[Doc] Translate the doc of Uniformer V1&V2 into Chinese

configs/recognition/uniformer/README.md

@@ -32,7 +32,7 @@ The models are ported from the repo [UniFormer](https:/Sense-X/UniFo
 2. The values in `top1/5 acc` is tested on the same data list as the original repo, and the label map is provided by [UniFormer](https://drive.google.com/drive/folders/17VB-XdF3Kfr9ORmnGyXCxTMs86n0L4QL). The total videos are available at [Kinetics400](https://pan.baidu.com/s/1t5K0FRz3PGAT-37-3FwAfg) (BaiduYun password: g5kp), which consists of 19787 videos.
 3. The values in columns named after "mm-Kinetics" are the testing results on the Kinetics dataset held by MMAction2, which is also used by other models in MMAction2. Due to the differences between various versions of Kinetics dataset, there is a little gap between `top1/5 acc` and `mm-Kinetics top1/5 acc`. For a fair comparison with other models, we report both results here. Note that we simply report the inference results, since the training set is different between UniFormer and other models, the results are lower than that tested on the author's version.
 4. Since the original models for Kinetics-400/600/700 adopt different [label file](https://drive.google.com/drive/folders/17VB-XdF3Kfr9ORmnGyXCxTMs86n0L4QL), we simply map the weight according to the label name. New label map for Kinetics-400/600/700 can be found [here](https:/open-mmlab/mmaction2/tree/main/tools/data/kinetics).
-5. Due to some difference between [SlowFast](https:/facebookresearch/SlowFast) and MMAction, there are some gaps between their performances.
+5. Due to some difference between [SlowFast](https:/facebookresearch/SlowFast) and MMAction2, there are some gaps between their performances.
 
 For more details on data preparation, you can refer to [preparing_kinetics](/tools/data/kinetics/README.md).
 

configs/recognition/uniformer/README_zh-CN.md

@@ -0,0 +1,55 @@
+# UniFormer
+
+[UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning](https://arxiv.org/abs/2201.04676)
+
+<!-- [ALGORITHM] -->
+
+## 简介
+
+```BibTeX
+@inproceedings{
+ li2022uniformer,
+ title={UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning},
+ author={Kunchang Li and Yali Wang and Gao Peng and Guanglu Song and Yu Liu and Hongsheng Li and Yu Qiao},
+ booktitle={International Conference on Learning Representations},
+ year={2022},
+ url={https://openreview.net/forum?id=nBU_u6DLvoK}
+}
+```
+
+## 模型库
+
+### Kinetics-400
+
+| 帧采样策略 | 分辨率 | 主干网络 | top1 准确率 | top5 准确率 | [参考文献](https:/Sense-X/UniFormer/blob/main/video_classification/README.md) top1 准确率 | [参考文献](https:/Sense-X/UniFormer/blob/main/video_classification/README.md) top5 准确率 | mm-Kinetics top1 准确率 | mm-Kinetics top5 准确率 | 测试方案 | FLOPs | 参数量 | 配置文件 | ckpt |
+| :--------: | :------------: | :---------: | :---------: | :---------: | :---------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------: | :---------------------: | :---------------------: | :--------------: | :---: | :----: | :-----------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| 16x4x1 | short-side 320 | UniFormer-S | 80.9 | 94.6 | 80.8 | 94.7 | 80.9 | 94.6 | 4 clips x 1 crop | 41.8G | 21.4M | [config](/configs/recognition/uniformer/uniformer-small_imagenet1k-pre_16x4x1_kinetics400-rgb.py) | [ckpt](https://download.openmmlab.com/mmaction/v1.0/recognition/uniformerv1/uniformer-small_imagenet1k-pre_16x4x1_kinetics400-rgb_20221219-c630a037.pth) |
+| 16x4x1 | short-side 320 | UniFormer-B | 82.0 | 95.0 | 82.0 | 95.1 | 82.0 | 95.0 | 4 clips x 1 crop | 96.7G | 49.8M | [config](/configs/recognition/uniformer/uniformer-base_imagenet1k-pre_16x4x1_kinetics400-rgb.py) | [ckpt](https://download.openmmlab.com/mmaction/v1.0/recognition/uniformerv1/uniformer-base_imagenet1k-pre_16x4x1_kinetics400-rgb_20221219-157c2e66.pth) |
+| 32x4x1 | short-side 320 | UniFormer-B | 83.1 | 95.3 | 82.9 | 95.4 | 83.0 | 95.3 | 4 clips x 1 crop | 59G | 49.8M | [config](/configs/recognition/uniformer/uniformer-base_imagenet1k-pre_32x4x1_kinetics400-rgb.py) | [ckpt](https://download.openmmlab.com/mmaction/v1.0/recognition/uniformerv1/uniformer-base_imagenet1k-pre_32x4x1_kinetics400-rgb_20221219-b776322c.pth) |
+
+这些模型迁移自 [UniFormer](https:/Sense-X/UniFormer/blob/main/video_classification/README.md)仓库，并在我们的数据上进行了测试。目前，我们仅支持对 UniFormer 模型的测试，训练功能将很快提供。
+
+1. 名称为"参考文献"的列中的值是原始仓库的结果。
+2. `top1/5 准确率`中的值是模型在与原始仓库相同的数据集上的测试结果，分类器结果-标签映射与[UniFormer](https://drive.google.com/drive/folders/17VB-XdF3Kfr9ORmnGyXCxTMs86n0L4QL)一致。数据集总共有19787个视频，可以在[Kinetics400](https://pan.baidu.com/s/1t5K0FRz3PGAT-37-3FwAfg)（百度云密码：g5kp）中获取。
+3. 名称为 "mm-Kinetics" 的列中的值是模型在 MMAction2 持有的 Kinetics 数据集上的测试结果，其他 MMAction2 模型也使用了该数据集。由于 Kinetics 数据集的各个版本之间存在差异，因此 `top1/5 准确率` 和 `mm-Kinetics top1/5 准确率` 之间存在一些差距。为了与其他模型进行公平比较，我们在这里报告了两个结果。请注意，我们只报告了推理结果，由于 UniFormer 和其他模型之间的训练集不同，该结果低于在作者版本上测试的结果。
+4. 由于 Kinetics-400/600/700 的原始模型采用了不同的[标签文件](https://drive.google.com/drive/folders/17VB-XdF3Kfr9ORmnGyXCxTMs86n0L4QL)，我们根据标签名称简单地映射了权重。Kinetics-400/600/700 的新标签映射可以在[这里](https:/open-mmlab/mmaction2/tree/main/tools/data/kinetics)找到。
+5. 由于 \[SlowFast\] (https:/facebookresearch/SlowFast)和 MMAction2 之间存在一些差异，它们的性能存在一些差距。
+
+有关数据准备的更多详细信息，您可以参考[准备_kinetics](/tools/data/kinetics/README_zh-CN.md)。
+
+## 如何测试
+
+您可以使用以下命令来测试模型：
+
+```shell
+python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]
+```
+
+示例：在Kinetics-400数据集上测试 UniFormer-S 模型，并将结果转储到一个 pkl 文件中。
+
+```shell
+python tools/test.py configs/recognition/uniformer/uniformer-small_imagenet1k-pre_16x4x1_kinetics400-rgb.py \
+ checkpoints/SOME_CHECKPOINT.pth --dump result.pkl
+```
+
+有关更多详细信息，请参考[训练和测试教程](/docs/zh_cn/user_guides/train_test.md)中的**测试**部分。