcls/.mim
+.DS_Store
+
+# Pytorch
+*.pth
+
+# IPU
+*.pvti
+*.pvti-journal
+/cache_engine
+/report

openmmlab_test/mmclassification-0.24.1/.pre-commit-config.yaml

+exclude: ^tests/data/
+repos:
+  - repo: https://github.com/PyCQA/flake8
+    rev: 4.0.1
+    hooks:
+      - id: flake8
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.10.1
+    hooks:
+      - id: isort
+  - repo: https://github.com/pre-commit/mirrors-yapf
+    rev: v0.30.0
+    hooks:
+      - id: yapf
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.1.0
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: double-quote-string-fixer
+      - id: check-merge-conflict
+      - id: fix-encoding-pragma
+        args: ["--remove"]
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 0.7.9
+    hooks:
+      - id: mdformat
+        args: ["--number", "--table-width", "200"]
+        additional_dependencies:
+          - mdformat-openmmlab
+          - mdformat_frontmatter
+          - linkify-it-py
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.1.0
+    hooks:
+      - id: codespell
+  - repo: https://github.com/myint/docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://github.com/open-mmlab/pre-commit-hooks
+    rev: v0.2.0
+    hooks:
+    -   id: check-copyright
+        args: ["mmcls", "tests", "demo", "tools"]
+  # - repo: local
+  #   hooks:
+  #     - id: clang-format
+  #       name: clang-format
+  #       description: Format files with ClangFormat
+  #       entry: clang-format -style=google -i
+  #       language: system
+  #       files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$

openmmlab_test/mmclassification-0.24.1/.readthedocs.yml

+version: 2
+
+formats: all
+
+python:
+  version: 3.7
+  install:
+    - requirements: requirements/docs.txt
+    - requirements: requirements/readthedocs.txt

openmmlab_test/mmclassification-0.24.1/CITATION.cff

+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+title: "OpenMMLab's Image Classification Toolbox and Benchmark"
+authors:
+  - name: "MMClassification Contributors"
+version: 0.15.0
+date-released: 2020-07-09
+repository-code: "https://github.com/open-mmlab/mmclassification"
+license: Apache-2.0

openmmlab_test/mmclassification-0.24.1/CONTRIBUTING.md

+# Contributing to OpenMMLab
+
+All kinds of contributions are welcome, including but not limited to the following.
+
+- Fix typo or bugs
+- Add documentation or translate the documentation into other languages
+- Add new features and components
+
+## Workflow
+
+1. fork and pull the latest OpenMMLab repository (MMClassification)
+2. checkout a new branch (do not use master branch for PRs)
+3. commit your changes
+4. create a PR
+
+```{note}
+If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.
+```
+
+## Code style
+
+### Python
+
+We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
+
+We use the following tools for linting and formatting:
+
+- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.
+- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
+- [yapf](https://github.com/google/yapf): A formatter for Python files.
+- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.
+- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.
+- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
+
+Style configurations can be found in [setup.cfg](./setup.cfg).
+
+We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,
+fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.
+The config for a pre-commit hook is stored in [.pre-commit-config](https://github.com/open-mmlab/mmclassification/blob/master/.pre-commit-config.yaml).
+
+After you clone the repository, you will need to install initialize pre-commit hook.
+
+```shell
+pip install -U pre-commit
+```
+
+From the repository folder
+
+```shell
+pre-commit install
+```
+
+After this on every commit check code linters and formatter will be enforced.
+
+```{important}
+Before you create a PR, make sure that your code lints and is formatted by yapf.
+```
+
+### C++ and CUDA
+
+We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).

openmmlab_test/mmclassification-speed-benchmark/LICENSE → openmmlab_test/mmclassification-0.24.1/LICENSE

-Copyright 2020 MMClassification Authors. All rights reserved.
+Copyright (c) OpenMMLab. All rights reserved
 
                                  Apache License
                            Version 2.0, January 2004

openmmlab_test/mmclassification-0.24.1/MANIFEST.in

+include requirements/*.txt
+include mmcls/.mim/model-index.yml
+recursive-include mmcls/.mim/configs *.py *.yml
+recursive-include mmcls/.mim/tools *.py *.sh

openmmlab_test/mmclassification-0.24.1/README.md

+<div align="center">
+
+<img src="resources/mmcls-logo.png" width="600"/>
+  <div>&nbsp;</div>
+  <div align="center">
+    <b><font size="5">OpenMMLab website</font></b>
+    <sup>
+      <a href="https://openmmlab.com">
+        <i><font size="4">HOT</font></i>
+      </a>
+    </sup>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <b><font size="5">OpenMMLab platform</font></b>
+    <sup>
+      <a href="https://platform.openmmlab.com">
+        <i><font size="4">TRY IT OUT</font></i>
+      </a>
+    </sup>
+  </div>
+  <div>&nbsp;</div>
+
+[![PyPI](https://img.shields.io/pypi/v/mmcls)](https://pypi.org/project/mmcls)
+[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://mmclassification.readthedocs.io/en/latest/)
+[![Build Status](https://github.com/open-mmlab/mmclassification/workflows/build/badge.svg)](https://github.com/open-mmlab/mmclassification/actions)
+[![codecov](https://codecov.io/gh/open-mmlab/mmclassification/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmclassification)
+[![license](https://img.shields.io/github/license/open-mmlab/mmclassification.svg)](https://github.com/open-mmlab/mmclassification/blob/master/LICENSE)
+[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmclassification.svg)](https://github.com/open-mmlab/mmclassification/issues)
+[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmclassification.svg)](https://github.com/open-mmlab/mmclassification/issues)
+
+[📘 Documentation](https://mmclassification.readthedocs.io/en/latest/) |
+[🛠️ Installation](https://mmclassification.readthedocs.io/en/latest/install.html) |
+[👀 Model Zoo](https://mmclassification.readthedocs.io/en/latest/model_zoo.html) |
+[🆕 Update News](https://mmclassification.readthedocs.io/en/latest/changelog.html) |
+[🤔 Reporting Issues](https://github.com/open-mmlab/mmclassification/issues/new/choose)
+
+:point_right: **MMClassification 1.0 branch is in trial, welcome every to [try it](https://github.com/open-mmlab/mmclassification/tree/1.x) and [discuss with us](https://github.com/open-mmlab/mmclassification/discussions)!** :point_left:
+
+</div>
+
+## Introduction
+
+English | [简体中文](/README_zh-CN.md) | [模型的测试方法及测试步骤](train.md)
+
+MMClassification is an open source image classification toolbox based on PyTorch. It is
+a part of the [OpenMMLab](https://openmmlab.com/) project.
+
+The master branch works with **PyTorch 1.5+**.
+
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/9102141/87268895-3e0d0780-c4fe-11ea-849e-6140b7e0d4de.gif" width="70%"/>
+</div>
+
+### Major features
+
+- Various backbones and pretrained models
+- Bag of training tricks
+- Large-scale training configs
+- High efficiency and extensibility
+- Powerful toolkits
+
+## What's new
+
+The MMClassification 1.0 has released! It's still unstable and in release candidate. If you want to try it, go
+to [the 1.x branch](https://github.com/open-mmlab/mmclassification/tree/1.x) and discuss it with us in
+[the discussion](https://github.com/open-mmlab/mmclassification/discussions).
+
+v0.24.1 was released in 31/10/2022.
+Highlights of the new version:
+
+- Support HUAWEI Ascend device.
+
+v0.24.0 was released in 30/9/2022.
+Highlights of the new version:
+
+- Support **HorNet**, **EfficientFormerm**, **SwinTransformer V2** and **MViT** backbones.
+- Support Standford Cars dataset.
+
+v0.23.0 was released in 1/5/2022.
+Highlights of the new version:
+
+- Support **DenseNet**, **VAN** and **PoolFormer**, and provide pre-trained models.
+- Support training on IPU.
+- New style API docs, welcome [view it](https://mmclassification.readthedocs.io/en/master/api/models.html).
+
+Please refer to [changelog.md](docs/en/changelog.md) for more details and other release history.
+
+## Installation
+
+Below are quick steps for installation:
+
+```shell
+conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision==0.11.0 -c pytorch -y
+conda activate open-mmlab
+pip3 install openmim
+mim install mmcv-full
+git clone https://github.com/open-mmlab/mmclassification.git
+cd mmclassification
+pip3 install -e .
+```
+
+Please refer to [install.md](https://mmclassification.readthedocs.io/en/latest/install.html) for more detailed installation and dataset preparation.
+
+## Getting Started
+
+Please see [Getting Started](https://mmclassification.readthedocs.io/en/latest/getting_started.html) for the basic usage of MMClassification. There are also tutorials:
+
+- [Learn about Configs](https://mmclassification.readthedocs.io/en/latest/tutorials/config.html)
+- [Fine-tune Models](https://mmclassification.readthedocs.io/en/latest/tutorials/finetune.html)
+- [Add New Dataset](https://mmclassification.readthedocs.io/en/latest/tutorials/new_dataset.html)
+- [Customizie Data Pipeline](https://mmclassification.readthedocs.io/en/latest/tutorials/data_pipeline.html)
+- [Add New Modules](https://mmclassification.readthedocs.io/en/latest/tutorials/new_modules.html)
+- [Customizie Schedule](https://mmclassification.readthedocs.io/en/latest/tutorials/schedule.html)
+- [Customizie Runtime Settings](https://mmclassification.readthedocs.io/en/latest/tutorials/runtime.html)
+
+Colab tutorials are also provided:
+
+- Learn about MMClassification **Python API**: [Preview the notebook](https://github.com/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_python.ipynb) or directly [run on Colab](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_python.ipynb).
+- Learn about MMClassification **CLI tools**: [Preview the notebook](https://github.com/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_tools.ipynb) or directly [run on Colab](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_tools.ipynb).
+
+## Model zoo
+
+Results and models are available in the [model zoo](https://mmclassification.readthedocs.io/en/latest/model_zoo.html).
+
+<details open>
+<summary>Supported backbones</summary>
+
+- [x] [VGG](https://github.com/open-mmlab/mmclassification/tree/master/configs/vgg)
+- [x] [ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet)
+- [x] [ResNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext)
+- [x] [SE-ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet)
+- [x] [SE-ResNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet)
+- [x] [RegNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/regnet)
+- [x] [ShuffleNetV1](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1)
+- [x] [ShuffleNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2)
+- [x] [MobileNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2)
+- [x] [MobileNetV3](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v3)
+- [x] [Swin-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/swin_transformer)
+- [x] [RepVGG](https://github.com/open-mmlab/mmclassification/tree/master/configs/repvgg)
+- [x] [Vision-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/vision_transformer)
+- [x] [Transformer-in-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/tnt)
+- [x] [Res2Net](https://github.com/open-mmlab/mmclassification/tree/master/configs/res2net)
+- [x] [MLP-Mixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/mlp_mixer)
+- [x] [DeiT](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit)
+- [x] [Conformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/conformer)
+- [x] [T2T-ViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/t2t_vit)
+- [x] [Twins](https://github.com/open-mmlab/mmclassification/tree/master/configs/twins)
+- [x] [EfficientNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientnet)
+- [x] [ConvNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/convnext)
+- [x] [HRNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hrnet)
+- [x] [VAN](https://github.com/open-mmlab/mmclassification/tree/master/configs/van)
+- [x] [ConvMixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/convmixer)
+- [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/cspnet)
+- [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer)
+- [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/mvit)
+- [x] [EfficientFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientformer)
+- [x] [HorNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hornet)
+
+</details>
+
+## Contributing
+
+We appreciate all contributions to improve MMClassification.
+Please refer to [CONTRUBUTING.md](https://mmclassification.readthedocs.io/en/latest/community/CONTRIBUTING.html) 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.
+
+## Citation
+
+If you find this project useful in your research, please consider cite:
+
+```BibTeX
+@misc{2020mmclassification,
+    title={OpenMMLab's Image Classification Toolbox and Benchmark},
+    author={MMClassification Contributors},
+    howpublished = {\url{https://github.com/open-mmlab/mmclassification}},
+    year={2020}
+}
+```
+
+## License
+
+This project is released under the [Apache 2.0 license](LICENSE).
+
+## Projects in OpenMMLab
+
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
+- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
+- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.

openmmlab_test/mmclassification-0.24.1/README_zh-CN.md

+<div align="center">
+
+<img src="resources/mmcls-logo.png" width="600"/>
+  <div>&nbsp;</div>
+  <div align="center">
+    <b><font size="5">OpenMMLab 官网</font></b>
+    <sup>
+      <a href="https://openmmlab.com">
+        <i><font size="4">HOT</font></i>
+      </a>
+    </sup>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <b><font size="5">OpenMMLab 开放平台</font></b>
+    <sup>
+      <a href="https://platform.openmmlab.com">
+        <i><font size="4">TRY IT OUT</font></i>
+      </a>
+    </sup>
+  </div>
+  <div>&nbsp;</div>
+
+[![PyPI](https://img.shields.io/pypi/v/mmcls)](https://pypi.org/project/mmcls)
+[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://mmclassification.readthedocs.io/zh_CN/latest/)
+[![Build Status](https://github.com/open-mmlab/mmclassification/workflows/build/badge.svg)](https://github.com/open-mmlab/mmclassification/actions)
+[![codecov](https://codecov.io/gh/open-mmlab/mmclassification/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmclassification)
+[![license](https://img.shields.io/github/license/open-mmlab/mmclassification.svg)](https://github.com/open-mmlab/mmclassification/blob/master/LICENSE)
+[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmclassification.svg)](https://github.com/open-mmlab/mmclassification/issues)
+[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmclassification.svg)](https://github.com/open-mmlab/mmclassification/issues)
+
+[📘 中文文档](https://mmclassification.readthedocs.io/zh_CN/latest/) |
+[🛠️ 安装教程](https://mmclassification.readthedocs.io/zh_CN/latest/install.html) |
+[👀 模型库](https://mmclassification.readthedocs.io/zh_CN/latest/model_zoo.html) |
+[🆕 更新日志](https://mmclassification.readthedocs.io/en/latest/changelog.html) |
+[🤔 报告问题](https://github.com/open-mmlab/mmclassification/issues/new/choose)
+
+:point_right: **MMClassification 1.0 版本即将正式发布，欢迎大家 [试用](https://github.com/open-mmlab/mmclassification/tree/1.x) 并 [参与讨论](https://github.com/open-mmlab/mmclassification/discussions)！** :point_left:
+
+</div>
+
+</div>
+
+## Introduction
+
+[English](/README.md) | 简体中文
+
+MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [OpenMMLab](https://openmmlab.com/) 项目的成员之一
+
+主分支代码目前支持 PyTorch 1.5 以上的版本。
+
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/9102141/87268895-3e0d0780-c4fe-11ea-849e-6140b7e0d4de.gif" width="70%"/>
+</div>
+
+### 主要特性
+
+- 支持多样的主干网络与预训练模型
+- 支持配置多种训练技巧
+- 大量的训练配置文件
+- 高效率和高可扩展性
+- 功能强大的工具箱
+
+## 更新日志
+
+MMClassification 1.0 已经发布！目前仍在公测中，如果希望试用，请切换到 [1.x 分支](https://github.com/open-mmlab/mmclassification/tree/1.x)，并在[讨论版](https://github.com/open-mmlab/mmclassification/discussions) 参加开发讨论！
+
+2022/10/31 发布了 v0.24.1 版本
+
+- 支持了华为昇腾 NPU 设备。
+
+2022/9/30 发布了 v0.24.0 版本
+
+- 支持了 **HorNet**，**EfficientFormerm**，**SwinTransformer V2**，**MViT** 等主干网络。
+- 支持了 Support Standford Cars 数据集。
+
+2022/5/1 发布了 v0.23.0 版本
+
+- 支持了 **DenseNet**，**VAN** 和 **PoolFormer** 三个网络，并提供了预训练模型。
+- 支持在 IPU 上进行训练。
+- 更新了 API 文档的样式，更方便查阅，[欢迎查阅](https://mmclassification.readthedocs.io/en/master/api/models.html)。
+
+发布历史和更新细节请参考 [更新日志](docs/en/changelog.md)
+
+## 安装
+
+以下是安装的简要步骤：
+
+```shell
+conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision==0.11.0 -c pytorch -y
+conda activate open-mmlab
+pip3 install openmim
+mim install mmcv-full
+git clone https://github.com/open-mmlab/mmclassification.git
+cd mmclassification
+pip3 install -e .
+```
+
+更详细的步骤请参考 [安装指南](https://mmclassification.readthedocs.io/zh_CN/latest/install.html) 进行安装。
+
+## 基础教程
+
+请参考 [基础教程](https://mmclassification.readthedocs.io/zh_CN/latest/getting_started.html) 来了解 MMClassification 的基本使用。MMClassification 也提供了其他更详细的教程：
+
+- [如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)
+- [如何微调模型](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/finetune.html)
+- [如何增加新数据集](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/new_dataset.html)
+- [如何设计数据处理流程](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/data_pipeline.html)
+- [如何增加新模块](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/new_modules.html)
+- [如何自定义优化策略](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/schedule.html)
+- [如何自定义运行参数](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/runtime.html)
+
+我们也提供了相应的中文 Colab 教程：
+
+- 了解 MMClassification **Python API**：[预览 Notebook](https://github.com/open-mmlab/mmclassification/blob/master/docs/zh_CN/tutorials/MMClassification_python_cn.ipynb) 或者直接[在 Colab 上运行](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/zh_CN/tutorials/MMClassification_python_cn.ipynb)。
+- 了解 MMClassification **命令行工具**：[预览 Notebook](https://github.com/open-mmlab/mmclassification/blob/master/docs/zh_CN/tutorials/MMClassification_tools_cn.ipynb) 或者直接[在 Colab 上运行](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/zh_CN/tutorials/MMClassification_tools_cn.ipynb)。
+
+## 模型库
+
+相关结果和模型可在 [model zoo](https://mmclassification.readthedocs.io/en/latest/model_zoo.html) 中获得
+
+<details open>
+<summary>支持的主干网络</summary>
+
+- [x] [VGG](https://github.com/open-mmlab/mmclassification/tree/master/configs/vgg)
+- [x] [ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet)
+- [x] [ResNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext)
+- [x] [SE-ResNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet)
+- [x] [SE-ResNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet)
+- [x] [RegNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/regnet)
+- [x] [ShuffleNetV1](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1)
+- [x] [ShuffleNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2)
+- [x] [MobileNetV2](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2)
+- [x] [MobileNetV3](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v3)
+- [x] [Swin-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/swin_transformer)
+- [x] [RepVGG](https://github.com/open-mmlab/mmclassification/tree/master/configs/repvgg)
+- [x] [Vision-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/vision_transformer)
+- [x] [Transformer-in-Transformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/tnt)
+- [x] [Res2Net](https://github.com/open-mmlab/mmclassification/tree/master/configs/res2net)
+- [x] [MLP-Mixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/mlp_mixer)
+- [x] [DeiT](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit)
+- [x] [Conformer](https://github.com/open-mmlab/mmclassification/tree/master/configs/conformer)
+- [x] [T2T-ViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/t2t_vit)
+- [x] [Twins](https://github.com/open-mmlab/mmclassification/tree/master/configs/twins)
+- [x] [EfficientNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientnet)
+- [x] [ConvNeXt](https://github.com/open-mmlab/mmclassification/tree/master/configs/convnext)
+- [x] [HRNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hrnet)
+- [x] [VAN](https://github.com/open-mmlab/mmclassification/tree/master/configs/van)
+- [x] [ConvMixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/convmixer)
+- [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/cspnet)
+- [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer)
+- [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/mvit)
+- [x] [EfficientFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientformer)
+- [x] [HorNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hornet)
+
+</details>
+
+## 参与贡献
+
+我们非常欢迎任何有助于提升 MMClassification 的贡献，请参考 [贡献指南](https://mmclassification.readthedocs.io/zh_CN/latest/community/CONTRIBUTING.html) 来了解如何参与贡献。
+
+## 致谢
+
+MMClassification 是一款由不同学校和公司共同贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。
+
+我们希望该工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现现有算法并开发自己的新模型，从而不断为开源社区提供贡献。
+
+## 引用
+
+如果你在研究中使用了本项目的代码或者性能基准，请参考如下 bibtex 引用 MMClassification。
+
+```BibTeX
+@misc{2020mmclassification,
+    title={OpenMMLab's Image Classification Toolbox and Benchmark},
+    author={MMClassification Contributors},
+    howpublished = {\url{https://github.com/open-mmlab/mmclassification}},
+    year={2020}
+}
+```
+
+## 许可证
+
+该项目开源自 [Apache 2.0 license](LICENSE).
+
+## OpenMMLab 的其他项目
+
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
+- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
+- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
+
+## 欢迎加入 OpenMMLab 社区
+
+扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=aCvMxdr3) 或联络 OpenMMLab 官方微信小助手
+
+<div align="center">
+<img src="https://github.com/open-mmlab/mmcv/raw/master/docs/en/_static/zhihu_qrcode.jpg" height="400" />  <img src="https://github.com/open-mmlab/mmcv/raw/master/docs/en/_static/qq_group_qrcode.jpg" height="400" /> <img src="https://github.com/open-mmlab/mmcv/raw/master/docs/en/_static/wechat_qrcode.jpg" height="400" />
+</div>
+
+我们会在 OpenMMLab 社区为大家
+
+- 📢 分享 AI 框架的前沿核心技术
+- 💻 解读 PyTorch 常用模块源码
+- 📰 发布 OpenMMLab 的相关新闻
+- 🚀 介绍 OpenMMLab 开发的前沿算法
+- 🏃 获取更高效的问题答疑和意见反馈
+- 🔥 提供与各行各业开发者充分交流的平台
+
+干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬

openmmlab_test/mmclassification-0.24.1/configs/_base_/datasets/cub_bs8_384.py

+# dataset settings
+dataset_type = 'CUB'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=510),
+    dict(type='RandomCrop', size=384),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=510),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data_root = 'data/CUB_200_2011/'
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'images.txt',
+        image_class_labels_file=data_root + 'image_class_labels.txt',
+        train_test_split_file=data_root + 'train_test_split.txt',
+        data_prefix=data_root + 'images',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'images.txt',
+        image_class_labels_file=data_root + 'image_class_labels.txt',
+        train_test_split_file=data_root + 'train_test_split.txt',
+        data_prefix=data_root + 'images',
+        test_mode=True,
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'images.txt',
+        image_class_labels_file=data_root + 'image_class_labels.txt',
+        train_test_split_file=data_root + 'train_test_split.txt',
+        data_prefix=data_root + 'images',
+        test_mode=True,
+        pipeline=test_pipeline))
+
+evaluation = dict(
+    interval=1, metric='accuracy',
+    save_best='auto')  # save the checkpoint with highest accuracy

openmmlab_test/mmclassification-0.24.1/configs/_base_/datasets/cub_bs8_448.py

+# dataset settings
+dataset_type = 'CUB'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=600),
+    dict(type='RandomCrop', size=448),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=600),
+    dict(type='CenterCrop', crop_size=448),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data_root = 'data/CUB_200_2011/'
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'images.txt',
+        image_class_labels_file=data_root + 'image_class_labels.txt',
+        train_test_split_file=data_root + 'train_test_split.txt',
+        data_prefix=data_root + 'images',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'images.txt',
+        image_class_labels_file=data_root + 'image_class_labels.txt',
+        train_test_split_file=data_root + 'train_test_split.txt',
+        data_prefix=data_root + 'images',
+        test_mode=True,
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'images.txt',
+        image_class_labels_file=data_root + 'image_class_labels.txt',
+        train_test_split_file=data_root + 'train_test_split.txt',
+        data_prefix=data_root + 'images',
+        test_mode=True,
+        pipeline=test_pipeline))
+
+evaluation = dict(
+    interval=1, metric='accuracy',
+    save_best='auto')  # save the checkpoint with highest accuracy

openmmlab_test/mmclassification-0.24.1/configs/_base_/datasets/imagenet21k_bs128.py

+# dataset settings
+dataset_type = 'ImageNet21k'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='RandomResizedCrop', size=224),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(256, -1)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=128,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet21k/train',
+        pipeline=train_pipeline,
+        recursion_subdir=True),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet21k/val',
+        ann_file='data/imagenet21k/meta/val.txt',
+        pipeline=test_pipeline,
+        recursion_subdir=True),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet21k/val',
+        ann_file='data/imagenet21k/meta/val.txt',
+        pipeline=test_pipeline,
+        recursion_subdir=True))
+evaluation = dict(interval=1, metric='accuracy')

openmmlab_test/mmclassification-0.24.1/configs/_base_/datasets/imagenet_bs128_poolformer_medium_224.py

+_base_ = ['./pipelines/rand_aug.py']
+
+# dataset settings
+dataset_type = 'ImageNet'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        size=224,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies={{_base_.rand_increasing_policies}},
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
+            interpolation='bicubic')),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=img_norm_cfg['mean'][::-1],
+        fill_std=img_norm_cfg['std'][::-1]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        size=(236, -1),
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=128,
+    workers_per_gpu=8,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+
+evaluation = dict(interval=10, metric='accuracy')

openmmlab_test/mmclassification-0.24.1/configs/_base_/datasets/imagenet_bs128_poolformer_small_224.py

+_base_ = ['./pipelines/rand_aug.py']
+
+# dataset settings
+dataset_type = 'ImageNet'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        size=224,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies={{_base_.rand_increasing_policies}},
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
+            interpolation='bicubic')),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=img_norm_cfg['mean'][::-1],
+        fill_std=img_norm_cfg['std'][::-1]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        size=(248, -1),
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=128,
+    workers_per_gpu=8,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+
+evaluation = dict(interval=10, metric='accuracy')

openmmlab_test/mmclassification-0.24.1/configs/_base_/datasets/imagenet_bs256_rsb_a12.py

+_base_ = ['./pipelines/rand_aug.py']
+
+# dataset settings
+dataset_type = 'ImageNet'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='RandomResizedCrop', size=224),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies={{_base_.rand_increasing_policies}},
+        num_policies=2,
+        total_level=10,
+        magnitude_level=7,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
+            interpolation='bicubic')),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(236, -1)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=256,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+
+evaluation = dict(interval=1, metric='accuracy')

openmmlab_test/mmclassification-0.24.1/configs/_base_/datasets/imagenet_bs256_rsb_a3.py

+_base_ = ['./pipelines/rand_aug.py']
+
+# dataset settings
+dataset_type = 'ImageNet'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='RandomResizedCrop', size=160),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies={{_base_.rand_increasing_policies}},
+        num_policies=2,
+        total_level=10,
+        magnitude_level=6,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
+            interpolation='bicubic')),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(236, -1)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=256,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+
+evaluation = dict(interval=1, metric='accuracy')

openmmlab_test/mmclassification-0.24.1/configs/_base_/datasets/imagenet_bs32.py

+# dataset settings
+dataset_type = 'ImageNet'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='RandomResizedCrop', size=224),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(256, -1)),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=128, #[32],
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='/public/DL_DATA/ImageNet-pytorch/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='/work/home/ac60ssbz5p/openmmlab_test_new/data/',
+        ann_file='/work/home/ac60ssbz5p/openmmlab_test_new/data/val_list.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='/work/home/ac60ssbz5p/openmmlab_test_new/data/',
+        ann_file='/work/home/ac60ssbz5p/openmmlab_test_new/data/val_list.txt',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='accuracy')