fix docs and links

README.md

@@ -27,7 +27,7 @@ The main branch works with **PyTorch 1.8** (required by some self-supervised met
 <summary>Major Features</summary>
 
 - **Modular Design.**
-  OpenMixup follows a similar code architecture of OpenMMLab projects, which decompose the framework into various components, and users can easily build a customized model by combining different modules. OpenMixup is also transplantable to OpenMMLab projects (e.g., [MMSelfSup](https://github.com/open-mmlab/mmselfsup)).
+  OpenMixup follows a similar code architecture of OpenMMLab projects, which decompose the framework into various components, and users can easily build a customized model by combining different modules. OpenMixup is also transplantable to OpenMMLab projects (e.g., [MMPreTrain](https://github.com/open-mmlab/mmpretrain)).
 
 - **All in One.**
   OpenMixup provides popular backbones, mixup methods, semi-supervised, and self-supervised algorithms. Users can perform image classification (CNN & Transformer) and self-supervised pre-training (contrastive and autoregressive) under the same framework.
@@ -246,8 +246,8 @@ This project is released under the [Apache 2.0 license](LICENSE). See `LICENSE`
 
 ## Acknowledgement
 
-- OpenMixup is an open-source project for mixup methods created by researchers in **CAIRI AI Lab**. We encourage researchers interested in visual representation learning and mixup methods to contribute to OpenMixup!
-- This repo borrows the architecture design and part of the code from [MMSelfSup](https://github.com/open-mmlab/mmselfsup) and [MMClassification](https://github.com/open-mmlab/mmclassification).
+- OpenMixup is an open-source project for mixup methods and visual representation learning created by researchers in **CAIRI AI Lab**. We encourage researchers interested in backbone architectures, mixup augmentations, and self-supervised learning methods to contribute to OpenMixup!
+- This project borrows the architecture design and part of the code from [MMPreTrain](https://github.com/open-mmlab/mmpretrain) and the official implementations of supported algorisms.
 
 <p align="right">(<a href="#top">back to top</a>)</p>
 
@@ -269,7 +269,7 @@ If you find this project useful in your research, please consider star `OpenMixu
 
 ## Contributors and Contact
 
-For help, new features, or reporting bugs associated with OpenMixup, please open a [GitHub issue](https://github.com/Westlake-AI/openmixup/issues) and [pull request](https://github.com/Westlake-AI/openmixup/pulls) with the tag "help wanted" or "enhancement". For now, the direct contributors include: Siyuan Li ([@Lupin1998](https://github.com/Lupin1998)), Zedong Wang ([@Jacky1128](https://github.com/Jacky1128)), and Zicheng Liu ([@pone7](https://github.com/pone7)). We thank all public contributors and contributors from MMSelfSup and MMClassification!
+For help, new features, or reporting bugs associated with OpenMixup, please open a [GitHub issue](https://github.com/Westlake-AI/openmixup/issues) and [pull request](https://github.com/Westlake-AI/openmixup/pulls) with the tag "help wanted" or "enhancement". For now, the direct contributors include: Siyuan Li ([@Lupin1998](https://github.com/Lupin1998)), Zedong Wang ([@Jacky1128](https://github.com/Jacky1128)), and Zicheng Liu ([@pone7](https://github.com/pone7)). We thank all public contributors and contributors from MMPreTrain (MMSelfSup and MMClassification)!
 
 This repo is currently maintained by:
 

configs/classification/imagenet/automix/README.md

@@ -30,8 +30,7 @@ Data mixing augmentation have proved to be effective in improving the generaliza
 |    Swin-T   | AutoMix |   224x224  |   28.29   |   300  |   81.80   |        [config](./swin/swin_t_l2_a2_near_lam_cat_switch0_8_8x128_ep300.py)        | model / log |
 |  ConvNeXt-T | AutoMix |   224x224  |   28.59   |   300  |   82.28   | [config](./convnext/convnext_t_l2_a2_near_lam_cat_switch0_8_8x128_accu4_ep300.py) | model / log |
 
-
-We will update configs and models for AutoMix soon. Please refer to [Model Zoo](https://github.com/Westlake-AI/openmixup/tree/main/docs/en/model_zoos/Model_Zoo_sup.md) for image classification results.
+We will update configs and models (ResNets, ViTs, Swin-T, and ConvNeXt-T) for AutoMix soon (please contact us if you want the models right now). Please refer to [Model Zoo](https://github.com/Westlake-AI/openmixup/tree/main/docs/en/model_zoos/Model_Zoo_sup.md) for image classification results.
 
 
 ## Citation

configs/classification/imagenet/context_cluster/README.md

@@ -18,12 +18,15 @@ This page is based on the [official repo](https://github.com/ma-xu/Context-Clust
 
 | Model | Params(M) | Flops(G) | Top-1 (%) | Throughputs | Config | Download |
 | :---: | :-------: | :------: | :-------: | :---------: | :----: | :------: |
-| ContextCluster-tiny\* | 5.3 | 1.0 | 71.8 | 518.4 | [config](coc_tiny_8xb256_ep300.py) | [model](https://drive.google.com/drive/folders/1Q_6W3xKMX63aQOBaqiwX5y1fCj4hVOIA?usp=sharing) |
-| ContextCluster-tiny_plain\* | 5.3 | 1.0 | 72.9 |  -  | [config](coc_tiny_plain_8xb256_ep300.py) | [model](https://web.northeastern.edu/smilelab/xuma/ContextCluster/checkpoints/coc_tiny_plain/coc_tiny_plain.pth.tar) |
-| ContextCluster-small\* | 5.3 | 1.0 | 71.8 | 518.4 | [config](coc_small_8xb256_ep300.py) | [model](https://drive.google.com/drive/folders/1WSmnbSgy1I1HOTTTAQgOKEzXSvd3Kmh-?usp=sharing) |
-| ContextCluster-medium\* | 5.3 | 1.0 | 71.8 | 518.4 | [config](coc_medium_8xb256_ep300.py) | [model](https://drive.google.com/drive/folders/1sPxnEHb2AHDD9bCQh6MA0I_-7EBrvlT5?usp=sharing) |
-
-We follow the original training setting provided by the [official repo](https://github.com/ma-xu/Context-Cluster). *Models with * are converted from the [official repo](https://github.com/ma-xu/Context-Cluster).*
+| ContextCluster-tiny\* | 5.6 | 1.10 | 71.8 | 518.4 | [config](coc_tiny_8xb256_ep300.py) | [model](https://drive.google.com/drive/folders/1Q_6W3xKMX63aQOBaqiwX5y1fCj4hVOIA?usp=sharing) |
+| ContextCluster-tiny_plain\* (w/o region partition) | 5.6 | 1.10 | 72.9 |  -  | [config](coc_tiny_plain_8xb256_ep300.py) | [model](https://web.northeastern.edu/smilelab/xuma/ContextCluster/checkpoints/coc_tiny_plain/coc_tiny_plain.pth.tar) |
+| ContextCluster-small\* | 14.7 | 2.78 | 77.5 | 513.0 | [config](coc_small_8xb256_ep300.py) | [model](https://drive.google.com/drive/folders/1WSmnbSgy1I1HOTTTAQgOKEzXSvd3Kmh-?usp=sharing) |
+| ContextCluster-medium\* | 29.3 | 5.90 | 81.0 | 325.2 | [config](coc_medium_8xb256_ep300.py) | [model](https://drive.google.com/drive/folders/1sPxnEHb2AHDD9bCQh6MA0I_-7EBrvlT5?usp=sharing) |
+| ContextCluster-tiny | 5.6 | 1.10 | 72.7 | 518.4 | [config](coc_tiny_8xb256_ep300.py) | [model](https://github.com/Westlake-AI/openmixup/releases/download/open-in1k-weights/coc_tiny_8xb256_ep300.pth) \| [log](https://github.com/Westlake-AI/openmixup/releases/download/open-in1k-weights/) |
+| ContextCluster-tiny_plain (w/o region partition) | 5.6 | 1.10 | 73.2 |  -  | [config](coc_tiny_plain_8xb256_ep300.py) | [model](https://github.com/Westlake-AI/openmixup/releases/download/open-in1k-weights/coc_tiny_plain_8xb256_ep300.pth) \| [log](https://github.com/Westlake-AI/openmixup/releases/download/open-in1k-weights/coc_tiny_plain_8xb256_ep300.log.json) |
+| ContextCluster-small | 14.7 | 2.78 | 77.7 | 513.0 | [config](coc_small_8xb256_ep300.py) | [model](https://github.com/Westlake-AI/openmixup/releases/download/open-in1k-weights/coc_small_8xb256_ep300.pth) \| [log](https://github.com/Westlake-AI/openmixup/releases/download/open-in1k-weights/coc_small_8xb256_ep300.log.json) |
+
+We follow the original training setting provided by the [official repo](https://github.com/ma-xu/Context-Cluster) to reproduce better performance of ContextCluster variants. *Models with * are converted from the [official repo](https://github.com/ma-xu/Context-Cluster).*
 
 ## Citation
 

configs/classification/imagenet/convnext/README.md

@@ -18,7 +18,7 @@ This page is based on documents in [MMClassification](https://github.com/open-mm
 
 |     Model     |   Pretrain   | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                                Config                                 |                                Download                                 |
 | :-----------: | :----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------: | :---------------------------------------------------------------------: |
-| ConvNeXt-T    | From scratch |   28.59   |   4.46   |   82.16   |   95.91   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/convnext/convnext_tiny_8xb256_accu2_fp16_ep300.py) | model | log |
+| ConvNeXt-T    | From scratch |   28.59   |   4.46   |   82.16   |   95.91   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/convnext/convnext_tiny_8xb256_accu2_fp16_ep300.py) | [model](https://github.com/Westlake-AI/openmixup/releases/download/open-in1k-weights/convnext_small_8xb128_accu4_fp16_ep300.pth) \| [log](https://github.com/Westlake-AI/openmixup/releases/download/open-in1k-weights/convnext_small_8xb128_accu4_fp16_ep300.log.json) |
 | ConvNeXt-T\*  | From scratch |   28.59   |   4.46   |   82.05   |   95.86   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/convnext/convnext_tiny_8xb256_accu2_fp16_ep300.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128_in1k_20220124-18abde00.pth) |
 | ConvNeXt-S\*  | From scratch |   50.22   |   8.69   |   83.13   |   96.44   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/convnext/convnext_small_8xb128_accu4_fp16_ep300.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128_in1k_20220124-d39b5192.pth) |
 | ConvNeXt-B\*  | From scratch |   88.59   |  15.36   |   83.85   |   96.74   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/convnext/convnext_base_8xb128_accu4_fp16_ep300.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128_in1k_20220124-d0915162.pth) |

configs/classification/imagenet/deit/README.md

@@ -20,7 +20,7 @@ This page is based on documents in [MMClassification](https://github.com/open-mm
 | :-------: | :----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------: | :---------------------------------------------------------------------: |
 |  DeiT-T   | From scratch |   5.72    |   1.08   |   73.56   |   91.16   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/deit/deit_tiny_8xb128_ep300.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth)  \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.log.json) |
 |  DeiT-T\* | From scratch |   5.72    |   1.08   |   72.20   |   91.10   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/deit/deit_tiny_8xb128_ep300.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth) |
-|  DeiT-S   | From scratch |   22.05   |   4.24   |   79.93   |   95.14   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/deit/deit_small_8xb128_ep300.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.pth)  \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.log.json) |
+|  DeiT-S   | From scratch |   22.05   |   4.24   |   79.93   |   95.14   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/deit/deit_small_8xb128_ep300.py) | [model](https://github.com/Westlake-AI/openmixup/releases/download/open-in1k-weights/deit_small_8xb128_ep300.pth) \| [log](https://github.com/Westlake-AI/openmixup/releases/download/open-in1k-weights/deit_small_8xb128_ep300.log.json) |
 |  DeiT-S\* | From scratch |   22.05   |   4.24   |   79.90   |   95.10   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/deit/deit_small_8xb128_ep300.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth) |
 |  DeiT-B   | From scratch |   86.57   |   16.86  |   81.82   |   95.57   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/deit/deit_base_8xb128_ep300.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.pth)  \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.log.json) |
 |  DeiT-B\* | From scratch |   86.57   |   16.86  |   81.80   |   95.60   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/deit/deit_base_8xb128_ep300.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_pt-16xb64_in1k_20211124-6f40c188.pth) |

configs/classification/imagenet/lit_v2/README.md

@@ -16,11 +16,10 @@ Vision Transformers (ViTs) have triggered the most recent and significant breakt
 
 |   Model   |   Pretrain   | resolution | Params(M) | Flops(G) | Throughput (imgs/s) | Top-1 (%) |                               Config                                |                               Download                                |
 | :-------: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :-----------------------------------------------------------------: | :-------------------------------------------------------------------: |
-|  LITv2-S  | From scratch |  224x224   |     28    |    3.7   | 1,471               |    81.7   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/lit_v2/lit_v2_small_8xb128_cos_fp16_ep300.py) | model / log |
-| LITv2-S\* | From scratch |  224x224   |     28    |    3.7   | 1,471               |    82.0   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/lit_v2/lit_v2_small_8xb128_cos_fp16_ep300.py) | [model](https://github.com/ziplab/LITv2/releases/download/v1.0/litv2_s.pth) / [log](https://github.com/ziplab/LITv2/releases/download/v1.0/litv2_m_log.txt) |
-| LITv2-M\* | From scratch |  224x224   |     49    |    7.5   | 812                 |    83.3   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/lit_v2/lit_v2_medium_8xb128_cos_fp16_ep300.py) | [model](https://github.com/ziplab/LITv2/releases/download/v1.0/litv2_m.pth) / [log](https://github.com/ziplab/LITv2/releases/download/v1.0/litv2_m_log.txt) |
-| LITv2-B\* | From scratch |  224x224   |     87    |   13.2   | 602                 |    84.7   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/lit_v2/lit_v2_base_8xb128_cos_fp16_ep300.py) | [model](https://github.com/ziplab/LITv2/releases/download/v1.0/litv2_b.pth) / [log](https://github.com/ziplab/LITv2/releases/download/v1.0/litv2_b_log.txt) |
-
+|  LITv2-S  | From scratch |  224x224   |     28    |    3.7   | 1,471 | 81.7 | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/lit_v2/lit_v2_small_8xb128_cos_fp16_ep300.py) | [model](https://github.com/Westlake-AI/openmixup/releases/download/open-in1k-weights/lit_v2_small_8xb128_cos_fp16_ep300.pth) \| [log](https://github.com/Westlake-AI/openmixup/releases/download/open-in1k-weights/lit_v2_small_8xb128_cos_fp16_ep300.log.json) |
+| LITv2-S\* | From scratch |  224x224   |     28    |    3.7   | 1,471 | 82.0 | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/lit_v2/lit_v2_small_8xb128_cos_fp16_ep300.py) | [model](https://github.com/ziplab/LITv2/releases/download/v1.0/litv2_s.pth) / [log](https://github.com/ziplab/LITv2/releases/download/v1.0/litv2_m_log.txt) |
+| LITv2-M\* | From scratch |  224x224   |     49    |    7.5   | 812 | 83.3 | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/lit_v2/lit_v2_medium_8xb128_cos_fp16_ep300.py) | [model](https://github.com/ziplab/LITv2/releases/download/v1.0/litv2_m.pth) / [log](https://github.com/ziplab/LITv2/releases/download/v1.0/litv2_m_log.txt) |
+| LITv2-B\* | From scratch |  224x224   |     87    |   13.2   | 602 | 84.7 | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/lit_v2/lit_v2_base_8xb128_cos_fp16_ep300.py) | [model](https://github.com/ziplab/LITv2/releases/download/v1.0/litv2_b.pth) / [log](https://github.com/ziplab/LITv2/releases/download/v1.0/litv2_b_log.txt) |
 
 We follow the original training setting provided by the [official repo](https://github.com/ziplab/LITv2) and throughput is averaged over 30 runs. *Note that models with \* are converted from the [official repo](https://github.com/ziplab/LITv2).* We reproduce LITv2-S training 300 epochs.
 

configs/classification/imagenet/samix/README.md

@@ -26,7 +26,7 @@ Mixup is a popular data-dependent augmentation technique for deep neural network
 |  ResNet-101   |  SAMix  |  224x224   |   42.51   |  300   |   80.98   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/samix/basic/r101_l2_a2_bili_val_dp01_mul_mb_mlr1e_3_bb_mlr0_4xb64.py) | model / log |
 |  ResNeXt-101  |  SAMix  |  224x224   |   44.18   |  100   |   80.89   | [config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/samix/basic/rx101_l2_a2_bili_val_dp01_mul_mb_mlr1e_3_bb_mlr0_4xb64.py) | model / log |
 
-We will update configs and models for SAMix soon. Please refer to [Model Zoo](https://github.com/Westlake-AI/openmixup/tree/main/docs/en/model_zoos/Model_Zoo_sup.md) for image classification results.
+We will update configs and models (ResNets, ViTs, Swin-T, and ConvNeXt-T) for SAMix soon (please contact us if you want the models right now). Please refer to [Model Zoo](https://github.com/Westlake-AI/openmixup/tree/main/docs/en/model_zoos/Model_Zoo_sup.md) for image classification results.
 
 ## Citation