Update isort hook

* upodate README

* fix typo

.circleci/config.yml

@@ -187,31 +187,12 @@ workflows:
             env: LRU_CACHE_CAPACITY=1
             requires:
               - lint
-        - build:
-            name: build_py36_torch1.6
-            torch: 1.6.0
-            torchvision: 0.7.0
-            requires:
-              - lint
         - build:
             name: build_py36_torch1.7
             torch: 1.7.0
             torchvision: 0.8.1
             requires:
               - lint
-        - build:
-            name: build_py36_torch1.8
-            torch: 1.8.0
-            torchvision: 0.9.0
-            requires:
-              - lint
-        - build:
-            name: build_py39_torch1.8
-            torch: 1.8.0
-            torchvision: 0.9.0
-            python: "3.9.0"
-            requires:
-              - lint
         - build:
             name: build_py39_torch1.9
             torch: 1.9.0
@@ -219,21 +200,10 @@ workflows:
             python: "3.9.0"
             requires:
               - lint
-        - build:
-            name: build_py39_torch1.10
-            torch: 1.10.0
-            torchvision: 0.11.1
-            python: "3.9.0"
-            requires:
-              - lint
         - build_with_cuda:
             name: build_py36_torch1.6_cu101
             requires:
               - build_with_timm
               - build_py36_torch1.5
-              - build_py36_torch1.6
               - build_py36_torch1.7
-              - build_py36_torch1.8
-              - build_py39_torch1.8
               - build_py39_torch1.9
-              - build_py39_torch1.10

.github/ISSUE_TEMPLATE/----.md

@@ -1,33 +0,0 @@
----
-name: 寻求帮助
-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
-
-\[填写这里\]
-
-### 相关信息
-
-1. `pip list | grep "mmcv\|mmcls\|^torch"` 命令的输出
-   \[填写这里\]
-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
-   \[填写这里\]
-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
-   \[填写这里\]

.github/ISSUE_TEMPLATE/---.md

@@ -1,34 +0,0 @@
----
-name: 新功能
-about: 为项目提一个建议
-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
-
-\[填写这里\]
-
-### 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
-
-\[填写这里\]
-
-### 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。
-
-\[填写这里\]
-
-### 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。
-
-\[填写这里\]

.github/ISSUE_TEMPLATE/---bug.md

@@ -1,44 +0,0 @@
----
-name: 报告 Bug
-about: 报告问题以帮助我们提升
-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
-
-\[填写这里\]
-
-### 复现流程
-
-在命令行中执行的详细操作
-
-```shell
-[填写这里]
-```
-
-### 相关信息
-
-1. `pip list | grep "mmcv\|mmcls\|^torch"` 命令的输出
-   \[填写这里\]
-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
-   \[填写这里\]
-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
-   \[填写这里\]
-
-### 附加内容
-
-任何其他有关该 bug 的信息、截图等
-
-\[填写这里\]

.github/ISSUE_TEMPLATE/1_bug-report.yml

@@ -0,0 +1,68 @@
+name: 🐞 Bug report
+description: Create a report to help us improve
+labels: ["bug"]
+title: "[Bug] "
+body:
+  - type: markdown
+    attributes:
+      value: |
+        If you have already identified the reason, we strongly appreciate you creating a new PR according to [the tutorial](https://mmclassification.readthedocs.io/en/master/community/CONTRIBUTING.html)!
+        If you need our help, please fill in the following form to help us to identify the bug.
+
+  - type: dropdown
+    id: version
+    attributes:
+      label: Branch
+      description: Which branch/version are you using?
+      options:
+        - master branch (0.24 or other 0.x version)
+        - 1.x branch (1.0.0rc2 or other 1.x version)
+    validations:
+      required: true
+
+  - type: textarea
+    id: describe
+    validations:
+      required: true
+    attributes:
+      label: Describe the bug
+      description: |
+        Please provide a clear and concise description of what the bug is.
+        Preferably a simple and minimal code snippet that we can reproduce the error by running the code.
+      placeholder: |
+        A clear and concise description of what the bug is.
+
+        ```python
+        # Sample code to reproduce the problem
+        ```
+
+        ```shell
+        The command or script you run.
+        ```
+
+        ```
+        The error message or logs you got, with the full traceback.
+        ```
+
+  - type: textarea
+    id: environment
+    validations:
+      required: true
+    attributes:
+      label: Environment
+      description: |
+        Please run `python -c "import mmcls.utils;import pprint;pprint.pp(dict(mmcls.utils.collect_env()))"` to collect necessary environment information and paste it here.
+      placeholder: |
+        ```python
+        # The output the above command
+        ```
+
+  - type: textarea
+    id: other
+    attributes:
+      label: Other information
+      description: |
+        Tell us anything else you think we should know.
+
+        1. Did you make any modifications on the code or config?
+        2. What do you think might be the reason?

.github/ISSUE_TEMPLATE/2_feature-request.yml

@@ -0,0 +1,40 @@
+name: 🚀 Feature request
+description: Suggest an idea for this project
+labels: ["enhancement"]
+title: "[Feature] "
+body:
+  - type: markdown
+    attributes:
+      value: |
+        If you have already implemented the feature, we strongly appreciate you creating a new PR according to [the tutorial](https://mmclassification.readthedocs.io/en/master/community/CONTRIBUTING.html)!
+
+  - type: dropdown
+    id: version
+    attributes:
+      label: Branch
+      description: Which branch/version are you using?
+      options:
+        - master branch (0.24 or other 0.x version)
+        - 1.x branch (1.0.0rc2 or other 1.x version)
+    validations:
+      required: true
+
+  - type: textarea
+    id: describe
+    validations:
+      required: true
+    attributes:
+      label: Describe the feature
+      description: |
+        What kind of feature do you want MMClassification to add. If there is an official code release or third-party implementation, please also provide the information here, which would be very helpful.
+      placeholder: |
+        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 \[....\].
+
+  - type: checkboxes
+    id: pr
+    attributes:
+      label: Will you implement it?
+      options:
+        - label: I would like to implement this feature and create a PR!

.github/ISSUE_TEMPLATE/3_bug-report_zh.yml

@@ -0,0 +1,69 @@
+name: 🐞 报告 Bug
+description: 报告你在使用中遇到的不合预期的情况
+labels: ["bug"]
+title: "[Bug] "
+body:
+  - type: markdown
+    attributes:
+      value: |
+        我们推荐使用英语模板 Bug report，以便你的问题帮助更多人。
+
+        如果你已经有了解决方案，我们非常欢迎你直接创建一个新的 PR 来解决这个问题。创建 PR 的流程可以参考[文档](https://mmclassification.readthedocs.io/zh_CN/master/community/CONTRIBUTING.html)。
+        如果你需要我们的帮助，请填写以下内容帮助我们定位 Bug。
+
+  - type: dropdown
+    id: version
+    attributes:
+      label: 分支
+      description: 你正在使用的分支/版本是哪个？
+      options:
+        - master 分支 (0.24 或其他 0.x 版本)
+        - 1.x 分支 (1.0.0rc2 或其他 1.x 版本)
+    validations:
+      required: true
+
+  - type: textarea
+    id: describe
+    validations:
+      required: true
+    attributes:
+      label: 描述该错误
+      description: |
+        请简要说明你遇到的错误。如果可以的话，请提供一个简短的代码片段帮助我们复现这一错误。
+      placeholder: |
+        问题的简要说明
+
+        ```python
+        # 复现错误的代码片段
+        ```
+
+        ```shell
+        # 发生错误时你的运行命令
+        ```
+
+        ```
+        错误信息和日志，请展示全部的错误日志和 traceback
+        ```
+
+  - type: textarea
+    id: environment
+    validations:
+      required: true
+    attributes:
+      label: 环境信息
+      description: |
+        请运行指令 `python -c "import mmcls.utils;import pprint;pprint.pp(dict(mmcls.utils.collect_env()))"` 来收集必要的环境信息，并贴在下方。
+      placeholder: |
+        ```python
+        # 上述命令的输出
+        ```
+
+  - type: textarea
+    id: other
+    attributes:
+      label: 其他信息
+      description: |
+        告诉我们其他有价值的信息。
+
+        1. 你是否对代码或配置文件做了任何改动？
+        2. 你认为可能的原因是什么？

.github/ISSUE_TEMPLATE/4_feature-request_zh.yml

@@ -0,0 +1,42 @@
+name: 🚀 功能建议
+description: 建议一项新的功能
+labels: ["enhancement"]
+title: "[Feature] "
+body:
+  - type: markdown
+    attributes:
+      value: |
+        推荐使用英语模板 Feature request，以便你的问题帮助更多人。
+
+        如果你已经实现了该功能，我们非常欢迎你直接创建一个新的 PR 来解决这个问题。创建 PR 的流程可以参考[文档](https://mmclassification.readthedocs.io/zh_CN/master/community/CONTRIBUTING.html)。
+
+  - type: dropdown
+    id: version
+    attributes:
+      label: 分支
+      description: 你正在使用的分支/版本是哪个？
+      options:
+        - master 分支 (0.24 或其他 0.x 版本)
+        - 1.x 分支 (1.0.0rc2 或其他 1.x 版本)
+    validations:
+      required: true
+
+  - type: textarea
+    id: describe
+    validations:
+      required: true
+    attributes:
+      label: 描述该功能
+      description: |
+        你希望 MMClassification 添加什么功能？如果存在相关的论文、官方实现或者第三方实现，请同时贴出链接，这将非常有帮助。
+      placeholder: |
+        简要说明该功能，及为什么需要该功能
+        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
+
+  - type: checkboxes
+    id: pr
+    attributes:
+      label: 是否希望自己实现该功能？
+      options:
+        - label: 我希望自己来实现这一功能，并向 MMClassification 贡献代码！

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,42 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: '[Bug]'
-labels: bug
-assignees: ''
----
-
-### Describe the bug
-
-A clear and concise description of what the bug is.
-
-\[here\]
-
-### To Reproduce
-
-The command you executed.
-
-```shell
-[here]
-```
-
-### Post related information
-
-1. The output of `pip list | grep "mmcv\|mmcls\|^torch"`
-   \[here\]
-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.
-   \[here\]
-4. Other code you modified in the `mmcls` folder.
-   \[here\]
-
-### Additional context
-
-Add any other context about the problem here.
-
-\[here\]

.github/ISSUE_TEMPLATE/config.yml

@@ -1,6 +1,12 @@
 blank_issues_enabled: false
 
 contact_links:
-  - name: MMClassification Documentation
+  - name: 📚 MMClassification Documentation (官方文档)
     url: https://mmclassification.readthedocs.io/en/latest/
     about: Check if your question is answered in docs
+  - name: 💬 General questions (寻求帮助)
+    url: https://github.com/open-mmlab/mmclassification/discussions
+    about: Ask general usage questions and discuss with other MMClassification community members
+  - name: 🌐 Explore OpenMMLab (官网)
+    url: https://openmmlab.com/
+    about: Get know more about OpenMMLab

.github/ISSUE_TEMPLATE/feature_request.md

@@ -1,32 +0,0 @@
----
-name: Feature request
-about: Suggest an idea for this project
-title: '[Feature]'
-labels: enhancement
-assignees: ''
----
-
-### Describe the feature
-
-\[here\]
-
-### 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 \[....\].
-
-\[here\]
-
-### Related resources
-
-If there is an official code release or third-party implementation, please also provide the information here, which would be very helpful.
-
-\[here\]
-
-### 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.
-
-\[here\]

.github/ISSUE_TEMPLATE/general-questions.md

@@ -1,31 +0,0 @@
----
-name: General questions
-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
-
-\[here\]
-
-### Post related information
-
-1. The output of `pip list | grep "mmcv\|mmcls\|^torch"`
-   \[here\]
-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.
-   \[here\]
-4. Other code you modified in the `mmcls` folder.
-   \[here\]

.github/workflows/build.yml

@@ -29,91 +29,18 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
-  build_without_timm:
+  build_with_timm:
     runs-on: ubuntu-latest
     env:
       UBUNTU_VERSION: ubuntu1804
     strategy:
       matrix:
-        python-version: [3.6]
-        torch: [1.5.0, 1.8.0, 1.9.0]
+        python-version: [3.8]
+        torch: [1.8.0]
         include:
-          - torch: 1.5.0
-            torchvision: 0.6.0
-            torch_major: 1.5.0
           - torch: 1.8.0
             torchvision: 0.9.0
             torch_major: 1.8.0
-          - torch: 1.9.0
-            torchvision: 0.10.0
-            torch_major: 1.9.0
-
-    steps:
-      - uses: actions/checkout@v2
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
-        with:
-          python-version: ${{ matrix.python-version }}
-      - name: Install PyTorch
-        run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
-      - name: Install MMCV
-        run: |
-          pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch${{matrix.torch_major}}/index.html
-          python -c 'import mmcv; print(mmcv.__version__)'
-      - name: Install mmcls dependencies
-        run: |
-          pip install -r requirements.txt
-      - name: Build and install
-        run: |
-          rm -rf .eggs
-          pip install -e . -U
-      - name: Run unittests
-        run: |
-          pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py
-
-  build:
-    runs-on: ubuntu-latest
-    env:
-      UBUNTU_VERSION: ubuntu1804
-    strategy:
-      matrix:
-        python-version: [3.7]
-        torch: [1.5.0, 1.6.0, 1.7.0, 1.8.0, 1.9.0]
-        include:
-          - torch: 1.5.0
-            torchvision: 0.6.0
-            torch_major: 1.5.0
-          - torch: 1.6.0
-            torchvision: 0.7.0
-            torch_major: 1.6.0
-          - torch: 1.7.0
-            torchvision: 0.8.1
-            torch_major: 1.7.0
-          - torch: 1.8.0
-            torchvision: 0.9.0
-            torch_major: 1.8.0
-          - torch: 1.8.0
-            torchvision: 0.9.0
-            torch_major: 1.8.0
-            python-version: 3.8
-          - torch: 1.8.0
-            torchvision: 0.9.0
-            torch_major: 1.8.0
-            python-version: 3.9
-          - torch: 1.9.0
-            torchvision: 0.10.0
-            torch_major: 1.9.0
-          - torch: 1.10.0
-            torchvision: 0.11.1
-            torch_major: 1.10.0
-          - torch: 1.10.0
-            torchvision: 0.11.1
-            torch_major: 1.10.0
-            python-version: 3.8
-          - torch: 1.10.0
-            torchvision: 0.11.1
-            torch_major: 1.10.0
-            python-version: 3.9
 
     steps:
       - uses: actions/checkout@v2
@@ -137,7 +64,7 @@ jobs:
         run: |
           rm -rf .eggs
           pip install -e . -U
-      - name: Run unittests and generate coverage report
+      - name: Run unittests
         run: |
           coverage run --branch --source mmcls -m pytest tests/
           coverage xml
@@ -151,6 +78,64 @@ jobs:
           name: codecov-umbrella
           fail_ci_if_error: false
 
+  build:
+    runs-on: ubuntu-latest
+    env:
+      UBUNTU_VERSION: ubuntu1804
+    strategy:
+      matrix:
+        torch: [1.5.0, 1.8.0, 1.10.0, 1.13.0]
+        include:
+          - torch: 1.5.0
+            torchvision: 0.6.0
+            torch_major: 1.5.0
+            python-version: '3.7'
+          - torch: 1.8.0
+            torchvision: 0.9.0
+            torch_major: 1.8.0
+            python-version: '3.8'
+          - torch: 1.10.0
+            torchvision: 0.11.1
+            torch_major: 1.10.0
+            python-version: '3.9'
+          - torch: 1.13.0
+            torchvision: 0.14.0
+            torch_major: 1.13.0
+            python-version: '3.10'
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install PyTorch
+        run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install MMCV
+        run: |
+          pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch${{matrix.torch_major}}/index.html
+          python -c 'import mmcv; print(mmcv.__version__)'
+      - name: Install mmcls dependencies
+        run: |
+          pip install -r requirements.txt
+      - name: Build and install
+        run: |
+          rm -rf .eggs
+          pip install -e . -U
+      - name: Run unittests and generate coverage report
+        run: |
+          coverage run --branch --source mmcls -m pytest tests/ -k "not timm"
+          coverage xml
+          coverage report -m --omit="mmcls/utils/*","mmcls/apis/*"
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v2
+        with:
+          file: ./coverage.xml
+          flags: unittests
+          env_vars: OS,PYTHON
+          name: codecov-umbrella
+          fail_ci_if_error: false
+
   build-windows:
     runs-on: windows-2022
     strategy:

.github/workflows/test-mim.yml

@@ -0,0 +1,44 @@
+name: test-mim
+
+on:
+  push:
+    paths:
+      - 'model-index.yml'
+      - 'configs/**'
+
+  pull_request:
+    paths:
+      - 'model-index.yml'
+      - 'configs/**'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  build_cpu:
+    runs-on: ubuntu-18.04
+    strategy:
+      matrix:
+        python-version: [3.7]
+        torch: [1.8.0]
+        include:
+          - torch: 1.8.0
+            torch_version: torch1.8
+            torchvision: 0.9.0
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade
+      - name: Install PyTorch
+        run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install openmim
+        run: pip install openmim
+      - name: Build and install
+        run: rm -rf .eggs && mim install -e .
+      - name: test commands of mim
+        run: mim search mmcls

.owners.yml

@@ -0,0 +1,18 @@
+# assign issues to owners automatically
+assign:
+  issues: enabled # or disabled
+  pull_requests: enabled # or disabled
+  # assign strategy, both issues and pull requests follow the same strategy
+  strategy:
+    # random
+    # round-robin
+    daily-shift-based
+  schedule:
+    '*/1 * * * *'
+  # assignees
+  assignees:
+    - mzr1996
+    - tonysy
+    - Ezra-Yu
+    - techmonsterwang
+    - yingfhu

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ repos:
     hooks:
       - id: flake8
   - repo: https://github.com/PyCQA/isort
-    rev: 5.10.1
+    rev: 5.11.5
     hooks:
       - id: isort
   - repo: https://github.com/pre-commit/mirrors-yapf
@@ -29,9 +29,9 @@ repos:
     rev: 0.7.9
     hooks:
       - id: mdformat
-        args: ["--number", "--table-width", "200"]
+        args: ["--number", "--table-width", "200", '--disable-escape', 'backslash', '--disable-escape', 'link-enclosure']
         additional_dependencies:
-          - mdformat-openmmlab
+          - "mdformat-openmmlab>=0.0.4"
           - mdformat_frontmatter
           - linkify-it-py
   - repo: https://github.com/codespell-project/codespell

README.md

@@ -33,6 +33,8 @@
 [🆕 Update News](https://mmclassification.readthedocs.io/en/latest/changelog.html) |
 [🤔 Reporting Issues](https://github.com/open-mmlab/mmclassification/issues/new/choose)
 
+:point_right: **MMPreTrain 1.0 branch is in trial, welcome every to [try it](https://github.com/open-mmlab/mmclassification/tree/pretrain) and [discuss with us](https://github.com/open-mmlab/mmclassification/discussions)!** :point_left:
+
 </div>
 
 ## Introduction
@@ -58,20 +60,26 @@ The master branch works with **PyTorch 1.5+**.
 
 ## What's new
 
-v0.23.0 was released in 1/5/2022.
+The MMClassification 1.0 has released! It's still unstable and in release candidate. If you want to try it, go
+to [the 1.x branch](https://github.com/open-mmlab/mmclassification/tree/1.x) and discuss it with us in
+[the discussion](https://github.com/open-mmlab/mmclassification/discussions).
+
+v0.25.0 was released in 06/12/2022.
 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).
-
-v0.22.0 was released in 30/3/2022.
+- Support MLU backend.
+- Add `dist_train_arm.sh` for ARM device.
 
+v0.24.1 was released in 31/10/2022.
 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**.
+- Support HUAWEI Ascend device.
+
+v0.24.0 was released in 30/9/2022.
+Highlights of the new version:
+
+- Support **HorNet**, **EfficientFormerm**, **SwinTransformer V2** and **MViT** backbones.
+- Support Standford Cars dataset.
 
 Please refer to [changelog.md](docs/en/changelog.md) for more details and other release history.
 
@@ -80,7 +88,7 @@ Please refer to [changelog.md](docs/en/changelog.md) for more details and other
 Below are quick steps for installation:
 
 ```shell
-conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
+conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision==0.11.0 -c pytorch -y
 conda activate open-mmlab
 pip3 install openmim
 mim install mmcv-full
@@ -142,6 +150,9 @@ Results and models are available in the [model zoo](https://mmclassification.rea
 - [x] [ConvMixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/convmixer)
 - [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/cspnet)
 - [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer)
+- [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/mvit)
+- [x] [EfficientFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientformer)
+- [x] [HorNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hornet)
 
 </details>
 

README_zh-CN.md

@@ -33,6 +33,10 @@
 [🆕 更新日志](https://mmclassification.readthedocs.io/en/latest/changelog.html) |
 [🤔 报告问题](https://github.com/open-mmlab/mmclassification/issues/new/choose)
 
+:point_right: **MMPreTrain 1.0 版本即将正式发布，欢迎大家 [试用](https://github.com/open-mmlab/mmclassification/tree/pretrain) 并 [参与讨论](https://github.com/open-mmlab/mmclassification/discussions)！** :point_left:
+
+</div>
+
 </div>
 
 ## Introduction
@@ -57,22 +61,28 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O
 
 ## 更新日志
 
-2022/5/1 发布了 v0.23.0 版本
+MMClassification 1.0 已经发布！目前仍在公测中，如果希望试用，请切换到 [1.x 分支](https://github.com/open-mmlab/mmclassification/tree/1.x)，并在[讨论版](https://github.com/open-mmlab/mmclassification/discussions) 参加开发讨论！
 
-新版本亮点：
+2022/12/06 发布了 v0.25.0 版本
+
+- 支持 MLU 设备
+- 添加了用于 ARM 设备训练的 `dist_train_arm.sh`
+
+2022/10/31 发布了 v0.24.1 版本
+
+- 支持了华为昇腾 NPU 设备。
+
+2022/9/30 发布了 v0.24.0 版本
+
+- 支持了 **HorNet**，**EfficientFormerm**，**SwinTransformer V2**，**MViT** 等主干网络。
+- 支持了 Support Standford Cars 数据集。
+
+2022/5/1 发布了 v0.23.0 版本
 
 - 支持了 **DenseNet**，**VAN** 和 **PoolFormer** 三个网络，并提供了预训练模型。
 - 支持在 IPU 上进行训练。
 - 更新了 API 文档的样式，更方便查阅，[欢迎查阅](https://mmclassification.readthedocs.io/en/master/api/models.html)。
 
-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**。
-
 发布历史和更新细节请参考 [更新日志](docs/en/changelog.md)
 
 ## 安装
@@ -80,7 +90,7 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O
 以下是安装的简要步骤：
 
 ```shell
-conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
+conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision==0.11.0 -c pytorch -y
 conda activate open-mmlab
 pip3 install openmim
 mim install mmcv-full
@@ -142,6 +152,9 @@ pip3 install -e .
 - [x] [ConvMixer](https://github.com/open-mmlab/mmclassification/tree/master/configs/convmixer)
 - [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/cspnet)
 - [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer)
+- [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/mvit)
+- [x] [EfficientFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientformer)
+- [x] [HorNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/hornet)
 
 </details>
 

configs/_base_/datasets/imagenet_bs64_swin_256.py

@@ -0,0 +1,71 @@
+_base_ = ['./pipelines/rand_aug.py']
+
+# dataset settings
+dataset_type = 'ImageNet'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        size=256,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies={{_base_.rand_increasing_policies}},
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
+            interpolation='bicubic')),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=img_norm_cfg['mean'][::-1],
+        fill_std=img_norm_cfg['std'][::-1]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        size=(292, -1),  # ( 256 / 224 * 256 )
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=256),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=8,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+
+evaluation = dict(interval=10, metric='accuracy')

configs/_base_/datasets/stanford_cars_bs8_448.py

@@ -0,0 +1,46 @@
+# dataset settings
+dataset_type = 'StanfordCars'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=512),
+    dict(type='RandomCrop', size=448),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=512),
+    dict(type='CenterCrop', crop_size=448),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data_root = 'data/stanfordcars'
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix=data_root,
+        test_mode=False,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix=data_root,
+        test_mode=True,
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_prefix=data_root,
+        test_mode=True,
+        pipeline=test_pipeline))
+
+evaluation = dict(
+    interval=1, metric='accuracy',
+    save_best='auto')  # save the checkpoint with highest accuracy

configs/_base_/models/hornet/hornet-base-gf.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='HorNet', arch='base-gf', drop_path_rate=0.5),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+        dict(type='Constant', layer=['LayerScale'], val=1e-6)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/hornet/hornet-base.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='HorNet', arch='base', drop_path_rate=0.5),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+        dict(type='Constant', layer=['LayerScale'], val=1e-6)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/hornet/hornet-large-gf.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='HorNet', arch='large-gf', drop_path_rate=0.2),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1536,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+        dict(type='Constant', layer=['LayerScale'], val=1e-6)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/hornet/hornet-large-gf384.py

@@ -0,0 +1,17 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='HorNet', arch='large-gf384', drop_path_rate=0.4),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1536,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+        dict(type='Constant', layer=['LayerScale'], val=1e-6)
+    ])

configs/_base_/models/hornet/hornet-large.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='HorNet', arch='large', drop_path_rate=0.2),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1536,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+        dict(type='Constant', layer=['LayerScale'], val=1e-6)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/hornet/hornet-small-gf.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='HorNet', arch='small-gf', drop_path_rate=0.4),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+        dict(type='Constant', layer=['LayerScale'], val=1e-6)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/hornet/hornet-small.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='HorNet', arch='small', drop_path_rate=0.4),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+        dict(type='Constant', layer=['LayerScale'], val=1e-6)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/hornet/hornet-tiny-gf.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='HorNet', arch='tiny-gf', drop_path_rate=0.2),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+        dict(type='Constant', layer=['LayerScale'], val=1e-6)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/hornet/hornet-tiny.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='HorNet', arch='tiny', drop_path_rate=0.2),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+        dict(type='Constant', layer=['LayerScale'], val=1e-6)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/mvit/mvitv2-base.py

@@ -0,0 +1,19 @@
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='MViT', arch='base', drop_path_rate=0.3),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        in_channels=768,
+        num_classes=1000,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/mvit/mvitv2-large.py

@@ -0,0 +1,23 @@
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='MViT',
+        arch='large',
+        drop_path_rate=0.5,
+        dim_mul_in_attention=False),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        in_channels=1152,
+        num_classes=1000,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/mvit/mvitv2-small.py

@@ -0,0 +1,19 @@
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='MViT', arch='small', drop_path_rate=0.1),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        in_channels=768,
+        num_classes=1000,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/mvit/mvitv2-tiny.py

@@ -0,0 +1,19 @@
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='MViT', arch='tiny', drop_path_rate=0.1),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        in_channels=768,
+        num_classes=1000,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/swin_transformer_v2/base_256.py

@@ -0,0 +1,25 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SwinTransformerV2',
+        arch='base',
+        img_size=256,
+        drop_path_rate=0.5),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/swin_transformer_v2/base_384.py

@@ -0,0 +1,17 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SwinTransformerV2',
+        arch='base',
+        img_size=384,
+        drop_path_rate=0.2),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1024,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False))

configs/_base_/models/swin_transformer_v2/large_256.py

@@ -0,0 +1,16 @@
+# model settings
+# Only for evaluation
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SwinTransformerV2',
+        arch='large',
+        img_size=256,
+        drop_path_rate=0.2),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1536,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5)))

configs/_base_/models/swin_transformer_v2/large_384.py

@@ -0,0 +1,16 @@
+# model settings
+# Only for evaluation
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SwinTransformerV2',
+        arch='large',
+        img_size=384,
+        drop_path_rate=0.2),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=1536,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        topk=(1, 5)))

configs/_base_/models/swin_transformer_v2/small_256.py

@@ -0,0 +1,25 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SwinTransformerV2',
+        arch='small',
+        img_size=256,
+        drop_path_rate=0.3),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/swin_transformer_v2/tiny_256.py

@@ -0,0 +1,25 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SwinTransformerV2',
+        arch='tiny',
+        img_size=256,
+        drop_path_rate=0.2),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/van/van_b0.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='b0', drop_path_rate=0.1),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=256,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/van/van_b1.py

@@ -0,0 +1,21 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='b1', drop_path_rate=0.1),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
+    ],
+    train_cfg=dict(augments=[
+        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
+        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
+    ]))

configs/_base_/models/van/van_b2.py

@@ -0,0 +1,13 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='b2', drop_path_rate=0.1),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False))

configs/_base_/models/van/van_b3.py

@@ -0,0 +1,13 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='b3', drop_path_rate=0.2),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False))

configs/_base_/models/van/van_b4.py

@@ -0,0 +1,13 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='b4', drop_path_rate=0.2),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=512,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False))

configs/_base_/models/van/van_b5.py

@@ -0,0 +1,13 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='b5', drop_path_rate=0.2),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False))

configs/_base_/models/van/van_b6.py

@@ -0,0 +1,13 @@
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VAN', arch='b6', drop_path_rate=0.3),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1000,
+        in_channels=768,
+        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False))

configs/_base_/models/van/van_base.py

@@ -1,13 +1 @@
-# model settings
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(type='VAN', arch='base', drop_path_rate=0.1),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='LinearClsHead',
-        num_classes=1000,
-        in_channels=512,
-        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
-        loss=dict(
-            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
-        cal_acc=False))
+_base_ = ['./van-b2.py']

configs/_base_/models/van/van_large.py

@@ -1,13 +1 @@
-# model settings
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(type='VAN', arch='large', drop_path_rate=0.2),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='LinearClsHead',
-        num_classes=1000,
-        in_channels=512,
-        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
-        loss=dict(
-            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
-        cal_acc=False))
+_base_ = ['./van-b3.py']

configs/_base_/models/van/van_small.py

@@ -1,21 +1 @@
-# model settings
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(type='VAN', arch='small', drop_path_rate=0.1),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='LinearClsHead',
-        num_classes=1000,
-        in_channels=512,
-        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
-        loss=dict(
-            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
-        cal_acc=False),
-    init_cfg=[
-        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
-        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
-    ],
-    train_cfg=dict(augments=[
-        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
-        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
-    ]))
+_base_ = ['./van-b1.py']

configs/_base_/models/van/van_tiny.py

@@ -1,21 +1 @@
-# model settings
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(type='VAN', arch='tiny', drop_path_rate=0.1),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='LinearClsHead',
-        num_classes=1000,
-        in_channels=256,
-        init_cfg=None,  # suppress the default init_cfg of LinearClsHead.
-        loss=dict(
-            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
-        cal_acc=False),
-    init_cfg=[
-        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
-        dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
-    ],
-    train_cfg=dict(augments=[
-        dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5),
-        dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5)
-    ]))
+_base_ = ['./van-b0.py']

configs/_base_/schedules/stanford_cars_bs8.py

@@ -0,0 +1,7 @@
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.003, momentum=0.9, weight_decay=0.0005, nesterov=True)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(policy='step', step=[40, 70, 90])
+runner = dict(type='EpochBasedRunner', max_epochs=100)

configs/csra/README.md

@@ -0,0 +1,36 @@
+# CSRA
+
+> [Residual Attention: A Simple but Effective Method for Multi-Label Recognition](https://arxiv.org/abs/2108.02456)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Multi-label image recognition is a challenging computer vision task of practical use. Progresses in this area, however, are often characterized by complicated methods, heavy computations, and lack of intuitive explanations. To effectively capture different spatial regions occupied by objects from different categories, we propose an embarrassingly simple module, named class-specific residual attention (CSRA). CSRA generates class-specific features for every category by proposing a simple spatial attention score, and then combines it with the class-agnostic average pooling feature. CSRA achieves state-of-the-art results on multilabel recognition, and at the same time is much simpler than them. Furthermore, with only 4 lines of code, CSRA also leads to consistent improvement across many diverse pretrained models and datasets without any extra training. CSRA is both easy to implement and light in computations, which also enjoys intuitive explanations and visualizations.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/84259897/176982245-3ffcff56-a4ea-4474-9967-bc2b612bbaa3.png" width="80%"/>
+</div>
+
+## Results and models
+
+### VOC2007
+
+|     Model      |                      Pretrain                      | Params(M) | Flops(G) |  mAP  | OF1 (%) | CF1 (%) |                      Config                       |                      Download                       |
+| :------------: | :------------------------------------------------: | :-------: | :------: | :---: | :-----: | :-----: | :-----------------------------------------------: | :-------------------------------------------------: |
+| Resnet101-CSRA | [ImageNet-1k](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth) |   23.55   |   4.12   | 94.98 |  90.80  |  89.16  | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/csra/resnet101-csra_1xb16_voc07-448px.py) | [model](https://download.openmmlab.com/mmclassification/v0/csra/resnet101-csra_1xb16_voc07-448px_20220722-29efb40a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/csra/resnet101-csra_1xb16_voc07-448px_20220722-29efb40a.log.json) |
+
+## Citation
+
+```bibtex
+@misc{https://doi.org/10.48550/arxiv.2108.02456,
+  doi = {10.48550/ARXIV.2108.02456},
+  url = {https://arxiv.org/abs/2108.02456},
+  author = {Zhu, Ke and Wu, Jianxin},
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {Residual Attention: A Simple but Effective Method for Multi-Label Recognition},
+  publisher = {arXiv},
+  year = {2021},
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+```

configs/csra/metafile.yml

@@ -0,0 +1,29 @@
+Collections:
+  - Name: CSRA
+    Metadata:
+      Training Data: PASCAL VOC 2007
+      Architecture:
+        - Class-specific Residual Attention
+    Paper:
+      URL: https://arxiv.org/abs/1911.11929
+      Title: 'Residual Attention: A Simple but Effective Method for Multi-Label Recognition'
+    README: configs/csra/README.md
+    Code:
+      Version: v0.24.0
+      URL: https://github.com/open-mmlab/mmclassification/blob/v0.24.0/mmcls/models/heads/multi_label_csra_head.py
+
+Models:
+  - Name: resnet101-csra_1xb16_voc07-448px
+    Metadata:
+      FLOPs: 4120000000
+      Parameters: 23550000
+    In Collections: CSRA
+    Results:
+      - Dataset: PASCAL VOC 2007
+        Metrics:
+          mAP: 94.98
+          OF1: 90.80
+          CF1: 89.16
+        Task: Multi-Label Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/csra/resnet101-csra_1xb16_voc07-448px_20220722-29efb40a.pth
+    Config: configs/csra/resnet101-csra_1xb16_voc07-448px.py

configs/csra/resnet101-csra_1xb16_voc07-448px.py

@@ -0,0 +1,75 @@
+_base_ = ['../_base_/datasets/voc_bs16.py', '../_base_/default_runtime.py']
+
+# Pre-trained Checkpoint Path
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth'  # noqa
+# If you want to use the pre-trained weight of ResNet101-CutMix from
+# the originary repo(https://github.com/Kevinz-code/CSRA). Script of
+# 'tools/convert_models/torchvision_to_mmcls.py' can help you convert weight
+# into mmcls format. The mAP result would hit 95.5 by using the weight.
+# checkpoint = 'PATH/TO/PRE-TRAINED_WEIGHT'
+
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNet',
+        depth=101,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint=checkpoint, prefix='backbone')),
+    neck=None,
+    head=dict(
+        type='CSRAClsHead',
+        num_classes=20,
+        in_channels=2048,
+        num_heads=1,
+        lam=0.1,
+        loss=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)))
+
+# dataset setting
+img_norm_cfg = dict(mean=[0, 0, 0], std=[255, 255, 255], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='RandomResizedCrop', size=448, scale=(0.7, 1.0)),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=448),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    # map the difficult examples as negative ones(0)
+    train=dict(pipeline=train_pipeline, difficult_as_postive=False),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+# optimizer
+# the lr of classifier.head is 10 * base_lr, which help convergence.
+optimizer = dict(
+    type='SGD',
+    lr=0.0002,
+    momentum=0.9,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10)}))
+
+optimizer_config = dict(grad_clip=None)
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    step=6,
+    gamma=0.1,
+    warmup='linear',
+    warmup_iters=1,
+    warmup_ratio=1e-7,
+    warmup_by_epoch=True)
+runner = dict(type='EpochBasedRunner', max_epochs=20)

configs/efficientformer/README.md

@@ -0,0 +1,47 @@
+# EfficientFormer
+
+> [EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/2206.01191)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Vision Transformers (ViT) have shown rapid progress in computer vision tasks, achieving promising results on various benchmarks. However, due to the massive number of parameters and model design, e.g., attention mechanism, ViT-based models are generally times slower than lightweight convolutional networks. Therefore, the deployment of ViT for real-time applications is particularly challenging, especially on resource-constrained hardware such as mobile devices. Recent efforts try to reduce the computation complexity of ViT through network architecture search or hybrid design with MobileNet block, yet the inference speed is still unsatisfactory. This leads to an important question: can transformers run as fast as MobileNet while obtaining high performance? To answer this, we first revisit the network architecture and operators used in ViT-based models and identify inefficient designs. Then we introduce a dimension-consistent pure transformer (without MobileNet blocks) as a design paradigm.  Finally, we perform latency-driven slimming to get a series of final models dubbed EfficientFormer. Extensive experiments show the superiority of EfficientFormer in performance and speed on mobile devices. Our fastest model, EfficientFormer-L1, achieves 79.2% top-1 accuracy on ImageNet-1K with only 1.6 ms inference latency on iPhone 12 (compiled with CoreML), which runs as fast as MobileNetV2×1.4 (1.6 ms, 74.7% top-1), and our largest model, EfficientFormer-L7, obtains 83.3% accuracy with only 7.0 ms latency. Our work proves that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/18586273/180713426-9d3d77e3-3584-42d8-9098-625b4170d796.png" width="100%"/>
+</div>
+
+## Results and models
+
+### ImageNet-1k
+
+|        Model         | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                                 Config                                  |                                  Download                                  |
+| :------------------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------------: | :------------------------------------------------------------------------: |
+| EfficientFormer-l1\* |   12.19   |   1.30   |   80.46   |   94.99   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l1_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l1_3rdparty_in1k_20220803-d66e61df.pth) |
+| EfficientFormer-l3\* |   31.41   |   3.93   |   82.45   |   96.18   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l3_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l3_3rdparty_in1k_20220803-dde1c8c5.pth) |
+| EfficientFormer-l7\* |   82.23   |  10.16   |   83.40   |   96.60   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l7_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l7_3rdparty_in1k_20220803-41a552bb.pth) |
+
+*Models with * are converted from the [official repo](https://github.com/snap-research/EfficientFormer). 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
+
+```bibtex
+@misc{https://doi.org/10.48550/arxiv.2206.01191,
+  doi = {10.48550/ARXIV.2206.01191},
+
+  url = {https://arxiv.org/abs/2206.01191},
+
+  author = {Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian},
+
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
+
+  title = {EfficientFormer: Vision Transformers at MobileNet Speed},
+
+  publisher = {arXiv},
+
+  year = {2022},
+
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```

configs/efficientformer/efficientformer-l1_8xb128_in1k.py

@@ -0,0 +1,24 @@
+_base_ = [
+    '../_base_/datasets/imagenet_bs128_poolformer_small_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py',
+]
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='EfficientFormer',
+        arch='l1',
+        drop_path_rate=0,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.),
+            dict(type='Constant', layer=['LayerScale'], val=1e-5)
+        ]),
+    neck=dict(type='GlobalAveragePooling', dim=1),
+    head=dict(
+        type='EfficientFormerClsHead', in_channels=448, num_classes=1000))

configs/efficientformer/efficientformer-l3_8xb128_in1k.py

@@ -0,0 +1,24 @@
+_base_ = [
+    '../_base_/datasets/imagenet_bs128_poolformer_small_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py',
+]
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='EfficientFormer',
+        arch='l3',
+        drop_path_rate=0,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.),
+            dict(type='Constant', layer=['LayerScale'], val=1e-5)
+        ]),
+    neck=dict(type='GlobalAveragePooling', dim=1),
+    head=dict(
+        type='EfficientFormerClsHead', in_channels=512, num_classes=1000))

configs/efficientformer/efficientformer-l7_8xb128_in1k.py

@@ -0,0 +1,24 @@
+_base_ = [
+    '../_base_/datasets/imagenet_bs128_poolformer_small_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py',
+]
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='EfficientFormer',
+        arch='l7',
+        drop_path_rate=0,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.),
+            dict(type='Constant', layer=['LayerScale'], val=1e-5)
+        ]),
+    neck=dict(type='GlobalAveragePooling', dim=1),
+    head=dict(
+        type='EfficientFormerClsHead', in_channels=768, num_classes=1000))

configs/efficientformer/metafile.yml

@@ -0,0 +1,67 @@
+Collections:
+  - Name: EfficientFormer
+    Metadata:
+      Training Data: ImageNet-1k
+      Architecture:
+        - Pooling
+        - 1x1 Convolution
+        - LayerScale
+        - MetaFormer
+    Paper:
+      URL: https://arxiv.org/pdf/2206.01191.pdf
+      Title: "EfficientFormer: Vision Transformers at MobileNet Speed"
+    README: configs/efficientformer/README.md
+    Code:
+      Version: v0.24.0
+      URL: https://github.com/open-mmlab/mmclassification/blob/v0.24.0/mmcls/models/backbones/efficientformer.py
+
+Models:
+  - Name: efficientformer-l1_3rdparty_8xb128_in1k
+    Metadata:
+      FLOPs: 1304601088     # 1.3G
+      Parameters: 12278696   # 12M
+    In Collections: EfficientFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 80.46
+          Top 5 Accuracy: 94.99
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l1_3rdparty_in1k_20220803-d66e61df.pth
+    Config: configs/efficientformer/efficientformer-l1_8xb128_in1k.py
+    Converted From:
+      Weights: https://drive.google.com/file/d/11SbX-3cfqTOc247xKYubrAjBiUmr818y/view?usp=sharing
+      Code: https://github.com/snap-research/EfficientFormer
+  - Name: efficientformer-l3_3rdparty_8xb128_in1k
+    Metadata:
+      Training Data: ImageNet-1k
+      FLOPs: 3737045760        # 3.7G
+      Parameters: 31406000     # 31M
+    In Collections: EfficientFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 82.45
+          Top 5 Accuracy: 96.18
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l3_3rdparty_in1k_20220803-dde1c8c5.pth
+    Config: configs/efficientformer/efficientformer-l3_8xb128_in1k.py
+    Converted From:
+      Weights: https://drive.google.com/file/d/1OyyjKKxDyMj-BcfInp4GlDdwLu3hc30m/view?usp=sharing
+      Code: https://github.com/snap-research/EfficientFormer
+  - Name: efficientformer-l7_3rdparty_8xb128_in1k
+    Metadata:
+      FLOPs: 10163951616      # 10.2G
+      Parameters: 82229328    # 82M
+    In Collections: EfficientFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 83.40
+          Top 5 Accuracy: 96.60
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l7_3rdparty_in1k_20220803-41a552bb.pth
+    Config: configs/efficientformer/efficientformer-l7_8xb128_in1k.py
+    Converted From:
+      Weights: https://drive.google.com/file/d/1cVw-pctJwgvGafeouynqWWCwgkcoFMM5/view?usp=sharing
+      Code: https://github.com/snap-research/EfficientFormer

configs/hornet/README.md

@@ -0,0 +1,51 @@
+# HorNet
+
+> [HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions](https://arxiv.org/pdf/2207.14284v2.pdf)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Recent progress in vision Transformers exhibits great success in various tasks driven by the new spatial modeling mechanism based on dot-product self-attention. In this paper, we show that the key ingredients behind the vision Transformers, namely input-adaptive, long-range and high-order spatial interactions, can also be efficiently implemented with a convolution-based framework. We present the Recursive Gated Convolution (g nConv) that performs high-order spatial interactions with gated convolutions and recursive designs. The new operation is highly flexible and customizable, which is compatible with various variants of convolution and extends the two-order interactions in self-attention to arbitrary orders without introducing significant extra computation. g nConv can serve as a plug-and-play module to improve various vision Transformers and convolution-based models. Based on the operation, we construct a new family of generic vision backbones named HorNet. Extensive experiments on ImageNet classification, COCO object detection and ADE20K semantic segmentation show HorNet outperform Swin Transformers and ConvNeXt by a significant margin with similar overall architecture and training configurations. HorNet also shows favorable scalability to more training data and a larger model size. Apart from the effectiveness in visual encoders, we also show g nConv can be applied to task-specific decoders and consistently improve dense prediction performance with less computation. Our results demonstrate that g nConv can be a new basic module for visual modeling that effectively combines the merits of both vision Transformers and CNNs. Code is available at https://github.com/raoyongming/HorNet.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/24734142/188356236-b8e3db94-eaa6-48e9-b323-15e5ba7f2991.png" width="80%"/>
+</div>
+
+## Results and models
+
+### ImageNet-1k
+
+|     Model     |   Pretrain   | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                              Config                              |                              Download                              |
+| :-----------: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :--------------------------------------------------------------: | :----------------------------------------------------------------: |
+|  HorNet-T\*   | From scratch |  224x224   |   22.41   |   3.98   |   82.84   |   96.24   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-tiny_3rdparty_in1k_20220915-0e8eedff.pth) |
+| HorNet-T-GF\* | From scratch |  224x224   |   22.99   |   3.9    |   82.98   |   96.38   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-tiny-gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-tiny-gf_3rdparty_in1k_20220915-4c35a66b.pth) |
+|  HorNet-S\*   | From scratch |  224x224   |   49.53   |   8.83   |   83.79   |   96.75   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-small_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-small_3rdparty_in1k_20220915-5935f60f.pth) |
+| HorNet-S-GF\* | From scratch |  224x224   |   50.4    |   8.71   |   83.98   |   96.77   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-small-gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-small-gf_3rdparty_in1k_20220915-649ca492.pth) |
+|  HorNet-B\*   | From scratch |  224x224   |   87.26   |  15.59   |   84.24   |   96.94   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-base_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-base_3rdparty_in1k_20220915-a06176bb.pth) |
+| HorNet-B-GF\* | From scratch |  224x224   |   88.42   |  15.42   |   84.32   |   96.95   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-base-gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-base-gf_3rdparty_in1k_20220915-82c06fa7.pth) |
+
+\*Models with * are converted from [the official repo](https://github.com/raoyongming/HorNet). 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.
+
+### Pre-trained Models
+
+The pre-trained models on ImageNet-21k are used to fine-tune on the downstream tasks.
+
+|      Model       |   Pretrain   | resolution | Params(M) | Flops(G) |                                                          Download                                                          |
+| :--------------: | :----------: | :--------: | :-------: | :------: | :------------------------------------------------------------------------------------------------------------------------: |
+|    HorNet-L\*    | ImageNet-21k |  224x224   |  194.54   |  34.83   |    [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-large_3rdparty_in21k_20220909-9ccef421.pth)    |
+|  HorNet-L-GF\*   | ImageNet-21k |  224x224   |  196.29   |  34.58   |  [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-large-gf_3rdparty_in21k_20220909-3aea3b61.pth)   |
+| HorNet-L-GF384\* | ImageNet-21k |  384x384   |  201.23   |  101.63  | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-large-gf384_3rdparty_in21k_20220909-80894290.pth) |
+
+\*Models with * are converted from [the official repo](https://github.com/raoyongming/HorNet).
+
+## Citation
+
+```
+@article{rao2022hornet,
+  title={HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions},
+  author={Rao, Yongming and Zhao, Wenliang and Tang, Yansong and Zhou, Jie and Lim, Ser-Lam and Lu, Jiwen},
+  journal={arXiv preprint arXiv:2207.14284},
+  year={2022}
+}
+```

configs/hornet/hornet-base-gf_8xb64_in1k.py

@@ -0,0 +1,13 @@
+_base_ = [
+    '../_base_/models/hornet/hornet-base-gf.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py',
+]
+
+data = dict(samples_per_gpu=64)
+
+optimizer = dict(lr=4e-3)
+optimizer_config = dict(grad_clip=dict(max_norm=1.0), _delete_=True)
+
+custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]

configs/hornet/hornet-base_8xb64_in1k.py

@@ -0,0 +1,13 @@
+_base_ = [
+    '../_base_/models/hornet/hornet-base.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py',
+]
+
+data = dict(samples_per_gpu=64)
+
+optimizer = dict(lr=4e-3)
+optimizer_config = dict(grad_clip=dict(max_norm=5.0), _delete_=True)
+
+custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]

configs/hornet/hornet-small-gf_8xb64_in1k.py

@@ -0,0 +1,13 @@
+_base_ = [
+    '../_base_/models/hornet/hornet-small-gf.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py',
+]
+
+data = dict(samples_per_gpu=64)
+
+optimizer = dict(lr=4e-3)
+optimizer_config = dict(grad_clip=dict(max_norm=1.0), _delete_=True)
+
+custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]

configs/hornet/hornet-small_8xb64_in1k.py

@@ -0,0 +1,13 @@
+_base_ = [
+    '../_base_/models/hornet/hornet-small.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py',
+]
+
+data = dict(samples_per_gpu=64)
+
+optimizer = dict(lr=4e-3)
+optimizer_config = dict(grad_clip=dict(max_norm=5.0), _delete_=True)
+
+custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]

configs/hornet/hornet-tiny-gf_8xb128_in1k.py

@@ -0,0 +1,13 @@
+_base_ = [
+    '../_base_/models/hornet/hornet-tiny-gf.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py',
+]
+
+data = dict(samples_per_gpu=128)
+
+optimizer = dict(lr=4e-3)
+optimizer_config = dict(grad_clip=dict(max_norm=1.0), _delete_=True)
+
+custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]

configs/hornet/hornet-tiny_8xb128_in1k.py

@@ -0,0 +1,13 @@
+_base_ = [
+    '../_base_/models/hornet/hornet-tiny.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py',
+]
+
+data = dict(samples_per_gpu=128)
+
+optimizer = dict(lr=4e-3)
+optimizer_config = dict(grad_clip=dict(max_norm=100.0), _delete_=True)
+
+custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]

configs/hornet/metafile.yml

@@ -0,0 +1,97 @@
+Collections:
+  - Name: HorNet
+    Metadata:
+      Training Data: ImageNet-1k
+      Training Techniques:
+        - AdamW
+        - Weight Decay
+      Architecture:
+        - HorNet
+        - gnConv
+    Paper:
+      URL: https://arxiv.org/pdf/2207.14284v2.pdf
+      Title: "HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions"
+    README: configs/hornet/README.md
+    Code:
+      Version: v0.24.0
+      URL: https://github.com/open-mmlab/mmclassification/blob/v0.24.0/mmcls/models/backbones/hornet.py
+
+Models:
+  - Name: hornet-tiny_3rdparty_in1k
+    Metadata:
+      FLOPs: 3980000000   # 3.98G
+      Parameters: 22410000      # 22.41M
+    In Collection: HorNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 82.84
+          Top 5 Accuracy: 96.24
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-tiny_3rdparty_in1k_20220915-0e8eedff.pth
+    Config: configs/hornet/hornet-tiny_8xb128_in1k.py
+  - Name: hornet-tiny-gf_3rdparty_in1k
+    Metadata:
+      FLOPs: 3900000000   # 3.9G
+      Parameters: 22990000      # 22.99M
+    In Collection: HorNet
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 82.98
+          Top 5 Accuracy: 96.38
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-tiny-gf_3rdparty_in1k_20220915-4c35a66b.pth
+    Config: configs/hornet/hornet-tiny-gf_8xb128_in1k.py
+  - Name: hornet-small_3rdparty_in1k
+    Metadata:
+      FLOPs:  8830000000    # 8.83G
+      Parameters: 49530000          # 49.53M
+    In Collection: HorNet
+    Results:
+        - Dataset: ImageNet-1k
+          Metrics:
+            Top 1 Accuracy: 83.79
+            Top 5 Accuracy: 96.75
+          Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-small_3rdparty_in1k_20220915-5935f60f.pth
+    Config: configs/hornet/hornet-small_8xb64_in1k.py
+  - Name: hornet-small-gf_3rdparty_in1k
+    Metadata:
+      FLOPs:  8710000000    # 8.71G
+      Parameters: 50400000          # 50.4M
+    In Collection: HorNet
+    Results:
+        - Dataset: ImageNet-1k
+          Metrics:
+            Top 1 Accuracy: 83.98
+            Top 5 Accuracy: 96.77
+          Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-small-gf_3rdparty_in1k_20220915-649ca492.pth
+    Config: configs/hornet/hornet-small-gf_8xb64_in1k.py
+  - Name: hornet-base_3rdparty_in1k
+    Metadata:
+      FLOPs: 15590000000              # 15.59G
+      Parameters: 87260000            # 87.26M
+    In Collection: HorNet
+    Results:
+        - Dataset: ImageNet-1k
+          Metrics:
+            Top 1 Accuracy: 84.24
+            Top 5 Accuracy: 96.94
+          Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-base_3rdparty_in1k_20220915-a06176bb.pth
+    Config: configs/hornet/hornet-base_8xb64_in1k.py
+  - Name: hornet-base-gf_3rdparty_in1k
+    Metadata:
+      FLOPs: 15420000000              # 15.42G
+      Parameters: 88420000            # 88.42M
+    In Collection: HorNet
+    Results:
+        - Dataset: ImageNet-1k
+          Metrics:
+            Top 1 Accuracy: 84.32
+            Top 5 Accuracy: 96.95
+          Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-base-gf_3rdparty_in1k_20220915-82c06fa7.pth
+    Config: configs/hornet/hornet-base-gf_8xb64_in1k.py

configs/mobilenet_v3/mobilenet-v3-small_8xb16_cifar10.py

@@ -1,5 +1,5 @@
 _base_ = [
-    '../_base_/models/mobilenet-v3-small_8xb16_cifar.py',
+    '../_base_/models/mobilenet-v3-small_cifar.py',
     '../_base_/datasets/cifar10_bs16.py',
     '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
 ]

configs/mvit/README.md

@@ -0,0 +1,44 @@
+# MViT V2
+
+> [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](http://openaccess.thecvf.com//content/CVPR2022/papers/Li_MViTv2_Improved_Multiscale_Vision_Transformers_for_Classification_and_Detection_CVPR_2022_paper.pdf)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for image and video
+classification, as well as object detection. We present an improved version of MViT that incorporates
+decomposed relative positional embeddings and residual pooling connections. We instantiate this architecture
+in five sizes and evaluate it for ImageNet classification, COCO detection and Kinetics video recognition where
+it outperforms prior work. We further compare MViTv2s' pooling attention to window attention mechanisms where
+it outperforms the latter in accuracy/compute. Without bells-and-whistles, MViTv2 has state-of-the-art
+performance in 3 domains: 88.8% accuracy on ImageNet classification, 58.7 boxAP on COCO object detection as
+well as 86.1% on Kinetics-400 video classification.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/26739999/180376227-755243fa-158e-4068-940a-416036519665.png" width="50%"/>
+</div>
+
+## Results and models
+
+### ImageNet-1k
+
+|     Model      |   Pretrain   | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                                Config                                |                                Download                                 |
+| :------------: | :----------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------------: | :---------------------------------------------------------------------: |
+| MViTv2-tiny\*  | From scratch |   24.17   |   4.70   |   82.33   |   96.15   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-tiny_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-tiny_3rdparty_in1k_20220722-db7beeef.pth) |
+| MViTv2-small\* | From scratch |   34.87   |   7.00   |   83.63   |   96.51   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-small_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-small_3rdparty_in1k_20220722-986bd741.pth) |
+| MViTv2-base\*  | From scratch |   51.47   |  10.20   |   84.34   |   96.86   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-base_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-base_3rdparty_in1k_20220722-9c4f0a17.pth) |
+| MViTv2-large\* | From scratch |  217.99   |  42.10   |   85.25   |   97.14   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-large_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-large_3rdparty_in1k_20220722-2b57b983.pth) |
+
+*Models with * are converted from the [official repo](https://github.com/facebookresearch/mvit). 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
+
+```bibtex
+@inproceedings{li2021improved,
+  title={MViTv2: Improved multiscale vision transformers for classification and detection},
+  author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph},
+  booktitle={CVPR},
+  year={2022}
+}
+```

configs/mvit/metafile.yml

@@ -0,0 +1,95 @@
+Collections:
+  - Name: MViT V2
+    Metadata:
+      Architecture:
+        - Attention Dropout
+        - Convolution
+        - Dense Connections
+        - GELU
+        - Layer Normalization
+        - Scaled Dot-Product Attention
+        - Attention Pooling
+    Paper:
+      URL: http://openaccess.thecvf.com//content/CVPR2022/papers/Li_MViTv2_Improved_Multiscale_Vision_Transformers_for_Classification_and_Detection_CVPR_2022_paper.pdf
+      Title: 'MViTv2: Improved Multiscale Vision Transformers for Classification and Detection'
+    README: configs/mvit/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmclassification/blob/v0.24.0/mmcls/models/backbones/mvit.py
+      Version: v0.24.0
+
+Models:
+  - Name: mvitv2-tiny_3rdparty_in1k
+    In Collection: MViT V2
+    Metadata:
+      FLOPs: 4700000000
+      Parameters: 24173320
+      Training Data:
+        - ImageNet-1k
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 82.33
+        Top 5 Accuracy: 96.15
+    Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-tiny_3rdparty_in1k_20220722-db7beeef.pth
+    Converted From:
+      Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_T_in1k.pyth
+      Code: https://github.com/facebookresearch/mvit
+    Config: configs/mvit/mvitv2-tiny_8xb256_in1k.py
+
+  - Name: mvitv2-small_3rdparty_in1k
+    In Collection: MViT V2
+    Metadata:
+      FLOPs: 7000000000
+      Parameters: 34870216
+      Training Data:
+        - ImageNet-1k
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 83.63
+        Top 5 Accuracy: 96.51
+    Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-small_3rdparty_in1k_20220722-986bd741.pth
+    Converted From:
+      Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_S_in1k.pyth
+      Code: https://github.com/facebookresearch/mvit
+    Config: configs/mvit/mvitv2-small_8xb256_in1k.py
+
+  - Name: mvitv2-base_3rdparty_in1k
+    In Collection: MViT V2
+    Metadata:
+      FLOPs: 10200000000
+      Parameters: 51472744
+      Training Data:
+        - ImageNet-1k
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 84.34
+        Top 5 Accuracy: 96.86
+    Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-base_3rdparty_in1k_20220722-9c4f0a17.pth
+    Converted From:
+      Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_B_in1k.pyth
+      Code: https://github.com/facebookresearch/mvit
+    Config: configs/mvit/mvitv2-base_8xb256_in1k.py
+
+  - Name: mvitv2-large_3rdparty_in1k
+    In Collection: MViT V2
+    Metadata:
+      FLOPs: 42100000000
+      Parameters: 217992952
+      Training Data:
+        - ImageNet-1k
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 85.25
+        Top 5 Accuracy: 97.14
+    Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-large_3rdparty_in1k_20220722-2b57b983.pth
+    Converted From:
+      Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_L_in1k.pyth
+      Code: https://github.com/facebookresearch/mvit
+    Config: configs/mvit/mvitv2-large_8xb256_in1k.py

configs/mvit/mvitv2-base_8xb256_in1k.py

@@ -0,0 +1,29 @@
+_base_ = [
+    '../_base_/models/mvit/mvitv2-base.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset settings
+data = dict(samples_per_gpu=256)
+
+# schedule settings
+paramwise_cfg = dict(
+    norm_decay_mult=0.0,
+    bias_decay_mult=0.0,
+    custom_keys={
+        '.pos_embed': dict(decay_mult=0.0),
+        '.rel_pos_h': dict(decay_mult=0.0),
+        '.rel_pos_w': dict(decay_mult=0.0)
+    })
+
+optimizer = dict(lr=0.00025, paramwise_cfg=paramwise_cfg)
+optimizer_config = dict(grad_clip=dict(max_norm=1.0))
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    warmup='linear',
+    warmup_iters=70,
+    warmup_by_epoch=True)

configs/mvit/mvitv2-large_8xb256_in1k.py

@@ -0,0 +1,29 @@
+_base_ = [
+    '../_base_/models/mvit/mvitv2-large.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs2048_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset settings
+data = dict(samples_per_gpu=256)
+
+# schedule settings
+paramwise_cfg = dict(
+    norm_decay_mult=0.0,
+    bias_decay_mult=0.0,
+    custom_keys={
+        '.pos_embed': dict(decay_mult=0.0),
+        '.rel_pos_h': dict(decay_mult=0.0),
+        '.rel_pos_w': dict(decay_mult=0.0)
+    })
+
+optimizer = dict(lr=0.00025, paramwise_cfg=paramwise_cfg)
+optimizer_config = dict(grad_clip=dict(max_norm=1.0))
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    warmup='linear',
+    warmup_iters=70,
+    warmup_by_epoch=True)

configs/mvit/mvitv2-small_8xb256_in1k.py

@@ -0,0 +1,29 @@
+_base_ = [
+    '../_base_/models/mvit/mvitv2-small.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs2048_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset settings
+data = dict(samples_per_gpu=256)
+
+# schedule settings
+paramwise_cfg = dict(
+    norm_decay_mult=0.0,
+    bias_decay_mult=0.0,
+    custom_keys={
+        '.pos_embed': dict(decay_mult=0.0),
+        '.rel_pos_h': dict(decay_mult=0.0),
+        '.rel_pos_w': dict(decay_mult=0.0)
+    })
+
+optimizer = dict(lr=0.00025, paramwise_cfg=paramwise_cfg)
+optimizer_config = dict(grad_clip=dict(max_norm=1.0))
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    warmup='linear',
+    warmup_iters=70,
+    warmup_by_epoch=True)

configs/mvit/mvitv2-tiny_8xb256_in1k.py

@@ -0,0 +1,29 @@
+_base_ = [
+    '../_base_/models/mvit/mvitv2-tiny.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs2048_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset settings
+data = dict(samples_per_gpu=256)
+
+# schedule settings
+paramwise_cfg = dict(
+    norm_decay_mult=0.0,
+    bias_decay_mult=0.0,
+    custom_keys={
+        '.pos_embed': dict(decay_mult=0.0),
+        '.rel_pos_h': dict(decay_mult=0.0),
+        '.rel_pos_w': dict(decay_mult=0.0)
+    })
+
+optimizer = dict(lr=0.00025, paramwise_cfg=paramwise_cfg)
+optimizer_config = dict(grad_clip=dict(max_norm=1.0))
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    warmup='linear',
+    warmup_iters=70,
+    warmup_by_epoch=True)

configs/resnet/README.md

@@ -72,6 +72,12 @@ The pre-trained models on ImageNet-21k are used to fine-tune, and therefore don'
 | :-------: | :--------------------------------------------------: | :--------: | :-------: | :------: | :-------: | :------------------------------------------------: | :---------------------------------------------------: |
 | 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) |
 
+### Stanford-Cars
+
+|   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   |   92.82   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb8_cars.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cars_20220812-9d85901a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cars_20220812-9d85901a.log.json) |
+
 ## Citation
 
 ```

configs/resnet/metafile.yml

@@ -298,44 +298,6 @@ Models:
           Top 5 Accuracy: 93.80
     Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.pth
     Config: configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py
-  - Name: wide-resnet50_3rdparty_8xb32_in1k
-    Metadata:
-      FLOPs: 11440000000  # 11.44G
-      Parameters: 68880000  # 68.88M
-      Training Techniques:
-        - SGD with Momentum
-        - Weight Decay
-    In Collection: ResNet
-    Results:
-      - Task: Image Classification
-        Dataset: ImageNet-1k
-        Metrics:
-          Top 1 Accuracy: 78.48
-          Top 5 Accuracy: 94.08
-    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/wide-resnet50_3rdparty_8xb32_in1k_20220304-66678344.pth
-    Config: configs/resnet/wide-resnet50_8xb32_in1k.py
-    Converted From:
-      Weights: https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth
-      Code: https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py
-  - Name: wide-resnet101_3rdparty_8xb32_in1k
-    Metadata:
-      FLOPs: 22810000000  # 22.81G
-      Parameters: 126890000 # 126.89M
-      Training Techniques:
-        - SGD with Momentum
-        - Weight Decay
-    In Collection: ResNet
-    Results:
-      - Task: Image Classification
-        Dataset: ImageNet-1k
-        Metrics:
-          Top 1 Accuracy: 78.84
-          Top 5 Accuracy: 94.28
-    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth
-    Config: configs/resnet/wide-resnet101_8xb32_in1k.py
-    Converted From:
-      Weights: https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth
-      Code: https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py
   - Name: resnetv1c50_8xb32_in1k
     Metadata:
       FLOPs: 4360000000
@@ -388,3 +350,16 @@ Models:
     Pretrain: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth
     Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.pth
     Config: configs/resnet/resnet50_8xb8_cub.py
+  - Name: resnet50_8xb8_cars
+    Metadata:
+      FLOPs: 16480000000
+      Parameters: 23920000
+    In Collection: ResNet
+    Results:
+      - Dataset: StanfordCars
+        Metrics:
+          Top 1 Accuracy: 92.82
+        Task: Image Classification
+    Pretrain: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cars_20220812-9d85901a.pth
+    Config: configs/resnet/resnet50_8xb8_cars.py

configs/resnet/resnet50_8xb8_cars.py

@@ -0,0 +1,19 @@
+_base_ = [
+    '../_base_/models/resnet50.py',
+    '../_base_/datasets/stanford_cars_bs8_448.py',
+    '../_base_/schedules/stanford_cars_bs8.py', '../_base_/default_runtime.py'
+]
+
+# use pre-train weight converted from https://github.com/Alibaba-MIIL/ImageNet21K # noqa
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth'  # noqa
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        init_cfg=dict(
+            type='Pretrained', checkpoint=checkpoint, prefix='backbone')),
+    head=dict(num_classes=196, ))
+
+log_config = dict(interval=50)
+checkpoint_config = dict(
+    interval=1, max_keep_ckpts=3)  # save last three checkpoints

configs/swin_transformer_v2/README.md

@@ -0,0 +1,58 @@
+# Swin Transformer V2
+
+> [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883.pdf)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Large-scale NLP models have been shown to significantly improve the performance on language tasks with no signs of saturation. They also demonstrate amazing few-shot capabilities like that of human beings. This paper aims to explore large-scale models in computer vision. We tackle three major issues in training and application of large vision models, including training instability, resolution gaps between pre-training and fine-tuning, and hunger on labelled data. Three main techniques are proposed: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) A log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) A self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images. Through these techniques, this paper successfully trained a 3 billion-parameter Swin Transformer V2 model, which is the largest dense vision model to date, and makes it capable of training with images of up to 1,536×1,536 resolution. It set new performance records on 4 representative vision tasks, including ImageNet-V2 image classification, COCO object detection, ADE20K semantic segmentation, and Kinetics-400 video action classification. Also note our training is much more efficient than that in Google's billion-level visual models, which consumes 40 times less labelled data and 40 times less training time.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/42952108/180748696-ee7ed23d-7fee-4ccf-9eb5-f117db228a42.png" width="100%"/>
+</div>
+
+## Results and models
+
+### ImageNet-21k
+
+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\* |  192x192   |   87.92   |   8.51   | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-base-w12_3rdparty_in21k-192px_20220803-f7dc9763.pth)  |
+| Swin-L\* |  192x192   |  196.74   |  19.04   | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-large-w12_3rdparty_in21k-192px_20220803-d9073fee.pth) |
+
+### ImageNet-1k
+
+|  Model   |   Pretrain   | resolution | window | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                             Config                              |                              Download                              |
+| :------: | :----------: | :--------: | :----: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------: | :----------------------------------------------------------------: |
+| Swin-T\* | From scratch |  256x256   |  8x8   |   28.35   |   4.35   |   81.76   |   95.87   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-tiny-w8_3rdparty_in1k-256px_20220803-e318968f.pth) |
+| Swin-T\* | From scratch |  256x256   | 16x16  |   28.35   |   4.4    |   82.81   |   96.23   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-tiny-w16_3rdparty_in1k-256px_20220803-9651cdd7.pth) |
+| Swin-S\* | From scratch |  256x256   |  8x8   |   49.73   |   8.45   |   83.74   |   96.6    | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-small-w8_3rdparty_in1k-256px_20220803-b01a4332.pth) |
+| Swin-S\* | From scratch |  256x256   | 16x16  |   49.73   |   8.57   |   84.13   |   96.83   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-small-w16_3rdparty_in1k-256px_20220803-b707d206.pth) |
+| Swin-B\* | From scratch |  256x256   |  8x8   |   87.92   |  14.99   |   84.2    |   96.86   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w8_3rdparty_in1k-256px_20220803-8ff28f2b.pth) |
+| Swin-B\* | From scratch |  256x256   | 16x16  |   87.92   |  15.14   |   84.6    |   97.05   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w16_3rdparty_in1k-256px_20220803-5a1886b7.pth) |
+| Swin-B\* | ImageNet-21k |  256x256   | 16x16  |   87.92   |  15.14   |   86.17   |   97.88   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w16_in21k-pre_3rdparty_in1k-256px_20220803-8d7aa8ad.pth) |
+| Swin-B\* | ImageNet-21k |  384x384   | 24x24  |   87.92   |  34.07   |   87.14   |   98.23   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w24_in21k-pre_3rdparty_in1k-384px_20220803-44eb70f8.pth) |
+| Swin-L\* | ImageNet-21k |  256X256   | 16x16  |  196.75   |  33.86   |   86.93   |   98.06   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-large-w16_in21k-pre_3rdparty_in1k-256px_20220803-c40cbed7.pth) |
+| Swin-L\* | ImageNet-21k |  384x384   | 24x24  |  196.75   |   76.2   |   87.59   |   98.27   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-large-w24_in21k-pre_3rdparty_in1k-384px_20220803-3b36c165.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.*
+
+*ImageNet-21k pretrained models with input resolution of 256x256 and 384x384 both fine-tuned from the same pre-training model using a smaller input resolution of 192x192.*
+
+## Citation
+
+```
+@article{https://doi.org/10.48550/arxiv.2111.09883,
+  doi = {10.48550/ARXIV.2111.09883},
+  url = {https://arxiv.org/abs/2111.09883},
+  author = {Liu, Ze and Hu, Han and Lin, Yutong and Yao, Zhuliang and Xie, Zhenda and Wei, Yixuan and Ning, Jia and Cao, Yue and Zhang, Zheng and Dong, Li and Wei, Furu and Guo, Baining},
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {Swin Transformer V2: Scaling Up Capacity and Resolution},
+  publisher = {arXiv},
+  year = {2021},
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```

configs/swin_transformer_v2/metafile.yml

@@ -0,0 +1,204 @@
+Collections:
+  - Name: Swin-Transformer-V2
+    Metadata:
+      Training Data: ImageNet-1k
+      Training Techniques:
+        - AdamW
+        - Weight Decay
+      Training Resources: 16x V100 GPUs
+      Epochs: 300
+      Batch Size: 1024
+      Architecture:
+        - Shift Window Multihead Self Attention
+    Paper:
+      URL: https://arxiv.org/abs/2111.09883.pdf
+      Title: "Swin Transformer V2: Scaling Up Capacity and Resolution"
+    README: configs/swin_transformer_v2/README.md
+
+Models:
+  - Name: swinv2-tiny-w8_3rdparty_in1k-256px
+    Metadata:
+      FLOPs: 4350000000
+      Parameters: 28350000
+    In Collection: Swin-Transformer-V2
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 81.76
+          Top 5 Accuracy: 95.87
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-tiny-w8_3rdparty_in1k-256px_20220803-e318968f.pth
+    Config: configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py
+    Converted From:
+      Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window8_256.pth
+      Code: https://github.com/microsoft/Swin-Transformer
+  - Name: swinv2-tiny-w16_3rdparty_in1k-256px
+    Metadata:
+      FLOPs: 4400000000
+      Parameters: 28350000
+    In Collection: Swin-Transformer-V2
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 82.81
+          Top 5 Accuracy: 96.23
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-tiny-w16_3rdparty_in1k-256px_20220803-9651cdd7.pth
+    Config: configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py
+    Converted From:
+      Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window16_256.pth
+      Code: https://github.com/microsoft/Swin-Transformer
+  - Name: swinv2-small-w8_3rdparty_in1k-256px
+    Metadata:
+      FLOPs: 8450000000
+      Parameters: 49730000
+    In Collection: Swin-Transformer-V2
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 83.74
+          Top 5 Accuracy: 96.6
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-small-w8_3rdparty_in1k-256px_20220803-b01a4332.pth
+    Config: configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py
+    Converted From:
+      Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window8_256.pth
+      Code: https://github.com/microsoft/Swin-Transformer
+  - Name: swinv2-small-w16_3rdparty_in1k-256px
+    Metadata:
+      FLOPs: 8570000000
+      Parameters: 49730000
+    In Collection: Swin-Transformer-V2
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 84.13
+          Top 5 Accuracy: 96.83
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-small-w16_3rdparty_in1k-256px_20220803-b707d206.pth
+    Config: configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py
+    Converted From:
+      Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window16_256.pth
+      Code: https://github.com/microsoft/Swin-Transformer
+  - Name: swinv2-base-w8_3rdparty_in1k-256px
+    Metadata:
+      FLOPs: 14990000000
+      Parameters: 87920000
+    In Collection: Swin-Transformer-V2
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 84.2
+          Top 5 Accuracy: 96.86
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w8_3rdparty_in1k-256px_20220803-8ff28f2b.pth
+    Config: configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py
+    Converted From:
+      Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window8_256.pth
+      Code: https://github.com/microsoft/Swin-Transformer
+  - Name: swinv2-base-w16_3rdparty_in1k-256px
+    Metadata:
+      FLOPs: 15140000000
+      Parameters: 87920000
+    In Collection: Swin-Transformer-V2
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 84.6
+          Top 5 Accuracy: 97.05
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w16_3rdparty_in1k-256px_20220803-5a1886b7.pth
+    Config: configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py
+    Converted From:
+      Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window16_256.pth
+      Code: https://github.com/microsoft/Swin-Transformer
+  - Name: swinv2-base-w16_in21k-pre_3rdparty_in1k-256px
+    Metadata:
+      Training Data: ImageNet-21k
+      FLOPs: 15140000000
+      Parameters: 87920000
+    In Collection: Swin-Transformer-V2
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 86.17
+          Top 5 Accuracy: 97.88
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w16_in21k-pre_3rdparty_in1k-256px_20220803-8d7aa8ad.pth
+    Config: configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py
+    Converted From:
+      Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.pth
+      Code: https://github.com/microsoft/Swin-Transformer
+  - Name: swinv2-base-w24_in21k-pre_3rdparty_in1k-384px
+    Metadata:
+      Training Data: ImageNet-21k
+      FLOPs: 34070000000
+      Parameters: 87920000
+    In Collection: Swin-Transformer-V2
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 87.14
+          Top 5 Accuracy: 98.23
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w24_in21k-pre_3rdparty_in1k-384px_20220803-44eb70f8.pth
+    Config: configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py
+    Converted From:
+      Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.pth
+      Code: https://github.com/microsoft/Swin-Transformer
+  - Name: swinv2-large-w16_in21k-pre_3rdparty_in1k-256px
+    Metadata:
+      Training Data: ImageNet-21k
+      FLOPs: 33860000000
+      Parameters: 196750000
+    In Collection: Swin-Transformer-V2
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 86.93
+          Top 5 Accuracy: 98.06
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-large-w16_in21k-pre_3rdparty_in1k-256px_20220803-c40cbed7.pth
+    Config: configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py
+    Converted From:
+      Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.pth
+      Code: https://github.com/microsoft/Swin-Transformer
+  - Name: swinv2-large-w24_in21k-pre_3rdparty_in1k-384px
+    Metadata:
+      Training Data: ImageNet-21k
+      FLOPs: 76200000000
+      Parameters: 196750000
+    In Collection: Swin-Transformer-V2
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 87.59
+          Top 5 Accuracy: 98.27
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-large-w24_in21k-pre_3rdparty_in1k-384px_20220803-3b36c165.pth
+    Config: configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py
+    Converted From:
+      Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.pth
+      Code: https://github.com/microsoft/Swin-Transformer
+  - Name: swinv2-base-w12_3rdparty_in21k-192px
+    Metadata:
+      Training Data: ImageNet-21k
+      FLOPs: 8510000000
+      Parameters: 87920000
+    In Collections: Swin-Transformer-V2
+    Results: null
+    Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-base-w12_3rdparty_in21k-192px_20220803-f7dc9763.pth
+    Converted From:
+      Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12_192_22k.pth
+      Code: https://github.com/microsoft/Swin-Transformer
+  - Name: swinv2-large-w12_3rdparty_in21k-192px
+    Metadata:
+      Training Data: ImageNet-21k
+      FLOPs: 19040000000
+      Parameters: 196740000
+    In Collections: Swin-Transformer-V2
+    Results: null
+    Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-large-w12_3rdparty_in21k-192px_20220803-d9073fee.pth
+    Converted From:
+      Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12_192_22k.pth
+      Code: https://github.com/microsoft/Swin-Transformer

configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py

@@ -0,0 +1,8 @@
+_base_ = [
+    '../_base_/models/swin_transformer_v2/base_256.py',
+    '../_base_/datasets/imagenet_bs64_swin_256.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+model = dict(backbone=dict(window_size=[16, 16, 16, 8]))

configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py

@@ -0,0 +1,13 @@
+_base_ = [
+    '../_base_/models/swin_transformer_v2/base_256.py',
+    '../_base_/datasets/imagenet_bs64_swin_256.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        window_size=[16, 16, 16, 8],
+        drop_path_rate=0.2,
+        pretrained_window_sizes=[12, 12, 12, 6]))

configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py

@@ -0,0 +1,14 @@
+_base_ = [
+    '../_base_/models/swin_transformer_v2/base_384.py',
+    '../_base_/datasets/imagenet_bs64_swin_384.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        img_size=384,
+        window_size=[24, 24, 24, 12],
+        drop_path_rate=0.2,
+        pretrained_window_sizes=[12, 12, 12, 6]))

configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py

@@ -0,0 +1,6 @@
+_base_ = [
+    '../_base_/models/swin_transformer_v2/base_256.py',
+    '../_base_/datasets/imagenet_bs64_swin_256.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]

configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py

@@ -0,0 +1,13 @@
+# Only for evaluation
+_base_ = [
+    '../_base_/models/swin_transformer_v2/large_256.py',
+    '../_base_/datasets/imagenet_bs64_swin_256.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        window_size=[16, 16, 16, 8], pretrained_window_sizes=[12, 12, 12, 6]),
+)

configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py

@@ -0,0 +1,15 @@
+# Only for evaluation
+_base_ = [
+    '../_base_/models/swin_transformer_v2/large_384.py',
+    '../_base_/datasets/imagenet_bs64_swin_384.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        img_size=384,
+        window_size=[24, 24, 24, 12],
+        pretrained_window_sizes=[12, 12, 12, 6]),
+)

configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py

@@ -0,0 +1,8 @@
+_base_ = [
+    '../_base_/models/swin_transformer_v2/small_256.py',
+    '../_base_/datasets/imagenet_bs64_swin_256.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+model = dict(backbone=dict(window_size=[16, 16, 16, 8]))

configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py

@@ -0,0 +1,6 @@
+_base_ = [
+    '../_base_/models/swin_transformer_v2/small_256.py',
+    '../_base_/datasets/imagenet_bs64_swin_256.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]

configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py

@@ -0,0 +1,8 @@
+_base_ = [
+    '../_base_/models/swin_transformer_v2/tiny_256.py',
+    '../_base_/datasets/imagenet_bs64_swin_256.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+model = dict(backbone=dict(window_size=[16, 16, 16, 8]))

configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py

@@ -0,0 +1,6 @@
+_base_ = [
+    '../_base_/models/swin_transformer_v2/tiny_256.py',
+    '../_base_/datasets/imagenet_bs64_swin_256.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]

configs/t2t_vit/README.md

@@ -6,7 +6,7 @@
 
 ## 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, e.g., 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.
 
 <div align=center>
 <img src="https://user-images.githubusercontent.com/26739999/142578381-e9040610-05d9-457c-8bf5-01c2fa94add2.png" width="60%"/>

configs/van/README.md

@@ -16,15 +16,28 @@ While originally designed for natural language processing (NLP) tasks, the self-
 
 ### 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-B0\* | From scratch |  224x224   |   4.11    |   0.88   |   75.41   |   93.02   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b0_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth) |
+| VAN-B1\* | From scratch |  224x224   |   13.86   |   2.52   |   81.01   |   95.63   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b1_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) |
+| VAN-B2\* | From scratch |  224x224   |   26.58   |   5.03   |   82.80   |   96.21   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b2_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) |
+| VAN-B3\* | From scratch |  224x224   |   44.77   |   8.99   |   83.86   |   96.73   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b3_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) |
+| VAN-B4\* | From scratch |  224x224   |   60.28   |  12.22   |   84.13   |   96.86   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b4_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-b4_3rdparty_in1k_20220909-f4665b92.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.
 
+### Pre-trained Models
+
+The pre-trained models on ImageNet-21k are used to fine-tune on the downstream tasks.
+
+|  Model   |   Pretrain   | resolution | Params(M) | Flops(G) |                                                  Download                                                   |
+| :------: | :----------: | :--------: | :-------: | :------: | :---------------------------------------------------------------------------------------------------------: |
+| VAN-B4\* | ImageNet-21k |  224x224   |   60.28   |  12.22   | [model](https://download.openmmlab.com/mmclassification/v0/van/van-b4_3rdparty_in21k_20220909-db926b18.pth) |
+| VAN-B5\* | ImageNet-21k |  224x224   |   89.97   |  17.21   | [model](https://download.openmmlab.com/mmclassification/v0/van/van-b5_3rdparty_in21k_20220909-18e904e3.pth) |
+| VAN-B6\* | ImageNet-21k |  224x224   |   283.9   |  55.28   | [model](https://download.openmmlab.com/mmclassification/v0/van/van-b6_3rdparty_in21k_20220909-96c2cb3a.pth) |
+
+\*Models with * are converted from [the official repo](https://github.com/Visual-Attention-Network/VAN-Classification).
+
 ## Citation
 
 ```

configs/van/metafile.yml

@@ -7,6 +7,7 @@ Collections:
         - Weight Decay
       Architecture:
         - Visual Attention Network
+        - LKA
     Paper:
       URL: https://arxiv.org/pdf/2202.09741v2.pdf
       Title: "Visual Attention Network"
@@ -16,10 +17,10 @@ Collections:
       Version: v0.23.0
 
 Models:
-  - Name: van-tiny_8xb128_in1k
+  - Name: van-b0_3rdparty_in1k
     Metadata:
-      FLOPs: 4110000      # 4.11M
-      Parameters: 880000000   # 0.88G
+      FLOPs: 880000000   # 0.88G
+      Parameters: 4110000      # 4.11M
     In Collection: Visual-Attention-Network
     Results:
       - Dataset: ImageNet-1k
@@ -28,11 +29,11 @@ Models:
           Top 5 Accuracy: 93.02
         Task: Image Classification
     Weights: https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth
-    Config: configs/van/van-tiny_8xb128_in1k.py
-  - Name: van-small_8xb128_in1k
+    Config: configs/van/van-b0_8xb128_in1k.py
+  - Name: van-b1_3rdparty_in1k
     Metadata:
-      FLOPs:  13860000          # 13.86M
-      Parameters: 2520000000    # 2.52G
+      FLOPs:  2520000000    # 2.52G
+      Parameters: 13860000          # 13.86M
     In Collection: Visual-Attention-Network
     Results:
         - Dataset: ImageNet-1k
@@ -41,11 +42,11 @@ Models:
             Top 5 Accuracy: 95.63
           Task: Image Classification
     Weights: https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth
-    Config: configs/van/van-small_8xb128_in1k.py
-  - Name: van-base_8xb128_in1k
+    Config: configs/van/van-b1_8xb128_in1k.py
+  - Name: van-b2_3rdparty_in1k
     Metadata:
-      FLOPs: 26580000            # 26.58M
-      Parameters: 5030000000                # 5.03G
+      FLOPs: 5030000000                # 5.03G
+      Parameters: 26580000            # 26.58M
     In Collection: Visual-Attention-Network
     Results:
         - Dataset: ImageNet-1k
@@ -54,11 +55,11 @@ Models:
             Top 5 Accuracy: 96.21
           Task: Image Classification
     Weights: https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth
-    Config: configs/van/van-base_8xb128_in1k.py
-  - Name: van-large_8xb128_in1k
+    Config: configs/van/van-b2_8xb128_in1k.py
+  - Name: van-b3_3rdparty_in1k
     Metadata:
-      FLOPs: 44770000              # 44.77 M
-      Parameters: 8990000000              # 8.99G
+      FLOPs: 8990000000              # 8.99G
+      Parameters: 44770000              # 44.77M
     In Collection: Visual-Attention-Network
     Results:
         - Dataset: ImageNet-1k
@@ -67,4 +68,17 @@ Models:
             Top 5 Accuracy: 96.73
           Task: Image Classification
     Weights: https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth
-    Config: configs/van/van-large_8xb128_in1k.py
+    Config: configs/van/van-b3_8xb128_in1k.py
+  - Name: van-b4_3rdparty_in1k
+    Metadata:
+      FLOPs: 12220000000              # 12.22G
+      Parameters: 60280000              # 60.28M
+    In Collection: Visual-Attention-Network
+    Results:
+        - Dataset: ImageNet-1k
+          Metrics:
+            Top 1 Accuracy: 84.13
+            Top 5 Accuracy: 96.86
+          Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/van/van-b4_3rdparty_in1k_20220909-f4665b92.pth
+    Config: configs/van/van-b4_8xb128_in1k.py

configs/van/van-b0_8xb128_in1k.py

@@ -0,0 +1,61 @@
+_base_ = [
+    '../_base_/models/van/van_b0.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# Note that the mean and variance used here are different from other configs
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        size=224,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies={{_base_.rand_increasing_policies}},
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
+            interpolation='bicubic')),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=img_norm_cfg['mean'][::-1],
+        fill_std=img_norm_cfg['std'][::-1]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        size=(248, -1),
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(
+    samples_per_gpu=128,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

configs/van/van-b1_8xb128_in1k.py

@@ -0,0 +1,61 @@
+_base_ = [
+    '../_base_/models/van/van_b1.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# Note that the mean and variance used here are different from other configs
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        size=224,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies={{_base_.rand_increasing_policies}},
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
+            interpolation='bicubic')),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=img_norm_cfg['mean'][::-1],
+        fill_std=img_norm_cfg['std'][::-1]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        size=(248, -1),
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(
+    samples_per_gpu=128,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

configs/van/van-b2_8xb128_in1k.py

@@ -0,0 +1,61 @@
+_base_ = [
+    '../_base_/models/van/van_b2.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# Note that the mean and variance used here are different from other configs
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        size=224,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies={{_base_.rand_increasing_policies}},
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
+            interpolation='bicubic')),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=img_norm_cfg['mean'][::-1],
+        fill_std=img_norm_cfg['std'][::-1]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        size=(248, -1),
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(
+    samples_per_gpu=128,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

configs/van/van-b3_8xb128_in1k.py

@@ -0,0 +1,61 @@
+_base_ = [
+    '../_base_/models/van/van_b3.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# Note that the mean and variance used here are different from other configs
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        size=224,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies={{_base_.rand_increasing_policies}},
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
+            interpolation='bicubic')),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=img_norm_cfg['mean'][::-1],
+        fill_std=img_norm_cfg['std'][::-1]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        size=(248, -1),
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(
+    samples_per_gpu=128,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

configs/van/van-b4_8xb128_in1k.py

@@ -0,0 +1,61 @@
+_base_ = [
+    '../_base_/models/van/van_b4.py',
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# Note that the mean and variance used here are different from other configs
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandomResizedCrop',
+        size=224,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies={{_base_.rand_increasing_policies}},
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
+            interpolation='bicubic')),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=img_norm_cfg['mean'][::-1],
+        fill_std=img_norm_cfg['std'][::-1]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='Resize',
+        size=(248, -1),
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(
+    samples_per_gpu=128,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

configs/van/van-base_8xb128_in1k.py

@@ -1,61 +1,6 @@
-_base_ = [
-    '../_base_/models/van/van_base.py',
-    '../_base_/datasets/imagenet_bs64_swin_224.py',
-    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
-    '../_base_/default_runtime.py'
-]
+_base_ = ['./van-b2_8xb128_in1k.py']
 
-# Note that the mean and variance used here are different from other configs
-img_norm_cfg = dict(
-    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='RandomResizedCrop',
-        size=224,
-        backend='pillow',
-        interpolation='bicubic'),
-    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
-    dict(
-        type='RandAugment',
-        policies={{_base_.rand_increasing_policies}},
-        num_policies=2,
-        total_level=10,
-        magnitude_level=9,
-        magnitude_std=0.5,
-        hparams=dict(
-            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
-            interpolation='bicubic')),
-    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
-    dict(
-        type='RandomErasing',
-        erase_prob=0.25,
-        mode='rand',
-        min_area_ratio=0.02,
-        max_area_ratio=1 / 3,
-        fill_color=img_norm_cfg['mean'][::-1],
-        fill_std=img_norm_cfg['std'][::-1]),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='ImageToTensor', keys=['img']),
-    dict(type='ToTensor', keys=['gt_label']),
-    dict(type='Collect', keys=['img', 'gt_label'])
-]
-
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='Resize',
-        size=(248, -1),
-        backend='pillow',
-        interpolation='bicubic'),
-    dict(type='CenterCrop', crop_size=224),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='ImageToTensor', keys=['img']),
-    dict(type='Collect', keys=['img'])
-]
-
-data = dict(
-    samples_per_gpu=128,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
+_deprecation_ = dict(
+    expected='van-b2_8xb128_in1k.p',
+    reference='https://github.com/open-mmlab/mmclassification/pull/1017',
+)

configs/van/van-large_8xb128_in1k.py

@@ -1,61 +1,6 @@
-_base_ = [
-    '../_base_/models/van/van_large.py',
-    '../_base_/datasets/imagenet_bs64_swin_224.py',
-    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
-    '../_base_/default_runtime.py'
-]
+_base_ = ['./van-b3_8xb128_in1k.py']
 
-# Note that the mean and variance used here are different from other configs
-img_norm_cfg = dict(
-    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='RandomResizedCrop',
-        size=224,
-        backend='pillow',
-        interpolation='bicubic'),
-    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
-    dict(
-        type='RandAugment',
-        policies={{_base_.rand_increasing_policies}},
-        num_policies=2,
-        total_level=10,
-        magnitude_level=9,
-        magnitude_std=0.5,
-        hparams=dict(
-            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
-            interpolation='bicubic')),
-    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
-    dict(
-        type='RandomErasing',
-        erase_prob=0.25,
-        mode='rand',
-        min_area_ratio=0.02,
-        max_area_ratio=1 / 3,
-        fill_color=img_norm_cfg['mean'][::-1],
-        fill_std=img_norm_cfg['std'][::-1]),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='ImageToTensor', keys=['img']),
-    dict(type='ToTensor', keys=['gt_label']),
-    dict(type='Collect', keys=['img', 'gt_label'])
-]
-
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='Resize',
-        size=(248, -1),
-        backend='pillow',
-        interpolation='bicubic'),
-    dict(type='CenterCrop', crop_size=224),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='ImageToTensor', keys=['img']),
-    dict(type='Collect', keys=['img'])
-]
-
-data = dict(
-    samples_per_gpu=128,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
+_deprecation_ = dict(
+    expected='van-b3_8xb128_in1k.p',
+    reference='https://github.com/open-mmlab/mmclassification/pull/1017',
+)

configs/van/van-small_8xb128_in1k.py

@@ -1,61 +1,6 @@
-_base_ = [
-    '../_base_/models/van/van_small.py',
-    '../_base_/datasets/imagenet_bs64_swin_224.py',
-    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
-    '../_base_/default_runtime.py'
-]
+_base_ = ['./van-b1_8xb128_in1k.py']
 
-# Note that the mean and variance used here are different from other configs
-img_norm_cfg = dict(
-    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='RandomResizedCrop',
-        size=224,
-        backend='pillow',
-        interpolation='bicubic'),
-    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
-    dict(
-        type='RandAugment',
-        policies={{_base_.rand_increasing_policies}},
-        num_policies=2,
-        total_level=10,
-        magnitude_level=9,
-        magnitude_std=0.5,
-        hparams=dict(
-            pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]],
-            interpolation='bicubic')),
-    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
-    dict(
-        type='RandomErasing',
-        erase_prob=0.25,
-        mode='rand',
-        min_area_ratio=0.02,
-        max_area_ratio=1 / 3,
-        fill_color=img_norm_cfg['mean'][::-1],
-        fill_std=img_norm_cfg['std'][::-1]),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='ImageToTensor', keys=['img']),
-    dict(type='ToTensor', keys=['gt_label']),
-    dict(type='Collect', keys=['img', 'gt_label'])
-]
-
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='Resize',
-        size=(248, -1),
-        backend='pillow',
-        interpolation='bicubic'),
-    dict(type='CenterCrop', crop_size=224),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='ImageToTensor', keys=['img']),
-    dict(type='Collect', keys=['img'])
-]
-
-data = dict(
-    samples_per_gpu=128,
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
+_deprecation_ = dict(
+    expected='van-b1_8xb128_in1k.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/1017',
+)