[Docs] update README and model_zoo.md (#165)

* [Docs] update README

* [Docs] add simsiam logs

* [Docs] add chinese version of model_zoo.md

* [Docs] fix typo

* [Benchmark] update SimSiam 100 epoch benchmark results

* [Benchmark] update simsiam 100 epoch seg results

README.md

@@ -150,3 +150,5 @@ Remarks:
 - [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab few shot learning toolbox and benchmark.
 - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
 - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.

README_zh-CN.md

@@ -40,6 +40,14 @@ MMSelfSup 是一个基于 PyTorch 实现的开源自监督表征学习工具箱
 
 该项目采用 [Apache 2.0 开源许可证](LICENSE).
 
+## 修改日志
+
+MMSelfSup **v0.5.0** 在 16/12/2021 发版.
+
+请参考 [changelog.md](docs/zh_cn/changelog.md) 获取更多细节和历史版本信息。
+
+MMSelfSup 和 OpenSelfSup 的不同点写在 [compatibility.md](docs/en/compatibility.md) 当中。
+
 ## 模型库和基准测试
 
 ### 模型库
@@ -136,13 +144,15 @@ MMSelfSup 是一款由不同学校和公司共同贡献的开源项目，我们
 - [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
 - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
 - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
 
 ## 欢迎加入 OpenMMLab 社区
 
 扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=GJP18SjI)，添加OpenMMLab 官方小助手微信，加入 MMSelfSup 微信社区。
 
 <div align="center">
-<img src="./resources/zhihu_qrcode.jpg" height="400"/>  <img src="./resources/qq_group_qrcode.jpg" height="400"/> <img src="./resources/xiaozhushou_weixin_qrcode.jpeg" height="400"/> <img src="./resources/weixin_qrcode.jpeg" height="400"/>
+<img src="./resources/zhihu_qrcode.jpg" height="400"/>  <img src="./resources/qq_group_qrcode.jpg" height="400"/> <img src="./resources/xiaozhushou_weixin_qrcode.jpeg" height="400"/>
 </div>
 
 我们会在 OpenMMLab 社区为大家

configs/selfsup/simsiam/README.md

@@ -42,7 +42,7 @@ Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
 
 | Self-Supervised Config                                                 | Best Layer | SVM   | k=1   | k=2   | k=4   | k=8   | k=16  | k=32  | k=64  | k=96  |
 | ---------------------------------------------------------------------- | ---------- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |
-| [resnet50_8xb32-coslr-100e](simsiam_resnet50_8xb32-coslr-100e_in1k.py) | feature5   | 84.21 | 39.71 | 49.65 | 62.79 | 69.97 | 74.73 | 78.30 | 81.06 | 82.44 |
+| [resnet50_8xb32-coslr-100e](simsiam_resnet50_8xb32-coslr-100e_in1k.py) | feature5   | 84.64 | 39.65 | 49.86 | 62.48 | 69.50 | 74.48 | 78.31 | 81.06 | 82.56 |
 | [resnet50_8xb32-coslr-200e](simsiam_resnet50_8xb32-coslr-200e_in1k.py) | feature5   | 85.20 | 39.85 | 50.44 | 63.73 | 70.93 | 75.74 | 79.42 | 82.02 | 83.44 |
 
 #### ImageNet Linear Evaluation
@@ -53,7 +53,7 @@ The **AvgPool** result is obtained from Linear Evaluation with GlobalAveragePool
 
 | Self-Supervised Config                                                 | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
 | ---------------------------------------------------------------------- | -------- | -------- | -------- | -------- | -------- | ------- |
-| [resnet50_8xb32-coslr-100e](simsiam_resnet50_8xb32-coslr-100e_in1k.py) | 15.85    | 34.02    | 46.00    | 60.90    | 67.92    | 67.88   |
+| [resnet50_8xb32-coslr-100e](simsiam_resnet50_8xb32-coslr-100e_in1k.py) | 16.27    | 33.77    | 45.80    | 60.83    | 68.21    | 68.20   |
 | [resnet50_8xb32-coslr-200e](simsiam_resnet50_8xb32-coslr-200e_in1k.py) | 15.57    | 37.21    | 47.28    | 62.21    | 69.85    | 69.80   |
 
 
@@ -67,7 +67,7 @@ Please refer to [faster_rcnn_r50_c4_mstrain_24k_voc0712.py](../../benchmarks/mmd
 
 | Self-Supervised Config                                                 | AP50  |
 | ---------------------------------------------------------------------- | ----- |
-| [resnet50_8xb32-coslr-100e](simsiam_resnet50_8xb32-coslr-100e_in1k.py) | 79.97 |
+| [resnet50_8xb32-coslr-100e](simsiam_resnet50_8xb32-coslr-100e_in1k.py) | 79.80 |
 | [resnet50_8xb32-coslr-200e](simsiam_resnet50_8xb32-coslr-200e_in1k.py) | 79.85 |
 
 #### COCO2017
@@ -76,7 +76,7 @@ Please refer to [mask_rcnn_r50_fpn_mstrain_1x_coco.py](../../benchmarks/mmdetect
 
 | Self-Supervised Config                                                 | mAP(Box) | AP50(Box) | AP75(Box) | mAP(Mask) | AP50(Mask) | AP75(Mask) |
 | ---------------------------------------------------------------------- | -------- | --------- | --------- | --------- | ---------- | ---------- |
-| [resnet50_8xb32-coslr-100e](simsiam_resnet50_8xb32-coslr-100e_in1k.py) | 38.3     | 57.6      | 41.7      | 34.4      | 54.8       | 36.9       |
+| [resnet50_8xb32-coslr-100e](simsiam_resnet50_8xb32-coslr-100e_in1k.py) | 38.6     | 57.6      | 42.3      | 34.6      | 54.8       | 36.9       |
 | [resnet50_8xb32-coslr-200e](simsiam_resnet50_8xb32-coslr-200e_in1k.py) | 38.8     | 58.0      | 42.3      | 34.9      | 55.3       | 37.6       |
 
 ### Segmentation
@@ -89,5 +89,5 @@ Please refer to [fcn_r50-d8_512x512_20k_voc12aug.py](../../benchmarks/mmsegmenta
 
 | Self-Supervised Config                                                 | mIOU  |
 | ---------------------------------------------------------------------- | ----- |
-| [resnet50_8xb32-coslr-100e](simsiam_resnet50_8xb32-coslr-100e_in1k.py) | 46.11 |
+| [resnet50_8xb32-coslr-100e](simsiam_resnet50_8xb32-coslr-100e_in1k.py) | 48.35 |
 | [resnet50_8xb32-coslr-200e](simsiam_resnet50_8xb32-coslr-200e_in1k.py) | 46.27 |

docs/en/model_zoo.md

@@ -1,6 +1,6 @@
 # Model Zoo
 
-All models and benchmarks results are recorded below.
+All models and part of benchmark results are recorded below.
 
 ## Pre-trained models
 
@@ -16,16 +16,19 @@ All models and benchmarks results are recorded below.
 | [Relative Location](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/relative_loc/README.md)    | [relative-loc_resnet50_8xb64-steplr-70e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k.py)            | [model](https://download.openmmlab.com/mmselfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k_20211213-cdd3162f.pth) | [log](https://download.openmmlab.com/mmselfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k_20210930_144754.log.json)     |
 | [Rotation Prediction](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/rotation_pred/README.md) | [rotation-pred_resnet50_8xb16-steplr-70e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/rotation_pred/rotation-pred_resnet50_8xb16-steplr-70e_in1k.py)         | [model](https://download.openmmlab.com/mmselfsup/rotation_pred/rotation-pred_resnet50_8xb16-steplr-70e_in1k_20211213-513972ac.pth) | [log](https://download.openmmlab.com/mmselfsup/rotation_pred/rotation-pred_resnet50_8xb16-steplr-70e_in1k_20210930_151459.log.json) |
 | [SimCLR](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simclr/README.md)                     | [simclr_resnet50_8xb32-coslr-200e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simclr/simclr_resnet50_8xb32-coslr-200e_in1k.py)                              | [model](https://download.openmmlab.com/mmselfsup/simclr/simclr_resnet50_8xb32-coslr-200e_in1k_20211213-d0e53669.pth)                                                                                                                                                          |
-| [SimSiam](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/README.md)                   | [simsiam_resnet50_8xb32-coslr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k.py)                           | [model](https://download.openmmlab.com/mmselfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k_20211213-925d628c.pth)                                                                                                                                                        |
-|                                                                                                                    | [simsiam_resnet50_8xb32-coslr-200e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-200e_in1k.py)                           | [model](https://download.openmmlab.com/mmselfsup/simsiam/simsiam_resnet50_8xb32-coslr-200e_in1k_20211213-b605f9f1.pth)                                                                                                                                                        |
+| [SimSiam](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/README.md)                   | [simsiam_resnet50_8xb32-coslr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k.py)                           | [model](https://download.openmmlab.com/mmselfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k_20211230-65a0eff4.pth) | [log](https://download.openmmlab.com/mmselfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k_20211225_132004.log.json)                         |
+|                                                                                                                    | [simsiam_resnet50_8xb32-coslr-200e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-200e_in1k.py)                           | [model](https://download.openmmlab.com/mmselfsup/simsiam/simsiam_resnet50_8xb32-coslr-200e_in1k_20211213-b605f9f1.pth) | [log](https://download.openmmlab.com/mmselfsup/simsiam/simsiam_resnet50_8xb32-coslr-200e_in1k_20211225_132031.log.json)                         |
 | [SwAV](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/swav/README.md)                         | [swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/swav/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96.py)  | [model](https://download.openmmlab.com/mmselfsup/swav/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96_20211213-0028900c.pth) | [log](https://download.openmmlab.com/mmselfsup/swav/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96_20211206_102636.log.json)   |
 
 Remarks:
 
-- If not specified, the models are trained 200 epochs.
+- The training details are recorded in the config names.
+
+- You can click algorithm name to obtain more information.
+
 ## Benchmarks
 
-In following tables, we only displayed ImageNet Linear Evaluation, COCO17 Object Detection and PASCAL VOC12 Aug Segmentation, you can click the model name above to get the comprehensive benchmark results.
+In following tables, we only displayed ImageNet Linear Evaluation, COCO17 Object Detection and PASCAL VOC12 Aug Segmentation, you can click algorithm name above to check the comprehensive benchmark results.
 
 ### ImageNet Linear Evaluation
 
@@ -42,7 +45,7 @@ If not specified, we use linear evaluation setting from [MoCo](http://openaccess
 | Relative Location   | [relative-loc_resnet50_8xb64-steplr-70e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k.py)               |                       | 39.65     |
 | Rotation Prediction | [rotation-pred_resnet50_8xb16-steplr-70e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/rotation_pred/rotation-pred_resnet50_8xb16-steplr-70e_in1k.py)            |                       | 44.35     |
 | SimCLR              | [simclr_resnet50_8xb32-coslr-200e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simclr/simclr_resnet50_8xb32-coslr-200e_in1k.py)                                 |                       | 58.92     |
-| SimSiam             | [simsiam_resnet50_8xb32-coslr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k.py)                              | SimSiam paper setting | 67.88     |
+| SimSiam             | [simsiam_resnet50_8xb32-coslr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k.py)                              | SimSiam paper setting | 68.20     |
 |                     | [simsiam_resnet50_8xb32-coslr-200e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-200e_in1k.py)                              | SimSiam paper setting | 69.80     |
 | SwAV                | [swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/swav/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96.py)     | SwAV paper setting    | 70.55     |
 
@@ -59,7 +62,7 @@ In COCO17 Object detection task, we choose the evluation protocol from [MoCo](ht
 | Relative Location   | [relative-loc_resnet50_8xb64-steplr-70e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k.py)           | 37.5      | 33.7       |
 | Rotation Prediction | [rotation-pred_resnet50_8xb16-steplr-70e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/rotation_pred/rotation-pred_resnet50_8xb16-steplr-70e_in1k.py)        | 37.9      | 34.2       |
 | SimCLR              | [simclr_resnet50_8xb32-coslr-200e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simclr/simclr_resnet50_8xb32-coslr-200e_in1k.py)                             | 38.7      | 34.9       |
-| SimSiam             | [simsiam_resnet50_8xb32-coslr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k.py)                          | 38.3      | 34.4       |
+| SimSiam             | [simsiam_resnet50_8xb32-coslr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k.py)                          | 38.6      | 34.6       |
 |                     | [simsiam_resnet50_8xb32-coslr-200e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-200e_in1k.py)                          | 38.8      | 34.9       |
 | SwAV                | [swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/swav/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96.py) | 40.2      | 36.3       |
 
@@ -78,6 +81,6 @@ In Pascal VOC12 Aug Segmentation task, we choose the evluation protocol from [MM
 | Relative Location   | [relative-loc_resnet50_8xb64-steplr-70e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k.py)            | 63.49 |
 | Rotation Prediction | [rotation-pred_resnet50_8xb16-steplr-70e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/rotation_pred/rotation-pred_resnet50_8xb16-steplr-70e_in1k.py)         | 64.31 |
 | SimCLR              | [simclr_resnet50_8xb32-coslr-200e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simclr/simclr_resnet50_8xb32-coslr-200e_in1k.py)                              | 64.03 |
-| SimSiam             | [simsiam_resnet50_8xb32-coslr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k.py)                           | 46.11 |
+| SimSiam             | [simsiam_resnet50_8xb32-coslr-100e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-100e_in1k.py)                           | 48.35 |
 |                     | [simsiam_resnet50_8xb32-coslr-200e_in1k](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/simsiam/simsiam_resnet50_8xb32-coslr-200e_in1k.py)                           | 46.27 |
 | SwAV                | [swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96](https://github.com/open-mmlab/mmselfsup/blob/master/configs/selfsup/swav/swav_resnet50_8xb32-mcrop-2-6-coslr-200e_in1k-224-96.py)  | 63.73 |