Bump version to v0.22.0. (#756)
Ma Zerun
GitHub
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README.md
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README.md
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@ -59,6 +59,13 @@ The master branch works with **PyTorch 1.5+**.
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## What's new
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v0.22.0 was released in 30/3/2022.
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Highlights of the new version:
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- Support a series of **CSP Network**, such as CSP-ResNet, CSP-ResNeXt and CSP-DarkNet.
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- A new `CustomDataset` class to help you **build dataset of yourself**!
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- Support new backbones - **ConvMixer**, **RepMLP** and new dataset - **CUB dataset**.
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v0.21.0 was released in 4/3/2022.
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Highlights of the new version:
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@ -66,13 +73,6 @@ Highlights of the new version:
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- Support **dynamic input shape** for ViT-based algorithms. Now our ViT, DeiT, Swin-Transformer and T2T-ViT support forwarding with any input shape.
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- Reproduce training results of DeiT. And our DeiT-T and DeiT-S have **higher accuracy** comparing with the official weights.
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v0.20.0 was released in 30/1/2022.
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Highlights of the new version:
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- Support **K-fold cross-validation**. The tutorial will be released later.
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- Support **HRNet**, **ConvNeXt**, **Twins** and **EfficientNet**.
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- Support model conversion from PyTorch to **Core ML** by a tool.
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Please refer to [changelog.md](docs/en/changelog.md) for more details and other release history.
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## Installation
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@ -57,20 +57,20 @@ MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [O
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## 更新日志
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2022/3/30 发布了 v0.22.0 版本
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新版本亮点：
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- 支持了一系列 **CSP Net**，包括 CSP-ResNet，CSP-ResNeXt 和 CSP-DarkNet。
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- 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**！
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- 支持了新的主干网络 **ConvMixer**、**RepMLP** 和一个新的数据集 **CUB dataset**。
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2022/3/4 发布了 v0.21.0 版本
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新版本亮点：
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- 支持了 **ResNetV1c** 和 **Wide-ResNet** 两个 ResNet 变种，并提供了预训练模型
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- ViT相关模型支持 **动态输入尺寸**。现在我们的 ViT，DeiT，Swin-Transformer 和 T2T-ViT 支持任意尺寸的输入。
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- ViT 相关模型支持 **动态输入尺寸**。现在我们的 ViT，DeiT，Swin-Transformer 和 T2T-ViT 支持任意尺寸的输入。
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- 复现了 DeiT 的训练结果，并且我们的 DeiT-T 和 DeiT-S 拥有比官方权重 **更高的精度**。
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2022/1/30 发布了 v0.20.0 版本
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新版本亮点：
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- 支持 **K 折交叉验证** 工具。相应文档会在后续添加。
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- 支持了 **HRNet**，**ConvNeXt**，**Twins** 以及 **EfficientNet** 四个主干网络，欢迎使用！
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- 支持了从 PyTorch 模型到 Core-ML 模型的转换工具。
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发布历史和更新细节请参考 [更新日志](docs/en/changelog.md)
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## 安装
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@ -91,7 +91,7 @@ pip3 install -e .
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## 基础教程
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请参考 [基础教程](https://mmclassification.readthedocs.io/zh_CN/latest/getting_started.html) 来了解 MMClassification 的基本使用。MMClassification 也提供了其他更详细的教程:
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请参考 [基础教程](https://mmclassification.readthedocs.io/zh_CN/latest/getting_started.html) 来了解 MMClassification 的基本使用。MMClassification 也提供了其他更详细的教程：
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- [如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)
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- [如何微调模型](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/finetune.html)
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@ -4,7 +4,7 @@ ARG CUDNN="7"
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FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
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ARG MMCV="1.4.2"
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ARG MMCLS="0.21.0"
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ARG MMCLS="0.22.0"
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ENV PYTHONUNBUFFERED TRUE
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@ -1,5 +1,40 @@
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# Changelog
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## v0.22.0(30/3/2022)
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### Highlights
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- Support a series of CSP Network, such as CSP-ResNet, CSP-ResNeXt and CSP-DarkNet.
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- A new `CustomDataset` class to help you build dataset of yourself!
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- Support ConvMixer, RepMLP and new dataset - CUB dataset.
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### New Features
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- [Feature] Add CSPNet and backbone and checkpoints ([#735](https://github.com/open-mmlab/mmclassification/pull/735))
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- [Feature] Add `CustomDataset`. ([#738](https://github.com/open-mmlab/mmclassification/pull/738))
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- [Feature] Add diff seeds to diff ranks. ([#744](https://github.com/open-mmlab/mmclassification/pull/744))
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- [Feature] Support ConvMixer. ([#716](https://github.com/open-mmlab/mmclassification/pull/716))
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- [Feature] Our `dist_train` & `dist_test` tools support distributed training on multiple machines. ([#734](https://github.com/open-mmlab/mmclassification/pull/734))
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- [Feature] Add RepMLP backbone and checkpoints. ([#709](https://github.com/open-mmlab/mmclassification/pull/709))
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- [Feature] Support CUB dataset. ([#703](https://github.com/open-mmlab/mmclassification/pull/703))
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- [Feature] Support ResizeMix. ([#676](https://github.com/open-mmlab/mmclassification/pull/676))
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### Improvements
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- [Enhance] Use `--a-b` instead of `--a_b` in arguments. ([#754](https://github.com/open-mmlab/mmclassification/pull/754))
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- [Enhance] Add `get_cat_ids` and `get_gt_labels` to KFoldDataset. ([#721](https://github.com/open-mmlab/mmclassification/pull/721))
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- [Enhance] Set torch seed in `worker_init_fn`. ([#733](https://github.com/open-mmlab/mmclassification/pull/733))
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### Bug Fixes
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- [Fix] Fix the discontiguous output feature map of ConvNeXt. ([#743](https://github.com/open-mmlab/mmclassification/pull/743))
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### Docs Update
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- [Docs] Add brief installation steps in README for copy&paste. ([#755](https://github.com/open-mmlab/mmclassification/pull/755))
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- [Docs] fix logo url link from mmocr to mmcls. ([#732](https://github.com/open-mmlab/mmclassification/pull/732))
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## v0.21.0(04/03/2022)
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### Highlights
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@ -10,8 +10,9 @@ The compatible MMClassification and MMCV versions are as below. Please install t
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| MMClassification version | MMCV version |
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|:------------------------:|:---------------------:|
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| dev | mmcv>=1.4.6, <=1.5.0 |
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| 0.21.0 (master) | mmcv>=1.4.2, <=1.5.0 |
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| dev | mmcv>=1.4.8, <=1.5.0 |
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| 0.22.0 (master) | mmcv>=1.4.2, <=1.5.0 |
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| 0.21.0 | mmcv>=1.4.2, <=1.5.0 |
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| 0.20.1 | mmcv>=1.4.2, <=1.5.0 |
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| 0.19.0 | mmcv>=1.3.16, <=1.5.0 |
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| 0.18.0 | mmcv>=1.3.16, <=1.5.0 |
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@ -10,8 +10,9 @@ MMClassification 和 MMCV 的适配关系如下，请安装正确版本的 MMCV
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| MMClassification 版本 | MMCV 版本 |
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|:---------------------:|:---------------------:|
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| dev | mmcv>=1.4.6, <=1.5.0 |
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| 0.21.0 (master)| mmcv>=1.4.2, <=1.5.0 |
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| dev | mmcv>=1.4.8, <=1.5.0 |
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| 0.22.0 (master)| mmcv>=1.4.2, <=1.5.0 |
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| 0.21.0 | mmcv>=1.4.2, <=1.5.0 |
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| 0.20.1 | mmcv>=1.4.2, <=1.5.0 |
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| 0.19.0 | mmcv>=1.3.16, <=1.5.0 |
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| 0.18.0 | mmcv>=1.3.16, <=1.5.0 |
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@ -109,7 +110,7 @@ MMClassification 和 MMCV 的适配关系如下，请安装正确版本的 MMCV
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cd mmclassification
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```
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2. [可选] 签出到 `dev` 分支
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2. 【可选】 签出到 `dev` 分支
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```shell
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git checkout dev
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@ -1,6 +1,6 @@
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# Copyright (c) OpenMMLab. All rights reserved
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__version__ = '0.21.0'
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__version__ = '0.22.0'
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def parse_version_info(version_str):
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