diff --git a/.github/ISSUE_TEMPLATE/----.md b/.github/ISSUE_TEMPLATE/----.md index bd1427fbc30..3a90404e9de 100644 --- a/.github/ISSUE_TEMPLATE/----.md +++ b/.github/ISSUE_TEMPLATE/----.md @@ -4,27 +4,30 @@ about: 遇到问题并寻求帮助 title: '' labels: help wanted assignees: '' - --- 推荐使用英语模板 General question，以便你的问题帮助更多人。 ### 首先确认以下内容 + - 我已经查询了相关的 issue，但没有找到需要的帮助。 - 我已经阅读了相关文档，但仍不知道如何解决。 ### 描述你遇到的问题 -[填写这里] +\[填写这里\] ### 相关信息 + 1. `pip list | grep "mmcv\|mmcls\|^torch"` 命令的输出 -[填写这里] + \[填写这里\] 2. 如果你修改了，或者使用了新的配置文件，请在这里写明 + ```python [填写这里] ``` + 3. 如果你是在训练过程中遇到的问题，请填写完整的训练日志和报错信息 -[填写这里] + \[填写这里\] 4. 如果你对 `mmcls` 文件夹下的代码做了其他相关的修改，请在这里写明 -[填写这里] + \[填写这里\] diff --git a/.github/ISSUE_TEMPLATE/---.md b/.github/ISSUE_TEMPLATE/---.md index c8d2880cd85..fe91547056b 100644 --- a/.github/ISSUE_TEMPLATE/---.md +++ b/.github/ISSUE_TEMPLATE/---.md @@ -1,32 +1,34 @@ --- name: 新功能 about: 为项目提一个建议 -title: "[Feature]" +title: '[Feature]' labels: enhancement assignees: '' - --- 推荐使用英语模板 Feature request，以便你的问题帮助更多人。 ### 描述这个功能 -[填写这里] +\[填写这里\] ### 动机 + 请简要说明以下为什么需要添加这个新功能 例 1. 现在进行 xxx 的时候不方便 例 2. 最近的论文中提出了有一个很有帮助的 xx -[填写这里] +\[填写这里\] ### 相关资源 + 是否有相关的官方实现或者第三方实现？这些会很有参考意义。 -[填写这里] +\[填写这里\] ### 其他相关信息 + 其他和这个功能相关的信息或者截图，请放在这里。 另外如果你愿意参与实现这个功能并提交 PR，请在这里说明，我们将非常欢迎。 -[填写这里] +\[填写这里\] diff --git a/.github/ISSUE_TEMPLATE/---bug.md b/.github/ISSUE_TEMPLATE/---bug.md index 21439b1ae16..a3ec4988c65 100644 --- a/.github/ISSUE_TEMPLATE/---bug.md +++ b/.github/ISSUE_TEMPLATE/---bug.md @@ -1,20 +1,21 @@ --- name: 报告 Bug about: 报告问题以帮助我们提升 -title: "[Bug]" +title: '[Bug]' labels: bug assignees: '' - --- 推荐使用英语模板 Bug report，以便你的问题帮助更多人。 ### 描述 bug + 简单地描述一下遇到了什么 bug -[填写这里] +\[填写这里\] ### 复现流程 + 在命令行中执行的详细操作 ```shell @@ -22,18 +23,22 @@ assignees: '' ``` ### 相关信息 + 1. `pip list | grep "mmcv\|mmcls\|^torch"` 命令的输出 -[填写这里] + \[填写这里\] 2. 如果你修改了，或者使用了新的配置文件，请在这里写明 + ```python [填写这里] ``` + 3. 如果你是在训练过程中遇到的问题，请填写完整的训练日志和报错信息 -[填写这里] + \[填写这里\] 4. 如果你对 `mmcls` 文件夹下的代码做了其他相关的修改，请在这里写明 -[填写这里] + \[填写这里\] ### 附加内容 + 任何其他有关该 bug 的信息、截图等 -[填写这里] +\[填写这里\] diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 3d31f203900..c00c1f59600 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -1,18 +1,19 @@ --- name: Bug report about: Create a report to help us improve -title: "[Bug]" +title: '[Bug]' labels: bug assignees: '' - --- ### Describe the bug + A clear and concise description of what the bug is. -[here] +\[here\] ### To Reproduce + The command you executed. ```shell @@ -20,18 +21,22 @@ The command you executed. ``` ### Post related information + 1. The output of `pip list | grep "mmcv\|mmcls\|^torch"` -[here] + \[here\] 2. Your config file if you modified it or created a new one. + ```python [here] ``` + 3. Your train log file if you meet the problem during training. -[here] + \[here\] 4. Other code you modified in the `mmcls` folder. -[here] + \[here\] ### Additional context + Add any other context about the problem here. -[here] +\[here\] diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md index f724b9fb6ce..23b7c097b8c 100644 --- a/.github/ISSUE_TEMPLATE/feature_request.md +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -1,30 +1,32 @@ --- name: Feature request about: Suggest an idea for this project -title: "[Feature]" +title: '[Feature]' labels: enhancement assignees: '' - --- ### Describe the feature -[here] +\[here\] ### Motivation + A clear and concise description of the motivation of the feature. -Ex1. It is inconvenient when [....]. -Ex2. There is a recent paper [....], which is very helpful for [....]. +Ex1. It is inconvenient when \[....\]. +Ex2. There is a recent paper \[....\], which is very helpful for \[....\]. -[here] +\[here\] ### Related resources + If there is an official code release or third-party implementation, please also provide the information here, which would be very helpful. -[here] +\[here\] ### Additional context + Add any other context or screenshots about the feature request here. If you would like to implement the feature and create a PR, please leave a comment here and that would be much appreciated. -[here] +\[here\] diff --git a/.github/ISSUE_TEMPLATE/general-questions.md b/.github/ISSUE_TEMPLATE/general-questions.md index 929da065e45..42d5fb2e4c2 100644 --- a/.github/ISSUE_TEMPLATE/general-questions.md +++ b/.github/ISSUE_TEMPLATE/general-questions.md @@ -4,25 +4,28 @@ about: 'Ask general questions to get help ' title: '' labels: help wanted assignees: '' - --- ### Checklist + - I have searched related issues but cannot get the expected help. - I have read related documents and don't know what to do. ### Describe the question you meet -[here] +\[here\] ### Post related information + 1. The output of `pip list | grep "mmcv\|mmcls\|^torch"` -[here] + \[here\] 2. Your config file if you modified it or created a new one. + ```python [here] ``` + 3. Your train log file if you meet the problem during training. -[here] + \[here\] 4. Other code you modified in the `mmcls` folder. -[here] + \[here\] diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 82fa87e6380..0d19d5f67bf 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -25,12 +25,15 @@ repos: args: ["--remove"] - id: mixed-line-ending args: ["--fix=lf"] - - repo: https://github.com/markdownlint/markdownlint - rev: v0.11.0 - hooks: - - id: markdownlint - args: ["-r", "~MD002,~MD013,~MD029,~MD033,~MD034", - "-t", "allow_different_nesting"] + - 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: diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000000..8a0c63299f0 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,61 @@ +# 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). diff --git a/README.md b/README.md index f593d2af1a1..f47fc145c31 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@
- +
 
OpenMMLab website @@ -19,20 +19,19 @@
 
+[![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) - [![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) +[📘 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)
@@ -61,6 +60,7 @@ The master branch works with **PyTorch 1.5+**. 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). @@ -68,6 +68,7 @@ Highlights of the new version: v0.22.0 was released in 30/3/2022. Highlights of the new version: + - Support a series of **CSP Network**, such as CSP-ResNet, CSP-ResNeXt and CSP-DarkNet. - A new `CustomDataset` class to help you **build dataset of yourself**! - Support new backbones - **ConvMixer**, **RepMLP** and new dataset - **CUB dataset**. diff --git a/README_zh-CN.md b/README_zh-CN.md index 7e78b37f0a4..592a1d1ec11 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -1,6 +1,6 @@
- +
 
OpenMMLab 官网 @@ -19,19 +19,19 @@
 
- [![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) +[![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) +[📘 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)
@@ -60,6 +60,7 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O 2022/5/1 发布了 v0.23.0 版本 新版本亮点： + - 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)。 @@ -67,6 +68,7 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O 2022/3/30 发布了 v0.22.0 版本 新版本亮点： + - 支持了一系列 **CSP Net**，包括 CSP-ResNet，CSP-ResNeXt 和 CSP-DarkNet。 - 我们提供了一个新的 `CustomDataset` 类，这个类将帮助你轻松使用**自己的数据集**！ - 支持了新的主干网络 **ConvMixer**、**RepMLP** 和一个新的数据集 **CUB dataset**。 diff --git a/configs/conformer/README.md b/configs/conformer/README.md index ff91ed2081e..5b7d96b7f71 100644 --- a/configs/conformer/README.md +++ b/configs/conformer/README.md @@ -1,6 +1,7 @@ # Conformer > [Conformer: Local Features Coupling Global Representations for Visual Recognition](https://arxiv.org/abs/2105.03889) + ## Abstract @@ -15,14 +16,14 @@ Within Convolutional Neural Network (CNN), the convolution operations are good a ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| Conformer-tiny-p16\* | 23.52 | 4.90 | 81.31 | 95.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-tiny-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-tiny-p16_3rdparty_8xb128_in1k_20211206-f6860372.pth) | -| Conformer-small-p32\* | 38.85 | 7.09 | 81.96 | 96.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p32_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p32_8xb128_in1k_20211206-947a0816.pth) | -| Conformer-small-p16\* | 37.67 | 10.31 | 83.32 | 96.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p16_3rdparty_8xb128_in1k_20211206-3065dcf5.pth) | -| Conformer-base-p16\* | 83.29 | 22.89 | 83.82 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-base-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-base-p16_3rdparty_8xb128_in1k_20211206-bfdf8637.pth) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------------------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------------: | :-----------------------------------------------------------------------: | +| Conformer-tiny-p16\* | 23.52 | 4.90 | 81.31 | 95.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-tiny-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-tiny-p16_3rdparty_8xb128_in1k_20211206-f6860372.pth) | +| Conformer-small-p32\* | 38.85 | 7.09 | 81.96 | 96.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p32_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p32_8xb128_in1k_20211206-947a0816.pth) | +| Conformer-small-p16\* | 37.67 | 10.31 | 83.32 | 96.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p16_3rdparty_8xb128_in1k_20211206-3065dcf5.pth) | +| Conformer-base-p16\* | 83.29 | 22.89 | 83.82 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-base-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-base-p16_3rdparty_8xb128_in1k_20211206-bfdf8637.pth) | -*Models with \* are converted from the [official repo](https://github.com/pengzhiliang/Conformer). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +*Models with * are converted from the [official repo](https://github.com/pengzhiliang/Conformer). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## Citation diff --git a/configs/convmixer/README.md b/configs/convmixer/README.md index 00834981cf7..763bad3ccf6 100644 --- a/configs/convmixer/README.md +++ b/configs/convmixer/README.md @@ -1,14 +1,17 @@ # ConvMixer > [Patches Are All You Need?](https://arxiv.org/abs/2201.09792) + ## Abstract + Although convolutional networks have been the dominant architecture for vision tasks for many years, recent experiments have shown that Transformer-based models, most notably the Vision Transformer (ViT), may exceed their performance in some settings. However, due to the quadratic runtime of the self-attention layers in Transformers, ViTs require the use of patch embeddings, which group together small regions of the image into single input features, in order to be applied to larger image sizes. This raises a question: Is the performance of ViTs due to the inherently-more-powerful Transformer architecture, or is it at least partly due to using patches as the input representation? In this paper, we present some evidence for the latter: specifically, we propose the ConvMixer, an extremely simple model that is similar in spirit to the ViT and the even-more-basic MLP-Mixer in that it operates directly on patches as input, separates the mixing of spatial and channel dimensions, and maintains equal size and resolution throughout the network. In contrast, however, the ConvMixer uses only standard convolutions to achieve the mixing steps. Despite its simplicity, we show that the ConvMixer outperforms the ViT, MLP-Mixer, and some of their variants for similar parameter counts and data set sizes, in addition to outperforming classical vision models such as the ResNet. +
@@ -17,15 +20,13 @@ Although convolutional networks have been the dominant architecture for vision t ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| ConvMixer-768/32\* | 21.11 | 19.62 | 80.16 | 95.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convmixer/convmixer-768-32_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-768-32_3rdparty_10xb64_in1k_20220323-bca1f7b8.pth) | -| ConvMixer-1024/20\* | 24.38 | 5.55 | 76.94 | 93.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convmixer/convmixer-1024-20_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-1024-20_3rdparty_10xb64_in1k_20220323-48f8aeba.pth) | -| ConvMixer-1536/20\* | 51.63 | 48.71 | 81.37 | 95.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convmixer/convmixer-1536-20_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-1536_20_3rdparty_10xb64_in1k_20220323-ea5786f3.pth) | - - -*Models with \* are converted from the [official repo](https://github.com/locuslab/convmixer). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------------: | :------------------------------------------------------------------------: | +| ConvMixer-768/32\* | 21.11 | 19.62 | 80.16 | 95.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convmixer/convmixer-768-32_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-768-32_3rdparty_10xb64_in1k_20220323-bca1f7b8.pth) | +| ConvMixer-1024/20\* | 24.38 | 5.55 | 76.94 | 93.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convmixer/convmixer-1024-20_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-1024-20_3rdparty_10xb64_in1k_20220323-48f8aeba.pth) | +| ConvMixer-1536/20\* | 51.63 | 48.71 | 81.37 | 95.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convmixer/convmixer-1536-20_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-1536_20_3rdparty_10xb64_in1k_20220323-ea5786f3.pth) | +*Models with * are converted from the [official repo](https://github.com/locuslab/convmixer). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## Citation diff --git a/configs/convnext/README.md b/configs/convnext/README.md index 357e8de40b0..7db81366aa4 100644 --- a/configs/convnext/README.md +++ b/configs/convnext/README.md @@ -1,14 +1,17 @@ # ConvNeXt > [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545v1) + ## Abstract + The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets. +
@@ -17,32 +20,32 @@ The "Roaring 20s" of visual recognition began with the introduction of Vision Tr ### ImageNet-1k -| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------:|:------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| ConvNeXt-T\* | From scratch | 28.59 | 4.46 | 82.05 | 95.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-tiny_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128_in1k_20220124-18abde00.pth) | -| ConvNeXt-S\* | From scratch | 50.22 | 8.69 | 83.13 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-small_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128_in1k_20220124-d39b5192.pth) | -| ConvNeXt-B\* | From scratch | 88.59 | 15.36 | 83.85 | 96.74 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128_in1k_20220124-d0915162.pth) | -| ConvNeXt-B\* | ImageNet-21k | 88.59 | 15.36 | 85.81 | 97.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_32xb128_in1k_20220124-eb2d6ada.pth) | -| ConvNeXt-L\* | From scratch | 197.77 | 34.37 | 84.30 | 96.89 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_64xb64_in1k_20220124-f8a0ded0.pth) | -| ConvNeXt-L\* | ImageNet-21k | 197.77 | 34.37 | 86.61 | 98.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_in21k-pre-3rdparty_64xb64_in1k_20220124-2412403d.pth) | -| ConvNeXt-XL\* | ImageNet-21k | 350.20 | 60.93 | 86.97 | 98.20 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-xlarge_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_in21k-pre-3rdparty_64xb64_in1k_20220124-76b6863d.pth) | +| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------: | :---------------------------------------------------------------------: | +| ConvNeXt-T\* | From scratch | 28.59 | 4.46 | 82.05 | 95.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-tiny_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128_in1k_20220124-18abde00.pth) | +| ConvNeXt-S\* | From scratch | 50.22 | 8.69 | 83.13 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-small_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128_in1k_20220124-d39b5192.pth) | +| ConvNeXt-B\* | From scratch | 88.59 | 15.36 | 83.85 | 96.74 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128_in1k_20220124-d0915162.pth) | +| ConvNeXt-B\* | ImageNet-21k | 88.59 | 15.36 | 85.81 | 97.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_32xb128_in1k_20220124-eb2d6ada.pth) | +| ConvNeXt-L\* | From scratch | 197.77 | 34.37 | 84.30 | 96.89 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_64xb64_in1k_20220124-f8a0ded0.pth) | +| ConvNeXt-L\* | ImageNet-21k | 197.77 | 34.37 | 86.61 | 98.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_in21k-pre-3rdparty_64xb64_in1k_20220124-2412403d.pth) | +| ConvNeXt-XL\* | ImageNet-21k | 350.20 | 60.93 | 86.97 | 98.20 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-xlarge_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_in21k-pre-3rdparty_64xb64_in1k_20220124-76b6863d.pth) | -*Models with \* are converted from the [official repo](https://github.com/facebookresearch/ConvNeXt). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +*Models with * are converted from the [official repo](https://github.com/facebookresearch/ConvNeXt). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ### Pre-trained Models The pre-trained models on ImageNet-1k or ImageNet-21k are used to fine-tune on the downstream tasks. -| Model | Training Data | Params(M) | Flops(G) | Download | -|:--------------:|:-------------:|:---------:|:--------:|:--------:| -| ConvNeXt-T\* | ImageNet-1k | 28.59 | 4.46 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128-noema_in1k_20220222-2908964a.pth) | -| ConvNeXt-S\* | ImageNet-1k | 50.22 | 8.69 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128-noema_in1k_20220222-fa001ca5.pth) | -| ConvNeXt-B\* | ImageNet-1k | 88.59 | 15.36 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128-noema_in1k_20220222-dba4f95f.pth) | -| ConvNeXt-B\* | ImageNet-21k | 88.59 | 15.36 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_in21k_20220124-13b83eec.pth) | -| ConvNeXt-L\* | ImageNet-21k | 197.77 | 34.37 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_in21k_20220124-41b5a79f.pth) | -| ConvNeXt-XL\* | ImageNet-21k | 350.20 | 60.93 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_3rdparty_in21k_20220124-f909bad7.pth) | +| Model | Training Data | Params(M) | Flops(G) | Download | +| :-----------: | :-----------: | :-------: | :------: | :-----------------------------------------------------------------------------------------------------------------------------------: | +| ConvNeXt-T\* | ImageNet-1k | 28.59 | 4.46 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128-noema_in1k_20220222-2908964a.pth) | +| ConvNeXt-S\* | ImageNet-1k | 50.22 | 8.69 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128-noema_in1k_20220222-fa001ca5.pth) | +| ConvNeXt-B\* | ImageNet-1k | 88.59 | 15.36 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128-noema_in1k_20220222-dba4f95f.pth) | +| ConvNeXt-B\* | ImageNet-21k | 88.59 | 15.36 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_in21k_20220124-13b83eec.pth) | +| ConvNeXt-L\* | ImageNet-21k | 197.77 | 34.37 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_in21k_20220124-41b5a79f.pth) | +| ConvNeXt-XL\* | ImageNet-21k | 350.20 | 60.93 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_3rdparty_in21k_20220124-f909bad7.pth) | -*Models with \* are converted from the [official repo](https://github.com/facebookresearch/ConvNeXt).* +*Models with * are converted from the [official repo](https://github.com/facebookresearch/ConvNeXt).* ## Citation diff --git a/configs/cspnet/README.md b/configs/cspnet/README.md index c2507e92205..10eb9d0d505 100644 --- a/configs/cspnet/README.md +++ b/configs/cspnet/README.md @@ -1,14 +1,17 @@ # CSPNet > [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://arxiv.org/abs/1911.11929) + ## Abstract + Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on ResNet, ResNeXt, and DenseNet. Source code is at this https URL. +
@@ -17,14 +20,13 @@ Neural networks have enabled state-of-the-art approaches to achieve incredible r ### ImageNet-1k -| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------:|:------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| CSPDarkNet50\* | From scratch | 27.64 | 5.04 | 80.05 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspdarknet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspdarknet50_3rdparty_8xb32_in1k_20220329-bd275287.pth) | -| CSPResNet50\* | From scratch | 21.62 | 3.48 | 79.55 | 94.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnet50_3rdparty_8xb32_in1k_20220329-dd6dddfb.pth) | -| CSPResNeXt50\* | From scratch | 20.57 | 3.11 | 79.96 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnext50_3rdparty_8xb32_in1k_20220329-2cc84d21.pth) | - +| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------------: | :----------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------------: | :---------------------------------------------------------------------: | +| CSPDarkNet50\* | From scratch | 27.64 | 5.04 | 80.05 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspdarknet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspdarknet50_3rdparty_8xb32_in1k_20220329-bd275287.pth) | +| CSPResNet50\* | From scratch | 21.62 | 3.48 | 79.55 | 94.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnet50_3rdparty_8xb32_in1k_20220329-dd6dddfb.pth) | +| CSPResNeXt50\* | From scratch | 20.57 | 3.11 | 79.96 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnext50_3rdparty_8xb32_in1k_20220329-2cc84d21.pth) | -*Models with \* are converted from the [timm repo](https://github.com/rwightman/pytorch-image-models). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +*Models with * are converted from the [timm repo](https://github.com/rwightman/pytorch-image-models). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## Citation diff --git a/configs/deit/README.md b/configs/deit/README.md index aa3b1544b86..e3103658a2a 100644 --- a/configs/deit/README.md +++ b/configs/deit/README.md @@ -1,6 +1,7 @@ # DeiT > [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) + ## Abstract @@ -17,19 +18,19 @@ Recently, neural networks purely based on attention were shown to address image The teacher of the distilled version DeiT is RegNetY-16GF. -| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| DeiT-tiny | From scratch | 5.72 | 1.08 | 74.50 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth) | [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.log.json) | -| DeiT-tiny distilled\* | From scratch | 5.72 | 1.08 | 74.51 | 91.90 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth) | -| DeiT-small | From scratch | 22.05 | 4.24 | 80.69 | 95.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.pth) | [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.log.json) | -| DeiT-small distilled\*| From scratch | 22.05 | 4.24 | 81.17 | 95.40 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-small-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth) | -| DeiT-base | From scratch | 86.57 | 16.86 | 81.76 | 95.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.log.json) | -| DeiT-base\* | From scratch | 86.57 | 16.86 | 81.79 | 95.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_pt-16xb64_in1k_20211124-6f40c188.pth) | -| DeiT-base distilled\* | From scratch | 86.57 | 16.86 | 83.33 | 96.49 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-base-distilled_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211216-42891296.pth) | -| DeiT-base 384px\* | ImageNet-1k | 86.86 | 49.37 | 83.04 | 96.31 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-base_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth) | -| DeiT-base distilled 384px\* | ImageNet-1k | 86.86 | 49.37 | 85.55 | 97.35 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211216-e48d6000.pth) | - -*Models with \* are converted from the [official repo](https://github.com/facebookresearch/deit). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------------------------: | :----------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------: | :--------------------------------------------------------------: | +| DeiT-tiny | From scratch | 5.72 | 1.08 | 74.50 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.log.json) | +| DeiT-tiny distilled\* | From scratch | 5.72 | 1.08 | 74.51 | 91.90 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth) | +| DeiT-small | From scratch | 22.05 | 4.24 | 80.69 | 95.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.log.json) | +| DeiT-small distilled\* | From scratch | 22.05 | 4.24 | 81.17 | 95.40 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-small-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth) | +| DeiT-base | From scratch | 86.57 | 16.86 | 81.76 | 95.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.log.json) | +| DeiT-base\* | From scratch | 86.57 | 16.86 | 81.79 | 95.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_pt-16xb64_in1k_20211124-6f40c188.pth) | +| DeiT-base distilled\* | From scratch | 86.57 | 16.86 | 83.33 | 96.49 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-base-distilled_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211216-42891296.pth) | +| DeiT-base 384px\* | ImageNet-1k | 86.86 | 49.37 | 83.04 | 96.31 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-base_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth) | +| DeiT-base distilled 384px\* | ImageNet-1k | 86.86 | 49.37 | 85.55 | 97.35 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211216-e48d6000.pth) | + +*Models with * are converted from the [official repo](https://github.com/facebookresearch/deit). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ```{warning} MMClassification doesn't support training the distilled version DeiT. diff --git a/configs/densenet/README.md b/configs/densenet/README.md index 77dfa2987d9..f07f25c9fdb 100644 --- a/configs/densenet/README.md +++ b/configs/densenet/README.md @@ -1,6 +1,7 @@ # DenseNet > [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993) + ## Abstract @@ -15,15 +16,14 @@ Recent work has shown that convolutional networks can be substantially deeper, m ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| DenseNet121\* | 7.98 | 2.88 | 74.96 | 92.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet121_4xb256_in1k_20220426-07450f99.pth) | -| DenseNet169\* | 14.15 | 3.42 | 76.08 | 93.11 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet169_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet169_4xb256_in1k_20220426-a2889902.pth) | -| DenseNet201\* | 20.01 | 4.37 | 77.32 | 93.64 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet201_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet201_4xb256_in1k_20220426-05cae4ef.pth) | -| DenseNet161\* | 28.68 | 7.82 | 77.61 | 93.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet161_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet161_4xb256_in1k_20220426-ee6a80a9.pth) | - -*Models with \* are converted from [pytorch](https://pytorch.org/vision/stable/models.html), guided by [original repo](https://github.com/liuzhuang13/DenseNet). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :---------------------------------------------------------------------------: | +| DenseNet121\* | 7.98 | 2.88 | 74.96 | 92.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet121_4xb256_in1k_20220426-07450f99.pth) | +| DenseNet169\* | 14.15 | 3.42 | 76.08 | 93.11 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet169_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet169_4xb256_in1k_20220426-a2889902.pth) | +| DenseNet201\* | 20.01 | 4.37 | 77.32 | 93.64 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet201_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet201_4xb256_in1k_20220426-05cae4ef.pth) | +| DenseNet161\* | 28.68 | 7.82 | 77.61 | 93.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet161_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet161_4xb256_in1k_20220426-ee6a80a9.pth) | +*Models with * are converted from [pytorch](https://pytorch.org/vision/stable/models.html), guided by [original repo](https://github.com/liuzhuang13/DenseNet). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## Citation diff --git a/configs/efficientnet/README.md b/configs/efficientnet/README.md index 846ff564e2b..832f5c6b2f9 100644 --- a/configs/efficientnet/README.md +++ b/configs/efficientnet/README.md @@ -1,6 +1,7 @@ # EfficientNet > [Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946v5) + ## Abstract @@ -19,33 +20,33 @@ In the result table, AA means trained with AutoAugment pre-processing, more deta Note: In MMClassification, we support training with AutoAugment, don't support AdvProp by now. -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| EfficientNet-B0\* | 5.29 | 0.02 | 76.74 | 93.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32_in1k_20220119-a7e2a0b1.pth) | -| EfficientNet-B0 (AA)\* | 5.29 | 0.02 | 77.26 | 93.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa_in1k_20220119-8d939117.pth) | -| EfficientNet-B0 (AA + AdvProp)\* | 5.29 | 0.02 | 77.53 | 93.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k_20220119-26434485.pth) | -| EfficientNet-B1\* | 7.79 | 0.03 | 78.68 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32_in1k_20220119-002556d9.pth) | -| EfficientNet-B1 (AA)\* | 7.79 | 0.03 | 79.20 | 94.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa_in1k_20220119-619d8ae3.pth) | -| EfficientNet-B1 (AA + AdvProp)\* | 7.79 | 0.03 | 79.52 | 94.43 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa-advprop_in1k_20220119-5715267d.pth) | -| EfficientNet-B2\* | 9.11 | 0.03 | 79.64 | 94.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32_in1k_20220119-ea374a30.pth) | -| EfficientNet-B2 (AA)\* | 9.11 | 0.03 | 80.21 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa_in1k_20220119-dd61e80b.pth) | -| EfficientNet-B2 (AA + AdvProp)\* | 9.11 | 0.03 | 80.45 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa-advprop_in1k_20220119-1655338a.pth) | -| EfficientNet-B3\* | 12.23 | 0.06 | 81.01 | 95.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32_in1k_20220119-4b4d7487.pth) | -| EfficientNet-B3 (AA)\* | 12.23 | 0.06 | 81.58 | 95.67 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k_20220119-5b4887a0.pth) | -| EfficientNet-B3 (AA + AdvProp)\* | 12.23 | 0.06 | 81.81 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa-advprop_in1k_20220119-53b41118.pth) | -| EfficientNet-B4\* | 19.34 | 0.12 | 82.57 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32_in1k_20220119-81fd4077.pth) | -| EfficientNet-B4 (AA)\* | 19.34 | 0.12 | 82.95 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa_in1k_20220119-45b8bd2b.pth) | -| EfficientNet-B4 (AA + AdvProp)\* | 19.34 | 0.12 | 83.25 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa-advprop_in1k_20220119-38c2238c.pth) | -| EfficientNet-B5\* | 30.39 | 0.24 | 83.18 | 96.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32_in1k_20220119-e9814430.pth) | -| EfficientNet-B5 (AA)\* | 30.39 | 0.24 | 83.82 | 96.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa_in1k_20220119-2cab8b78.pth) | -| EfficientNet-B5 (AA + AdvProp)\* | 30.39 | 0.24 | 84.21 | 96.98 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa-advprop_in1k_20220119-f57a895a.pth) | -| EfficientNet-B6 (AA)\* | 43.04 | 0.41 | 84.05 | 96.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa_in1k_20220119-45b03310.pth) | -| EfficientNet-B6 (AA + AdvProp)\* | 43.04 | 0.41 | 84.74 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa-advprop_in1k_20220119-bfe3485e.pth) | -| EfficientNet-B7 (AA)\* | 66.35 | 0.72 | 84.38 | 96.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa_in1k_20220119-bf03951c.pth) | -| EfficientNet-B7 (AA + AdvProp)\* | 66.35 | 0.72 | 85.14 | 97.23 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa-advprop_in1k_20220119-c6dbff10.pth) | -| EfficientNet-B8 (AA + AdvProp)\* | 87.41 | 1.09 | 85.38 | 97.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b8_3rdparty_8xb32-aa-advprop_in1k_20220119-297ce1b7.pth) | - -*Models with \* are converted from the [official repo](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------------------------------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------: | :------------------------------------------------------------------: | +| EfficientNet-B0\* | 5.29 | 0.02 | 76.74 | 93.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32_in1k_20220119-a7e2a0b1.pth) | +| EfficientNet-B0 (AA)\* | 5.29 | 0.02 | 77.26 | 93.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa_in1k_20220119-8d939117.pth) | +| EfficientNet-B0 (AA + AdvProp)\* | 5.29 | 0.02 | 77.53 | 93.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k_20220119-26434485.pth) | +| EfficientNet-B1\* | 7.79 | 0.03 | 78.68 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32_in1k_20220119-002556d9.pth) | +| EfficientNet-B1 (AA)\* | 7.79 | 0.03 | 79.20 | 94.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa_in1k_20220119-619d8ae3.pth) | +| EfficientNet-B1 (AA + AdvProp)\* | 7.79 | 0.03 | 79.52 | 94.43 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa-advprop_in1k_20220119-5715267d.pth) | +| EfficientNet-B2\* | 9.11 | 0.03 | 79.64 | 94.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32_in1k_20220119-ea374a30.pth) | +| EfficientNet-B2 (AA)\* | 9.11 | 0.03 | 80.21 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa_in1k_20220119-dd61e80b.pth) | +| EfficientNet-B2 (AA + AdvProp)\* | 9.11 | 0.03 | 80.45 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa-advprop_in1k_20220119-1655338a.pth) | +| EfficientNet-B3\* | 12.23 | 0.06 | 81.01 | 95.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32_in1k_20220119-4b4d7487.pth) | +| EfficientNet-B3 (AA)\* | 12.23 | 0.06 | 81.58 | 95.67 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k_20220119-5b4887a0.pth) | +| EfficientNet-B3 (AA + AdvProp)\* | 12.23 | 0.06 | 81.81 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa-advprop_in1k_20220119-53b41118.pth) | +| EfficientNet-B4\* | 19.34 | 0.12 | 82.57 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32_in1k_20220119-81fd4077.pth) | +| EfficientNet-B4 (AA)\* | 19.34 | 0.12 | 82.95 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa_in1k_20220119-45b8bd2b.pth) | +| EfficientNet-B4 (AA + AdvProp)\* | 19.34 | 0.12 | 83.25 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa-advprop_in1k_20220119-38c2238c.pth) | +| EfficientNet-B5\* | 30.39 | 0.24 | 83.18 | 96.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32_in1k_20220119-e9814430.pth) | +| EfficientNet-B5 (AA)\* | 30.39 | 0.24 | 83.82 | 96.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa_in1k_20220119-2cab8b78.pth) | +| EfficientNet-B5 (AA + AdvProp)\* | 30.39 | 0.24 | 84.21 | 96.98 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa-advprop_in1k_20220119-f57a895a.pth) | +| EfficientNet-B6 (AA)\* | 43.04 | 0.41 | 84.05 | 96.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa_in1k_20220119-45b03310.pth) | +| EfficientNet-B6 (AA + AdvProp)\* | 43.04 | 0.41 | 84.74 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa-advprop_in1k_20220119-bfe3485e.pth) | +| EfficientNet-B7 (AA)\* | 66.35 | 0.72 | 84.38 | 96.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa_in1k_20220119-bf03951c.pth) | +| EfficientNet-B7 (AA + AdvProp)\* | 66.35 | 0.72 | 85.14 | 97.23 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa-advprop_in1k_20220119-c6dbff10.pth) | +| EfficientNet-B8 (AA + AdvProp)\* | 87.41 | 1.09 | 85.38 | 97.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b8_3rdparty_8xb32-aa-advprop_in1k_20220119-297ce1b7.pth) | + +*Models with * are converted from the [official repo](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## Citation diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md index e3144cc4a23..0a30ccd16d4 100644 --- a/configs/hrnet/README.md +++ b/configs/hrnet/README.md @@ -1,6 +1,7 @@ # HRNet > [Deep High-Resolution Representation Learning for Visual Recognition](https://arxiv.org/abs/1908.07919v2) + ## Abstract @@ -15,19 +16,19 @@ High-resolution representations are essential for position-sensitive vision prob ## ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| HRNet-W18\* | 21.30 | 4.33 | 76.75 | 93.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32_in1k_20220120-0c10b180.pth) | -| HRNet-W30\* | 37.71 | 8.17 | 78.19 | 94.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w30_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w30_3rdparty_8xb32_in1k_20220120-8aa3832f.pth) | -| HRNet-W32\* | 41.23 | 8.99 | 78.44 | 94.19 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w32_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w32_3rdparty_8xb32_in1k_20220120-c394f1ab.pth) | -| HRNet-W40\* | 57.55 | 12.77 | 78.94 | 94.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w40_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w40_3rdparty_8xb32_in1k_20220120-9a2dbfc5.pth) | -| HRNet-W44\* | 67.06 | 14.96 | 78.88 | 94.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w44_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w44_3rdparty_8xb32_in1k_20220120-35d07f73.pth) | -| HRNet-W48\* | 77.47 | 17.36 | 79.32 | 94.52 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32_in1k_20220120-e555ef50.pth) | -| HRNet-W64\* | 128.06 | 29.00 | 79.46 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w64_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w64_3rdparty_8xb32_in1k_20220120-19126642.pth) | -| HRNet-W18 (ssld)\* | 21.30 | 4.33 | 81.06 | 95.70 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32-ssld_in1k_20220120-455f69ea.pth) | -| HRNet-W48 (ssld)\* | 77.47 | 17.36 | 83.63 | 96.79 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32-ssld_in1k_20220120-d0459c38.pth) | - -*Models with \* are converted from the [official repo](https://github.com/HRNet/HRNet-Image-Classification). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :----------------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------------: | :-------------------------------------------------------------------------: | +| HRNet-W18\* | 21.30 | 4.33 | 76.75 | 93.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32_in1k_20220120-0c10b180.pth) | +| HRNet-W30\* | 37.71 | 8.17 | 78.19 | 94.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w30_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w30_3rdparty_8xb32_in1k_20220120-8aa3832f.pth) | +| HRNet-W32\* | 41.23 | 8.99 | 78.44 | 94.19 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w32_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w32_3rdparty_8xb32_in1k_20220120-c394f1ab.pth) | +| HRNet-W40\* | 57.55 | 12.77 | 78.94 | 94.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w40_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w40_3rdparty_8xb32_in1k_20220120-9a2dbfc5.pth) | +| HRNet-W44\* | 67.06 | 14.96 | 78.88 | 94.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w44_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w44_3rdparty_8xb32_in1k_20220120-35d07f73.pth) | +| HRNet-W48\* | 77.47 | 17.36 | 79.32 | 94.52 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32_in1k_20220120-e555ef50.pth) | +| HRNet-W64\* | 128.06 | 29.00 | 79.46 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w64_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w64_3rdparty_8xb32_in1k_20220120-19126642.pth) | +| HRNet-W18 (ssld)\* | 21.30 | 4.33 | 81.06 | 95.70 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32-ssld_in1k_20220120-455f69ea.pth) | +| HRNet-W48 (ssld)\* | 77.47 | 17.36 | 83.63 | 96.79 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32-ssld_in1k_20220120-d0459c38.pth) | + +*Models with * are converted from the [official repo](https://github.com/HRNet/HRNet-Image-Classification). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## Citation diff --git a/configs/lenet/README.md b/configs/lenet/README.md index 241bedab173..2cd68eac42e 100644 --- a/configs/lenet/README.md +++ b/configs/lenet/README.md @@ -1,6 +1,7 @@ # LeNet > [Backpropagation Applied to Handwritten Zip Code Recognition](https://ieeexplore.ieee.org/document/6795724) + ## Abstract diff --git a/configs/mlp_mixer/README.md b/configs/mlp_mixer/README.md index dc8866e8447..5ec98871b6d 100644 --- a/configs/mlp_mixer/README.md +++ b/configs/mlp_mixer/README.md @@ -1,6 +1,7 @@ # Mlp-Mixer > [MLP-Mixer: An all-MLP Architecture for Vision](https://arxiv.org/abs/2105.01601) + ## Abstract @@ -15,12 +16,12 @@ Convolutional Neural Networks (CNNs) are the go-to model for computer vision. Re ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:--------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| Mixer-B/16\* | 59.88 | 12.61 | 76.68 | 92.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-base-p16_3rdparty_64xb64_in1k_20211124-1377e3e0.pth) | -| Mixer-L/16\* | 208.2 | 44.57 | 72.34 | 88.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-large-p16_3rdparty_64xb64_in1k_20211124-5a2519d2.pth) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | +| Mixer-B/16\* | 59.88 | 12.61 | 76.68 | 92.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-base-p16_3rdparty_64xb64_in1k_20211124-1377e3e0.pth) | +| Mixer-L/16\* | 208.2 | 44.57 | 72.34 | 88.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-large-p16_3rdparty_64xb64_in1k_20211124-5a2519d2.pth) | -*Models with \* are converted from [timm](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mlp_mixer.py). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +*Models with * are converted from [timm](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mlp_mixer.py). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## Citation diff --git a/configs/mobilenet_v2/README.md b/configs/mobilenet_v2/README.md index 9a0cd8a6549..675c8dd4d43 100644 --- a/configs/mobilenet_v2/README.md +++ b/configs/mobilenet_v2/README.md @@ -1,6 +1,7 @@ # MobileNet V2 > [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) + ## Abstract @@ -17,9 +18,9 @@ The MobileNetV2 architecture is based on an inverted residual structure where th ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:| -| MobileNet V2 | 3.5 | 0.319 | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth) | [log](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.log.json) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | +| MobileNet V2 | 3.5 | 0.319 | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.log.json) | ## Citation diff --git a/configs/mobilenet_v3/README.md b/configs/mobilenet_v3/README.md index 36392b91c3b..737c4d32ec0 100644 --- a/configs/mobilenet_v3/README.md +++ b/configs/mobilenet_v3/README.md @@ -1,11 +1,12 @@ # MobileNet V3 > [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244) + ## Abstract -We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2\% more accurate on ImageNet classification while reducing latency by 15\% compared to MobileNetV2. MobileNetV3-Small is 4.6\% more accurate while reducing latency by 5\% compared to MobileNetV2. MobileNetV3-Large detection is 25\% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 30\% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation. +We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 15% compared to MobileNetV2. MobileNetV3-Small is 4.6% more accurate while reducing latency by 5% compared to MobileNetV2. MobileNetV3-Large detection is 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation.
@@ -15,12 +16,12 @@ We present the next generation of MobileNets based on a combination of complemen ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| MobileNetV3-Small\* | 2.54 | 0.06 | 67.66 | 87.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v3/mobilenet-v3-small_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth) | -| MobileNetV3-Large\* | 5.48 | 0.23 | 74.04 | 91.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v3/mobilenet-v3-large_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_large-3ea3c186.pth) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------------: | :------------------------------------------------------------------------: | +| MobileNetV3-Small\* | 2.54 | 0.06 | 67.66 | 87.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v3/mobilenet-v3-small_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth) | +| MobileNetV3-Large\* | 5.48 | 0.23 | 74.04 | 91.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v3/mobilenet-v3-large_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_large-3ea3c186.pth) | -*Models with \* are converted from [torchvision](https://pytorch.org/vision/stable/_modules/torchvision/models/mobilenetv3.html). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +*Models with * are converted from [torchvision](https://pytorch.org/vision/stable/_modules/torchvision/models/mobilenetv3.html). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## Citation diff --git a/configs/poolformer/README.md b/configs/poolformer/README.md index ed1a0664060..cc557e107df 100644 --- a/configs/poolformer/README.md +++ b/configs/poolformer/README.md @@ -1,6 +1,7 @@ # PoolFormer > [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) + ## Abstract @@ -15,15 +16,15 @@ Transformers have shown great potential in computer vision tasks. A common belie ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| PoolFormer-S12\* | 11.92 | 1.87 | 77.24 | 93.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-s12_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s12_3rdparty_32xb128_in1k_20220414-f8d83051.pth) | -| PoolFormer-S24\* | 21.39 | 3.51 | 80.33 | 95.05 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-s24_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s24_3rdparty_32xb128_in1k_20220414-d7055904.pth) | -| PoolFormer-S36\* | 30.86 | 5.15 | 81.43 | 95.45 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-s36_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s36_3rdparty_32xb128_in1k_20220414-d78ff3e8.pth) | -| PoolFormer-M36\* | 56.17 | 8.96 | 82.14 | 95.71 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-m36_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m36_3rdparty_32xb128_in1k_20220414-c55e0949.pth) | -| PoolFormer-M48\* | 73.47 | 11.80 | 82.51 | 95.95 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-m48_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m48_3rdparty_32xb128_in1k_20220414-9378f3eb.pth) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :--------------: | :-------: | :------: | :-------: | :-------: | :-----------------------------------------------------------------------: | :--------------------------------------------------------------------------: | +| PoolFormer-S12\* | 11.92 | 1.87 | 77.24 | 93.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-s12_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s12_3rdparty_32xb128_in1k_20220414-f8d83051.pth) | +| PoolFormer-S24\* | 21.39 | 3.51 | 80.33 | 95.05 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-s24_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s24_3rdparty_32xb128_in1k_20220414-d7055904.pth) | +| PoolFormer-S36\* | 30.86 | 5.15 | 81.43 | 95.45 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-s36_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s36_3rdparty_32xb128_in1k_20220414-d78ff3e8.pth) | +| PoolFormer-M36\* | 56.17 | 8.96 | 82.14 | 95.71 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-m36_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m36_3rdparty_32xb128_in1k_20220414-c55e0949.pth) | +| PoolFormer-M48\* | 73.47 | 11.80 | 82.51 | 95.95 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-m48_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m48_3rdparty_32xb128_in1k_20220414-9378f3eb.pth) | -*Models with \* are converted from the [official repo](https://github.com/sail-sg/poolformer). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +*Models with * are converted from the [official repo](https://github.com/sail-sg/poolformer). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## Citation diff --git a/configs/regnet/README.md b/configs/regnet/README.md index b7bbc3f0904..1ae074d644c 100644 --- a/configs/regnet/README.md +++ b/configs/regnet/README.md @@ -1,6 +1,7 @@ # RegNet > [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) + ## Abstract @@ -15,26 +16,26 @@ In this work, we present a new network design paradigm. Our goal is to help adva ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| RegNetX-400MF | 5.16 | 0.41 | 72.56 | 90.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-400mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211213-89bfc226.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211208_143316.log.json) | -| RegNetX-800MF | 7.26 | 0.81 | 74.76 | 92.32 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-800mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211213-222b0f11.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211207_143037.log.json) | -| RegNetX-1.6GF | 9.19 | 1.63 | 76.84 | 93.31 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-1.6gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211213-d1b89758.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211208_143018.log.json) | -| RegNetX-3.2GF | 15.3 | 3.21 | 78.09 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-3.2gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211213-1fdd82ae.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211208_142720.log.json) | -| RegNetX-4.0GF | 22.12 | 4.0 | 78.60 | 94.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-4.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211213-efed675c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211207_150431.log.json) | -| RegNetX-6.4GF | 26.21 | 6.51 | 79.38 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-6.4gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211215-5c6089da.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211213_172748.log.json) | -| RegNetX-8.0GF | 39.57 | 8.03 | 79.12 | 94.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-8.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211213-9a9fcc76.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211208_103250.log.json) | -| RegNetX-12GF | 46.11 | 12.15 | 79.67 | 95.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-12gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211213-5df8c2f8.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211208_143713.log.json) | -| RegNetX-400MF\* | 5.16 | 0.41 | 72.55 | 90.91 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-400mf_8xb128_in1k) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-400MF-0db9f35c.pth) | -| RegNetX-800MF\* | 7.26 | 0.81 | 75.21 | 92.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-800mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-800MF-4f9d1e8a.pth) | -| RegNetX-1.6GF\* | 9.19 | 1.63 | 77.04 | 93.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-1.6gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-1.6GF-cfb32375.pth) | -| RegNetX-3.2GF\* | 15.3 | 3.21 | 78.26 | 94.20 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-3.2gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-3.2GF-82c43fd5.pth) | -| RegNetX-4.0GF\* | 22.12 | 4.0 | 78.72 | 94.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-4.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-4.0GF-ef8bb32c.pth) | -| RegNetX-6.4GF\* | 26.21 | 6.51 | 79.22 | 94.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-6.4gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-6.4GF-6888c0ea.pth) | -| RegNetX-8.0GF\* | 39.57 | 8.03 | 79.31 | 94.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-8.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-8.0GF-cb4c77ec.pth) | -| RegNetX-12GF\* | 46.11 | 12.15 | 79.91 | 94.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-12gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-12GF-0574538f.pth) | - -*Models with \* are converted from [pycls](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md). The config files of these models are only for validation.* +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------------------: | :--------------------------------------------------------------------------: | +| RegNetX-400MF | 5.16 | 0.41 | 72.56 | 90.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-400mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211213-89bfc226.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211208_143316.log.json) | +| RegNetX-800MF | 7.26 | 0.81 | 74.76 | 92.32 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-800mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211213-222b0f11.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211207_143037.log.json) | +| RegNetX-1.6GF | 9.19 | 1.63 | 76.84 | 93.31 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-1.6gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211213-d1b89758.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211208_143018.log.json) | +| RegNetX-3.2GF | 15.3 | 3.21 | 78.09 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-3.2gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211213-1fdd82ae.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211208_142720.log.json) | +| RegNetX-4.0GF | 22.12 | 4.0 | 78.60 | 94.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-4.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211213-efed675c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211207_150431.log.json) | +| RegNetX-6.4GF | 26.21 | 6.51 | 79.38 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-6.4gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211215-5c6089da.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211213_172748.log.json) | +| RegNetX-8.0GF | 39.57 | 8.03 | 79.12 | 94.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-8.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211213-9a9fcc76.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211208_103250.log.json) | +| RegNetX-12GF | 46.11 | 12.15 | 79.67 | 95.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-12gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211213-5df8c2f8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211208_143713.log.json) | +| RegNetX-400MF\* | 5.16 | 0.41 | 72.55 | 90.91 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-400mf_8xb128_in1k) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-400MF-0db9f35c.pth) | +| RegNetX-800MF\* | 7.26 | 0.81 | 75.21 | 92.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-800mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-800MF-4f9d1e8a.pth) | +| RegNetX-1.6GF\* | 9.19 | 1.63 | 77.04 | 93.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-1.6gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-1.6GF-cfb32375.pth) | +| RegNetX-3.2GF\* | 15.3 | 3.21 | 78.26 | 94.20 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-3.2gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-3.2GF-82c43fd5.pth) | +| RegNetX-4.0GF\* | 22.12 | 4.0 | 78.72 | 94.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-4.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-4.0GF-ef8bb32c.pth) | +| RegNetX-6.4GF\* | 26.21 | 6.51 | 79.22 | 94.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-6.4gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-6.4GF-6888c0ea.pth) | +| RegNetX-8.0GF\* | 39.57 | 8.03 | 79.31 | 94.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-8.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-8.0GF-cb4c77ec.pth) | +| RegNetX-12GF\* | 46.11 | 12.15 | 79.91 | 94.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-12gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-12GF-0574538f.pth) | + +*Models with * are converted from [pycls](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md). The config files of these models are only for validation.* ## Citation diff --git a/configs/repmlp/README.md b/configs/repmlp/README.md index 5fee27df544..453346352cd 100644 --- a/configs/repmlp/README.md +++ b/configs/repmlp/README.md @@ -1,6 +1,7 @@ # RepMLP > [RepMLP: Re-parameterizing Convolutions into Fully-connected Layers forImage Recognition](https://arxiv.org/abs/2105.01883) + ## Abstract @@ -11,18 +12,16 @@ We propose RepMLP, a multi-layer-perceptron-style neural network building block
- ## Results and models ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:--------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| RepMLP-B224\* | 68.24 | 6.71 | 80.41 | 95.12 | [train_cfg](https://github.com/open-mmlab/mmclassification/blob/master/configs/repmlp/repmlp-base_8xb64_in1k.py) \| [deploy_cfg](https://github.com/open-mmlab/mmclassification/blob/master/configs/repmlp/repmlp-base_delopy_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repmlp/repmlp-base_3rdparty_8xb64_in1k_20220330-1cb1f11b.pth) | -| RepMLP-B256\* | 96.45 | 9.69 | 81.11 | 95.5 | [train_cfg](https://github.com/open-mmlab/mmclassification/blob/master/configs/repmlp/repmlp-base_8xb64_in1k-256px.py) \| [deploy_cfg](https://github.com/open-mmlab/mmclassification/blob/master/configs/repmlp/repmlp-b256_deploy_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repmlp/repmlp-base_3rdparty_8xb64_in1k-256px_20220330-7c5a91ce.pth) | - -*Models with \* are converted from [the official repo.](https://github.com/DingXiaoH/RepMLP). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :---------------------------------------------------------------------------: | +| RepMLP-B224\* | 68.24 | 6.71 | 80.41 | 95.12 | [train_cfg](https://github.com/open-mmlab/mmclassification/blob/master/configs/repmlp/repmlp-base_8xb64_in1k.py) \| [deploy_cfg](https://github.com/open-mmlab/mmclassification/blob/master/configs/repmlp/repmlp-base_delopy_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repmlp/repmlp-base_3rdparty_8xb64_in1k_20220330-1cb1f11b.pth) | +| RepMLP-B256\* | 96.45 | 9.69 | 81.11 | 95.5 | [train_cfg](https://github.com/open-mmlab/mmclassification/blob/master/configs/repmlp/repmlp-base_8xb64_in1k-256px.py) \| [deploy_cfg](https://github.com/open-mmlab/mmclassification/blob/master/configs/repmlp/repmlp-b256_deploy_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repmlp/repmlp-base_3rdparty_8xb64_in1k-256px_20220330-7c5a91ce.pth) | +*Models with * are converted from [the official repo.](https://github.com/DingXiaoH/RepMLP). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## How to use @@ -82,7 +81,6 @@ classifier = build_classifier(cfg) classifier.backbone.switch_to_deploy() ``` - ## Citation ``` diff --git a/configs/repvgg/README.md b/configs/repvgg/README.md index f484f128908..b934132626d 100644 --- a/configs/repvgg/README.md +++ b/configs/repvgg/README.md @@ -1,6 +1,7 @@ # RepVGG > [Repvgg: Making vgg-style convnets great again](https://arxiv.org/abs/2101.03697) + ## Abstract @@ -11,27 +12,26 @@ We present a simple but powerful architecture of convolutional neural network, w - ## Results and models ### ImageNet-1k -| Model | Epochs | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -| :---------: | :----: | :-------------------------------: | :-----------------------------: | :-------: | :-------: | :----------------------------------------------------------: | :----------------------------------------------------------: | +| Model | Epochs | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :----: | :-------------------------------: | :-----------------------------: | :-------: | :-------: | :----------------------------------------------: | :-------------------------------------------------: | | RepVGG-A0\* | 120 | 9.11（train) \| 8.31 (deploy) | 1.52 (train) \| 1.36 (deploy) | 72.41 | 90.50 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_3rdparty_4xb64-coslr-120e_in1k_20210909-883ab98c.pth) | | RepVGG-A1\* | 120 | 14.09 (train) \| 12.79 (deploy) | 2.64 (train) \| 2.37 (deploy) | 74.47 | 91.85 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_3rdparty_4xb64-coslr-120e_in1k_20210909-24003a24.pth) | -| RepVGG-A2\* | 120 | 28.21 (train) \| 25.5 (deploy) | 5.7 (train) \| 5.12 (deploy) | 76.48 | 93.01 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_3rdparty_4xb64-coslr-120e_in1k_20210909-97d7695a.pth) | +| RepVGG-A2\* | 120 | 28.21 (train) \| 25.5 (deploy) | 5.7 (train) \| 5.12 (deploy) | 76.48 | 93.01 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_3rdparty_4xb64-coslr-120e_in1k_20210909-97d7695a.pth) | | RepVGG-B0\* | 120 | 15.82 (train) \| 14.34 (deploy) | 3.42 (train) \| 3.06 (deploy) | 75.14 | 92.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_3rdparty_4xb64-coslr-120e_in1k_20210909-446375f4.pth) | -| RepVGG-B1\* | 120 | 57.42 (train) \| 51.83 (deploy) | 13.16 (train) \| 11.82 (deploy) | 78.37 | 94.11 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_3rdparty_4xb64-coslr-120e_in1k_20210909-750cdf67.pth) | +| RepVGG-B1\* | 120 | 57.42 (train) \| 51.83 (deploy) | 13.16 (train) \| 11.82 (deploy) | 78.37 | 94.11 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_3rdparty_4xb64-coslr-120e_in1k_20210909-750cdf67.pth) | | RepVGG-B1g2\* | 120 | 45.78 (train) \| 41.36 (deploy) | 9.82 (train) \| 8.82 (deploy) | 77.79 | 93.88 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k_20210909-344f6422.pth) | | RepVGG-B1g4\* | 120 | 39.97 (train) \| 36.13 (deploy) | 8.15 (train) \| 7.32 (deploy) | 77.58 | 93.84 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k_20210909-d4c1a642.pth) | | RepVGG-B2\* | 120 | 89.02 (train) \| 80.32 (deploy) | 20.46 (train) \| 18.39 (deploy) | 78.78 | 94.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_3rdparty_4xb64-coslr-120e_in1k_20210909-bd6b937c.pth) | | RepVGG-B2g4\* | 200 | 61.76 (train) \| 55.78 (deploy) | 12.63 (train) \| 11.34 (deploy) | 79.38 | 94.68 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-7b7955f0.pth) | | RepVGG-B3\* | 200 | 123.09 (train) \| 110.96 (deploy) | 29.17 (train) \| 26.22 (deploy) | 80.52 | 95.26 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-dda968bf.pth) | | RepVGG-B3g4\* | 200 | 83.83 (train) \| 75.63 (deploy) | 17.9 (train) \| 16.08 (deploy) | 80.22 | 95.10 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-4e54846a.pth) | -| RepVGG-D2se\* | 200 | 133.33 (train) \| 120.39 (deploy) | 36.56 (train) \| 32.85 (deploy) | 81.81 | 95.94 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth) | +| RepVGG-D2se\* | 200 | 133.33 (train) \| 120.39 (deploy) | 36.56 (train) \| 32.85 (deploy) | 81.81 | 95.94 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth) | -*Models with \* are converted from the [official repo](https://github.com/DingXiaoH/RepVGG). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +*Models with * are converted from the [official repo](https://github.com/DingXiaoH/RepVGG). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## How to use diff --git a/configs/res2net/README.md b/configs/res2net/README.md index c7cebaa7379..61190092886 100644 --- a/configs/res2net/README.md +++ b/configs/res2net/README.md @@ -1,6 +1,7 @@ # Res2Net > [Res2Net: A New Multi-scale Backbone Architecture](https://arxiv.org/pdf/1904.01169.pdf) + ## Abstract @@ -15,13 +16,13 @@ Representing features at multiple scales is of great importance for numerous vis ### ImageNet-1k -| Model | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:------:|:--------:| -| Res2Net-50-14w-8s\* | 224x224 | 25.06 | 4.22 | 78.14 | 93.85 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w14-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w14-s8_3rdparty_8xb32_in1k_20210927-bc967bf1.pth) | [log]()| -| Res2Net-50-26w-8s\* | 224x224 | 48.40 | 8.39 | 79.20 | 94.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w26-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w26-s8_3rdparty_8xb32_in1k_20210927-f547a94b.pth) | [log]()| -| Res2Net-101-26w-4s\* | 224x224 | 45.21 | 8.12 | 79.19 | 94.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net101-w26-s4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net101-w26-s4_3rdparty_8xb32_in1k_20210927-870b6c36.pth) | [log]()| +| Model | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------------------: | :--------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------: | :-------------------------------------------------------------------: | +| Res2Net-50-14w-8s\* | 224x224 | 25.06 | 4.22 | 78.14 | 93.85 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w14-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w14-s8_3rdparty_8xb32_in1k_20210927-bc967bf1.pth) \| [log](<>) | +| Res2Net-50-26w-8s\* | 224x224 | 48.40 | 8.39 | 79.20 | 94.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w26-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w26-s8_3rdparty_8xb32_in1k_20210927-f547a94b.pth) \| [log](<>) | +| Res2Net-101-26w-4s\* | 224x224 | 45.21 | 8.12 | 79.19 | 94.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net101-w26-s4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net101-w26-s4_3rdparty_8xb32_in1k_20210927-870b6c36.pth) \| [log](<>) | -*Models with \* are converted from the [official repo](https://github.com/Res2Net/Res2Net-PretrainedModels). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +*Models with * are converted from the [official repo](https://github.com/Res2Net/Res2Net-PretrainedModels). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## Citation diff --git a/configs/resnest/README.md b/configs/resnest/README.md index 55c0e7f2843..eb6c5fd728c 100644 --- a/configs/resnest/README.md +++ b/configs/resnest/README.md @@ -1,6 +1,7 @@ # ResNeSt > [ResNeSt: Split-Attention Networks](https://arxiv.org/abs/2004.08955) + ## Abstract diff --git a/configs/resnet/README.md b/configs/resnet/README.md index e5f90fc950e..f1d32effde7 100644 --- a/configs/resnet/README.md +++ b/configs/resnet/README.md @@ -1,6 +1,7 @@ # ResNet > [Deep Residual Learning for Image Recognition](https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html) + ## Abstract @@ -17,60 +18,59 @@ The depth of representations is of central importance for many visual recognitio The pre-trained models on ImageNet-21k are used to fine-tune, and therefore don't have evaluation results. -| Model | resolution | Params(M) | Flops(G) | Download | -|:---------------:|:-----------:|:---------:|:---------:|:--------:| -| ResNet-50-mill | 224x224 | 86.74 | 15.14 | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth)| +| Model | resolution | Params(M) | Flops(G) | Download | +| :------------: | :--------: | :-------: | :------: | :-------------------------------------------------------------------------------------------------------------------: | +| ResNet-50-mill | 224x224 | 86.74 | 15.14 | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth) | *The "mill" means using the mutil-label pretrain weight from [ImageNet-21K Pretraining for the Masses](https://github.com/Alibaba-MIIL/ImageNet21K).* ### Cifar10 -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:| -| ResNet-18 | 11.17 | 0.56 | 94.82 | 99.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.log.json) | -| ResNet-34 | 21.28 | 1.16 | 95.34 | 99.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.log.json) | -| ResNet-50 | 23.52 | 1.31 | 95.55 | 99.91 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.log.json) | -| ResNet-101 | 42.51 | 2.52 | 95.58 | 99.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.log.json) | -| ResNet-152 | 58.16 | 3.74 | 95.76 | 99.89 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.log.json) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :--------: | :-------: | :------: | :-------: | :-------: | :--------------------------------------------------------------------------: | :-----------------------------------------------------------------------------: | +| ResNet-18 | 11.17 | 0.56 | 94.82 | 99.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.log.json) | +| ResNet-34 | 21.28 | 1.16 | 95.34 | 99.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.log.json) | +| ResNet-50 | 23.52 | 1.31 | 95.55 | 99.91 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.log.json) | +| ResNet-101 | 42.51 | 2.52 | 95.58 | 99.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.log.json) | +| ResNet-152 | 58.16 | 3.74 | 95.76 | 99.89 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.log.json) | ### Cifar100 -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:| -| ResNet-50 | 23.71 | 1.31 | 79.90 | 95.19 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb16_cifar100.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar100_20210528-67b58a1b.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar100_20210528-67b58a1b.log.json) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------------------: | :-----------------------------------------------------------------------------: | +| ResNet-50 | 23.71 | 1.31 | 79.90 | 95.19 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb16_cifar100.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar100_20210528-67b58a1b.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar100_20210528-67b58a1b.log.json) | ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:| -| ResNet-18 | 11.69 | 1.82 | 69.90 | 89.43 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.log.json) | -| ResNet-34 | 21.8 | 3.68 | 73.62 | 91.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.log.json) | -| ResNet-50 | 25.56 | 4.12 | 76.55 | 93.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.log.json) | -| ResNet-101 | 44.55 | 7.85 | 77.97 | 94.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.log.json) | -| ResNet-152 | 60.19 | 11.58 | 78.48 | 94.13 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.log.json) | -| ResNetV1C-50 | 25.58 | 4.36 | 77.01 | 93.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.log.json) | -| ResNetV1C-101 | 44.57 | 8.09 | 78.30 | 94.27 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.log.json) | -| ResNetV1C-152 | 60.21 | 11.82 | 78.76 | 94.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.log.json) | -| ResNetV1D-50 | 25.58 | 4.36 | 77.54 | 93.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.log.json) | -| ResNetV1D-101 | 44.57 | 8.09 | 78.93 | 94.48 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.log.json) | -| ResNetV1D-152 | 60.21 | 11.82 | 79.41 | 94.70 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.log.json) | -| ResNet-50 (fp16) | 25.56 | 4.12 | 76.30 | 93.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32-fp16_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth) | [log](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.log.json) | -| Wide-ResNet-50\* | 68.88 | 11.44 | 78.48 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/wide-resnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/wide-resnet50_3rdparty_8xb32_in1k_20220304-66678344.pth) | -| Wide-ResNet-101\* | 126.89 | 22.81 | 78.84 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth) | -| ResNet-50 (rsb-a1) | 25.56 | 4.12 | 80.12 | 94.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.log.json) | -| ResNet-50 (rsb-a2) | 25.56 | 4.12 | 79.55 | 94.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a2-300e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a2-300e_in1k_20211228-0fd8be6e.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a2-300e_in1k_20211228-0fd8be6e.log.json) | -| ResNet-50 (rsb-a3) | 25.56 | 4.12 | 78.30 | 93.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.log.json) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :----------------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------------: | :-------------------------------------------------------------------------: | +| ResNet-18 | 11.69 | 1.82 | 69.90 | 89.43 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.log.json) | +| ResNet-34 | 21.8 | 3.68 | 73.62 | 91.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.log.json) | +| ResNet-50 | 25.56 | 4.12 | 76.55 | 93.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.log.json) | +| ResNet-101 | 44.55 | 7.85 | 77.97 | 94.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.log.json) | +| ResNet-152 | 60.19 | 11.58 | 78.48 | 94.13 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.log.json) | +| ResNetV1C-50 | 25.58 | 4.36 | 77.01 | 93.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.log.json) | +| ResNetV1C-101 | 44.57 | 8.09 | 78.30 | 94.27 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.log.json) | +| ResNetV1C-152 | 60.21 | 11.82 | 78.76 | 94.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.log.json) | +| ResNetV1D-50 | 25.58 | 4.36 | 77.54 | 93.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.log.json) | +| ResNetV1D-101 | 44.57 | 8.09 | 78.93 | 94.48 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.log.json) | +| ResNetV1D-152 | 60.21 | 11.82 | 79.41 | 94.70 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.log.json) | +| ResNet-50 (fp16) | 25.56 | 4.12 | 76.30 | 93.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32-fp16_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.log.json) | +| Wide-ResNet-50\* | 68.88 | 11.44 | 78.48 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/wide-resnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/wide-resnet50_3rdparty_8xb32_in1k_20220304-66678344.pth) | +| Wide-ResNet-101\* | 126.89 | 22.81 | 78.84 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth) | +| ResNet-50 (rsb-a1) | 25.56 | 4.12 | 80.12 | 94.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.log.json) | +| ResNet-50 (rsb-a2) | 25.56 | 4.12 | 79.55 | 94.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a2-300e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a2-300e_in1k_20211228-0fd8be6e.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a2-300e_in1k_20211228-0fd8be6e.log.json) | +| ResNet-50 (rsb-a3) | 25.56 | 4.12 | 78.30 | 93.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.log.json) | *The "rsb" means using the training settings from [ResNet strikes back: An improved training procedure in timm](https://arxiv.org/abs/2110.00476).* -*Models with \* are converted from the [official repo](https://github.com/pytorch/vision). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +*Models with * are converted from the [official repo](https://github.com/pytorch/vision). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ### CUB-200-2011 -| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Config | Download | -|:---------------------:|:------------:|:---------:|:---------:|:--------:|:---------:|:---------:|:---------:| -| ResNet-50 | [ImageNet-21k-mill](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth) | 448x448 | 23.92 | 16.48 | 88.45 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb8_cub.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.log.json) | - +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Config | Download | +| :-------: | :--------------------------------------------------: | :--------: | :-------: | :------: | :-------: | :------------------------------------------------: | :---------------------------------------------------: | +| ResNet-50 | [ImageNet-21k-mill](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth) | 448x448 | 23.92 | 16.48 | 88.45 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb8_cub.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.log.json) | ## Citation diff --git a/configs/resnext/README.md b/configs/resnext/README.md index 2d0b51527ba..56df277e5ec 100644 --- a/configs/resnext/README.md +++ b/configs/resnext/README.md @@ -1,6 +1,7 @@ # ResNeXt > [Aggregated Residual Transformations for Deep Neural Networks](https://openaccess.thecvf.com/content_cvpr_2017/html/Xie_Aggregated_Residual_Transformations_CVPR_2017_paper.html) + ## Abstract @@ -15,12 +16,12 @@ We present a simple, highly modularized network architecture for image classific ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:| -| ResNeXt-32x4d-50 | 25.03 | 4.27 | 77.90 | 93.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.log.json) | -| ResNeXt-32x4d-101 | 44.18 | 8.03 | 78.61 | 94.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.log.json) | -| ResNeXt-32x8d-101 | 88.79 | 16.5 | 79.27 | 94.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x8d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.log.json) | -| ResNeXt-32x4d-152 | 59.95 | 11.8 | 78.88 | 94.33 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext152-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.log.json) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :---------------: | :-------: | :------: | :-------: | :-------: | :-----------------------------------------------------------------------: | :-------------------------------------------------------------------------: | +| ResNeXt-32x4d-50 | 25.03 | 4.27 | 77.90 | 93.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.log.json) | +| ResNeXt-32x4d-101 | 44.18 | 8.03 | 78.61 | 94.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.log.json) | +| ResNeXt-32x8d-101 | 88.79 | 16.5 | 79.27 | 94.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x8d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.log.json) | +| ResNeXt-32x4d-152 | 59.95 | 11.8 | 78.88 | 94.33 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext152-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.log.json) | ## Citation diff --git a/configs/seresnet/README.md b/configs/seresnet/README.md index 345bd63f6c6..ccfd1d156ed 100644 --- a/configs/seresnet/README.md +++ b/configs/seresnet/README.md @@ -1,6 +1,7 @@ # SE-ResNet > [Squeeze-and-Excitation Networks](https://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.html) + ## Abstract @@ -15,10 +16,10 @@ The central building block of convolutional neural networks (CNNs) is the convol ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:| -| SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth) | [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200708-657b3c36.log.json) | -| SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth) | [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200708-038a4d04.log.json) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :---------------------------------------------------------------------------: | +| SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200708-657b3c36.log.json) | +| SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200708-038a4d04.log.json) | ## Citation diff --git a/configs/shufflenet_v1/README.md b/configs/shufflenet_v1/README.md index 34b33e43d10..fd131279210 100644 --- a/configs/shufflenet_v1/README.md +++ b/configs/shufflenet_v1/README.md @@ -1,6 +1,7 @@ # ShuffleNet V1 > [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_ShuffleNet_An_Extremely_CVPR_2018_paper.html) + ## Abstract @@ -15,9 +16,9 @@ We introduce an extremely computation-efficient CNN architecture named ShuffleNe ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:| -| ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth) | [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.log.json) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------------------------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------------: | :--------------------------------------------------------------------: | +| ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.log.json) | ## Citation diff --git a/configs/shufflenet_v2/README.md b/configs/shufflenet_v2/README.md index 8da3d186908..78271543984 100644 --- a/configs/shufflenet_v2/README.md +++ b/configs/shufflenet_v2/README.md @@ -1,6 +1,7 @@ # ShuffleNet V2 > [Shufflenet v2: Practical guidelines for efficient cnn architecture design](https://openaccess.thecvf.com/content_ECCV_2018/papers/Ningning_Light-weight_CNN_Architecture_ECCV_2018_paper.pdf) + ## Abstract @@ -15,9 +16,9 @@ Currently, the neural network architecture design is mostly guided by the *indir ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:| -| ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth) | [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200804-8860eec9.log.json) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :---------------: | :-------: | :------: | :-------: | :-------: | :-----------------------------------------------------------------------: | :-------------------------------------------------------------------------: | +| ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200804-8860eec9.log.json) | ## Citation diff --git a/configs/swin_transformer/README.md b/configs/swin_transformer/README.md index c561c407c93..86975ec89ba 100644 --- a/configs/swin_transformer/README.md +++ b/configs/swin_transformer/README.md @@ -1,6 +1,7 @@ # Swin Transformer > [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/pdf/2103.14030.pdf) + ## Abstract @@ -17,36 +18,35 @@ This paper presents a new vision Transformer, called Swin Transformer, that capa The pre-trained models on ImageNet-21k are used to fine-tune, and therefore don't have evaluation results. -| Model | resolution | Params(M) | Flops(G) | Download | -|:---------:|:-----------:|:---------:|:---------:|:--------:| -| Swin-B | 224x224 | 86.74 | 15.14 | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k.pth)| -| Swin-B | 384x384 | 86.88 | 44.49 | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k-384px.pth)| -| Swin-L | 224x224 | 195.00 | 34.04 | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-large_3rdparty_in21k.pth)| -| Swin-L | 384x384 | 195.20 | 100.04 | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k-384px.pth)| +| Model | resolution | Params(M) | Flops(G) | Download | +| :----: | :--------: | :-------: | :------: | :---------------------------------------------------------------------------------------------------------------------: | +| Swin-B | 224x224 | 86.74 | 15.14 | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k.pth) | +| Swin-B | 384x384 | 86.88 | 44.49 | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k-384px.pth) | +| Swin-L | 224x224 | 195.00 | 34.04 | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-large_3rdparty_in21k.pth) | +| Swin-L | 384x384 | 195.20 | 100.04 | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k-384px.pth) | ### ImageNet-1k -| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------:|:------------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:------:|:--------:| -| Swin-T | From scratch | 224x224 | 28.29 | 4.36 | 81.18 | 95.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-tiny_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth) | [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925.log.json)| -| Swin-S | From scratch | 224x224 | 49.61 | 8.52 | 83.02 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-small_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219-7f9d988b.pth) | [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219.log.json)| -| Swin-B | From scratch | 224x224 | 87.77 | 15.14 | 83.36 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742-93230b0d.pth) | [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742.log.json)| -| Swin-S\* | From scratch | 224x224 | 49.61 | 8.52 | 83.21 | 96.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-small_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_small_patch4_window7_224-cc7a01c9.pth) | -| Swin-B\* | From scratch | 224x224 | 87.77 | 15.14 | 83.42 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-base_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window7_224-4670dd19.pth)| -| Swin-B\* | From scratch | 384x384 | 87.90 | 44.49 | 84.49 | 96.95 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-base_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window12_384-02c598a4.pth)| -| Swin-B\* | ImageNet-21k | 224x224 | 87.77 | 15.14 | 85.16 | 97.50 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-base_16xb64_in1k.py)| [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window7_224_22kto1k-f967f799.pth)| -| Swin-B\* | ImageNet-21k | 384x384 | 87.90 | 44.49 | 86.44 | 98.05 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-base_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window12_384_22kto1k-d59b0d1d.pth)| -| Swin-L\* | ImageNet-21k | 224x224 | 196.53 | 34.04 | 86.24 | 97.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_large_patch4_window7_224_22kto1k-5f0996db.pth)| -| Swin-L\* | ImageNet-21k | 384x384 | 196.74 | 100.04 | 87.25 | 98.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-large_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_large_patch4_window12_384_22kto1k-0a40944b.pth)| - -*Models with \* are converted from the [official repo](https://github.com/microsoft/Swin-Transformer#main-results-on-imagenet-with-pretrained-models). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------: | :-------------------------------------------------------------------: | +| Swin-T | From scratch | 224x224 | 28.29 | 4.36 | 81.18 | 95.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-tiny_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925.log.json) | +| Swin-S | From scratch | 224x224 | 49.61 | 8.52 | 83.02 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-small_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219-7f9d988b.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219.log.json) | +| Swin-B | From scratch | 224x224 | 87.77 | 15.14 | 83.36 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742-93230b0d.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742.log.json) | +| Swin-S\* | From scratch | 224x224 | 49.61 | 8.52 | 83.21 | 96.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-small_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_small_patch4_window7_224-cc7a01c9.pth) | +| Swin-B\* | From scratch | 224x224 | 87.77 | 15.14 | 83.42 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-base_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window7_224-4670dd19.pth) | +| Swin-B\* | From scratch | 384x384 | 87.90 | 44.49 | 84.49 | 96.95 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-base_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window12_384-02c598a4.pth) | +| Swin-B\* | ImageNet-21k | 224x224 | 87.77 | 15.14 | 85.16 | 97.50 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-base_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window7_224_22kto1k-f967f799.pth) | +| Swin-B\* | ImageNet-21k | 384x384 | 87.90 | 44.49 | 86.44 | 98.05 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-base_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window12_384_22kto1k-d59b0d1d.pth) | +| Swin-L\* | ImageNet-21k | 224x224 | 196.53 | 34.04 | 86.24 | 97.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_large_patch4_window7_224_22kto1k-5f0996db.pth) | +| Swin-L\* | ImageNet-21k | 384x384 | 196.74 | 100.04 | 87.25 | 98.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-large_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_large_patch4_window12_384_22kto1k-0a40944b.pth) | + +*Models with * are converted from the [official repo](https://github.com/microsoft/Swin-Transformer#main-results-on-imagenet-with-pretrained-models). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ### CUB-200-2011 -| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Config | Download | -|:----------------:|:------------:|:---------:|:---------:|:--------:|:---------:|:---------:|:---------:| -| Swin-L | [ImageNet-21k](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k-384px.pth) | 384x384 | 195.51 | 100.04 | 91.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-large_8xb8_cub_384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin-large_8xb8_cub_384px_20220307-1bbaee6a.pth) | [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin-large_8xb8_cub_384px_20220307-1bbaee6a.log.json) | - +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Config | Download | +| :----: | :---------------------------------------------------: | :--------: | :-------: | :------: | :-------: | :-------------------------------------------------: | :----------------------------------------------------: | +| Swin-L | [ImageNet-21k](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k-384px.pth) | 384x384 | 195.51 | 100.04 | 91.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-large_8xb8_cub_384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin-large_8xb8_cub_384px_20220307-1bbaee6a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin-large_8xb8_cub_384px_20220307-1bbaee6a.log.json) | ## Citation diff --git a/configs/t2t_vit/README.md b/configs/t2t_vit/README.md index b971a3057b4..1e3a082760e 100644 --- a/configs/t2t_vit/README.md +++ b/configs/t2t_vit/README.md @@ -1,11 +1,12 @@ # Tokens-to-Token ViT > [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://arxiv.org/abs/2101.11986) + ## Abstract -Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, \eg, the Vision Transformer (ViT) for image classification. The ViT model splits each image into a sequence of tokens with fixed length and then applies multiple Transformer layers to model their global relation for classification. However, ViT achieves inferior performance to CNNs when trained from scratch on a midsize dataset like ImageNet. We find it is because: 1) the simple tokenization of input images fails to model the important local structure such as edges and lines among neighboring pixels, leading to low training sample efficiency; 2) the redundant attention backbone design of ViT leads to limited feature richness for fixed computation budgets and limited training samples. To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-ViT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study. Notably, T2T-ViT reduces the parameter count and MACs of vanilla ViT by half, while achieving more than 3.0\% improvement when trained from scratch on ImageNet. It also outperforms ResNets and achieves comparable performance with MobileNets by directly training on ImageNet. For example, T2T-ViT with comparable size to ResNet50 (21.5M parameters) can achieve 83.3\% top1 accuracy in image resolution 384×384 on ImageNet. +Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, \\eg, the Vision Transformer (ViT) for image classification. The ViT model splits each image into a sequence of tokens with fixed length and then applies multiple Transformer layers to model their global relation for classification. However, ViT achieves inferior performance to CNNs when trained from scratch on a midsize dataset like ImageNet. We find it is because: 1) the simple tokenization of input images fails to model the important local structure such as edges and lines among neighboring pixels, leading to low training sample efficiency; 2) the redundant attention backbone design of ViT leads to limited feature richness for fixed computation budgets and limited training samples. To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-ViT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study. Notably, T2T-ViT reduces the parameter count and MACs of vanilla ViT by half, while achieving more than 3.0% improvement when trained from scratch on ImageNet. It also outperforms ResNets and achieves comparable performance with MobileNets by directly training on ImageNet. For example, T2T-ViT with comparable size to ResNet50 (21.5M parameters) can achieve 83.3% top1 accuracy in image resolution 384×384 on ImageNet.
@@ -15,11 +16,11 @@ Transformers, which are popular for language modeling, have been explored for so ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:--------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| T2T-ViT_t-14 | 21.47 | 4.34 | 81.83 | 95.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.pth) | [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.log.json)| -| T2T-ViT_t-19 | 39.08 | 7.80 | 82.63 | 96.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-19_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.pth) | [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.log.json)| -| T2T-ViT_t-24 | 64.00 | 12.69 | 82.71 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-24_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.pth) | [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.log.json)| +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | +| T2T-ViT_t-14 | 21.47 | 4.34 | 81.83 | 95.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.log.json) | +| T2T-ViT_t-19 | 39.08 | 7.80 | 82.63 | 96.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-19_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.log.json) | +| T2T-ViT_t-24 | 64.00 | 12.69 | 82.71 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-24_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.log.json) | *In consistent with the [official repo](https://github.com/yitu-opensource/T2T-ViT), we adopt the best checkpoints during training.* diff --git a/configs/tnt/README.md b/configs/tnt/README.md index 72af174c0b3..948eef747ce 100644 --- a/configs/tnt/README.md +++ b/configs/tnt/README.md @@ -1,6 +1,7 @@ # TNT > [Transformer in Transformer](https://arxiv.org/abs/2103.00112) + ## Abstract @@ -15,11 +16,11 @@ Transformer is a new kind of neural architecture which encodes the input data as ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:-----------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| TNT-small\* | 23.76 | 3.36 | 81.52 | 95.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/tnt/tnt-s-p16_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/tnt/tnt-small-p16_3rdparty_in1k_20210903-c56ee7df.pth) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :---------: | :-------: | :------: | :-------: | :-------: | :--------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | +| TNT-small\* | 23.76 | 3.36 | 81.52 | 95.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/tnt/tnt-s-p16_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/tnt/tnt-small-p16_3rdparty_in1k_20210903-c56ee7df.pth) | -*Models with \* are converted from [timm](https://github.com/rwightman/pytorch-image-models/). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +*Models with * are converted from [timm](https://github.com/rwightman/pytorch-image-models/). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## Citation diff --git a/configs/twins/README.md b/configs/twins/README.md index 9eeeca08363..87e72941f4a 100644 --- a/configs/twins/README.md +++ b/configs/twins/README.md @@ -1,6 +1,7 @@ # Twins > [Twins: Revisiting the Design of Spatial Attention in Vision Transformers](http://arxiv-export-lb.library.cornell.edu/abs/2104.13840) + ## Abstract @@ -15,16 +16,16 @@ Very recently, a variety of vision transformer architectures for dense predictio ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:--------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| PCPVT-small\* | 24.11 | 3.67 | 81.14 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-small_3rdparty_8xb128_in1k_20220126-ef23c132.pth) | -| PCPVT-base\* | 43.83 | 6.45 | 82.66 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-base_3rdparty_8xb128_in1k_20220126-f8c4b0d5.pth) | -| PCPVT-large\* | 60.99 | 9.51 | 83.09 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-large_3rdparty_16xb64_in1k_20220126-c1ef8d80.pth) | -| SVT-small\* | 24.06 | 2.82 | 81.77 | 95.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-small_3rdparty_8xb128_in1k_20220126-8fe5205b.pth) | -| SVT-base\* | 56.07 | 8.35 | 83.13 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-base_3rdparty_8xb128_in1k_20220126-e31cc8e9.pth) | -| SVT-large\* | 99.27 | 14.82 | 83.60 | 96.50 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-large_3rdparty_16xb64_in1k_20220126-4817645f.pth) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :---------------------------------------------------------------------------: | +| PCPVT-small\* | 24.11 | 3.67 | 81.14 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-small_3rdparty_8xb128_in1k_20220126-ef23c132.pth) | +| PCPVT-base\* | 43.83 | 6.45 | 82.66 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-base_3rdparty_8xb128_in1k_20220126-f8c4b0d5.pth) | +| PCPVT-large\* | 60.99 | 9.51 | 83.09 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-large_3rdparty_16xb64_in1k_20220126-c1ef8d80.pth) | +| SVT-small\* | 24.06 | 2.82 | 81.77 | 95.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-small_3rdparty_8xb128_in1k_20220126-8fe5205b.pth) | +| SVT-base\* | 56.07 | 8.35 | 83.13 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-base_3rdparty_8xb128_in1k_20220126-e31cc8e9.pth) | +| SVT-large\* | 99.27 | 14.82 | 83.60 | 96.50 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-large_3rdparty_16xb64_in1k_20220126-4817645f.pth) | -*Models with \* are converted from [the official repo](https://github.com/Meituan-AutoML/Twins). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results. The validation accuracy is a little different from the official paper because of the PyTorch version. This result is get in PyTorch=1.9 while the official result is get in PyTorch=1.7* +*Models with * are converted from [the official repo](https://github.com/Meituan-AutoML/Twins). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results. The validation accuracy is a little different from the official paper because of the PyTorch version. This result is get in PyTorch=1.9 while the official result is get in PyTorch=1.7* ## Citation diff --git a/configs/van/README.md b/configs/van/README.md index 99ac9e07fdf..e39dfc445a1 100644 --- a/configs/van/README.md +++ b/configs/van/README.md @@ -1,6 +1,7 @@ # Visual Attention Network > [Visual Attention Network](https://arxiv.org/pdf/2202.09741v2.pdf) + ## Abstract @@ -11,19 +12,18 @@ While originally designed for natural language processing (NLP) tasks, the self-
- ## Results and models ### ImageNet-1k -| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------:|:------------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:------:|:--------:| -| VAN-T\* | From scratch | 224x224 | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth) | -| VAN-S\* | From scratch | 224x224 | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) | -| VAN-B\* | From scratch | 224x224 | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) | -| VAN-L\* | From scratch | 224x224 | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-large_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) | +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :-----------------------------------------------------------------: | :-------------------------------------------------------------------: | +| VAN-T\* | From scratch | 224x224 | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth) | +| VAN-S\* | From scratch | 224x224 | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) | +| VAN-B\* | From scratch | 224x224 | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) | +| VAN-L\* | From scratch | 224x224 | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-large_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) | -*Models with \* are converted from [the official repo](https://github.com/Visual-Attention-Network/VAN-Classification). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results. +\*Models with * are converted from [the official repo](https://github.com/Visual-Attention-Network/VAN-Classification). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results. ## Citation diff --git a/configs/vgg/README.md b/configs/vgg/README.md index 7747de5894e..454489ff310 100644 --- a/configs/vgg/README.md +++ b/configs/vgg/README.md @@ -1,6 +1,7 @@ # VGG > [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556) + ## Abstract @@ -15,16 +16,16 @@ In this work we investigate the effect of the convolutional network depth on its ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:| -| VGG-11 | 132.86 | 7.63 | 68.75 | 88.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.log.json) | -| VGG-13 | 133.05 | 11.34 | 70.02 | 89.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.log.json) | -| VGG-16 | 138.36 | 15.5 | 71.62 | 90.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.log.json) | -| VGG-19 | 143.67 | 19.67 | 72.41 | 90.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.log.json)| -| VGG-11-BN | 132.87 | 7.64 | 70.67 | 90.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.log.json) | -| VGG-13-BN | 133.05 | 11.36 | 72.12 | 90.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.log.json) | -| VGG-16-BN | 138.37 | 15.53 | 73.74 | 91.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.log.json) | -| VGG-19-BN | 143.68 | 19.7 | 74.68 | 92.27 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json)| +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------------------: | :-----------------------------------------------------------------------------: | +| VGG-11 | 132.86 | 7.63 | 68.75 | 88.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.log.json) | +| VGG-13 | 133.05 | 11.34 | 70.02 | 89.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.log.json) | +| VGG-16 | 138.36 | 15.5 | 71.62 | 90.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.log.json) | +| VGG-19 | 143.67 | 19.67 | 72.41 | 90.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.log.json) | +| VGG-11-BN | 132.87 | 7.64 | 70.67 | 90.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.log.json) | +| VGG-13-BN | 133.05 | 11.36 | 72.12 | 90.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.log.json) | +| VGG-16-BN | 138.37 | 15.53 | 73.74 | 91.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.log.json) | +| VGG-19-BN | 143.68 | 19.7 | 74.68 | 92.27 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json) | ## Citation diff --git a/configs/vision_transformer/README.md b/configs/vision_transformer/README.md index ef6dc45bcde..e1a63e8af1d 100644 --- a/configs/vision_transformer/README.md +++ b/configs/vision_transformer/README.md @@ -1,6 +1,7 @@ # Vision Transformer > [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/pdf/2010.11929.pdf) + ## Abstract @@ -23,23 +24,23 @@ pre-trained models and fine-tuned models. The pre-trained models on ImageNet-21k are used to fine-tune, and therefore don't have evaluation results. -| Model | resolution | Params(M) | Flops(G) | Download | -|:----------:|:-----------:|:---------:|:---------:|:--------:| -| ViT-B16\* | 224x224 | 86.86 | 33.03 | [model](https://download.openmmlab.com/mmclassification/v0/vit/pretrain/vit-base-p16_3rdparty_pt-64xb64_in1k-224_20210928-02284250.pth)| -| ViT-B32\* | 224x224 | 88.30 | 8.56 | [model](https://download.openmmlab.com/mmclassification/v0/vit/pretrain/vit-base-p32_3rdparty_pt-64xb64_in1k-224_20210928-eee25dd4.pth)| -| ViT-L16\* | 224x224 | 304.72 | 116.68 | [model](https://download.openmmlab.com/mmclassification/v0/vit/pretrain/vit-large-p16_3rdparty_pt-64xb64_in1k-224_20210928-0001f9a1.pth)| +| Model | resolution | Params(M) | Flops(G) | Download | +| :-------: | :--------: | :-------: | :------: | :--------------------------------------------------------------------------------------------------------------------------------------: | +| ViT-B16\* | 224x224 | 86.86 | 33.03 | [model](https://download.openmmlab.com/mmclassification/v0/vit/pretrain/vit-base-p16_3rdparty_pt-64xb64_in1k-224_20210928-02284250.pth) | +| ViT-B32\* | 224x224 | 88.30 | 8.56 | [model](https://download.openmmlab.com/mmclassification/v0/vit/pretrain/vit-base-p32_3rdparty_pt-64xb64_in1k-224_20210928-eee25dd4.pth) | +| ViT-L16\* | 224x224 | 304.72 | 116.68 | [model](https://download.openmmlab.com/mmclassification/v0/vit/pretrain/vit-large-p16_3rdparty_pt-64xb64_in1k-224_20210928-0001f9a1.pth) | -*Models with \* are converted from the [official repo](https://github.com/google-research/vision_transformer#available-vit-models).* +*Models with * are converted from the [official repo](https://github.com/google-research/vision_transformer#available-vit-models).* ### ImageNet-1k -| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:----------:|:------------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:----------:|:--------:| -| ViT-B16\* | ImageNet-21k | 384x384 | 86.86 | 33.03 | 85.43 | 97.77 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-98e8652b.pth)| -| ViT-B32\* | ImageNet-21k | 384x384 | 88.30 | 8.56 | 84.01 | 97.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-9cea8599.pth)| -| ViT-L16\* | ImageNet-21k | 384x384 | 304.72 | 116.68 | 85.63 | 97.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth)| +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------: | :------------------------------------------------------------------: | +| ViT-B16\* | ImageNet-21k | 384x384 | 86.86 | 33.03 | 85.43 | 97.77 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-98e8652b.pth) | +| ViT-B32\* | ImageNet-21k | 384x384 | 88.30 | 8.56 | 84.01 | 97.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-9cea8599.pth) | +| ViT-L16\* | ImageNet-21k | 384x384 | 304.72 | 116.68 | 85.63 | 97.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth) | -*Models with \* are converted from the [official repo](https://github.com/google-research/vision_transformer#available-vit-models). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +*Models with * are converted from the [official repo](https://github.com/google-research/vision_transformer#available-vit-models). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## Citation diff --git a/configs/wrn/README.md b/configs/wrn/README.md index 43902c20dfd..b036caafe0a 100644 --- a/configs/wrn/README.md +++ b/configs/wrn/README.md @@ -1,6 +1,7 @@ # Wide-ResNet > [Wide Residual Networks](https://arxiv.org/abs/1605.07146) + ## Abstract @@ -15,13 +16,13 @@ Deep residual networks were shown to be able to scale up to thousands of layers ### ImageNet-1k -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:------:|:--------:| -| WRN-50\* | 68.88 | 11.44 | 78.48 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet50_3rdparty_8xb32_in1k_20220304-66678344.pth) | -| WRN-101\* | 126.89 | 22.81 | 78.84 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth) | -| WRN-50 (timm)\* | 68.88 | 11.44 | 81.45 | 95.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet50_timm_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet50_3rdparty-timm_8xb32_in1k_20220304-83ae4399.pth) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------------------: | :--------------------------------------------------------------------------: | +| WRN-50\* | 68.88 | 11.44 | 78.48 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet50_3rdparty_8xb32_in1k_20220304-66678344.pth) | +| WRN-101\* | 126.89 | 22.81 | 78.84 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth) | +| WRN-50 (timm)\* | 68.88 | 11.44 | 81.45 | 95.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet50_timm_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet50_3rdparty-timm_8xb32_in1k_20220304-83ae4399.pth) | -*Models with \* are converted from the [TorchVision](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) and [TIMM](https://github.com/rwightman/pytorch-image-models/blob/master). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* +*Models with * are converted from the [TorchVision](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) and [TIMM](https://github.com/rwightman/pytorch-image-models/blob/master). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* ## Citation diff --git a/docs/en/changelog.md b/docs/en/changelog.md index e3b0bf6f5b7..fdf0f774c8e 100644 --- a/docs/en/changelog.md +++ b/docs/en/changelog.md @@ -19,13 +19,13 @@ ### New Features -- [Feature] Support resize relative position embedding in `SwinTransformer`. ([#749](https://github.com/open-mmlab/mmclassification/pull/749)) -- [Feature] Add PoolFormer backbone and checkpoints. ([#746](https://github.com/open-mmlab/mmclassification/pull/746)) +- \[Feature\] Support resize relative position embedding in `SwinTransformer`. ([#749](https://github.com/open-mmlab/mmclassification/pull/749)) +- \[Feature\] Add PoolFormer backbone and checkpoints. ([#746](https://github.com/open-mmlab/mmclassification/pull/746)) ### Improvements -- [Enhance] Improve CPE performance by reduce memory copy. ([#762](https://github.com/open-mmlab/mmclassification/pull/762)) -- [Enhance] Add extra dataloader settings in configs. ([#752](https://github.com/open-mmlab/mmclassification/pull/752)) +- \[Enhance\] Improve CPE performance by reduce memory copy. ([#762](https://github.com/open-mmlab/mmclassification/pull/762)) +- \[Enhance\] Add extra dataloader settings in configs. ([#752](https://github.com/open-mmlab/mmclassification/pull/752)) ## v0.22.0(30/3/2022) @@ -37,30 +37,29 @@ ### New Features -- [Feature] Add CSPNet and backbone and checkpoints ([#735](https://github.com/open-mmlab/mmclassification/pull/735)) -- [Feature] Add `CustomDataset`. ([#738](https://github.com/open-mmlab/mmclassification/pull/738)) -- [Feature] Add diff seeds to diff ranks. ([#744](https://github.com/open-mmlab/mmclassification/pull/744)) -- [Feature] Support ConvMixer. ([#716](https://github.com/open-mmlab/mmclassification/pull/716)) -- [Feature] Our `dist_train` & `dist_test` tools support distributed training on multiple machines. ([#734](https://github.com/open-mmlab/mmclassification/pull/734)) -- [Feature] Add RepMLP backbone and checkpoints. ([#709](https://github.com/open-mmlab/mmclassification/pull/709)) -- [Feature] Support CUB dataset. ([#703](https://github.com/open-mmlab/mmclassification/pull/703)) -- [Feature] Support ResizeMix. ([#676](https://github.com/open-mmlab/mmclassification/pull/676)) - +- \[Feature\] Add CSPNet and backbone and checkpoints ([#735](https://github.com/open-mmlab/mmclassification/pull/735)) +- \[Feature\] Add `CustomDataset`. ([#738](https://github.com/open-mmlab/mmclassification/pull/738)) +- \[Feature\] Add diff seeds to diff ranks. ([#744](https://github.com/open-mmlab/mmclassification/pull/744)) +- \[Feature\] Support ConvMixer. ([#716](https://github.com/open-mmlab/mmclassification/pull/716)) +- \[Feature\] Our `dist_train` & `dist_test` tools support distributed training on multiple machines. ([#734](https://github.com/open-mmlab/mmclassification/pull/734)) +- \[Feature\] Add RepMLP backbone and checkpoints. ([#709](https://github.com/open-mmlab/mmclassification/pull/709)) +- \[Feature\] Support CUB dataset. ([#703](https://github.com/open-mmlab/mmclassification/pull/703)) +- \[Feature\] Support ResizeMix. ([#676](https://github.com/open-mmlab/mmclassification/pull/676)) ### Improvements -- [Enhance] Use `--a-b` instead of `--a_b` in arguments. ([#754](https://github.com/open-mmlab/mmclassification/pull/754)) -- [Enhance] Add `get_cat_ids` and `get_gt_labels` to KFoldDataset. ([#721](https://github.com/open-mmlab/mmclassification/pull/721)) -- [Enhance] Set torch seed in `worker_init_fn`. ([#733](https://github.com/open-mmlab/mmclassification/pull/733)) +- \[Enhance\] Use `--a-b` instead of `--a_b` in arguments. ([#754](https://github.com/open-mmlab/mmclassification/pull/754)) +- \[Enhance\] Add `get_cat_ids` and `get_gt_labels` to KFoldDataset. ([#721](https://github.com/open-mmlab/mmclassification/pull/721)) +- \[Enhance\] Set torch seed in `worker_init_fn`. ([#733](https://github.com/open-mmlab/mmclassification/pull/733)) ### Bug Fixes -- [Fix] Fix the discontiguous output feature map of ConvNeXt. ([#743](https://github.com/open-mmlab/mmclassification/pull/743)) +- \[Fix\] Fix the discontiguous output feature map of ConvNeXt. ([#743](https://github.com/open-mmlab/mmclassification/pull/743)) ### Docs Update -- [Docs] Add brief installation steps in README for copy&paste. ([#755](https://github.com/open-mmlab/mmclassification/pull/755)) -- [Docs] fix logo url link from mmocr to mmcls. ([#732](https://github.com/open-mmlab/mmclassification/pull/732)) +- \[Docs\] Add brief installation steps in README for copy&paste. ([#755](https://github.com/open-mmlab/mmclassification/pull/755)) +- \[Docs\] fix logo url link from mmocr to mmcls. ([#732](https://github.com/open-mmlab/mmclassification/pull/732)) ## v0.21.0(04/03/2022) @@ -163,18 +162,18 @@ ### Improvements -- [Reproduction] Reproduce RegNetX training accuracy. ([#587](https://github.com/open-mmlab/mmclassification/pull/587)) -- [Reproduction] Reproduce training results of T2T-ViT. ([#610](https://github.com/open-mmlab/mmclassification/pull/610)) -- [Enhance] Provide high-acc training settings of ResNet. ([#572](https://github.com/open-mmlab/mmclassification/pull/572)) -- [Enhance] Set a random seed when the user does not set a seed. ([#554](https://github.com/open-mmlab/mmclassification/pull/554)) -- [Enhance] Added `NumClassCheckHook` and unit tests. ([#559](https://github.com/open-mmlab/mmclassification/pull/559)) -- [Enhance] Enhance feature extraction function. ([#593](https://github.com/open-mmlab/mmclassification/pull/593)) -- [Enhance] Improve efficiency of precision, recall, f1_score and support. ([#595](https://github.com/open-mmlab/mmclassification/pull/595)) -- [Enhance] Improve accuracy calculation performance. ([#592](https://github.com/open-mmlab/mmclassification/pull/592)) -- [Refactor] Refactor `analysis_log.py`. ([#529](https://github.com/open-mmlab/mmclassification/pull/529)) -- [Refactor] Use new API of matplotlib to handle blocking input in visualization. ([#568](https://github.com/open-mmlab/mmclassification/pull/568)) -- [CI] Cancel previous runs that are not completed. ([#583](https://github.com/open-mmlab/mmclassification/pull/583)) -- [CI] Skip build CI if only configs or docs modification. ([#575](https://github.com/open-mmlab/mmclassification/pull/575)) +- \[Reproduction\] Reproduce RegNetX training accuracy. ([#587](https://github.com/open-mmlab/mmclassification/pull/587)) +- \[Reproduction\] Reproduce training results of T2T-ViT. ([#610](https://github.com/open-mmlab/mmclassification/pull/610)) +- \[Enhance\] Provide high-acc training settings of ResNet. ([#572](https://github.com/open-mmlab/mmclassification/pull/572)) +- \[Enhance\] Set a random seed when the user does not set a seed. ([#554](https://github.com/open-mmlab/mmclassification/pull/554)) +- \[Enhance\] Added `NumClassCheckHook` and unit tests. ([#559](https://github.com/open-mmlab/mmclassification/pull/559)) +- \[Enhance\] Enhance feature extraction function. ([#593](https://github.com/open-mmlab/mmclassification/pull/593)) +- \[Enhance\] Improve efficiency of precision, recall, f1_score and support. ([#595](https://github.com/open-mmlab/mmclassification/pull/595)) +- \[Enhance\] Improve accuracy calculation performance. ([#592](https://github.com/open-mmlab/mmclassification/pull/592)) +- \[Refactor\] Refactor `analysis_log.py`. ([#529](https://github.com/open-mmlab/mmclassification/pull/529)) +- \[Refactor\] Use new API of matplotlib to handle blocking input in visualization. ([#568](https://github.com/open-mmlab/mmclassification/pull/568)) +- \[CI\] Cancel previous runs that are not completed. ([#583](https://github.com/open-mmlab/mmclassification/pull/583)) +- \[CI\] Skip build CI if only configs or docs modification. ([#575](https://github.com/open-mmlab/mmclassification/pull/575)) ### Bug Fixes @@ -285,7 +284,7 @@ - Fix TnT compatibility and verbose warning. ([#436](https://github.com/open-mmlab/mmclassification/pull/436)) - Support setting `--out-items` in `tools/test.py`. ([#437](https://github.com/open-mmlab/mmclassification/pull/437)) -- Add datetime info and saving model using torch<1.6 format. ([#439](https://github.com/open-mmlab/mmclassification/pull/439)) +- Add datetime info and saving model using torch\<1.6 format. ([#439](https://github.com/open-mmlab/mmclassification/pull/439)) - Improve downstream repositories compatibility. ([#421](https://github.com/open-mmlab/mmclassification/pull/421)) - Rename the option `--options` to `--cfg-options` in some tools. ([#425](https://github.com/open-mmlab/mmclassification/pull/425)) - Add PyTorch 1.9 and Python 3.9 build workflow, and remove some CI. ([#422](https://github.com/open-mmlab/mmclassification/pull/422)) @@ -308,6 +307,7 @@ ## v0.15.0(31/8/2021) ### Highlights + - Support `hparams` argument in `AutoAugment` and `RandAugment` to provide hyperparameters for sub-policies. - Support custom squeeze channels in `SELayer`. - Support classwise weight in losses. @@ -338,6 +338,7 @@ ## v0.14.0(4/8/2021) ### Highlights + - Add transformer-in-transformer backbone and pretrain checkpoints, refers to [the paper](https://arxiv.org/abs/2103.00112). - Add Chinese colab tutorial. - Provide dockerfile to build mmcls dev docker image. diff --git a/docs/en/community/CONTRIBUTING.md b/docs/en/community/CONTRIBUTING.md deleted file mode 100644 index 1044a050f3c..00000000000 --- a/docs/en/community/CONTRIBUTING.md +++ /dev/null @@ -1,71 +0,0 @@ -# Contributing to OpenMMLab - -All kinds of contributions are welcome, including but not limited to the following. - -- Fixes (typo, bugs) -- 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](http://flake8.pycqa.org/en/latest/): A wrapper around some linter tools. -- [yapf](https://github.com/google/yapf): A formatter for Python files. -- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports. -- [markdownlint](https://github.com/markdownlint/markdownlint): A linter to check markdown files and flag style issues. -- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring. - -Style configurations of yapf and isort can be found in [setup.cfg](https://github.com/open-mmlab/mmclassification/blob/master/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 -``` - -Try the following steps to install ruby when you encounter an issue on installing markdownlint - -```shell -# install rvm -curl -L https://get.rvm.io | bash -s -- --autolibs=read-fail -[[ -s "$HOME/.rvm/scripts/rvm" ]] && source "$HOME/.rvm/scripts/rvm" -rvm autolibs disable - -# install ruby -rvm install 2.7.1 -``` - -Or refer to [this repo](https://github.com/innerlee/setup) and take [`zzruby.sh`](https://github.com/innerlee/setup/blob/master/zzruby.sh) according its instruction. - -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). diff --git a/docs/en/community/CONTRIBUTING.md b/docs/en/community/CONTRIBUTING.md new file mode 120000 index 00000000000..c97564d93a7 --- /dev/null +++ b/docs/en/community/CONTRIBUTING.md @@ -0,0 +1 @@ +../../../CONTRIBUTING.md \ No newline at end of file diff --git a/docs/en/faq.md b/docs/en/faq.md index a2e0f7bf6c1..437c347f138 100644 --- a/docs/en/faq.md +++ b/docs/en/faq.md @@ -10,34 +10,34 @@ and make sure you fill in all required information in the template. ## Installation - Compatibility issue between MMCV and MMClassification; "AssertionError: - MMCV==xxx is used but incompatible. Please install mmcv>=xxx, <=xxx." + MMCV==xxx is used but incompatible. Please install mmcv>=xxx, \<=xxx." Compatible MMClassification and MMCV versions are shown as below. Please choose the correct version of MMCV to avoid installation issues. - | MMClassification version | MMCV version | - |:------------------------:|:---------------------:| - | dev | mmcv>=1.5.0, <1.6.0 | - | 0.23.0 (master) | mmcv>=1.4.2, <1.6.0 | - | 0.22.1 | mmcv>=1.4.2, <1.6.0 | - | 0.21.0 | mmcv>=1.4.2, <=1.5.0 | - | 0.20.1 | mmcv>=1.4.2, <=1.5.0 | - | 0.19.0 | mmcv>=1.3.16, <=1.5.0 | - | 0.18.0 | mmcv>=1.3.16, <=1.5.0 | - | 0.17.0 | mmcv>=1.3.8, <=1.5.0 | - | 0.16.0 | mmcv>=1.3.8, <=1.5.0 | - | 0.15.0 | mmcv>=1.3.8, <=1.5.0 | - | 0.15.0 | mmcv>=1.3.8, <=1.5.0 | - | 0.14.0 | mmcv>=1.3.8, <=1.5.0 | - | 0.13.0 | mmcv>=1.3.8, <=1.5.0 | - | 0.12.0 | mmcv>=1.3.1, <=1.5.0 | - | 0.11.1 | mmcv>=1.3.1, <=1.5.0 | - | 0.11.0 | mmcv>=1.3.0 | - | 0.10.0 | mmcv>=1.3.0 | - | 0.9.0 | mmcv>=1.1.4 | - | 0.8.0 | mmcv>=1.1.4 | - | 0.7.0 | mmcv>=1.1.4 | - | 0.6.0 | mmcv>=1.1.4 | + | MMClassification version | MMCV version | + | :----------------------: | :--------------------: | + | dev | mmcv>=1.5.0, \<1.6.0 | + | 0.23.0 (master) | mmcv>=1.4.2, \<1.6.0 | + | 0.22.1 | mmcv>=1.4.2, \<1.6.0 | + | 0.21.0 | mmcv>=1.4.2, \<=1.5.0 | + | 0.20.1 | mmcv>=1.4.2, \<=1.5.0 | + | 0.19.0 | mmcv>=1.3.16, \<=1.5.0 | + | 0.18.0 | mmcv>=1.3.16, \<=1.5.0 | + | 0.17.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.16.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.15.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.15.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.14.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.13.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.12.0 | mmcv>=1.3.1, \<=1.5.0 | + | 0.11.1 | mmcv>=1.3.1, \<=1.5.0 | + | 0.11.0 | mmcv>=1.3.0 | + | 0.10.0 | mmcv>=1.3.0 | + | 0.9.0 | mmcv>=1.1.4 | + | 0.8.0 | mmcv>=1.1.4 | + | 0.7.0 | mmcv>=1.1.4 | + | 0.6.0 | mmcv>=1.1.4 | ```{note} Since the `dev` branch is under frequent development, the MMCV @@ -70,7 +70,7 @@ and make sure you fill in all required information in the template. can also use the same environment to develop MMClassification in different folders, like mmcls-0.21, mmcls-0.23. When you run the train or test shell script, it will adopt the mmcls package in the current folder. And when you run other Python - script, you can also add ``PYTHONPATH=`pwd` `` at the beginning of your command + script, you can also add `` PYTHONPATH=`pwd` `` at the beginning of your command to use the package in the current folder. Conversely, to use the default MMClassification installed in the environment diff --git a/docs/en/getting_started.md b/docs/en/getting_started.md index 0aa73073e4d..ec12da3cfdb 100644 --- a/docs/en/getting_started.md +++ b/docs/en/getting_started.md @@ -32,8 +32,8 @@ For ImageNet, it has multiple versions, but the most commonly used one is [ILSVR 1. Register an account and login to the [download page](http://www.image-net.org/download-images). 2. Find download links for ILSVRC2012 and download the following two files - - ILSVRC2012_img_train.tar (~138GB) - - ILSVRC2012_img_val.tar (~6.3GB) + - ILSVRC2012_img_train.tar (~138GB) + - ILSVRC2012_img_val.tar (~6.3GB) 3. Untar the downloaded files 4. Download meta data using this [script](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh) @@ -245,6 +245,7 @@ This tool is still experimental and we do not guarantee that the number is corre ### Publish a model Before you publish a model, you may want to + 1. Convert model weights to CPU tensors. 2. Delete the optimizer states. 3. Compute the hash of the checkpoint file and append the hash id to the filename. diff --git a/docs/en/install.md b/docs/en/install.md index 034556d2798..bde1a815af2 100644 --- a/docs/en/install.md +++ b/docs/en/install.md @@ -175,7 +175,7 @@ commands. !mim install mmcv-full ``` - **Step 2.** Install MMClassification from the source. +**Step 2.** Install MMClassification from the source. ```shell !git clone https://github.com/open-mmlab/mmclassification.git @@ -183,7 +183,7 @@ commands. !pip install -e . ``` - **Step 3.** Verification. +**Step 3.** Verification. ```python import mmcls @@ -195,7 +195,7 @@ print(mmcls.__version__) Within Jupyter, the exclamation mark `!` is used to call external executables and `%cd` is a [magic command](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-cd) to change the current working directory of Python. ``` - ### Using MMClassification with Docker +### Using MMClassification with Docker We provide a [Dockerfile](https://github.com/open-mmlab/mmclassification/blob/master/docker/Dockerfile) to build an image. Ensure that your [docker version](https://docs.docker.com/engine/install/) >=19.03. diff --git a/docs/en/model_zoo.md b/docs/en/model_zoo.md index 8a039dd83ea..83e6ec5d59a 100644 --- a/docs/en/model_zoo.md +++ b/docs/en/model_zoo.md @@ -5,151 +5,151 @@ ImageNet has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/). The ResNet family models below are trained by standard data augmentations, i.e., RandomResizedCrop, RandomHorizontalFlip and Normalize. -| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:| -| VGG-11 | 132.86 | 7.63 | 68.75 | 88.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.log.json) | -| VGG-13 | 133.05 | 11.34 | 70.02 | 89.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.log.json) | -| VGG-16 | 138.36 | 15.5 | 71.62 | 90.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.log.json) | -| VGG-19 | 143.67 | 19.67 | 72.41 | 90.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.log.json)| -| VGG-11-BN | 132.87 | 7.64 | 70.75 | 90.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.log.json) | -| VGG-13-BN | 133.05 | 11.36 | 72.15 | 90.71 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.log.json) | -| VGG-16-BN | 138.37 | 15.53 | 73.72 | 91.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.log.json) | -| VGG-19-BN | 143.68 | 19.7 | 74.70 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json)| -| RepVGG-A0\* | 9.11（train) | 8.31 (deploy) | 1.52 (train) | 1.36 (deploy) | 72.41 | 90.50 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py) | [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_3rdparty_4xb64-coslr-120e_in1k_20210909-883ab98c.pth) | -| RepVGG-A1\* | 14.09 (train) | 12.79 (deploy) | 2.64 (train) | 2.37 (deploy) | 74.47 | 91.85 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py) | [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_3rdparty_4xb64-coslr-120e_in1k_20210909-24003a24.pth) | -| RepVGG-A2\* | 28.21 (train) | 25.5 (deploy) | 5.7 (train) | 5.12 (deploy) | 76.48 | 93.01 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py) | [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_3rdparty_4xb64-coslr-120e_in1k_20210909-97d7695a.pth) | -| RepVGG-B0\* | 15.82 (train) | 14.34 (deploy) | 3.42 (train) | 3.06 (deploy) | 75.14 | 92.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py) | [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_3rdparty_4xb64-coslr-120e_in1k_20210909-446375f4.pth) | -| RepVGG-B1\* | 57.42 (train) | 51.83 (deploy) | 13.16 (train) | 11.82 (deploy) | 78.37 | 94.11 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py) | [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_3rdparty_4xb64-coslr-120e_in1k_20210909-750cdf67.pth) | -| RepVGG-B1g2\* | 45.78 (train) | 41.36 (deploy) | 9.82 (train) | 8.82 (deploy) | 77.79 | 93.88 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py) | [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k_20210909-344f6422.pth) | -| RepVGG-B1g4\* | 39.97 (train) | 36.13 (deploy) | 8.15 (train) | 7.32 (deploy) | 77.58 | 93.84 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py) | [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k_20210909-d4c1a642.pth) | -| RepVGG-B2\* | 89.02 (train) | 80.32 (deploy) | 20.46 (train) | 18.39 (deploy) | 78.78 | 94.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py) | [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_3rdparty_4xb64-coslr-120e_in1k_20210909-bd6b937c.pth) | -| RepVGG-B2g4\* | 61.76 (train) | 55.78 (deploy) | 12.63 (train) | 11.34 (deploy) | 79.38 | 94.68 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-7b7955f0.pth) | -| RepVGG-B3\* | 123.09 (train) | 110.96 (deploy) | 29.17 (train) | 26.22 (deploy) | 80.52 | 95.26 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-dda968bf.pth) | -| RepVGG-B3g4\* | 83.83 (train) | 75.63 (deploy) | 17.9 (train) | 16.08 (deploy) | 80.22 | 95.10 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-4e54846a.pth) | -| RepVGG-D2se\* | 133.33 (train) | 120.39 (deploy) | 36.56 (train) | 32.85 (deploy) | 81.81 | 95.94 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth) | -| ResNet-18 | 11.69 | 1.82 | 70.07 | 89.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.log.json) | -| ResNet-34 | 21.8 | 3.68 | 73.85 | 91.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.log.json) | -| ResNet-50 (rsb-a1) | 25.56 | 4.12 | 80.12 | 94.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.log.json) | -| ResNet-101 | 44.55 | 7.85 | 78.18 | 94.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.log.json) | -| ResNet-152 | 60.19 | 11.58 | 78.63 | 94.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.log.json) | -| Res2Net-50-14w-8s\* | 25.06 | 4.22 | 78.14 | 93.85 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w14-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w14-s8_3rdparty_8xb32_in1k_20210927-bc967bf1.pth) | -| Res2Net-50-26w-8s\* | 48.40 | 8.39 | 79.20 | 94.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w26-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w26-s8_3rdparty_8xb32_in1k_20210927-f547a94b.pth) | -| Res2Net-101-26w-4s\* | 45.21 | 8.12 | 79.19 | 94.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net101-w26-s4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net101-w26-s4_3rdparty_8xb32_in1k_20210927-870b6c36.pth) | -| ResNeSt-50\* | 27.48 | 5.41 | 81.13 | 95.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest50_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest50_imagenet_converted-1ebf0afe.pth)| -| ResNeSt-101\* | 48.28 | 10.27 | 82.32 | 96.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest101_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest101_imagenet_converted-032caa52.pth)| -| ResNeSt-200\* | 70.2 | 17.53 | 82.41 | 96.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest200_64xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest200_imagenet_converted-581a60f2.pth)| -| ResNeSt-269\* | 110.93 | 22.58 | 82.70 | 96.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest269_64xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest269_imagenet_converted-59930960.pth)| -| ResNetV1D-50 | 25.58 | 4.36 | 77.54 | 93.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.log.json) | -| ResNetV1D-101 | 44.57 | 8.09 | 78.93 | 94.48 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.log.json) | -| ResNetV1D-152 | 60.21 | 11.82 | 79.41 | 94.7 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.log.json) | -| ResNeXt-32x4d-50 | 25.03 | 4.27 | 77.90 | 93.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.log.json) | -| ResNeXt-32x4d-101 | 44.18 | 8.03 | 78.71 | 94.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.log.json) | -| ResNeXt-32x8d-101 | 88.79 | 16.5 | 79.23 | 94.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x8d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.log.json) | -| ResNeXt-32x4d-152 | 59.95 | 11.8 | 78.93 | 94.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext152-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.log.json) | -| SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth) | [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200708-657b3c36.log.json) | -| SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth) | [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200708-038a4d04.log.json) | -| RegNetX-400MF | 5.16 | 0.41 | 72.56 | 90.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-400mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211213-89bfc226.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211208_143316.log.json) | -| RegNetX-800MF | 7.26 | 0.81 | 74.76 | 92.32 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-800mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211213-222b0f11.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211207_143037.log.json) | -| RegNetX-1.6GF | 9.19 | 1.63 | 76.84 | 93.31 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-1.6gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211213-d1b89758.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211208_143018.log.json) | -| RegNetX-3.2GF | 15.3 | 3.21 | 78.09 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-3.2gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211213-1fdd82ae.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211208_142720.log.json) | -| RegNetX-4.0GF | 22.12 | 4.0 | 78.60 | 94.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-4.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211213-efed675c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211207_150431.log.json) | -| RegNetX-6.4GF | 26.21 | 6.51 | 79.38 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-6.4gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211215-5c6089da.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211213_172748.log.json) | -| RegNetX-8.0GF | 39.57 | 8.03 | 79.12 | 94.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-8.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211213-9a9fcc76.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211208_103250.log.json) | -| RegNetX-12GF | 46.11 | 12.15 | 79.67 | 95.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-12gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211213-5df8c2f8.pth) | [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211208_143713.log.json) | -| ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth) | [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.log.json) | -| ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth) | [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200804-8860eec9.log.json) | -| MobileNet V2 | 3.5 | 0.319 | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth) | [log](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.log.json) | -| ViT-B/16\* | 86.86 | 33.03 | 85.43 | 97.77 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-98e8652b.pth)| -| ViT-B/32\* | 88.3 | 8.56 | 84.01 | 97.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-9cea8599.pth)| -| ViT-L/16\* | 304.72 | 116.68 | 85.63 | 97.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth)| -| Swin-Transformer tiny | 28.29 | 4.36 | 81.18 | 95.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-tiny_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth) | [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925.log.json)| -| Swin-Transformer small| 49.61 | 8.52 | 83.02 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-small_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219-7f9d988b.pth) | [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219.log.json)| -| Swin-Transformer base | 87.77 | 15.14 | 83.36 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742-93230b0d.pth) | [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742.log.json)| -| Transformer in Transformer small\* | 23.76 | 3.36 | 81.52 | 95.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/tnt/tnt-s-p16_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/tnt/tnt-small-p16_3rdparty_in1k_20210903-c56ee7df.pth) | -| T2T-ViT_t-14 | 21.47 | 4.34 | 81.83 | 95.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.pth) | [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.log.json)| -| T2T-ViT_t-19 | 39.08 | 7.80 | 82.63 | 96.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-19_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.pth) | [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.log.json)| -| T2T-ViT_t-24 | 64.00 | 12.69 | 82.71 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-24_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.pth) | [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.log.json)| -| Mixer-B/16\* | 59.88 | 12.61 | 76.68 | 92.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-base-p16_3rdparty_64xb64_in1k_20211124-1377e3e0.pth) | -| Mixer-L/16\* | 208.2 | 44.57 | 72.34 | 88.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-large-p16_3rdparty_64xb64_in1k_20211124-5a2519d2.pth) | -| DeiT-tiny | 5.72 | 1.08 | 74.50 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth) | [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.log.json) | -| DeiT-tiny distilled\* | 5.72 | 1.08 | 74.51 | 91.90 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth) | -| DeiT-small | 22.05 | 4.24 | 80.69 | 95.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.pth) | [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.log.json) | -| DeiT-small distilled\* | 22.05 | 4.24 | 81.17 | 95.40 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth) | -| DeiT-base | 86.57 | 16.86 | 81.76 | 95.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.log.json) | -| DeiT-base distilled\* | 86.57 | 16.86 | 83.33 | 96.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base-distilled_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211216-42891296.pth) | -| DeiT-base 384px\* | 86.86 | 49.37 | 83.04 | 96.31 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth) | -| DeiT-base distilled 384px\* | 86.86 | 49.37 | 85.55 | 97.35 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211216-e48d6000.pth) | -| Conformer-tiny-p16\* | 23.52 | 4.90 | 81.31 | 95.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-tiny-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-tiny-p16_3rdparty_8xb128_in1k_20211206-f6860372.pth) | -| Conformer-small-p32\* | 38.85 | 7.09 | 81.96 | 96.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p32_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p32_8xb128_in1k_20211206-947a0816.pth) | -| Conformer-small-p16\* | 37.67 | 10.31 | 83.32 | 96.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p16_3rdparty_8xb128_in1k_20211206-3065dcf5.pth) | -| Conformer-base-p16\* | 83.29 | 22.89 | 83.82 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-base-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-base-p16_3rdparty_8xb128_in1k_20211206-bfdf8637.pth) | -| PCPVT-small\* | 24.11 | 3.67 | 81.14 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-small_3rdparty_8xb128_in1k_20220126-ef23c132.pth) | -| PCPVT-base\* | 43.83 | 6.45 | 82.66 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-base_3rdparty_8xb128_in1k_20220126-f8c4b0d5.pth) | -| PCPVT-large\* | 60.99 | 9.51 | 83.09 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-large_3rdparty_16xb64_in1k_20220126-c1ef8d80.pth) | -| SVT-small\* | 24.06 | 2.82 | 81.77 | 95.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-small_3rdparty_8xb128_in1k_20220126-8fe5205b.pth) | -| SVT-base\* | 56.07 | 8.35 | 83.13 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-base_3rdparty_8xb128_in1k_20220126-e31cc8e9.pth) | -| SVT-large\* | 99.27 | 14.82 | 83.60 | 96.50 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-large_3rdparty_16xb64_in1k_20220126-4817645f.pth) | -| EfficientNet-B0\* | 5.29 | 0.02 | 76.74 | 93.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32_in1k_20220119-a7e2a0b1.pth) | -| EfficientNet-B0 (AA)\* | 5.29 | 0.02 | 77.26 | 93.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa_in1k_20220119-8d939117.pth) | -| EfficientNet-B0 (AA + AdvProp)\* | 5.29 | 0.02 | 77.53 | 93.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k_20220119-26434485.pth) | -| EfficientNet-B1\* | 7.79 | 0.03 | 78.68 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32_in1k_20220119-002556d9.pth) | -| EfficientNet-B1 (AA)\* | 7.79 | 0.03 | 79.20 | 94.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa_in1k_20220119-619d8ae3.pth) | -| EfficientNet-B1 (AA + AdvProp)\* | 7.79 | 0.03 | 79.52 | 94.43 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa-advprop_in1k_20220119-5715267d.pth) | -| EfficientNet-B2\* | 9.11 | 0.03 | 79.64 | 94.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32_in1k_20220119-ea374a30.pth) | -| EfficientNet-B2 (AA)\* | 9.11 | 0.03 | 80.21 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa_in1k_20220119-dd61e80b.pth) | -| EfficientNet-B2 (AA + AdvProp)\* | 9.11 | 0.03 | 80.45 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa-advprop_in1k_20220119-1655338a.pth) | -| EfficientNet-B3\* | 12.23 | 0.06 | 81.01 | 95.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32_in1k_20220119-4b4d7487.pth) | -| EfficientNet-B3 (AA)\* | 12.23 | 0.06 | 81.58 | 95.67 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k_20220119-5b4887a0.pth) | -| EfficientNet-B3 (AA + AdvProp)\* | 12.23 | 0.06 | 81.81 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa-advprop_in1k_20220119-53b41118.pth) | -| EfficientNet-B4\* | 19.34 | 0.12 | 82.57 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32_in1k_20220119-81fd4077.pth) | -| EfficientNet-B4 (AA)\* | 19.34 | 0.12 | 82.95 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa_in1k_20220119-45b8bd2b.pth) | -| EfficientNet-B4 (AA + AdvProp)\* | 19.34 | 0.12 | 83.25 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa-advprop_in1k_20220119-38c2238c.pth) | -| EfficientNet-B5\* | 30.39 | 0.24 | 83.18 | 96.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32_in1k_20220119-e9814430.pth) | -| EfficientNet-B5 (AA)\* | 30.39 | 0.24 | 83.82 | 96.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa_in1k_20220119-2cab8b78.pth) | -| EfficientNet-B5 (AA + AdvProp)\* | 30.39 | 0.24 | 84.21 | 96.98 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa-advprop_in1k_20220119-f57a895a.pth) | -| EfficientNet-B6 (AA)\* | 43.04 | 0.41 | 84.05 | 96.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa_in1k_20220119-45b03310.pth) | -| EfficientNet-B6 (AA + AdvProp)\* | 43.04 | 0.41 | 84.74 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa-advprop_in1k_20220119-bfe3485e.pth) | -| EfficientNet-B7 (AA)\* | 66.35 | 0.72 | 84.38 | 96.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa_in1k_20220119-bf03951c.pth) | -| EfficientNet-B7 (AA + AdvProp)\* | 66.35 | 0.72 | 85.14 | 97.23 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa-advprop_in1k_20220119-c6dbff10.pth) | -| EfficientNet-B8 (AA + AdvProp)\* | 87.41 | 1.09 | 85.38 | 97.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b8_3rdparty_8xb32-aa-advprop_in1k_20220119-297ce1b7.pth) | -| ConvNeXt-T\* | 28.59 | 4.46 | 82.05 | 95.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-tiny_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128_in1k_20220124-18abde00.pth) | -| ConvNeXt-S\* | 50.22 | 8.69 | 83.13 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-small_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128_in1k_20220124-d39b5192.pth) | -| ConvNeXt-B\* | 88.59 | 15.36 | 83.85 | 96.74 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128_in1k_20220124-d0915162.pth) | -| ConvNeXt-B\* | 88.59 | 15.36 | 85.81 | 97.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_32xb128_in1k_20220124-eb2d6ada.pth) | -| ConvNeXt-L\* | 197.77 | 34.37 | 84.30 | 96.89 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_64xb64_in1k_20220124-f8a0ded0.pth) | -| ConvNeXt-L\* | 197.77 | 34.37 | 86.61 | 98.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_in21k-pre-3rdparty_64xb64_in1k_20220124-2412403d.pth) | -| ConvNeXt-XL\* | 350.20 | 60.93 | 86.97 | 98.20 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-xlarge_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_in21k-pre-3rdparty_64xb64_in1k_20220124-76b6863d.pth) | -| HRNet-W18\* | 21.30 | 4.33 | 76.75 | 93.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32_in1k_20220120-0c10b180.pth) | -| HRNet-W30\* | 37.71 | 8.17 | 78.19 | 94.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w30_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w30_3rdparty_8xb32_in1k_20220120-8aa3832f.pth) | -| HRNet-W32\* | 41.23 | 8.99 | 78.44 | 94.19 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w32_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w32_3rdparty_8xb32_in1k_20220120-c394f1ab.pth) | -| HRNet-W40\* | 57.55 | 12.77 | 78.94 | 94.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w40_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w40_3rdparty_8xb32_in1k_20220120-9a2dbfc5.pth) | -| HRNet-W44\* | 67.06 | 14.96 | 78.88 | 94.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w44_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w44_3rdparty_8xb32_in1k_20220120-35d07f73.pth) | -| HRNet-W48\* | 77.47 | 17.36 | 79.32 | 94.52 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32_in1k_20220120-e555ef50.pth) | -| HRNet-W64\* | 128.06 | 29.00 | 79.46 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w64_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w64_3rdparty_8xb32_in1k_20220120-19126642.pth) | -| HRNet-W18 (ssld)\* | 21.30 | 4.33 | 81.06 | 95.70 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32-ssld_in1k_20220120-455f69ea.pth) | -| HRNet-W48 (ssld)\* | 77.47 | 17.36 | 83.63 | 96.79 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32-ssld_in1k_20220120-d0459c38.pth) | -| WRN-50\* | 68.88 | 11.44 | 81.45 | 95.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet50_timm_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet50_3rdparty-timm_8xb32_in1k_20220304-83ae4399.pth) | -| WRN-101\* | 126.89| 22.81 | 78.84 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth) | -| CSPDarkNet50\* | 27.64 | 5.04 | 80.05 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspdarknet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspdarknet50_3rdparty_8xb32_in1k_20220329-bd275287.pth) | -| CSPResNet50\* | 21.62 | 3.48 | 79.55 | 94.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnet50_3rdparty_8xb32_in1k_20220329-dd6dddfb.pth) | -| CSPResNeXt50\* | 20.57 | 3.11 | 79.96 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnext50_3rdparty_8xb32_in1k_20220329-2cc84d21.pth) | -| DenseNet121\* | 7.98 | 2.88 | 74.96 | 92.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet121_4xb256_in1k_20220426-07450f99.pth) | -| DenseNet169\* | 14.15 | 3.42 | 76.08 | 93.11 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet169_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet169_4xb256_in1k_20220426-a2889902.pth) | -| DenseNet201\* | 20.01 | 4.37 | 77.32 | 93.64 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet201_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet201_4xb256_in1k_20220426-05cae4ef.pth) | -| DenseNet161\* | 28.68 | 7.82 | 77.61 | 93.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet161_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet161_4xb256_in1k_20220426-ee6a80a9.pth) | -| VAN-T\* | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth) | -| VAN-S\* | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) | -| VAN-B\* | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) | -| VAN-L\* | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-large_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :--------------------------------: | :-------------------------------: | :-----------------------------: | :-------: | :-------: | :---------------------------------------: | :-----------------------------------------: | +| VGG-11 | 132.86 | 7.63 | 68.75 | 88.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.log.json) | +| VGG-13 | 133.05 | 11.34 | 70.02 | 89.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.log.json) | +| VGG-16 | 138.36 | 15.5 | 71.62 | 90.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.log.json) | +| VGG-19 | 143.67 | 19.67 | 72.41 | 90.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.log.json) | +| VGG-11-BN | 132.87 | 7.64 | 70.75 | 90.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.log.json) | +| VGG-13-BN | 133.05 | 11.36 | 72.15 | 90.71 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.log.json) | +| VGG-16-BN | 138.37 | 15.53 | 73.72 | 91.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.log.json) | +| VGG-19-BN | 143.68 | 19.7 | 74.70 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json) | +| RepVGG-A0\* | 9.11（train) \| 8.31 (deploy) | 1.52 (train) \| 1.36 (deploy) | 72.41 | 90.50 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_3rdparty_4xb64-coslr-120e_in1k_20210909-883ab98c.pth) | +| RepVGG-A1\* | 14.09 (train) \| 12.79 (deploy) | 2.64 (train) \| 2.37 (deploy) | 74.47 | 91.85 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_3rdparty_4xb64-coslr-120e_in1k_20210909-24003a24.pth) | +| RepVGG-A2\* | 28.21 (train) \| 25.5 (deploy) | 5.7 (train) \| 5.12 (deploy) | 76.48 | 93.01 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_3rdparty_4xb64-coslr-120e_in1k_20210909-97d7695a.pth) | +| RepVGG-B0\* | 15.82 (train) \| 14.34 (deploy) | 3.42 (train) \| 3.06 (deploy) | 75.14 | 92.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_3rdparty_4xb64-coslr-120e_in1k_20210909-446375f4.pth) | +| RepVGG-B1\* | 57.42 (train) \| 51.83 (deploy) | 13.16 (train) \| 11.82 (deploy) | 78.37 | 94.11 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_3rdparty_4xb64-coslr-120e_in1k_20210909-750cdf67.pth) | +| RepVGG-B1g2\* | 45.78 (train) \| 41.36 (deploy) | 9.82 (train) \| 8.82 (deploy) | 77.79 | 93.88 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k_20210909-344f6422.pth) | +| RepVGG-B1g4\* | 39.97 (train) \| 36.13 (deploy) | 8.15 (train) \| 7.32 (deploy) | 77.58 | 93.84 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k_20210909-d4c1a642.pth) | +| RepVGG-B2\* | 89.02 (train) \| 80.32 (deploy) | 20.46 (train) \| 18.39 (deploy) | 78.78 | 94.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_3rdparty_4xb64-coslr-120e_in1k_20210909-bd6b937c.pth) | +| RepVGG-B2g4\* | 61.76 (train) \| 55.78 (deploy) | 12.63 (train) \| 11.34 (deploy) | 79.38 | 94.68 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-7b7955f0.pth) | +| RepVGG-B3\* | 123.09 (train) \| 110.96 (deploy) | 29.17 (train) \| 26.22 (deploy) | 80.52 | 95.26 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-dda968bf.pth) | +| RepVGG-B3g4\* | 83.83 (train) \| 75.63 (deploy) | 17.9 (train) \| 16.08 (deploy) | 80.22 | 95.10 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-4e54846a.pth) | +| RepVGG-D2se\* | 133.33 (train) \| 120.39 (deploy) | 36.56 (train) \| 32.85 (deploy) | 81.81 | 95.94 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth) | +| ResNet-18 | 11.69 | 1.82 | 70.07 | 89.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.log.json) | +| ResNet-34 | 21.8 | 3.68 | 73.85 | 91.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.log.json) | +| ResNet-50 (rsb-a1) | 25.56 | 4.12 | 80.12 | 94.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.log.json) | +| ResNet-101 | 44.55 | 7.85 | 78.18 | 94.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.log.json) | +| ResNet-152 | 60.19 | 11.58 | 78.63 | 94.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.log.json) | +| Res2Net-50-14w-8s\* | 25.06 | 4.22 | 78.14 | 93.85 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w14-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w14-s8_3rdparty_8xb32_in1k_20210927-bc967bf1.pth) | +| Res2Net-50-26w-8s\* | 48.40 | 8.39 | 79.20 | 94.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w26-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w26-s8_3rdparty_8xb32_in1k_20210927-f547a94b.pth) | +| Res2Net-101-26w-4s\* | 45.21 | 8.12 | 79.19 | 94.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net101-w26-s4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net101-w26-s4_3rdparty_8xb32_in1k_20210927-870b6c36.pth) | +| ResNeSt-50\* | 27.48 | 5.41 | 81.13 | 95.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest50_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest50_imagenet_converted-1ebf0afe.pth) | +| ResNeSt-101\* | 48.28 | 10.27 | 82.32 | 96.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest101_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest101_imagenet_converted-032caa52.pth) | +| ResNeSt-200\* | 70.2 | 17.53 | 82.41 | 96.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest200_64xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest200_imagenet_converted-581a60f2.pth) | +| ResNeSt-269\* | 110.93 | 22.58 | 82.70 | 96.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest269_64xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest269_imagenet_converted-59930960.pth) | +| ResNetV1D-50 | 25.58 | 4.36 | 77.54 | 93.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.log.json) | +| ResNetV1D-101 | 44.57 | 8.09 | 78.93 | 94.48 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.log.json) | +| ResNetV1D-152 | 60.21 | 11.82 | 79.41 | 94.7 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.log.json) | +| ResNeXt-32x4d-50 | 25.03 | 4.27 | 77.90 | 93.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.log.json) | +| ResNeXt-32x4d-101 | 44.18 | 8.03 | 78.71 | 94.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.log.json) | +| ResNeXt-32x8d-101 | 88.79 | 16.5 | 79.23 | 94.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x8d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.log.json) | +| ResNeXt-32x4d-152 | 59.95 | 11.8 | 78.93 | 94.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext152-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.log.json) | +| SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200708-657b3c36.log.json) | +| SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200708-038a4d04.log.json) | +| RegNetX-400MF | 5.16 | 0.41 | 72.56 | 90.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-400mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211213-89bfc226.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211208_143316.log.json) | +| RegNetX-800MF | 7.26 | 0.81 | 74.76 | 92.32 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-800mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211213-222b0f11.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211207_143037.log.json) | +| RegNetX-1.6GF | 9.19 | 1.63 | 76.84 | 93.31 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-1.6gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211213-d1b89758.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211208_143018.log.json) | +| RegNetX-3.2GF | 15.3 | 3.21 | 78.09 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-3.2gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211213-1fdd82ae.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211208_142720.log.json) | +| RegNetX-4.0GF | 22.12 | 4.0 | 78.60 | 94.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-4.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211213-efed675c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211207_150431.log.json) | +| RegNetX-6.4GF | 26.21 | 6.51 | 79.38 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-6.4gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211215-5c6089da.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211213_172748.log.json) | +| RegNetX-8.0GF | 39.57 | 8.03 | 79.12 | 94.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-8.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211213-9a9fcc76.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211208_103250.log.json) | +| RegNetX-12GF | 46.11 | 12.15 | 79.67 | 95.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-12gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211213-5df8c2f8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211208_143713.log.json) | +| ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.log.json) | +| ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200804-8860eec9.log.json) | +| MobileNet V2 | 3.5 | 0.319 | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.log.json) | +| ViT-B/16\* | 86.86 | 33.03 | 85.43 | 97.77 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-98e8652b.pth) | +| ViT-B/32\* | 88.3 | 8.56 | 84.01 | 97.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-9cea8599.pth) | +| ViT-L/16\* | 304.72 | 116.68 | 85.63 | 97.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth) | +| Swin-Transformer tiny | 28.29 | 4.36 | 81.18 | 95.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-tiny_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925.log.json) | +| Swin-Transformer small | 49.61 | 8.52 | 83.02 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-small_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219-7f9d988b.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219.log.json) | +| Swin-Transformer base | 87.77 | 15.14 | 83.36 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742-93230b0d.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742.log.json) | +| Transformer in Transformer small\* | 23.76 | 3.36 | 81.52 | 95.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/tnt/tnt-s-p16_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/tnt/tnt-small-p16_3rdparty_in1k_20210903-c56ee7df.pth) | +| T2T-ViT_t-14 | 21.47 | 4.34 | 81.83 | 95.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.log.json) | +| T2T-ViT_t-19 | 39.08 | 7.80 | 82.63 | 96.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-19_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.log.json) | +| T2T-ViT_t-24 | 64.00 | 12.69 | 82.71 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-24_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.log.json) | +| Mixer-B/16\* | 59.88 | 12.61 | 76.68 | 92.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-base-p16_3rdparty_64xb64_in1k_20211124-1377e3e0.pth) | +| Mixer-L/16\* | 208.2 | 44.57 | 72.34 | 88.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-large-p16_3rdparty_64xb64_in1k_20211124-5a2519d2.pth) | +| DeiT-tiny | 5.72 | 1.08 | 74.50 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.log.json) | +| DeiT-tiny distilled\* | 5.72 | 1.08 | 74.51 | 91.90 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth) | +| DeiT-small | 22.05 | 4.24 | 80.69 | 95.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.log.json) | +| DeiT-small distilled\* | 22.05 | 4.24 | 81.17 | 95.40 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth) | +| DeiT-base | 86.57 | 16.86 | 81.76 | 95.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.log.json) | +| DeiT-base distilled\* | 86.57 | 16.86 | 83.33 | 96.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base-distilled_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211216-42891296.pth) | +| DeiT-base 384px\* | 86.86 | 49.37 | 83.04 | 96.31 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth) | +| DeiT-base distilled 384px\* | 86.86 | 49.37 | 85.55 | 97.35 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211216-e48d6000.pth) | +| Conformer-tiny-p16\* | 23.52 | 4.90 | 81.31 | 95.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-tiny-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-tiny-p16_3rdparty_8xb128_in1k_20211206-f6860372.pth) | +| Conformer-small-p32\* | 38.85 | 7.09 | 81.96 | 96.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p32_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p32_8xb128_in1k_20211206-947a0816.pth) | +| Conformer-small-p16\* | 37.67 | 10.31 | 83.32 | 96.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p16_3rdparty_8xb128_in1k_20211206-3065dcf5.pth) | +| Conformer-base-p16\* | 83.29 | 22.89 | 83.82 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-base-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-base-p16_3rdparty_8xb128_in1k_20211206-bfdf8637.pth) | +| PCPVT-small\* | 24.11 | 3.67 | 81.14 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-small_3rdparty_8xb128_in1k_20220126-ef23c132.pth) | +| PCPVT-base\* | 43.83 | 6.45 | 82.66 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-base_3rdparty_8xb128_in1k_20220126-f8c4b0d5.pth) | +| PCPVT-large\* | 60.99 | 9.51 | 83.09 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-large_3rdparty_16xb64_in1k_20220126-c1ef8d80.pth) | +| SVT-small\* | 24.06 | 2.82 | 81.77 | 95.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-small_3rdparty_8xb128_in1k_20220126-8fe5205b.pth) | +| SVT-base\* | 56.07 | 8.35 | 83.13 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-base_3rdparty_8xb128_in1k_20220126-e31cc8e9.pth) | +| SVT-large\* | 99.27 | 14.82 | 83.60 | 96.50 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-large_3rdparty_16xb64_in1k_20220126-4817645f.pth) | +| EfficientNet-B0\* | 5.29 | 0.02 | 76.74 | 93.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32_in1k_20220119-a7e2a0b1.pth) | +| EfficientNet-B0 (AA)\* | 5.29 | 0.02 | 77.26 | 93.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa_in1k_20220119-8d939117.pth) | +| EfficientNet-B0 (AA + AdvProp)\* | 5.29 | 0.02 | 77.53 | 93.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k_20220119-26434485.pth) | +| EfficientNet-B1\* | 7.79 | 0.03 | 78.68 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32_in1k_20220119-002556d9.pth) | +| EfficientNet-B1 (AA)\* | 7.79 | 0.03 | 79.20 | 94.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa_in1k_20220119-619d8ae3.pth) | +| EfficientNet-B1 (AA + AdvProp)\* | 7.79 | 0.03 | 79.52 | 94.43 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa-advprop_in1k_20220119-5715267d.pth) | +| EfficientNet-B2\* | 9.11 | 0.03 | 79.64 | 94.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32_in1k_20220119-ea374a30.pth) | +| EfficientNet-B2 (AA)\* | 9.11 | 0.03 | 80.21 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa_in1k_20220119-dd61e80b.pth) | +| EfficientNet-B2 (AA + AdvProp)\* | 9.11 | 0.03 | 80.45 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa-advprop_in1k_20220119-1655338a.pth) | +| EfficientNet-B3\* | 12.23 | 0.06 | 81.01 | 95.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32_in1k_20220119-4b4d7487.pth) | +| EfficientNet-B3 (AA)\* | 12.23 | 0.06 | 81.58 | 95.67 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k_20220119-5b4887a0.pth) | +| EfficientNet-B3 (AA + AdvProp)\* | 12.23 | 0.06 | 81.81 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa-advprop_in1k_20220119-53b41118.pth) | +| EfficientNet-B4\* | 19.34 | 0.12 | 82.57 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32_in1k_20220119-81fd4077.pth) | +| EfficientNet-B4 (AA)\* | 19.34 | 0.12 | 82.95 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa_in1k_20220119-45b8bd2b.pth) | +| EfficientNet-B4 (AA + AdvProp)\* | 19.34 | 0.12 | 83.25 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa-advprop_in1k_20220119-38c2238c.pth) | +| EfficientNet-B5\* | 30.39 | 0.24 | 83.18 | 96.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32_in1k_20220119-e9814430.pth) | +| EfficientNet-B5 (AA)\* | 30.39 | 0.24 | 83.82 | 96.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa_in1k_20220119-2cab8b78.pth) | +| EfficientNet-B5 (AA + AdvProp)\* | 30.39 | 0.24 | 84.21 | 96.98 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa-advprop_in1k_20220119-f57a895a.pth) | +| EfficientNet-B6 (AA)\* | 43.04 | 0.41 | 84.05 | 96.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa_in1k_20220119-45b03310.pth) | +| EfficientNet-B6 (AA + AdvProp)\* | 43.04 | 0.41 | 84.74 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa-advprop_in1k_20220119-bfe3485e.pth) | +| EfficientNet-B7 (AA)\* | 66.35 | 0.72 | 84.38 | 96.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa_in1k_20220119-bf03951c.pth) | +| EfficientNet-B7 (AA + AdvProp)\* | 66.35 | 0.72 | 85.14 | 97.23 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa-advprop_in1k_20220119-c6dbff10.pth) | +| EfficientNet-B8 (AA + AdvProp)\* | 87.41 | 1.09 | 85.38 | 97.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b8_3rdparty_8xb32-aa-advprop_in1k_20220119-297ce1b7.pth) | +| ConvNeXt-T\* | 28.59 | 4.46 | 82.05 | 95.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-tiny_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128_in1k_20220124-18abde00.pth) | +| ConvNeXt-S\* | 50.22 | 8.69 | 83.13 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-small_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128_in1k_20220124-d39b5192.pth) | +| ConvNeXt-B\* | 88.59 | 15.36 | 83.85 | 96.74 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128_in1k_20220124-d0915162.pth) | +| ConvNeXt-B\* | 88.59 | 15.36 | 85.81 | 97.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_32xb128_in1k_20220124-eb2d6ada.pth) | +| ConvNeXt-L\* | 197.77 | 34.37 | 84.30 | 96.89 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_64xb64_in1k_20220124-f8a0ded0.pth) | +| ConvNeXt-L\* | 197.77 | 34.37 | 86.61 | 98.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_in21k-pre-3rdparty_64xb64_in1k_20220124-2412403d.pth) | +| ConvNeXt-XL\* | 350.20 | 60.93 | 86.97 | 98.20 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-xlarge_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_in21k-pre-3rdparty_64xb64_in1k_20220124-76b6863d.pth) | +| HRNet-W18\* | 21.30 | 4.33 | 76.75 | 93.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32_in1k_20220120-0c10b180.pth) | +| HRNet-W30\* | 37.71 | 8.17 | 78.19 | 94.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w30_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w30_3rdparty_8xb32_in1k_20220120-8aa3832f.pth) | +| HRNet-W32\* | 41.23 | 8.99 | 78.44 | 94.19 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w32_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w32_3rdparty_8xb32_in1k_20220120-c394f1ab.pth) | +| HRNet-W40\* | 57.55 | 12.77 | 78.94 | 94.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w40_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w40_3rdparty_8xb32_in1k_20220120-9a2dbfc5.pth) | +| HRNet-W44\* | 67.06 | 14.96 | 78.88 | 94.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w44_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w44_3rdparty_8xb32_in1k_20220120-35d07f73.pth) | +| HRNet-W48\* | 77.47 | 17.36 | 79.32 | 94.52 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32_in1k_20220120-e555ef50.pth) | +| HRNet-W64\* | 128.06 | 29.00 | 79.46 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w64_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w64_3rdparty_8xb32_in1k_20220120-19126642.pth) | +| HRNet-W18 (ssld)\* | 21.30 | 4.33 | 81.06 | 95.70 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32-ssld_in1k_20220120-455f69ea.pth) | +| HRNet-W48 (ssld)\* | 77.47 | 17.36 | 83.63 | 96.79 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32-ssld_in1k_20220120-d0459c38.pth) | +| WRN-50\* | 68.88 | 11.44 | 81.45 | 95.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet50_timm_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet50_3rdparty-timm_8xb32_in1k_20220304-83ae4399.pth) | +| WRN-101\* | 126.89 | 22.81 | 78.84 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth) | +| CSPDarkNet50\* | 27.64 | 5.04 | 80.05 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspdarknet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspdarknet50_3rdparty_8xb32_in1k_20220329-bd275287.pth) | +| CSPResNet50\* | 21.62 | 3.48 | 79.55 | 94.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnet50_3rdparty_8xb32_in1k_20220329-dd6dddfb.pth) | +| CSPResNeXt50\* | 20.57 | 3.11 | 79.96 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnext50_3rdparty_8xb32_in1k_20220329-2cc84d21.pth) | +| DenseNet121\* | 7.98 | 2.88 | 74.96 | 92.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet121_4xb256_in1k_20220426-07450f99.pth) | +| DenseNet169\* | 14.15 | 3.42 | 76.08 | 93.11 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet169_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet169_4xb256_in1k_20220426-a2889902.pth) | +| DenseNet201\* | 20.01 | 4.37 | 77.32 | 93.64 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet201_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet201_4xb256_in1k_20220426-05cae4ef.pth) | +| DenseNet161\* | 28.68 | 7.82 | 77.61 | 93.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet161_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet161_4xb256_in1k_20220426-ee6a80a9.pth) | +| VAN-T\* | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth) | +| VAN-S\* | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) | +| VAN-B\* | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) | +| VAN-L\* | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-large_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) | -*Models with \* are converted from other repos, others are trained by ourselves.* +*Models with * are converted from other repos, others are trained by ourselves.* ## CIFAR10 -| Model | Params(M) | Flops(G) | Top-1 (%) | Config | Download | -|:---------------------:|:---------:|:--------:|:---------:|:--------:|:--------:| -| ResNet-18-b16x8 | 11.17 | 0.56 | 94.82 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.log.json) | -| ResNet-34-b16x8 | 21.28 | 1.16 | 95.34 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.log.json) | -| ResNet-50-b16x8 | 23.52 | 1.31 | 95.55 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.log.json) | -| ResNet-101-b16x8 | 42.51 | 2.52 | 95.58 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.log.json) | -| ResNet-152-b16x8 | 58.16 | 3.74 | 95.76 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.log.json) | +| Model | Params(M) | Flops(G) | Top-1 (%) | Config | Download | +| :--------------: | :-------: | :------: | :-------: | :----: | :------------------------------------------------------------------------------------------------------------: | +| ResNet-18-b16x8 | 11.17 | 0.56 | 94.82 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb16_cifar10.py) | +| ResNet-34-b16x8 | 21.28 | 1.16 | 95.34 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb16_cifar10.py) | +| ResNet-50-b16x8 | 23.52 | 1.31 | 95.55 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb16_cifar10.py) | +| ResNet-101-b16x8 | 42.51 | 2.52 | 95.58 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb16_cifar10.py) | +| ResNet-152-b16x8 | 58.16 | 3.74 | 95.76 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb16_cifar10.py) | diff --git a/docs/en/tools/analysis.md b/docs/en/tools/analysis.md index f335aed800f..13eeea0a4c3 100644 --- a/docs/en/tools/analysis.md +++ b/docs/en/tools/analysis.md @@ -52,21 +52,21 @@ Examples: - Plot the loss curve in training. - ```shell - python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys loss --legend loss - ``` + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys loss --legend loss + ``` - Plot the top-1 accuracy and top-5 accuracy curves, and save the figure to results.jpg. - ```shell - python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys accuracy_top-1 accuracy_top-5 --legend top1 top5 --out results.jpg - ``` + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys accuracy_top-1 accuracy_top-5 --legend top1 top5 --out results.jpg + ``` - Compare the top-1 accuracy of two log files in the same figure. - ```shell - python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys accuracy_top-1 --legend exp1 exp2 - ``` + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys accuracy_top-1 --legend exp1 exp2 + ``` ```{note} The tool will automatically select to find keys in training logs or validation logs according to the keys. diff --git a/docs/en/tools/onnx2tensorrt.md b/docs/en/tools/onnx2tensorrt.md index 44aeeb6641d..ea0f148460a 100644 --- a/docs/en/tools/onnx2tensorrt.md +++ b/docs/en/tools/onnx2tensorrt.md @@ -59,11 +59,11 @@ python tools/deployment/onnx2tensorrt.py \ The table below lists the models that are guaranteed to be convertible to TensorRT. -| Model | Config | Status | -| :----------: | :--------------------------------------------------------------------------: | :----: | -| MobileNetV2 | `configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py` | Y | -| ResNet | `configs/resnet/resnet18_8xb16_cifar10.py` | Y | -| ResNeXt | `configs/resnext/resnext50-32x4d_8xb32_in1k.py` | Y | +| Model | Config | Status | +| :----------: | :-----------------------------------------------------: | :----: | +| MobileNetV2 | `configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py` | Y | +| ResNet | `configs/resnet/resnet18_8xb16_cifar10.py` | Y | +| ResNeXt | `configs/resnext/resnext50-32x4d_8xb32_in1k.py` | Y | | ShuffleNetV1 | `configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py` | Y | | ShuffleNetV2 | `configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py` | Y | diff --git a/docs/en/tools/pytorch2onnx.md b/docs/en/tools/pytorch2onnx.md index 5b6b80c3f4d..7352d453bb7 100644 --- a/docs/en/tools/pytorch2onnx.md +++ b/docs/en/tools/pytorch2onnx.md @@ -25,9 +25,9 @@ 1. Please refer to [install](https://mmclassification.readthedocs.io/en/latest/install.html#install-mmclassification) for installation of MMClassification. 2. Install onnx and onnxruntime - ```shell - pip install onnx onnxruntime==1.5.1 - ``` +```shell +pip install onnx onnxruntime==1.5.1 +``` ### Usage @@ -182,12 +182,12 @@ This part selects ImageNet for onnxruntime verification. ImageNet has multiple v The table below lists the models that are guaranteed to be exportable to ONNX and runnable in ONNX Runtime. -| Model | Config | Batch Inference | Dynamic Shape | Note | -| :----------: | :----------------------------------------------------------: | :-------------: | :-----------: | ---- | -| MobileNetV2 | [mobilenet-v2_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | Y | Y | | -| ResNet | [resnet18_8xb16_cifar10.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet/resnet18_8xb16_cifar10.py) | Y | Y | | -| ResNeXt | [resnext50-32x4d_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | Y | Y | | -| SE-ResNet | [seresnet50_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet/seresnet50_8xb32_in1k.py) | Y | Y | | +| Model | Config | Batch Inference | Dynamic Shape | Note | +| :----------: | :-------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------: | :-----------: | ---- | +| MobileNetV2 | [mobilenet-v2_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | Y | Y | | +| ResNet | [resnet18_8xb16_cifar10.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet/resnet18_8xb16_cifar10.py) | Y | Y | | +| ResNeXt | [resnext50-32x4d_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | Y | Y | | +| SE-ResNet | [seresnet50_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet/seresnet50_8xb32_in1k.py) | Y | Y | | | ShuffleNetV1 | [shufflenet-v1-1x_16xb64_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py) | Y | Y | | | ShuffleNetV2 | [shufflenet-v2-1x_16xb64_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | Y | Y | | diff --git a/docs/en/tools/visualization.md b/docs/en/tools/visualization.md index f224fe68eb0..01282453f89 100644 --- a/docs/en/tools/visualization.md +++ b/docs/en/tools/visualization.md @@ -8,6 +8,7 @@ - [FAQs](#faqs) + ## Pipeline Visualization ```bash @@ -54,35 +55,35 @@ python tools/visualizations/vis_pipeline.py \ 1. In **'original'** mode, visualize 100 original pictures in the `CIFAR100` validation set, then display and save them in the `./tmp` folder： - ```shell - python ./tools/visualizations/vis_pipeline.py configs/resnet/resnet50_8xb16_cifar100.py --phase val --output-dir tmp --mode original --number 100 --show --adaptive --bgr2rgb - ``` +```shell +python ./tools/visualizations/vis_pipeline.py configs/resnet/resnet50_8xb16_cifar100.py --phase val --output-dir tmp --mode original --number 100 --show --adaptive --bgr2rgb +``` -
+
2. In **'transformed'** mode, visualize all the transformed pictures of the `ImageNet` training set and display them in pop-up windows： - ```shell - python ./tools/visualizations/vis_pipeline.py ./configs/resnet/resnet50_8xb32_in1k.py --show --mode transformed - ``` +```shell +python ./tools/visualizations/vis_pipeline.py ./configs/resnet/resnet50_8xb32_in1k.py --show --mode transformed +``` -
+
3. In **'concat'** mode, visualize 10 pairs of origin and transformed images for comparison in the `ImageNet` train set and save them in the `./tmp` folder： - ```shell - python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --output-dir tmp --number 10 --adaptive - ``` +```shell +python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --output-dir tmp --number 10 --adaptive +``` -
+
4. In **'pipeline'** mode, visualize all the intermediate pictures in the `ImageNet` train set through the pipeline： - ```shell - python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --adaptive --mode pipeline --show - ``` +```shell +python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --adaptive --mode pipeline --show +``` -
+
## Learning Rate Schedule Visualization @@ -135,14 +136,14 @@ MMClassification provides `tools\visualizations\vis_cam.py` tool to visualize cl The supported methods are as follows: -| Method | What it does | -|----------|--------------| -| GradCAM | Weight the 2D activations by the average gradient | -| GradCAM++ | Like GradCAM but uses second order gradients | -| XGradCAM | Like GradCAM but scale the gradients by the normalized activations | -| EigenCAM | Takes the first principle component of the 2D Activations (no class discrimination, but seems to give great results)| -| EigenGradCAM | Like EigenCAM but with class discrimination: First principle component of Activations\*Grad. Looks like GradCAM, but cleaner| -| LayerCAM | Spatially weight the activations by positive gradients. Works better especially in lower layers | +| Method | What it does | +| ------------ | ---------------------------------------------------------------------------------------------------------------------------- | +| GradCAM | Weight the 2D activations by the average gradient | +| GradCAM++ | Like GradCAM but uses second order gradients | +| XGradCAM | Like GradCAM but scale the gradients by the normalized activations | +| EigenCAM | Takes the first principle component of the 2D Activations (no class discrimination, but seems to give great results) | +| EigenGradCAM | Like EigenCAM but with class discrimination: First principle component of Activations\*Grad. Looks like GradCAM, but cleaner | +| LayerCAM | Spatially weight the activations by positive gradients. Works better especially in lower layers | **Command**： @@ -210,9 +211,9 @@ For example, the `backbone.layer4[-1]` is the same as `backbone.layer4.2` since # GradCAM++, XGradCAM, EigenCAM, EigenGradCAM, LayerCAM ``` - | Image | GradCAM | GradCAM++ | EigenGradCAM | LayerCAM | - |-------|----------|------------|-------------- |------------| - |
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| + | Image | GradCAM | GradCAM++ | EigenGradCAM | LayerCAM | + | ------------------------------------ | --------------------------------------- | ----------------------------------------- | -------------------------------------------- | ---------------------------------------- | + |
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| 2. Use different `target-category` to get CAM from the same picture. In `ImageNet` dataset, the category 238 is 'Greater Swiss Mountain dog', the category 281 is 'tabby, tabby cat'. @@ -226,10 +227,10 @@ For example, the `backbone.layer4[-1]` is the same as `backbone.layer4.2` since # --target-category 281 ``` - | Category | Image | GradCAM | XGradCAM | LayerCAM | - | --------- |-------|----------|-------------- |------------| - | Dog |
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| + | Category | Image | GradCAM | XGradCAM | LayerCAM | + | -------- | ---------------------------------------------- | ------------------------------------------------ | ------------------------------------------------- | ------------------------------------------------- | + | Dog |
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| 3. Use `--eigen-smooth` and `--aug-smooth` to improve visual effects. @@ -243,9 +244,9 @@ For example, the `backbone.layer4[-1]` is the same as `backbone.layer4.2` since --eigen-smooth --aug-smooth ``` - | Image | LayerCAM | eigen-smooth | aug-smooth | eigen&aug | - |-------|----------|------------|-------------- |------------| - |
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| + | Image | LayerCAM | eigen-smooth | aug-smooth | eigen&aug | + | ------------------------------------ | --------------------------------------- | ------------------------------------------- | ----------------------------------------- | ----------------------------------------- | + |
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| **Examples(Transformer)**： @@ -292,9 +293,9 @@ To exclude these extra tokens, we need know the number of extra tokens. Almost a --target-layers 'backbone.encoder[-1].ln1' ``` -| Image | ResNet50 | ViT | Swin | T2T-ViT | -|-------|----------|------------|-------------- |------------| -|
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| +| Image | ResNet50 | ViT | Swin | T2T-ViT | +| --------------------------------------- | ------------------------------------------ | -------------------------------------- | --------------------------------------- | ------------------------------------------ | +|
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| ## FAQs diff --git a/docs/en/tutorials/config.md b/docs/en/tutorials/config.md index 280f31a12f2..26740467d3b 100644 --- a/docs/en/tutorials/config.md +++ b/docs/en/tutorials/config.md @@ -32,30 +32,39 @@ We follow the below convention to name config files. Contributors are advised to - `data info`：Data information, dataset name, input size and so on, such as imagenet, cifar, etc.; ### Algorithm information + The main algorithm name and the corresponding branch architecture information. E.g： + - `resnet50` - `mobilenet-v3-large` - `vit-small-patch32` : `patch32` represents the size of the partition in `ViT` algorithm; - `seresnext101-32x4d` : `SeResNet101` network structure, `32x4d` means that `groups` and `width_per_group` are 32 and 4 respectively in `Bottleneck`; ### Module information + Some special `neck`, `head` and `pretrain` information. In classification tasks, `pretrain` information is the most commonly used: + - `in21k-pre` : pre-trained on ImageNet21k; - `in21k-pre-3rd-party` : pre-trained on ImageNet21k and the checkpoint is converted from a third-party repository; ### Training information + Training schedule, including training type, `batch size`, `lr schedule`, data augment, special loss functions and so on: + - format `{gpu x batch_per_gpu}`, such as `8xb32` Training type (mainly seen in the transformer network, such as the `ViT` algorithm, which is usually divided into two training type: pre-training and fine-tuning): + - `ft` : configuration file for fine-tuning - `pt` : configuration file for pretraining Training recipe. Usually, only the part that is different from the original paper will be marked. These methods will be arranged in the order `{pipeline aug}-{train aug}-{loss trick}-{scheduler}-{epochs}`. + - `coslr-200e` : use cosine scheduler to train 200 epochs - `autoaug-mixup-lbs-coslr-50e` : use `autoaug`, `mixup`, `label smooth`, `cosine scheduler` to train 50 epochs ### Data information + - `in1k` : `ImageNet1k` dataset, default to use the input image size of 224x224; - `in21k` : `ImageNet21k` dataset, also called `ImageNet22k` dataset, default to use the input image size of 224x224; - `in1k-384px` : Indicates that the input image size is 384x384; @@ -68,16 +77,16 @@ repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py ``` - `repvgg-D2se`: Algorithm information - + `repvgg`: The main algorithm. - + `D2se`: The architecture. + - `repvgg`: The main algorithm. + - `D2se`: The architecture. - `deploy`: Module information, means the backbone is in the deploy state. - `4xb64-autoaug-lbs-mixup-coslr-200e`: Training information. - + `4xb64`: Use 4 GPUs and the size of batches per GPU is 64. - + `autoaug`: Use `AutoAugment` in training pipeline. - + `lbs`: Use label smoothing loss. - + `mixup`: Use `mixup` training augment method. - + `coslr`: Use cosine learning rate scheduler. - + `200e`: Train the model for 200 epochs. + - `4xb64`: Use 4 GPUs and the size of batches per GPU is 64. + - `autoaug`: Use `AutoAugment` in training pipeline. + - `lbs`: Use label smoothing loss. + - `mixup`: Use `mixup` training augment method. + - `coslr`: Use cosine learning rate scheduler. + - `200e`: Train the model for 200 epochs. - `in1k`: Dataset information. The config is for `ImageNet1k` dataset and the input size is `224x224`. ```{note} @@ -117,14 +126,15 @@ _base_ = [ The four parts are explained separately below, and the above-mentioned ResNet50 primitive config are also used as an example. ### model + The parameter `"model"` is a python dictionary in the configuration file, which mainly includes information such as network structure and loss function: -- `type` ： Classifier name, MMCls supports `ImageClassifier`, refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api.html#module-mmcls.models.classifiers). -- `backbone` ： Backbone configs, refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api.html#module-mmcls.models.backbones) for available options. -- `neck` ：Neck network name, MMCls supports `GlobalAveragePooling`, please refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api.html#module-mmcls.models.necks). -- `head`: Head network name, MMCls supports single-label and multi-label classification head networks, available options refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api.html#module-mmcls.models.heads). - - `loss`: Loss function type, supports `CrossEntropyLoss`, [`LabelSmoothLoss`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_label_smooth.py) etc., For available options, refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api.html#module-mmcls.models.losses). -- `train_cfg` ：Training augment config, MMCls supports [`mixup`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_mixup.py), [`cutmix`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_cutmix.py) and other augments. +- `type` ： Classifier name, MMCls supports `ImageClassifier`, refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api/models.html#classifier). +- `backbone` ： Backbone configs, refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api/models.html#backbones) for available options. +- `neck` ：Neck network name, MMCls supports `GlobalAveragePooling`, please refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api/models.html#necks). +- `head`: Head network name, MMCls supports single-label and multi-label classification head networks, available options refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api/models.html#heads). + - `loss`: Loss function type, supports `CrossEntropyLoss`, [`LabelSmoothLoss`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_label_smooth.py) etc., For available options, refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api/models.html#losses). +- `train_cfg` ：Training augment config, MMCls supports [`mixup`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_mixup.py), [`cutmix`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_cutmix.py) and other augments. ```{note} The 'type' in the configuration file is not a constructed parameter, but a class name. @@ -151,11 +161,13 @@ model = dict( ``` ### data + The parameter `"data"` is a python dictionary in the configuration file, which mainly includes information to construct dataloader: + - `samples_per_gpu` : the BatchSize of each GPU when building the dataloader - `workers_per_gpu` : the number of threads per GPU when building dataloader - `train ｜ val ｜ test` : config to construct dataset - - `type`: Dataset name, MMCls supports `ImageNet`, `Cifar` etc., refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api.html#module-mmcls.datasets) + - `type`: Dataset name, MMCls supports `ImageNet`, `Cifar` etc., refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api/datasets.html) - `data_prefix` : Dataset root directory - `pipeline` : Data processing pipeline, refer to related tutorial [CUSTOM DATA PIPELINES](https://mmclassification.readthedocs.io/en/latest/tutorials/data_pipeline.html) @@ -211,7 +223,9 @@ evaluation = dict( # The config to build the evaluation hook, refer to htt ``` ### training schedule + Mainly include optimizer settings, `optimizer hook` settings, learning rate schedule and `runner` settings: + - `optimizer`: optimizer setting , support all optimizers in `pytorch`, refer to related [mmcv](https://mmcv.readthedocs.io/en/latest/_modules/mmcv/runner/optimizer/default_constructor.html#DefaultOptimizerConstructor) documentation. - `optimizer_config`: `optimizer hook` configuration file, such as setting gradient limit, refer to related [mmcv](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8) code. - `lr_config`: Learning rate scheduler, supports "CosineAnnealing", "Step", "Cyclic", etc. refer to related [mmcv](https://mmcv.readthedocs.io/en/latest/_modules/mmcv/runner/hooks/lr_updater.html#LrUpdaterHook) documentation for more options. @@ -376,10 +390,9 @@ When users use the script "tools/train.py" or "tools/test.py" to submit tasks or - Update values of list/tuples. If the value to be updated is a list or a tuple. For example, the config file normally sets `workflow=[('train', 1)]`. If you want to - change this key, you may specify `--cfg-options workflow="[(train,1),(val,1)]"`. Note that the quotation mark \" is necessary to + change this key, you may specify `--cfg-options workflow="[(train,1),(val,1)]"`. Note that the quotation mark " is necessary to support list/tuple data types, and that **NO** white space is allowed inside the quotation marks in the specified value. - ## Import user-defined modules ```{note} @@ -400,4 +413,5 @@ custom_imports = dict( ``` ## FAQ + - None diff --git a/docs/en/tutorials/data_pipeline.md b/docs/en/tutorials/data_pipeline.md index 319d95f6474..4b32280e283 100644 --- a/docs/en/tutorials/data_pipeline.md +++ b/docs/en/tutorials/data_pipeline.md @@ -106,44 +106,44 @@ For more information about other data transformation classes, please refer to [D the folder `mmcls/datasets/pipelines/`. The pipeline class needs to override the `__call__` method which takes a dict as input and returns a dict. - ```python - from mmcls.datasets import PIPELINES + ```python + from mmcls.datasets import PIPELINES - @PIPELINES.register_module() - class MyTransform(object): + @PIPELINES.register_module() + class MyTransform(object): - def __call__(self, results): - # apply transforms on results['img'] - return results - ``` + def __call__(self, results): + # apply transforms on results['img'] + return results + ``` 2. Import the new class in `mmcls/datasets/pipelines/__init__.py`. - ```python - ... - from .my_pipeline import MyTransform + ```python + ... + from .my_pipeline import MyTransform - __all__ = [ - ..., 'MyTransform' - ] - ``` + __all__ = [ + ..., 'MyTransform' + ] + ``` 3. Use it in config files. - ```python - 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='MyTransform'), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='ToTensor', keys=['gt_label']), - dict(type='Collect', keys=['img', 'gt_label']) - ] - ``` + ```python + 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='MyTransform'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) + ] + ``` ## Pipeline visualization diff --git a/docs/en/tutorials/finetune.md b/docs/en/tutorials/finetune.md index 22954e1e129..2925be76226 100644 --- a/docs/en/tutorials/finetune.md +++ b/docs/en/tutorials/finetune.md @@ -92,11 +92,10 @@ model = dict( ```{note} Not all backbones support the `frozen_stages` argument by now. Please check -[the docs](https://mmclassification.readthedocs.io/en/latest/api.html#module-mmcls.models.backbones) +[the docs](https://mmclassification.readthedocs.io/en/latest/api/models.html#backbones) to confirm if your backbone supports it. ``` - ## Modify dataset When fine-tuning on a new dataset, usually we need to modify some dataset diff --git a/docs/en/tutorials/new_dataset.md b/docs/en/tutorials/new_dataset.md index 25fe3bff19e..24e6fe9ecf8 100644 --- a/docs/en/tutorials/new_dataset.md +++ b/docs/en/tutorials/new_dataset.md @@ -1,7 +1,7 @@ # Tutorial 3: Customize Dataset We support many common public datasets for image classification task, you can find them in -[this page](https://mmclassification.readthedocs.io/en/master/api/datasets.html). +[this page](https://mmclassification.readthedocs.io/en/latest/api/datasets.html). In this section, we demonstrate how to [use your own dataset](#use-your-own-dataset) and [use dataset wrapper](#use-dataset-wrapper). @@ -13,7 +13,7 @@ and [use dataset wrapper](#use-dataset-wrapper). The simplest way to use your own dataset is to convert it to existing dataset formats. For multi-class classification task, we recommend to use the format of -[`CustomDataset`](https://mmclassification.readthedocs.io/en/master/api/datasets.html#mmcls.datasets.CustomDataset). +[`CustomDataset`](https://mmclassification.readthedocs.io/en/latest/api/datasets.html#mmcls.datasets.CustomDataset). The `CustomDataset` supports two kinds of format: @@ -236,4 +236,4 @@ data = dict( ) ``` -You may refer to [API reference](https://mmclassification.readthedocs.io/en/master/api/datasets.html#mmcls.datasets.ClassBalancedDataset) for details. +You may refer to [API reference](https://mmclassification.readthedocs.io/en/latest/api/datasets.html#mmcls.datasets.ClassBalancedDataset) for details. diff --git a/docs/en/tutorials/new_modules.md b/docs/en/tutorials/new_modules.md index 33bfde569f2..5ac89de3643 100644 --- a/docs/en/tutorials/new_modules.md +++ b/docs/en/tutorials/new_modules.md @@ -96,42 +96,42 @@ To add a new neck, we mainly implement the `forward` function, which applies som 1. Create a new file in `mmcls/models/necks/gap.py`. - ```python - import torch.nn as nn + ```python + import torch.nn as nn - from ..builder import NECKS + from ..builder import NECKS - @NECKS.register_module() - class GlobalAveragePooling(nn.Module): + @NECKS.register_module() + class GlobalAveragePooling(nn.Module): - def __init__(self): - self.gap = nn.AdaptiveAvgPool2d((1, 1)) + def __init__(self): + self.gap = nn.AdaptiveAvgPool2d((1, 1)) - def forward(self, inputs): - # we regard inputs as tensor for simplicity - outs = self.gap(inputs) - outs = outs.view(inputs.size(0), -1) - return outs - ``` + def forward(self, inputs): + # we regard inputs as tensor for simplicity + outs = self.gap(inputs) + outs = outs.view(inputs.size(0), -1) + return outs + ``` 2. Import the module in `mmcls/models/necks/__init__.py`. - ```python - ... - from .gap import GlobalAveragePooling + ```python + ... + from .gap import GlobalAveragePooling - __all__ = [ - ..., 'GlobalAveragePooling' - ] - ``` + __all__ = [ + ..., 'GlobalAveragePooling' + ] + ``` 3. Modify the config file. - ```python - model = dict( - neck=dict(type='GlobalAveragePooling'), - ) - ``` + ```python + model = dict( + neck=dict(type='GlobalAveragePooling'), + ) + ``` ### Add new heads @@ -140,52 +140,52 @@ To implement a new head, basically we need to implement `forward_train`, which t 1. Create a new file in `mmcls/models/heads/linear_head.py`. - ```python - from ..builder import HEADS - from .cls_head import ClsHead + ```python + from ..builder import HEADS + from .cls_head import ClsHead - @HEADS.register_module() - class LinearClsHead(ClsHead): + @HEADS.register_module() + class LinearClsHead(ClsHead): - def __init__(self, - num_classes, - in_channels, - loss=dict(type='CrossEntropyLoss', loss_weight=1.0), - topk=(1, )): - super(LinearClsHead, self).__init__(loss=loss, topk=topk) - self.in_channels = in_channels - self.num_classes = num_classes + def __init__(self, + num_classes, + in_channels, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, )): + super(LinearClsHead, self).__init__(loss=loss, topk=topk) + self.in_channels = in_channels + self.num_classes = num_classes - if self.num_classes <= 0: - raise ValueError( - f'num_classes={num_classes} must be a positive integer') + if self.num_classes <= 0: + raise ValueError( + f'num_classes={num_classes} must be a positive integer') - self._init_layers() + self._init_layers() - def _init_layers(self): - self.fc = nn.Linear(self.in_channels, self.num_classes) + def _init_layers(self): + self.fc = nn.Linear(self.in_channels, self.num_classes) - def init_weights(self): - normal_init(self.fc, mean=0, std=0.01, bias=0) + def init_weights(self): + normal_init(self.fc, mean=0, std=0.01, bias=0) - def forward_train(self, x, gt_label): - cls_score = self.fc(x) - losses = self.loss(cls_score, gt_label) - return losses + def forward_train(self, x, gt_label): + cls_score = self.fc(x) + losses = self.loss(cls_score, gt_label) + return losses - ``` + ``` 2. Import the module in `mmcls/models/heads/__init__.py`. - ```python - ... - from .linear_head import LinearClsHead + ```python + ... + from .linear_head import LinearClsHead - __all__ = [ - ..., 'LinearClsHead' - ] - ``` + __all__ = [ + ..., 'LinearClsHead' + ] + ``` 3. Modify the config file. @@ -219,54 +219,54 @@ Assuming that we want to mimic a probabilistic distribution generated from anoth 1. Create a new file in `mmcls/models/losses/l1_loss.py`. - ```python - import torch - import torch.nn as nn - - from ..builder import LOSSES - from .utils import weighted_loss - - @weighted_loss - def l1_loss(pred, target): - assert pred.size() == target.size() and target.numel() > 0 - loss = torch.abs(pred - target) - return loss - - @LOSSES.register_module() - class L1Loss(nn.Module): - - def __init__(self, reduction='mean', loss_weight=1.0): - super(L1Loss, self).__init__() - self.reduction = reduction - self.loss_weight = loss_weight - - def forward(self, - pred, - target, - weight=None, - avg_factor=None, - reduction_override=None): - assert reduction_override in (None, 'none', 'mean', 'sum') - reduction = ( - reduction_override if reduction_override else self.reduction) - loss = self.loss_weight * l1_loss( - pred, target, weight, reduction=reduction, avg_factor=avg_factor) - return loss - ``` + ```python + import torch + import torch.nn as nn + + from ..builder import LOSSES + from .utils import weighted_loss + + @weighted_loss + def l1_loss(pred, target): + assert pred.size() == target.size() and target.numel() > 0 + loss = torch.abs(pred - target) + return loss + + @LOSSES.register_module() + class L1Loss(nn.Module): + + def __init__(self, reduction='mean', loss_weight=1.0): + super(L1Loss, self).__init__() + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None): + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + loss = self.loss_weight * l1_loss( + pred, target, weight, reduction=reduction, avg_factor=avg_factor) + return loss + ``` 2. Import the module in `mmcls/models/losses/__init__.py`. - ```python - ... - from .l1_loss import L1Loss, l1_loss + ```python + ... + from .l1_loss import L1Loss, l1_loss - __all__ = [ - ..., 'L1Loss', 'l1_loss' - ] - ``` + __all__ = [ + ..., 'L1Loss', 'l1_loss' + ] + ``` 3. Modify loss field in the config. - ```python - loss=dict(type='L1Loss', loss_weight=1.0)) - ``` + ```python + loss=dict(type='L1Loss', loss_weight=1.0)) + ``` diff --git a/docs/en/tutorials/runtime.md b/docs/en/tutorials/runtime.md index 836a5d2a13c..b2127448520 100644 --- a/docs/en/tutorials/runtime.md +++ b/docs/en/tutorials/runtime.md @@ -58,17 +58,17 @@ The custom hooks are registered through `custom_hooks`. Generally, they are hook **Priority list** -| Level | Value | -|:--:|:--:| -| HIGHEST | 0 | -| VERY_HIGH | 10 | -| HIGH | 30 | -| ABOVE_NORMAL | 40 | -| NORMAL(default) | 50 | -| BELOW_NORMAL | 60 | -| LOW | 70 | -| VERY_LOW | 90 | -| LOWEST | 100 | +| Level | Value | +| :-------------: | :---: | +| HIGHEST | 0 | +| VERY_HIGH | 10 | +| HIGH | 30 | +| ABOVE_NORMAL | 40 | +| NORMAL(default) | 50 | +| BELOW_NORMAL | 60 | +| LOW | 70 | +| VERY_LOW | 90 | +| LOWEST | 100 | The priority determines the execution order of the hooks. Before training, the log will print out the execution order of the hooks at each stage to facilitate debugging. @@ -76,15 +76,15 @@ The priority determines the execution order of the hooks. Before training, the l Some common hooks are not registered through `custom_hooks`, they are -| Hooks | Priority | -|:--:|:--:| -| `LrUpdaterHook` | VERY_HIGH (10) | -| `MomentumUpdaterHook` | HIGH (30) | -| `OptimizerHook` | ABOVE_NORMAL (40) | -| `CheckpointHook` | NORMAL (50) | -| `IterTimerHook` | LOW (70) | -| `EvalHook` | LOW (70) | -| `LoggerHook(s)` | VERY_LOW (90) | +| Hooks | Priority | +| :-------------------: | :---------------: | +| `LrUpdaterHook` | VERY_HIGH (10) | +| `MomentumUpdaterHook` | HIGH (30) | +| `OptimizerHook` | ABOVE_NORMAL (40) | +| `CheckpointHook` | NORMAL (50) | +| `IterTimerHook` | LOW (70) | +| `EvalHook` | LOW (70) | +| `LoggerHook(s)` | VERY_LOW (90) | `OptimizerHook`, `MomentumUpdaterHook` and `LrUpdaterHook` have been introduced in [sehedule strategy](./schedule.md). `IterTimerHook` is used to record elapsed time and does not support modification. @@ -156,7 +156,6 @@ Some hooks have been already implemented in MMCV and MMClassification, they are: - [ProfilerHook](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/profiler.py) - ...... - If the hook is already implemented in MMCV, you can directly modify the config to use the hook as below ```python @@ -216,8 +215,8 @@ Then we need to make `MyHook` imported. Assuming the file is in `mmcls/core/util - Modify `mmcls/core/utils/__init__.py` to import it. - The newly defined module should be imported in `mmcls/core/utils/__init__.py` so that the registry will - find the new module and add it: + The newly defined module should be imported in `mmcls/core/utils/__init__.py` so that the registry will + find the new module and add it: ```python from .my_hook import MyHook @@ -247,7 +246,6 @@ custom_hooks = [ By default, the hook's priority is set as `NORMAL` during registration. - ## FAQ ### 1. `resume_from` and `load_from` and `init_cfg.Pretrained` diff --git a/docs/en/tutorials/schedule.md b/docs/en/tutorials/schedule.md index 04afbf20cdc..1afc4b7f35e 100644 --- a/docs/en/tutorials/schedule.md +++ b/docs/en/tutorials/schedule.md @@ -19,7 +19,6 @@ In this tutorial, we will introduce some methods about how to construct optimize - ## Customize optimizer supported by PyTorch We already support to use all the optimizers implemented by PyTorch, and to use and modify them, please change the `optimizer` field of config files. @@ -58,20 +57,20 @@ We also support many other learning rate schedules [here](https://github.com/ope - ConsineAnnealing schedule: - ```python - lr_config = dict( - policy='CosineAnnealing', - warmup='linear', - warmup_iters=1000, - warmup_ratio=1.0 / 10, - min_lr_ratio=1e-5) - ``` + ```python + lr_config = dict( + policy='CosineAnnealing', + warmup='linear', + warmup_iters=1000, + warmup_ratio=1.0 / 10, + min_lr_ratio=1e-5) + ``` - Poly schedule: - ```python - lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) - ``` + ```python + lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) + ``` ### Warmup strategy @@ -90,28 +89,28 @@ Here are some examples 1. linear & warmup by iter - ```python - lr_config = dict( - policy='CosineAnnealing', - by_epoch=False, - min_lr_ratio=1e-2, - warmup='linear', - warmup_ratio=1e-3, - warmup_iters=20 * 1252, - warmup_by_epoch=False) - ``` + ```python + lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, + min_lr_ratio=1e-2, + warmup='linear', + warmup_ratio=1e-3, + warmup_iters=20 * 1252, + warmup_by_epoch=False) + ``` 2. exp & warmup by epoch - ```python - lr_config = dict( - policy='CosineAnnealing', - min_lr=0, - warmup='exp', - warmup_iters=5, - warmup_ratio=0.1, - warmup_by_epoch=True) - ``` + ```python + lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='exp', + warmup_iters=5, + warmup_ratio=0.1, + warmup_by_epoch=True) + ``` ```{tip} After completing your configuration file，you could use [learning rate visualization tool](https://mmclassification.readthedocs.io/en/latest/tools/visualization.html#learning-rate-schedule-visualization) to draw the corresponding learning rate adjustment curve. @@ -151,49 +150,49 @@ We provide some examples here and more usages refer to [DefaultOptimizerConstruc - Using specified options - The `DefaultOptimizerConstructor` provides options including `bias_lr_mult`, `bias_decay_mult`, `norm_decay_mult`, `dwconv_decay_mult`, `dcn_offset_lr_mult` and `bypass_duplicate` to configure special optimizer behaviors of bias, normalization, depth-wise convolution, deformable convolution and duplicated parameter. E.g: + The `DefaultOptimizerConstructor` provides options including `bias_lr_mult`, `bias_decay_mult`, `norm_decay_mult`, `dwconv_decay_mult`, `dcn_offset_lr_mult` and `bypass_duplicate` to configure special optimizer behaviors of bias, normalization, depth-wise convolution, deformable convolution and duplicated parameter. E.g: - 1. No weight decay to the BatchNorm layer + 1. No weight decay to the BatchNorm layer - ```python - optimizer = dict( - type='SGD', - lr=0.8, - weight_decay=1e-4, - paramwise_cfg=dict(norm_decay_mult=0.)) - ``` + ```python + optimizer = dict( + type='SGD', + lr=0.8, + weight_decay=1e-4, + paramwise_cfg=dict(norm_decay_mult=0.)) + ``` - Using `custom_keys` dict - MMClassification can use `custom_keys` to specify different parameters to use different learning rates or weight decays, for example: + MMClassification can use `custom_keys` to specify different parameters to use different learning rates or weight decays, for example: - 1. No weight decay for specific parameters + 1. No weight decay for specific parameters - ```python - paramwise_cfg = dict( - custom_keys={ - 'backbone.cls_token': dict(decay_mult=0.0), - 'backbone.pos_embed': dict(decay_mult=0.0) - }) + ```python + paramwise_cfg = dict( + custom_keys={ + 'backbone.cls_token': dict(decay_mult=0.0), + 'backbone.pos_embed': dict(decay_mult=0.0) + }) - optimizer = dict( - type='SGD', - lr=0.8, - weight_decay=1e-4, - paramwise_cfg=paramwise_cfg) - ``` + optimizer = dict( + type='SGD', + lr=0.8, + weight_decay=1e-4, + paramwise_cfg=paramwise_cfg) + ``` - 2. Using a smaller learning rate and a weight decay for the backbone layers + 2. Using a smaller learning rate and a weight decay for the backbone layers - ```python - optimizer = dict( - type='SGD', - lr=0.8, - weight_decay=1e-4, - # 'lr' for backbone and 'weight_decay' are 0.1 * lr and 0.9 * weight_decay - paramwise_cfg=dict( - custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=0.9)})) - ``` + ```python + optimizer = dict( + type='SGD', + lr=0.8, + weight_decay=1e-4, + # 'lr' for backbone and 'weight_decay' are 0.1 * lr and 0.9 * weight_decay + paramwise_cfg=dict( + custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=0.9)})) + ``` ## Gradient clipping and gradient accumulation @@ -279,7 +278,7 @@ To find the above module defined above, this module should be imported into the - Modify `mmcls/core/optimizer/__init__.py` to import it into `optimizer` package, and then modify `mmcls/core/__init__.py` to import the new `optimizer` package. - Create the `mmcls/core/optimizer` folder and the `mmcls/core/optimizer/__init__.py` file if they don't exist. The newly defined module should be imported in `mmcls/core/optimizer/__init__.py` and `mmcls/core/__init__.py` so that the registry will find the new module and add it: + Create the `mmcls/core/optimizer` folder and the `mmcls/core/optimizer/__init__.py` file if they don't exist. The newly defined module should be imported in `mmcls/core/optimizer/__init__.py` and `mmcls/core/__init__.py` so that the registry will find the new module and add it: ```python # In mmcls/core/optimizer/__init__.py diff --git a/docs/zh_CN/community/CONTRIBUTING.md b/docs/zh_CN/community/CONTRIBUTING.md index b5d9b809fc8..5554800822f 100644 --- a/docs/zh_CN/community/CONTRIBUTING.md +++ b/docs/zh_CN/community/CONTRIBUTING.md @@ -1,18 +1,21 @@ # 参与贡献 OpenMMLab -欢迎各种形式的贡献，包括但不限于以下内容。 +欢迎任何类型的贡献，包括但不限于 -- 修复（文本错误，bug) -- 新的功能和组件 +- 修改拼写错误或代码错误 +- 添加文档或将文档翻译成其他语言 +- 添加新功能和新组件 ## 工作流程 -1. fork 并 pull 最新的 OpenMMLab 仓库 (mmclassification) +1. fork 并 pull 最新的 OpenMMLab 仓库 (MMClassification) 2. 签出到一个新分支（不要使用 master 分支提交 PR） 3. 进行修改并提交至 fork 出的自己的远程仓库 4. 在我们的仓库中创建一个 PR -注意：如果你计划添加一些新的功能，并引入大量改动，请尽量首先创建一个 issue 来进行讨论。 +```{note} +如果你计划添加一些新的功能，并引入大量改动，请尽量首先创建一个 issue 来进行讨论。 +``` ## 代码风格 @@ -22,17 +25,17 @@ 我们使用下列工具来进行代码风格检查与格式化： -- [flake8](http://flake8.pycqa.org/en/latest/): 一个包含了多个代码风格检查工具的封装。 +- [flake8](https://github.com/PyCQA/flake8): Python 官方发布的代码规范检查工具，是多个检查工具的封装 +- [isort](https://github.com/timothycrosley/isort): 自动调整模块导入顺序的工具 - [yapf](https://github.com/google/yapf): 一个 Python 文件的格式化工具。 -- [isort](https://github.com/timothycrosley/isort): 一个对 import 进行排序的 Python 工具。 -- [markdownlint](https://github.com/markdownlint/markdownlint): 一个对 markdown 文件进行格式检查与提示的工具。 +- [codespell](https://github.com/codespell-project/codespell): 检查单词拼写是否有误 +- [mdformat](https://github.com/executablebooks/mdformat): 检查 markdown 文件的工具 - [docformatter](https://github.com/myint/docformatter): 一个 docstring 格式化工具。 yapf 和 isort 的格式设置位于 [setup.cfg](https://github.com/open-mmlab/mmclassification/blob/master/setup.cfg) 我们使用 [pre-commit hook](https://pre-commit.com/) 来保证每次提交时自动进行代 -码检查和格式化，启用的功能包括 `flake8`, `yapf`, `isort`, `trailing -whitespaces`, `markdown files`, 修复 `end-of-files`, `double-quoted-strings`, +码检查和格式化，启用的功能包括 `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`, 修复 `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, 对 `requirments.txt`的排序等。 pre-commit hook 的配置文件位于 [.pre-commit-config](https://github.com/open-mmlab/mmclassification/blob/master/.pre-commit-config.yaml) @@ -48,20 +51,6 @@ pip install -U pre-commit pre-commit install ``` -如果你在安装 markdownlint 的时候遇到问题，请尝试按照以下步骤安装 ruby - -```shell -# 安装 rvm -curl -L https://get.rvm.io | bash -s -- --autolibs=read-fail -[[ -s "$HOME/.rvm/scripts/rvm" ]] && source "$HOME/.rvm/scripts/rvm" -rvm autolibs disable - -# 安装 ruby -rvm install 2.7.1 -``` - -或者参照 [该仓库](https://github.com/innerlee/setup) 并按照指引执行 [`zzruby.sh`](https://github.com/innerlee/setup/blob/master/zzruby.sh) - 在此之后，每次提交，代码规范检查和格式化工具都将被强制执行。 ```{important} @@ -70,4 +59,4 @@ rvm install 2.7.1 ### C++ 和 CUDA -我们遵照 [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html) +C++ 和 CUDA 的代码规范遵从 [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html) diff --git a/docs/zh_CN/faq.md b/docs/zh_CN/faq.md index 82af2f51aa9..479957bda1e 100644 --- a/docs/zh_CN/faq.md +++ b/docs/zh_CN/faq.md @@ -8,34 +8,34 @@ ## 安装 - MMCV 与 MMClassification 的兼容问题。如遇到 - "AssertionError: MMCV==xxx is used but incompatible. Please install mmcv>=xxx, <=xxx." + "AssertionError: MMCV==xxx is used but incompatible. Please install mmcv>=xxx, \<=xxx." 这里我们列举了各版本 MMClassification 对 MMCV 版本的依赖，请选择合适的 MMCV 版本来避免安装和使用中的问题。 - | MMClassification version | MMCV version | - |:------------------------:|:---------------------:| - | dev | mmcv>=1.5.0, <1.6.0 | - | 0.23.0 (master) | mmcv>=1.4.2, <1.6.0 | - | 0.22.1 | mmcv>=1.4.2, <1.6.0 | - | 0.21.0 | mmcv>=1.4.2, <=1.5.0 | - | 0.20.1 | mmcv>=1.4.2, <=1.5.0 | - | 0.19.0 | mmcv>=1.3.16, <=1.5.0 | - | 0.18.0 | mmcv>=1.3.16, <=1.5.0 | - | 0.17.0 | mmcv>=1.3.8, <=1.5.0 | - | 0.16.0 | mmcv>=1.3.8, <=1.5.0 | - | 0.15.0 | mmcv>=1.3.8, <=1.5.0 | - | 0.15.0 | mmcv>=1.3.8, <=1.5.0 | - | 0.14.0 | mmcv>=1.3.8, <=1.5.0 | - | 0.13.0 | mmcv>=1.3.8, <=1.5.0 | - | 0.12.0 | mmcv>=1.3.1, <=1.5.0 | - | 0.11.1 | mmcv>=1.3.1, <=1.5.0 | - | 0.11.0 | mmcv>=1.3.0 | - | 0.10.0 | mmcv>=1.3.0 | - | 0.9.0 | mmcv>=1.1.4 | - | 0.8.0 | mmcv>=1.1.4 | - | 0.7.0 | mmcv>=1.1.4 | - | 0.6.0 | mmcv>=1.1.4 | + | MMClassification version | MMCV version | + | :----------------------: | :--------------------: | + | dev | mmcv>=1.5.0, \<1.6.0 | + | 0.23.0 (master) | mmcv>=1.4.2, \<1.6.0 | + | 0.22.1 | mmcv>=1.4.2, \<1.6.0 | + | 0.21.0 | mmcv>=1.4.2, \<=1.5.0 | + | 0.20.1 | mmcv>=1.4.2, \<=1.5.0 | + | 0.19.0 | mmcv>=1.3.16, \<=1.5.0 | + | 0.18.0 | mmcv>=1.3.16, \<=1.5.0 | + | 0.17.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.16.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.15.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.15.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.14.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.13.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.12.0 | mmcv>=1.3.1, \<=1.5.0 | + | 0.11.1 | mmcv>=1.3.1, \<=1.5.0 | + | 0.11.0 | mmcv>=1.3.0 | + | 0.10.0 | mmcv>=1.3.0 | + | 0.9.0 | mmcv>=1.1.4 | + | 0.8.0 | mmcv>=1.1.4 | + | 0.7.0 | mmcv>=1.1.4 | + | 0.6.0 | mmcv>=1.1.4 | ```{note} 由于 `dev` 分支处于频繁开发中，MMCV 版本依赖可能不准确。如果您在使用 @@ -62,7 +62,7 @@ 通常来说，我们推荐通过不同虚拟环境来管理多个开发目录下的 MMClassification。 但如果你希望在不同目录（如 mmcls-0.21, mmcls-0.23 等）使用同一个环境进行开发， 我们提供的训练和测试 shell 脚本会自动使用当前目录的 mmcls，其他 Python 脚本 - 则可以在命令前添加 ``PYTHONPATH=`pwd` `` 来使用当前目录的代码。 + 则可以在命令前添加 `` PYTHONPATH=`pwd` `` 来使用当前目录的代码。 反过来，如果你希望 shell 脚本使用环境中安装的 MMClassification，而不是当前目录的， 则可以去掉 shell 脚本中如下一行代码： diff --git a/docs/zh_CN/getting_started.md b/docs/zh_CN/getting_started.md index f7e101c6bc0..e88a7ba34de 100644 --- a/docs/zh_CN/getting_started.md +++ b/docs/zh_CN/getting_started.md @@ -32,8 +32,8 @@ mmclassification 1. 注册账号并登录 [下载页面](http://www.image-net.org/download-images) 2. 获取 ILSVRC2012 下载链接并下载以下文件 - - ILSVRC2012_img_train.tar (~138GB) - - ILSVRC2012_img_val.tar (~6.3GB) + - ILSVRC2012_img_train.tar (~138GB) + - ILSVRC2012_img_val.tar (~6.3GB) 3. 解压下载的文件 4. 使用 [该脚本](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh) 获取元数据 @@ -235,6 +235,7 @@ Params: 25.56 M ### 模型发布 在发布模型之前，你也许会需要 + 1. 转换模型权重至 CPU 张量 2. 删除优化器状态 3. 计算模型权重文件的哈希值，并添加至文件名之后 diff --git a/docs/zh_CN/tools/analysis.md b/docs/zh_CN/tools/analysis.md index 2dfc7cb59da..5f7fcfa3a5b 100644 --- a/docs/zh_CN/tools/analysis.md +++ b/docs/zh_CN/tools/analysis.md @@ -52,21 +52,21 @@ python tools/analysis_tools/analyze_logs.py plot_curve \ - 绘制某日志文件对应的损失曲线图。 - ```shell - python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys loss --legend loss - ``` + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys loss --legend loss + ``` - 绘制某日志文件对应的 top-1 和 top-5 准确率曲线图，并将曲线图导出为 results.jpg 文件。 - ```shell - python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys accuracy_top-1 accuracy_top-5 --legend top1 top5 --out results.jpg - ``` + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys accuracy_top-1 accuracy_top-5 --legend top1 top5 --out results.jpg + ``` - 在同一图像内绘制两份日志文件对应的 top-1 准确率曲线图。 - ```shell - python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys accuracy_top-1 --legend run1 run2 - ``` + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys accuracy_top-1 --legend run1 run2 + ``` ```{note} 本工具会自动根据关键字段选择从日志的训练部分还是验证部分读取，因此如果你添加了 diff --git a/docs/zh_CN/tools/onnx2tensorrt.md b/docs/zh_CN/tools/onnx2tensorrt.md index fdcbf13976a..f6a25fa483b 100644 --- a/docs/zh_CN/tools/onnx2tensorrt.md +++ b/docs/zh_CN/tools/onnx2tensorrt.md @@ -54,13 +54,13 @@ python tools/deployment/onnx2tensorrt.py \ 下表列出了保证可转换为 TensorRT 的模型。 -| 模型 | 配置文件 | 状态 | -| :----------: | :--------------------------------------------------------------------------: | :----: | -| MobileNetV2 | `configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py` | Y | -| ResNet | `configs/resnet/resnet18_8xb16_cifar10.py` | Y | -| ResNeXt | `configs/resnext/resnext50-32x4d_8xb32_in1k.py` | Y | -| ShuffleNetV1 | `configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py` | Y | -| ShuffleNetV2 | `configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py` | Y | +| 模型 | 配置文件 | 状态 | +| :----------: | :-----------------------------------------------------: | :--: | +| MobileNetV2 | `configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py` | Y | +| ResNet | `configs/resnet/resnet18_8xb16_cifar10.py` | Y | +| ResNeXt | `configs/resnext/resnext50-32x4d_8xb32_in1k.py` | Y | +| ShuffleNetV1 | `configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py` | Y | +| ShuffleNetV2 | `configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py` | Y | 注： diff --git a/docs/zh_CN/tools/pytorch2onnx.md b/docs/zh_CN/tools/pytorch2onnx.md index c217407e0e7..c66991a2f93 100644 --- a/docs/zh_CN/tools/pytorch2onnx.md +++ b/docs/zh_CN/tools/pytorch2onnx.md @@ -18,9 +18,9 @@ 1. 请参照 [安装指南](https://mmclassification.readthedocs.io/zh_CN/latest/install.html#mmclassification) 从源码安装 MMClassification。 2. 安装 onnx 和 onnxruntime。 - ```shell - pip install onnx onnxruntime==1.5.1 - ``` +```shell +pip install onnx onnxruntime==1.5.1 +``` ### 使用方法 @@ -66,14 +66,14 @@ python tools/deployment/pytorch2onnx.py \ 下表列出了保证可导出至 ONNX，并在 ONNX Runtime 中运行的模型。 -| 模型 | 配置文件 | 批推理 | 动态输入尺寸 | 备注 | -| :----------: | :--------------------------------------------------------------------------: | :-------------: | :-----------: | ---- | -| MobileNetV2 | `configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py` | Y | Y | | -| ResNet | `configs/resnet/resnet18_8xb16_cifar10.py` | Y | Y | | -| ResNeXt | `configs/resnext/resnext50-32x4d_8xb32_in1k.py` | Y | Y | | -| SE-ResNet | `configs/seresnet/seresnet50_8xb32_in1k.py` | Y | Y | | -| ShuffleNetV1 | `configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py` | Y | Y | | -| ShuffleNetV2 | `configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py` | Y | Y | | +| 模型 | 配置文件 | 批推理 | 动态输入尺寸 | 备注 | +| :----------: | :-----------------------------------------------------: | :----: | :----------: | ---- | +| MobileNetV2 | `configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py` | Y | Y | | +| ResNet | `configs/resnet/resnet18_8xb16_cifar10.py` | Y | Y | | +| ResNeXt | `configs/resnext/resnext50-32x4d_8xb32_in1k.py` | Y | Y | | +| SE-ResNet | `configs/seresnet/seresnet50_8xb32_in1k.py` | Y | Y | | +| ShuffleNetV1 | `configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py` | Y | Y | | +| ShuffleNetV2 | `configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py` | Y | Y | | 注： diff --git a/docs/zh_CN/tools/visualization.md b/docs/zh_CN/tools/visualization.md index 9caaf24ae2e..75d81421705 100644 --- a/docs/zh_CN/tools/visualization.md +++ b/docs/zh_CN/tools/visualization.md @@ -55,35 +55,35 @@ python tools/visualizations/vis_pipeline.py \ 1. **'original'** 模式，可视化 `CIFAR100` 验证集中的100张原始图片，显示并保存在 `./tmp` 文件夹下： - ```shell - python ./tools/visualizations/vis_pipeline.py configs/resnet/resnet50_8xb16_cifar100.py --phase val --output-dir tmp --mode original --number 100 --show --adaptive --bgr2rgb - ``` +```shell +python ./tools/visualizations/vis_pipeline.py configs/resnet/resnet50_8xb16_cifar100.py --phase val --output-dir tmp --mode original --number 100 --show --adaptive --bgr2rgb +``` -
+
2. **'transformed'** 模式，可视化 `ImageNet` 训练集的所有经过预处理的图片，并以弹窗形式显示： - ```shell - python ./tools/visualizations/vis_pipeline.py ./configs/resnet/resnet50_8xb32_in1k.py --show --mode transformed - ``` +```shell +python ./tools/visualizations/vis_pipeline.py ./configs/resnet/resnet50_8xb32_in1k.py --show --mode transformed +``` -
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3. **'concat'** 模式，可视化 `ImageNet` 训练集的10张原始图片与预处理后图片对比图，保存在 `./tmp` 文件夹下： - ```shell - python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --output-dir tmp --number 10 --adaptive - ``` +```shell +python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --output-dir tmp --number 10 --adaptive +``` -
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4. **'pipeline'** 模式，可视化 `ImageNet` 训练集经过数据流水线的过程图像： - ```shell - python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --adaptive --mode pipeline --show - ``` +```shell +python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --adaptive --mode pipeline --show +``` -
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## 学习率策略可视化 @@ -138,14 +138,14 @@ MMClassification 提供 `tools\visualizations\vis_cam.py` 工具来可视化类 目前支持的方法有： -| Method | What it does | -|:----------:|:------------:| -| GradCAM | 使用平均梯度对 2D 激活进行加权 | -| GradCAM++ | 类似 GradCAM，但使用了二阶梯度 | -| XGradCAM | 类似 GradCAM，但通过归一化的激活对梯度进行了加权 | -| EigenCAM | 使用 2D 激活的第一主成分（无法区分类别，但效果似乎不错）| -| EigenGradCAM | 类似 EigenCAM，但支持类别区分，使用了激活 \* 梯度的第一主成分，看起来和 GradCAM 差不多，但是更干净 | -| LayerCAM | 使用正梯度对激活进行空间加权，对于浅层有更好的效果 | +| Method | What it does | +| :----------: | :-----------------------------------------------------------------------------------------------: | +| GradCAM | 使用平均梯度对 2D 激活进行加权 | +| GradCAM++ | 类似 GradCAM，但使用了二阶梯度 | +| XGradCAM | 类似 GradCAM，但通过归一化的激活对梯度进行了加权 | +| EigenCAM | 使用 2D 激活的第一主成分（无法区分类别，但效果似乎不错） | +| EigenGradCAM | 类似 EigenCAM，但支持类别区分，使用了激活 * 梯度的第一主成分，看起来和 GradCAM 差不多，但是更干净 | +| LayerCAM | 使用正梯度对激活进行空间加权，对于浅层有更好的效果 | **命令行**： @@ -211,9 +211,9 @@ python tools/visualizations/vis_cam.py \ # GradCAM++, XGradCAM, EigenCAM, EigenGradCAM, LayerCAM ``` - | Image | GradCAM | GradCAM++ | EigenGradCAM | LayerCAM | - |-------|----------|------------|-------------- |------------| - |
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| + | Image | GradCAM | GradCAM++ | EigenGradCAM | LayerCAM | + | ------------------------------------ | --------------------------------------- | ----------------------------------------- | -------------------------------------------- | ---------------------------------------- | + |
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| 2. 同一张图不同类别的激活图效果图，在 `ImageNet` 数据集中，类别238为 'Greater Swiss Mountain dog'，类别281为 'tabby, tabby cat'。 @@ -227,10 +227,10 @@ python tools/visualizations/vis_cam.py \ # --target-category 281 ``` - | Category | Image | GradCAM | XGradCAM | LayerCAM | - | --------- |-------|----------|-------------- |------------| - | Dog |
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| + | Category | Image | GradCAM | XGradCAM | LayerCAM | + | -------- | ---------------------------------------------- | ------------------------------------------------ | ------------------------------------------------- | ------------------------------------------------- | + | Dog |
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| 3. 使用 `--eigen-smooth` 以及 `--aug-smooth` 获取更好的可视化效果。 @@ -244,9 +244,9 @@ python tools/visualizations/vis_cam.py \ --eigen-smooth --aug-smooth ``` - | Image | LayerCAM | eigen-smooth | aug-smooth | eigen&aug | - |-------|----------|------------|-------------- |------------| - |
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| + | Image | LayerCAM | eigen-smooth | aug-smooth | eigen&aug | + | ------------------------------------ | --------------------------------------- | ------------------------------------------- | ----------------------------------------- | ----------------------------------------- | + |
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| **示例（Transformer）**： @@ -293,9 +293,9 @@ python tools/visualizations/vis_cam.py \ --target-layers 'backbone.encoder[-1].ln1' ``` -| Image | ResNet50 | ViT | Swin | T2T-ViT | -|-------|----------|------------|-------------- |------------| -|
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| +| Image | ResNet50 | ViT | Swin | T2T-ViT | +| --------------------------------------- | ------------------------------------------ | -------------------------------------- | --------------------------------------- | ------------------------------------------ | +|
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| ## 常见问题 diff --git a/docs/zh_CN/tutorials/config.md b/docs/zh_CN/tutorials/config.md index 48bffb0d7a2..9e9c87e8649 100644 --- a/docs/zh_CN/tutorials/config.md +++ b/docs/zh_CN/tutorials/config.md @@ -2,7 +2,7 @@ MMClassification 主要使用 python 文件作为配置文件。其配置文件系统的设计将模块化与继承整合进来，方便用户进行各种实验。所有配置文件都放置在 `configs` 文件夹下，主要包含 `_base_` 原始配置文件夹 以及 `resnet`, `swin_transformer`，`vision_transformer` 等诸多算法文件夹。 -可以使用 ```python tools/misc/print_config.py /PATH/TO/CONFIG``` 命令来查看完整的配置信息，从而方便检查所对应的配置文件。 +可以使用 `python tools/misc/print_config.py /PATH/TO/CONFIG` 命令来查看完整的配置信息，从而方便检查所对应的配置文件。 @@ -32,31 +32,40 @@ MMClassification 按照以下风格进行配置文件命名，代码库的贡献 - `data info`：数据信息，数据集名称、模态、输入尺寸等，如 imagenet, cifar 等； ### 算法信息 + 指论文中的算法名称缩写，以及相应的分支架构信息。例如： + - `resnet50` - `mobilenet-v3-large` - `vit-small-patch32` : `patch32` 表示 `ViT` 切分的分块大小 - `seresnext101-32x4d` : `SeResNet101` 基本网络结构，`32x4d` 表示在 `Bottleneck` 中 `groups` 和 `width_per_group` 分别为32和4 ### 模块信息 + 指一些特殊的 `neck` 、`head` 或者 `pretrain` 的信息， 在分类中常见为预训练信息，比如： + - `in21k-pre` : 在 `ImageNet21k` 上预训练 - `in21k-pre-3rd-party` : 在 `ImageNet21k` 上预训练，其权重来自其他仓库 ### 训练信息 + 训练策略的一些设置，包括训练类型、 `batch size`、 `lr schedule`、 数据增强以及特殊的损失函数等等,比如: Batch size 信息： + - 格式为`{gpu x batch_per_gpu}`, 如 `8xb32` 训练类型(主要见于 transformer 网络，如 `ViT` 算法，这类算法通常分为预训练和微调两种模式): + - `ft` : Finetune config，用于微调的配置文件 - `pt` : Pretrain config，用于预训练的配置文件 训练策略信息，训练策略以复现配置文件为基础，此基础不必标注训练策略。但如果在此基础上进行改进，则需注明训练策略，按照应用点位顺序排列，如：`{pipeline aug}-{train aug}-{loss trick}-{scheduler}-{epochs}` + - `coslr-200e` : 使用 cosine scheduler, 训练 200 个 epoch - `autoaug-mixup-lbs-coslr-50e` : 使用了 `autoaug`、`mixup`、`label smooth`、`cosine scheduler`, 训练了 50 个轮次 ### 数据信息 + - `in1k` : `ImageNet1k` 数据集，默认使用 `224x224` 大小的图片 - `in21k` : `ImageNet21k` 数据集，有些地方也称为 `ImageNet22k` 数据集，默认使用 `224x224` 大小的图片 - `in1k-384px` : 表示训练的输出图片大小为 `384x384` @@ -69,16 +78,16 @@ repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py ``` - `repvgg-D2se`: 算法信息 - + `repvgg`: 主要算法名称。 - + `D2se`: 模型的结构。 + - `repvgg`: 主要算法名称。 + - `D2se`: 模型的结构。 - `deploy`:模块信息，该模型为推理状态。 - `4xb64-autoaug-lbs-mixup-coslr-200e`: 训练信息 - + `4xb64`: 使用4块 GPU 并且 每块 GPU 的批大小为64。 - + `autoaug`: 使用 `AutoAugment` 数据增强方法。 - + `lbs`: 使用 `label smoothing` 损失函数。 - + `mixup`: 使用 `mixup` 训练增强方法。 - + `coslr`: 使用 `cosine scheduler` 优化策略。 - + `200e`: 训练 200 轮次。 + - `4xb64`: 使用4块 GPU 并且 每块 GPU 的批大小为64。 + - `autoaug`: 使用 `AutoAugment` 数据增强方法。 + - `lbs`: 使用 `label smoothing` 损失函数。 + - `mixup`: 使用 `mixup` 训练增强方法。 + - `coslr`: 使用 `cosine scheduler` 优化策略。 + - `200e`: 训练 200 轮次。 - `in1k`: 数据信息。 配置文件用于 `ImageNet1k` 数据集上使用 `224x224` 大小图片训练。 ```{note} @@ -93,7 +102,6 @@ repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py {config_name}_{date}-{hash}.pth ``` - ## 配置文件结构 在 `configs/_base_` 文件夹下有 4 个基本组件类型，分别是： @@ -118,15 +126,15 @@ _base_ = [ 下面对这四个部分分别进行说明，仍然以上述 ResNet50 原始配置文件作为案例。 - ### 模型 模型参数 `model` 在配置文件中为一个 `python` 字典，主要包括网络结构、损失函数等信息： -- `type` ： 分类器名称, 目前 MMClassification 只支持 `ImageClassifier`， 参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api.html#module-mmcls.models.classifiers)。 -- `backbone` ： 主干网类型，可用选项参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api.html#module-mmcls.models.backbones)。 -- `neck` ： 颈网络类型，目前 MMClassification 只支持 `GlobalAveragePooling`， 参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api.html#module-mmcls.models.necks)。 -- `head` ： 头网络类型， 包括单标签分类与多标签分类头网络，可用选项参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api.html#module-mmcls.models.heads)。 - - `loss` ： 损失函数类型， 支持 `CrossEntropyLoss`, [`LabelSmoothLoss`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_label_smooth.py) 等，可用选项参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api.html#module-mmcls.models.losses)。 + +- `type` ： 分类器名称, 目前 MMClassification 只支持 `ImageClassifier`， 参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/models.html#classifier)。 +- `backbone` ： 主干网类型，可用选项参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/models.html#backbones)。 +- `neck` ： 颈网络类型，目前 MMClassification 只支持 `GlobalAveragePooling`， 参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/models.html#necks)。 +- `head` ： 头网络类型， 包括单标签分类与多标签分类头网络，可用选项参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/models.html#heads)。 + - `loss` ： 损失函数类型， 支持 `CrossEntropyLoss`, [`LabelSmoothLoss`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_label_smooth.py) 等，可用选项参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/models.html#losses)。 - `train_cfg` ：训练配置, 支持 [`mixup`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_mixup.py), [`cutmix`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_cutmix.py) 等训练增强。 ```{note} @@ -154,11 +162,13 @@ model = dict( ``` ### 数据 + 数据参数 `data` 在配置文件中为一个 `python` 字典，主要包含构造数据集加载器(dataloader)配置信息： + - `samples_per_gpu` : 构建 dataloader 时，每个 GPU 的 Batch Size - `workers_per_gpu` : 构建 dataloader 时，每个 GPU 的 线程数 - `train ｜ val ｜ test` : 构造数据集 - - `type` : 数据集类型， MMClassification 支持 `ImageNet`、 `Cifar` 等 ，参考[API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api.html#module-mmcls.datasets) + - `type` : 数据集类型， MMClassification 支持 `ImageNet`、 `Cifar` 等 ，参考[API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/datasets.html) - `data_prefix` : 数据集根目录 - `pipeline` : 数据处理流水线，参考相关教程文档 [如何设计数据处理流水线](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/data_pipeline.html) @@ -213,11 +223,14 @@ evaluation = dict( # evaluation hook 的配置 ``` ### 训练策略 + 主要包含 优化器设置、 `optimizer hook` 设置、学习率策略和 `runner`设置： + - `optimizer` : 优化器设置信息, 支持 `pytorch` 所有的优化器，参考相关 [mmcv](https://mmcv.readthedocs.io/zh_CN/latest/_modules/mmcv/runner/optimizer/default_constructor.html#DefaultOptimizerConstructor) 文档 - `optimizer_config` : `optimizer hook` 的配置文件,如设置梯度限制，参考相关 [mmcv](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8) 代码 - `lr_config` : 学习率策略，支持 "CosineAnnealing"、 "Step"、 "Cyclic" 等等，参考相关 [mmcv](https://mmcv.readthedocs.io/zh_CN/latest/_modules/mmcv/runner/hooks/lr_updater.html#LrUpdaterHook) 文档 - `runner` : 有关 `runner` 可以参考 `mmcv` 对于 [`runner`](https://mmcv.readthedocs.io/zh_CN/latest/understand_mmcv/runner.html) 介绍文档 + ```python # 用于构建优化器的配置文件。支持 PyTorch 中的所有优化器，同时它们的参数与 PyTorch 里的优化器参数一致。 optimizer = dict(type='SGD', # 优化器类型 @@ -283,7 +296,6 @@ data = dict( 例如数据集里的 `train_pipeline` / `test_pipeline` 是作为数据流水线的中间变量。我们首先要定义 `train_pipeline` / `test_pipeline`，然后将它们传递到 `data` 中。如果想修改训练或测试时输入图片的大小，就需要修改 `train_pipeline` / `test_pipeline` 这些中间变量。 - ```python img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) @@ -313,7 +325,6 @@ data = dict( 有时，您需要设置 `_delete_=True` 去忽略基础配置文件里的一些域内容。 可以参照 [mmcv](https://mmcv.readthedocs.io/zh_CN/latest/understand_mmcv/config.html#inherit-from-base-config-with-ignored-fields) 来获得一些简单的指导。 - 以下是一个简单应用案例。 如果在上述 ResNet50 案例中 使用 cosine schedule ,使用继承并直接修改会报 `get unexcepected keyword 'step'` 错, 因为基础配置文件 lr_config 域信息的 `'step'` 字段被保留下来了，需要加入 `_delete_=True` 去忽略基础配置文件里的 `lr_config` 相关域内容： ```python @@ -402,4 +413,5 @@ custom_imports = dict( ``` ## 常见问题 + - 无 diff --git a/docs/zh_CN/tutorials/data_pipeline.md b/docs/zh_CN/tutorials/data_pipeline.md index e50a2c0b84e..bbcf9d58de9 100644 --- a/docs/zh_CN/tutorials/data_pipeline.md +++ b/docs/zh_CN/tutorials/data_pipeline.md @@ -104,44 +104,44 @@ train_pipeline = [ 一个文件中，例如 `my_pipeline.py`。这个类需要重载 `__call__` 方法，接受一个 字典作为输入，并返回一个字典。 - ```python - from mmcls.datasets import PIPELINES + ```python + from mmcls.datasets import PIPELINES - @PIPELINES.register_module() - class MyTransform(object): + @PIPELINES.register_module() + class MyTransform(object): - def __call__(self, results): - # 对 results['img'] 进行变换操作 - return results - ``` + def __call__(self, results): + # 对 results['img'] 进行变换操作 + return results + ``` 2. 在 `mmcls/datasets/pipelines/__init__.py` 中导入这个新的类。 - ```python - ... - from .my_pipeline import MyTransform + ```python + ... + from .my_pipeline import MyTransform - __all__ = [ - ..., 'MyTransform' - ] - ``` + __all__ = [ + ..., 'MyTransform' + ] + ``` 3. 在数据流水线的配置中添加这一操作。 - ```python - 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='MyTransform'), - dict(type='Normalize', **img_norm_cfg), - dict(type='ImageToTensor', keys=['img']), - dict(type='ToTensor', keys=['gt_label']), - dict(type='Collect', keys=['img', 'gt_label']) - ] - ``` + ```python + 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='MyTransform'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) + ] + ``` ## 流水线可视化 diff --git a/docs/zh_CN/tutorials/finetune.md b/docs/zh_CN/tutorials/finetune.md index d0611faf853..e6b62005941 100644 --- a/docs/zh_CN/tutorials/finetune.md +++ b/docs/zh_CN/tutorials/finetune.md @@ -83,7 +83,7 @@ model = dict( ```{note} 目前还不是所有的网络都支持 `frozen_stages` 参数，在使用之前，请先检查 -[文档](https://mmclassification.readthedocs.io/zh_CN/latest/api.html#module-mmcls.models.backbones) +[文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/models.html#backbones) 以确认你所使用的主干网络是否支持。 ``` diff --git a/docs/zh_CN/tutorials/new_dataset.md b/docs/zh_CN/tutorials/new_dataset.md index 8d5a6ef0e55..86782a13b02 100644 --- a/docs/zh_CN/tutorials/new_dataset.md +++ b/docs/zh_CN/tutorials/new_dataset.md @@ -1,7 +1,7 @@ # 教程 3：如何自定义数据集 我们支持许多常用的图像分类领域公开数据集，你可以在 -[此页面](https://mmclassification.readthedocs.io/zh_CN/master/api/datasets.html)中找到它们。 +[此页面](https://mmclassification.readthedocs.io/zh_CN/latest/api/datasets.html)中找到它们。 在本节中，我们将介绍如何[使用自己的数据集](#使用自己的数据集)以及如何[使用数据集包装](#使用数据集包装)。 @@ -11,7 +11,7 @@ 想要使用自己的数据集，最简单的方法就是将数据集转换为现有的数据集格式。 -对于多分类任务，我们推荐使用 [`CustomDataset`](https://mmclassification.readthedocs.io/zh_CN/master/api/datasets.html#mmcls.datasets.CustomDataset) 格式。 +对于多分类任务，我们推荐使用 [`CustomDataset`](https://mmclassification.readthedocs.io/zh_CN/latest/api/datasets.html#mmcls.datasets.CustomDataset) 格式。 `CustomDataset` 支持两种类型的数据格式： @@ -227,4 +227,4 @@ data = dict( ) ``` -更加具体的细节，请参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/master/api/datasets.html#mmcls.datasets.ClassBalancedDataset)。 +更加具体的细节，请参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/datasets.html#mmcls.datasets.ClassBalancedDataset)。 diff --git a/docs/zh_CN/tutorials/new_modules.md b/docs/zh_CN/tutorials/new_modules.md index f731b43c686..14ee32c1677 100644 --- a/docs/zh_CN/tutorials/new_modules.md +++ b/docs/zh_CN/tutorials/new_modules.md @@ -12,8 +12,7 @@ 这里，我们以 ResNet_CIFAR 为例，展示了如何开发一个新的主干网络组件。 -ResNet_CIFAR 针对 CIFAR 32x32 的图像输入，将 ResNet 中 `kernel_size=7, -stride=2` 的设置替换为 `kernel_size=3, stride=1`，并移除了 stem 层之后的 +ResNet_CIFAR 针对 CIFAR 32x32 的图像输入，将 ResNet 中 `kernel_size=7, stride=2` 的设置替换为 `kernel_size=3, stride=1`，并移除了 stem 层之后的 `MaxPooling`，以避免传递过小的特征图到残差块中。 它继承自 `ResNet` 并只修改了 stem 层。 @@ -103,42 +102,42 @@ model = dict( 1. 创建一个新文件 `mmcls/models/necks/gap.py` - ```python - import torch.nn as nn + ```python + import torch.nn as nn - from ..builder import NECKS + from ..builder import NECKS - @NECKS.register_module() - class GlobalAveragePooling(nn.Module): + @NECKS.register_module() + class GlobalAveragePooling(nn.Module): - def __init__(self): - self.gap = nn.AdaptiveAvgPool2d((1, 1)) + def __init__(self): + self.gap = nn.AdaptiveAvgPool2d((1, 1)) - def forward(self, inputs): - # 简单起见，我们默认输入是一个张量 - outs = self.gap(inputs) - outs = outs.view(inputs.size(0), -1) - return outs - ``` + def forward(self, inputs): + # 简单起见，我们默认输入是一个张量 + outs = self.gap(inputs) + outs = outs.view(inputs.size(0), -1) + return outs + ``` 2. 在 `mmcls/models/necks/__init__.py` 中导入新模块 - ```python - ... - from .gap import GlobalAveragePooling + ```python + ... + from .gap import GlobalAveragePooling - __all__ = [ - ..., 'GlobalAveragePooling' - ] - ``` + __all__ = [ + ..., 'GlobalAveragePooling' + ] + ``` 3. 修改配置文件以使用新的颈部组件 - ```python - model = dict( - neck=dict(type='GlobalAveragePooling'), - ) - ``` + ```python + model = dict( + neck=dict(type='GlobalAveragePooling'), + ) + ``` ### 添加新的头部组件 @@ -149,52 +148,52 @@ model = dict( 1. 创建一个文件 `mmcls/models/heads/linear_head.py`. - ```python - from ..builder import HEADS - from .cls_head import ClsHead + ```python + from ..builder import HEADS + from .cls_head import ClsHead - @HEADS.register_module() - class LinearClsHead(ClsHead): + @HEADS.register_module() + class LinearClsHead(ClsHead): - def __init__(self, - num_classes, - in_channels, - loss=dict(type='CrossEntropyLoss', loss_weight=1.0), - topk=(1, )): - super(LinearClsHead, self).__init__(loss=loss, topk=topk) - self.in_channels = in_channels - self.num_classes = num_classes + def __init__(self, + num_classes, + in_channels, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, )): + super(LinearClsHead, self).__init__(loss=loss, topk=topk) + self.in_channels = in_channels + self.num_classes = num_classes - if self.num_classes <= 0: - raise ValueError( - f'num_classes={num_classes} must be a positive integer') + if self.num_classes <= 0: + raise ValueError( + f'num_classes={num_classes} must be a positive integer') - self._init_layers() + self._init_layers() - def _init_layers(self): - self.fc = nn.Linear(self.in_channels, self.num_classes) + def _init_layers(self): + self.fc = nn.Linear(self.in_channels, self.num_classes) - def init_weights(self): - normal_init(self.fc, mean=0, std=0.01, bias=0) + def init_weights(self): + normal_init(self.fc, mean=0, std=0.01, bias=0) - def forward_train(self, x, gt_label): - cls_score = self.fc(x) - losses = self.loss(cls_score, gt_label) - return losses + def forward_train(self, x, gt_label): + cls_score = self.fc(x) + losses = self.loss(cls_score, gt_label) + return losses - ``` + ``` 2. 在 `mmcls/models/heads/__init__.py` 中导入这个模块 - ```python - ... - from .linear_head import LinearClsHead + ```python + ... + from .linear_head import LinearClsHead - __all__ = [ - ..., 'LinearClsHead' - ] - ``` + __all__ = [ + ..., 'LinearClsHead' + ] + ``` 3. 修改配置文件以使用新的头部组件。 @@ -228,54 +227,54 @@ model = dict( 1. 创建一个新文件 `mmcls/models/losses/l1_loss.py` - ```python - import torch - import torch.nn as nn - - from ..builder import LOSSES - from .utils import weighted_loss - - @weighted_loss - def l1_loss(pred, target): - assert pred.size() == target.size() and target.numel() > 0 - loss = torch.abs(pred - target) - return loss - - @LOSSES.register_module() - class L1Loss(nn.Module): - - def __init__(self, reduction='mean', loss_weight=1.0): - super(L1Loss, self).__init__() - self.reduction = reduction - self.loss_weight = loss_weight - - def forward(self, - pred, - target, - weight=None, - avg_factor=None, - reduction_override=None): - assert reduction_override in (None, 'none', 'mean', 'sum') - reduction = ( - reduction_override if reduction_override else self.reduction) - loss = self.loss_weight * l1_loss( - pred, target, weight, reduction=reduction, avg_factor=avg_factor) - return loss - ``` + ```python + import torch + import torch.nn as nn + + from ..builder import LOSSES + from .utils import weighted_loss + + @weighted_loss + def l1_loss(pred, target): + assert pred.size() == target.size() and target.numel() > 0 + loss = torch.abs(pred - target) + return loss + + @LOSSES.register_module() + class L1Loss(nn.Module): + + def __init__(self, reduction='mean', loss_weight=1.0): + super(L1Loss, self).__init__() + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None): + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + loss = self.loss_weight * l1_loss( + pred, target, weight, reduction=reduction, avg_factor=avg_factor) + return loss + ``` 2. 在文件 `mmcls/models/losses/__init__.py` 中导入这个模块 - ```python - ... - from .l1_loss import L1Loss, l1_loss + ```python + ... + from .l1_loss import L1Loss, l1_loss - __all__ = [ - ..., 'L1Loss', 'l1_loss' - ] - ``` + __all__ = [ + ..., 'L1Loss', 'l1_loss' + ] + ``` 3. 修改配置文件中的 `loss` 字段以使用新的损失函数 - ```python - loss=dict(type='L1Loss', loss_weight=1.0)) - ``` + ```python + loss=dict(type='L1Loss', loss_weight=1.0)) + ``` diff --git a/docs/zh_CN/tutorials/runtime.md b/docs/zh_CN/tutorials/runtime.md index 47556c1a025..0be7999eefe 100644 --- a/docs/zh_CN/tutorials/runtime.md +++ b/docs/zh_CN/tutorials/runtime.md @@ -60,17 +60,17 @@ workflow = [('train', 1)] **优先级列表** -| Level | Value | -|:--:|:--:| -| HIGHEST | 0 | -| VERY_HIGH | 10 | -| HIGH | 30 | -| ABOVE_NORMAL | 40 | -| NORMAL(default) | 50 | -| BELOW_NORMAL | 60 | -| LOW | 70 | -| VERY_LOW | 90 | -| LOWEST | 100 | +| Level | Value | +| :-------------: | :---: | +| HIGHEST | 0 | +| VERY_HIGH | 10 | +| HIGH | 30 | +| ABOVE_NORMAL | 40 | +| NORMAL(default) | 50 | +| BELOW_NORMAL | 60 | +| LOW | 70 | +| VERY_LOW | 90 | +| LOWEST | 100 | 优先级确定钩子的执行顺序，每次训练前，日志会打印出各个阶段钩子的执行顺序，方便调试。 @@ -78,16 +78,15 @@ workflow = [('train', 1)] 有一些常见的钩子未通过 `custom_hooks` 注册，但会在运行器（`Runner`）中默认注册，它们是： -| Hooks | Priority | -|:--:|:--:| -| `LrUpdaterHook` | VERY_HIGH (10) | -| `MomentumUpdaterHook` | HIGH (30) | -| `OptimizerHook` | ABOVE_NORMAL (40) | -| `CheckpointHook` | NORMAL (50) | -| `IterTimerHook` | LOW (70) | -| `EvalHook` | LOW (70) | -| `LoggerHook(s)` | VERY_LOW (90) | - +| Hooks | Priority | +| :-------------------: | :---------------: | +| `LrUpdaterHook` | VERY_HIGH (10) | +| `MomentumUpdaterHook` | HIGH (30) | +| `OptimizerHook` | ABOVE_NORMAL (40) | +| `CheckpointHook` | NORMAL (50) | +| `IterTimerHook` | LOW (70) | +| `EvalHook` | LOW (70) | +| `LoggerHook(s)` | VERY_LOW (90) | `OptimizerHook`，`MomentumUpdaterHook`和 `LrUpdaterHook` 在 [优化策略](./schedule.md) 部分进行了介绍， `IterTimerHook` 用于记录所用时间，目前不支持修改; @@ -218,7 +217,7 @@ class MyHook(Hook): - 修改 `mmcls/core/utils/__init__.py` 进行导入 - 新定义的模块应导入到 `mmcls/core/utils/__init__py` 中，以便注册器能找到并添加新模块： + 新定义的模块应导入到 `mmcls/core/utils/__init__py` 中，以便注册器能找到并添加新模块： ```python from .my_hook import MyHook @@ -250,7 +249,6 @@ custom_hooks = [ 默认情况下，在注册过程中，钩子的优先级设置为“NORMAL”。 - ## 常见问题 ### 1. resume_from， load_from，init_cfg.Pretrained 区别 diff --git a/docs/zh_CN/tutorials/schedule.md b/docs/zh_CN/tutorials/schedule.md index 9e96d5f561a..931edd09e87 100644 --- a/docs/zh_CN/tutorials/schedule.md +++ b/docs/zh_CN/tutorials/schedule.md @@ -60,15 +60,15 @@ lr_config = dict(policy='step', step=[100, 150]) - ConsineAnnealing: - ```python - lr_config = dict(policy='CosineAnnealing', min_lr_ratio=1e-5) - ``` + ```python + lr_config = dict(policy='CosineAnnealing', min_lr_ratio=1e-5) + ``` - Poly: - ```python - lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) - ``` + ```python + lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) + ``` ### 定制学习率预热策略 @@ -85,28 +85,28 @@ lr_config = dict(policy='step', step=[100, 150]) 1. 逐**迭代次数**地**线性**预热 - ```python - lr_config = dict( - policy='CosineAnnealing', - by_epoch=False, - min_lr_ratio=1e-2, - warmup='linear', - warmup_ratio=1e-3, - warmup_iters=20 * 1252, - warmup_by_epoch=False) - ``` + ```python + lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, + min_lr_ratio=1e-2, + warmup='linear', + warmup_ratio=1e-3, + warmup_iters=20 * 1252, + warmup_by_epoch=False) + ``` 2. 逐**轮次**地**指数**预热 - ```python - lr_config = dict( - policy='CosineAnnealing', - min_lr=0, - warmup='exp', - warmup_iters=5, - warmup_ratio=0.1, - warmup_by_epoch=True) - ``` + ```python + lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='exp', + warmup_iters=5, + warmup_ratio=0.1, + warmup_by_epoch=True) + ``` ```{tip} 配置完成后，可以使用 MMClassification 提供的 [学习率可视化工具](https://mmclassification.readthedocs.io/zh_CN/latest/tools/visualization.html#id3) 画出对应学习率调整曲线。 @@ -143,45 +143,45 @@ momentum_config = dict( - 使用指定选项 - MMClassification 提供了包括 `bias_lr_mult`、 `bias_decay_mult`、 `norm_decay_mult`、 `dwconv_decay_mult`、 `dcn_offset_lr_mult` 和 `bypass_duplicate` 选项，指定相关所有的 `bais`、 `norm`、 `dwconv`、 `dcn` 和 `bypass` 参数。例如令模型中所有的 BN 不进行参数衰减： + MMClassification 提供了包括 `bias_lr_mult`、 `bias_decay_mult`、 `norm_decay_mult`、 `dwconv_decay_mult`、 `dcn_offset_lr_mult` 和 `bypass_duplicate` 选项，指定相关所有的 `bais`、 `norm`、 `dwconv`、 `dcn` 和 `bypass` 参数。例如令模型中所有的 BN 不进行参数衰减： - ```python - optimizer = dict( - type='SGD', - lr=0.8, - weight_decay=1e-4, - paramwise_cfg=dict(norm_decay_mult=0.) - ) - ``` + ```python + optimizer = dict( + type='SGD', + lr=0.8, + weight_decay=1e-4, + paramwise_cfg=dict(norm_decay_mult=0.) + ) + ``` - 使用 `custom_keys` 指定参数 - MMClassification 可通过 `custom_keys` 指定不同的参数使用不同的学习率或者权重衰减，例如对特定的参数不使用权重衰减： - - ```python - paramwise_cfg = dict( - custom_keys={ - 'backbone.cls_token': dict(decay_mult=0.0), - 'backbone.pos_embed': dict(decay_mult=0.0) - }) - - optimizer = dict( - type='SGD', - lr=0.8, - weight_decay=1e-4, - paramwise_cfg=paramwise_cfg) - ``` - - 对 backbone 使用更小的学习率与衰减系数： - - ```python - optimizer = dict( - type='SGD', - lr=0.8, - weight_decay=1e-4, - # backbone 的 'lr' and 'weight_decay' 分别为 0.1 * lr 和 0.9 * weight_decay - paramwise_cfg = dict(custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=0.9)})) - ``` + MMClassification 可通过 `custom_keys` 指定不同的参数使用不同的学习率或者权重衰减，例如对特定的参数不使用权重衰减： + + ```python + paramwise_cfg = dict( + custom_keys={ + 'backbone.cls_token': dict(decay_mult=0.0), + 'backbone.pos_embed': dict(decay_mult=0.0) + }) + + optimizer = dict( + type='SGD', + lr=0.8, + weight_decay=1e-4, + paramwise_cfg=paramwise_cfg) + ``` + + 对 backbone 使用更小的学习率与衰减系数： + + ```python + optimizer = dict( + type='SGD', + lr=0.8, + weight_decay=1e-4, + # backbone 的 'lr' and 'weight_decay' 分别为 0.1 * lr 和 0.9 * weight_decay + paramwise_cfg = dict(custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=0.9)})) + ``` ## 梯度裁剪与梯度累计 @@ -268,8 +268,8 @@ class MyOptimizer(Optimizer): - 修改 `mmcls/core/optimizer/__init__.py`，将其导入至 `optimizer` 包；再修改 `mmcls/core/__init__.py` 以导入 `optimizer` 包 - 创建 `mmcls/core/optimizer/__init__.py` 文件。 - 新定义的模块应导入到 `mmcls/core/optimizer/__init__.py` 中，以便注册器能找到新模块并将其添加： + 创建 `mmcls/core/optimizer/__init__.py` 文件。 + 新定义的模块应导入到 `mmcls/core/optimizer/__init__.py` 中，以便注册器能找到新模块并将其添加： ```python # 在 mmcls/core/optimizer/__init__.py 中