add part code

configs/resnext/resnext101-32x4d_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnext101_32x4d.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnext/resnext101-32x8d_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnext101_32x8d.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnext/resnext152-32x4d_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnext152_32x4d.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnext/resnext50-32x4d_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnext50_32x4d.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/revvit/README.md

+# Reversible Vision Transformers
+
+> [Reversible Vision Transformers](https://openaccess.thecvf.com/content/CVPR2022/papers/Mangalam_Reversible_Vision_Transformers_CVPR_2022_paper.pdf)
+
+<!-- [ALGORITHM] -->
+
+## Introduction
+
+**RevViT** is initially described in [Reversible Vision Tranformers](https://openaccess.thecvf.com/content/CVPR2022/papers/Mangalam_Reversible_Vision_Transformers_CVPR_2022_paper.pdf), which introduce the reversible idea into vision transformer, to reduce the GPU memory footprint required for training.
+
+<!-- [IMAGE] -->
+
+<div align=center>
+<img src="https://github.com/facebookresearch/SlowFast/raw/main/projects/rev/teaser.png" width="70%"/>
+</div>
+
+## Abstract
+
+<details>
+
+<summary>Show the paper's abstract</summary>
+
+<br>
+We present Reversible Vision Transformers, a memory efficient architecture design for visual recognition. By decoupling the GPU memory footprint from the depth of the model, Reversible Vision Transformers enable memory efficient scaling of transformer architectures. We adapt two popular models, namely Vision Transformer and Multiscale Vision Transformers, to reversible variants and benchmark extensively across both model sizes and tasks of image classification, object detection and video classification. Reversible Vision Transformers achieve a reduced memory footprint of up to 15.5× at identical model complexity, parameters and accuracy, demonstrating the promise of reversible vision transformers as an efficient backbone for resource limited training regimes. Finally, we find that the additional computational burden of recomputing activations is more than overcome for deeper models, where throughput can increase up to 3.9× over their non-reversible counterparts.
+</br>
+
+</details>
+
+## How to use it?
+
+<!-- [TABS-BEGIN] -->
+
+**Predict image**
+
+```python
+from mmpretrain import inference_model
+
+predict = inference_model('revvit-small_3rdparty_in1k', 'demo/bird.JPEG')
+print(predict['pred_class'])
+print(predict['pred_score'])
+```
+
+**Use the model**
+
+```python
+import torch
+from mmpretrain import get_model
+
+model = get_model('revvit-small_3rdparty_in1k', pretrained=True)
+inputs = torch.rand(1, 3, 224, 224)
+out = model(inputs)
+print(type(out))
+# To extract features.
+feats = model.extract_feat(inputs)
+print(type(feats))
+```
+
+**Test Command**
+
+Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
+
+Test:
+
+```shell
+python tools/test.py configs/revvit/revvit-small_8xb256_in1k.py https://download.openmmlab.com/mmclassification/v0/revvit/revvit-base_3rdparty_in1k_20221213-87a7b0a5.pth
+```
+
+<!-- [TABS-END] -->
+
+## Models and results
+
+### Image Classification on ImageNet-1k
+
+| Model                          |   Pretrain   | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) |                Config                 |                                       Download                                       |
+| :----------------------------- | :----------: | :--------: | :-------: | :-------: | :-------: | :-----------------------------------: | :----------------------------------------------------------------------------------: |
+| `revvit-small_3rdparty_in1k`\* | From scratch |   22.44    |   4.58    |   79.87   |   94.90   | [config](revvit-small_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/revvit/revvit-base_3rdparty_in1k_20221213-87a7b0a5.pth) |
+| `revvit-base_3rdparty_in1k`\*  | From scratch |   87.34    |   17.49   |   81.81   |   95.56   | [config](revvit-base_8xb256_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/revvit/revvit-small_3rdparty_in1k_20221213-a3a34f5c.pth) |
+
+*Models with * are converted from the [official repo](https://github.com/facebookresearch/SlowFast). The config files of these models are only for inference. We haven't reproduce the training results.*
+
+## Citation
+
+```bibtex
+@inproceedings{mangalam2022reversible,
+  title={Reversible Vision Transformers},
+  author={Mangalam, Karttikeya and Fan, Haoqi and Li, Yanghao and Wu, Chao-Yuan and Xiong, Bo and Feichtenhofer, Christoph and Malik, Jitendra},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={10830--10840},
+  year={2022}
+}
+```

configs/revvit/metafile.yml

+Collections:
+  - Name: RevViT
+    Metadata:
+      Training Data: ImageNet-1k
+      Architecture:
+        - Vision Transformer
+        - Reversible
+    Paper:
+      URL: https://openaccess.thecvf.com/content/CVPR2022/papers/Mangalam_Reversible_Vision_Transformers_CVPR_2022_paper.pdf
+      Title: Reversible Vision Transformers
+    README: configs/revvit/README.md
+    Code:
+      Version: v1.0.0rc5
+      URL: https://github.com/open-mmlab/mmpretrain/blob/1.0.0rc5/mmcls/models/backbones/revvit.py
+
+Models:
+  - Name: revvit-small_3rdparty_in1k
+    Metadata:
+      FLOPs: 4583427072
+      Parameters: 22435432
+    In Collection: RevViT
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 79.87
+          Top 5 Accuracy: 94.90
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/revvit/revvit-small_3rdparty_in1k_20221213-a3a34f5c.pth
+    Config: configs/revvit/revvit-small_8xb256_in1k.py
+    Converted From:
+      Weights: https://dl.fbaipublicfiles.com/pyslowfast/rev/REV_VIT_S.pyth
+      Code: https://github.com/facebookresearch/SlowFast
+  - Name: revvit-base_3rdparty_in1k
+    Metadata:
+      FLOPs: 17490450432
+      Parameters: 87337192
+    In Collection: RevViT
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 81.81
+          Top 5 Accuracy: 95.56
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/revvit/revvit-base_3rdparty_in1k_20221213-87a7b0a5.pth
+    Config: configs/revvit/revvit-base_8xb256_in1k.py
+    Converted From:
+      Weights: https://dl.fbaipublicfiles.com/pyslowfast/rev/REV_VIT_B.pyth
+      Code: https://github.com/facebookresearch/SlowFast

configs/revvit/revvit-base_8xb256_in1k.py

+_base_ = [
+    '../_base_/models/revvit/revvit-base.py',
+    '../_base_/datasets/imagenet_bs128_revvit_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_revvit.py',
+    '../_base_/default_runtime.py'
+]

configs/revvit/revvit-small_8xb256_in1k.py

+_base_ = [
+    '../_base_/models/revvit/revvit-small.py',
+    '../_base_/datasets/imagenet_bs128_revvit_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_revvit.py',
+    '../_base_/default_runtime.py'
+]

configs/riformer/README.md

+# RIFormer
+
+> [RIFormer: Keep Your Vision Backbone Effective But Removing Token Mixer](https://arxiv.org/abs/2304.05659)
+
+<!-- [ALGORITHM] -->
+
+## Introduction
+
+RIFormer is a way to keep a vision backbone effective while removing token mixers in its basic building blocks. Equipped with our proposed optimization strategy, we are able to build an extremely simple vision backbone with encouraging performance, while enjoying the high efficiency during inference. RIFormer shares nearly the same macro and micro design as MetaFormer, but safely removing all token mixers. The quantitative results show that our networks outperform many prevailing backbones with faster inference speed on ImageNet-1K.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/48375204/223930120-dc075c8e-0513-42eb-9830-469a45c1d941.png" width="65%"/>
+</div>
+
+## Abstract
+
+<details>
+
+<summary>Show the paper's abstract</summary>
+
+<br>
+This paper studies how to keep a vision backbone effective while removing token mixers in its basic building blocks. Token mixers, as self-attention for vision transformers (ViTs), are intended to perform information communication between different spatial tokens but suffer from considerable computational cost and latency. However, directly removing them will lead to an incomplete model structure prior, and thus brings a significant accuracy drop. To this end, we first develop an RepIdentityFormer base on the re-parameterizing idea, to study the token mixer free model architecture. And we then explore the improved learning paradigm to break the limitation of simple token mixer free backbone, and summarize the empirical practice into 5 guidelines. Equipped with the proposed optimization strategy, we are able to build an extremely simple vision backbone with encouraging performance, while enjoying the high efficiency during inference. Extensive experiments and ablative analysis also demonstrate that the inductive bias of network architecture, can be incorporated into simple network structure with appropriate optimization strategy. We hope this work can serve as a starting point for the exploration of optimization-driven efficient network design.
+</br>
+
+</details>
+
+## How to use
+
+The checkpoints provided are all `training-time` models. Use the reparameterize tool or `switch_to_deploy` interface to switch them to more efficient `inference-time` architecture, which not only has fewer parameters but also less calculations.
+
+<!-- [TABS-BEGIN] -->
+
+**Predict image**
+
+Use `classifier.backbone.switch_to_deploy()` interface to switch the RIFormer models into inference mode.
+
+```python
+>>> import torch
+>>> from mmpretrain import get_model, inference_model
+>>>
+>>> model = get_model("riformer-s12_in1k", pretrained=True)
+>>> results = inference_model(model, 'demo/demo.JPEG')
+>>> print( (results['pred_class'], results['pred_score']) )
+('sea snake', 0.7827484011650085)
+>>>
+>>> # switch to deploy mode
+>>> model.backbone.switch_to_deploy()
+>>> results = inference_model(model, 'demo/demo.JPEG')
+>>> print( (results['pred_class'], results['pred_score']) )
+('sea snake', 0.7827480435371399)
+```
+
+**Use the model**
+
+```python
+>>> import torch
+>>>
+>>> model = get_model("riformer-s12_in1k", pretrained=True)
+>>> model.eval()
+>>> inputs = torch.rand(1, 3, 224, 224).to(model.data_preprocessor.device)
+>>> # To get classification scores.
+>>> out = model(inputs)
+>>> print(out.shape)
+torch.Size([1, 1000])
+>>> # To extract features.
+>>> outs = model.extract_feat(inputs)
+>>> print(outs[0].shape)
+torch.Size([1, 512])
+>>>
+>>> # switch to deploy mode
+>>> model.backbone.switch_to_deploy()
+>>> out_deploy = model(inputs)
+>>> print(out.shape)
+torch.Size([1, 1000])
+>>> assert torch.allclose(out, out_deploy, rtol=1e-4, atol=1e-5) # pass without error
+```
+
+**Test Command**
+
+Place the ImageNet dataset to the `data/imagenet/` directory, or prepare datasets according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
+
+*224×224*
+
+Download Checkpoint:
+
+```shell
+wget https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k_20230406-6741ce71.pth
+```
+
+Test use unfused model:
+
+```shell
+python tools/test.py configs/riformer/riformer-s12_8xb128_in1k.py riformer-s12_32xb128_in1k_20230406-6741ce71.pth
+```
+
+Reparameterize checkpoint:
+
+```shell
+python tools/model_converters/reparameterize_model.py configs/riformer/riformer-s12_8xb128_in1k.py riformer-s12_32xb128_in1k_20230406-6741ce71.pth riformer-s12_deploy.pth
+```
+
+Test use fused model:
+
+```shell
+python tools/test.py configs/riformer/deploy/riformer-s12-deploy_8xb128_in1k.py riformer-s12_deploy.pth
+```
+
+<!-- [TABS-END] -->
+
+For more configurable parameters, please refer to the [API](https://mmpretrain.readthedocs.io/en/latest/api/generated/mmpretrain.models.backbones.RIFormer.html#mmpretrain.models.backbones.RIFormer).
+
+<details>
+
+<summary><b>How to use the reparameterization tool</b>(click to show)</summary>
+
+<br>
+
+Use provided tool to reparameterize the given model and save the checkpoint:
+
+```bash
+python tools/convert_models/reparameterize_model.py ${CFG_PATH} ${SRC_CKPT_PATH} ${TARGET_CKPT_PATH}
+```
+
+`${CFG_PATH}` is the config file path, `${SRC_CKPT_PATH}` is the source chenpoint file path, `${TARGET_CKPT_PATH}` is the target deploy weight file path.
+
+For example:
+
+```shell
+# download the weight
+wget https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k_20230406-6741ce71.pth
+
+# reparameterize unfused weight to fused weight
+python tools/model_converters/reparameterize_model.py configs/riformer/riformer-s12_8xb128_in1k.py riformer-s12_32xb128_in1k_20230406-6741ce71.pth riformer-s12_deploy.pth
+```
+
+To use reparameterized weights, you can use the deploy model config file such as the [s12_deploy example](./deploy/riformer-s12-deploy_8xb128_in1k.py):
+
+```text
+# in riformer-s12-deploy_8xb128_in1k.py
+_base_ = '../deploy/riformer-s12-deploy_8xb128_in1k.py'  # basic s12 config
+
+model = dict(backbone=dict(deploy=True))  # switch model into deploy mode
+```
+
+```shell
+python tools/test.py configs/riformer/deploy/riformer-s12-deploy_8xb128_in1k.py riformer-s12_deploy.pth
+```
+
+</br>
+
+</details>
+
+## Results and models
+
+### ImageNet-1k
+
+|         Model         | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                    Config                     |                                         Download                                          |
+| :-------------------: | :--------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------: | :---------------------------------------------------------------------------------------: |
+|   riformer-s12_in1k   |  224x224   |   11.92   |   1.82   |   76.90   |   93.06   |    [config](./riformer-s12_8xb128_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k_20230406-6741ce71.pth) |
+|   riformer-s24_in1k   |  224x224   |   21.39   |   3.41   |   80.28   |   94.80   |    [config](./riformer-s24_8xb128_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s24_32xb128_in1k_20230406-fdab072a.pth) |
+|   riformer-s36_in1k   |  224x224   |   30.86   |   5.00   |   81.29   |   95.41   |    [config](./riformer-s36_8xb128_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s36_32xb128_in1k_20230406-fdfcd3b0.pth) |
+|   riformer-m36_in1k   |  224x224   |   56.17   |   8.80   |   82.57   |   95.99   |    [config](./riformer-m36_8xb128_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m36_32xb128_in1k_20230406-2fcb9d9b.pth) |
+|   riformer-m48_in1k   |  224x224   |   73.47   |  11.59   |   82.75   |   96.11   |    [config](./riformer-m48_8xb64_in1k.py)     | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m48_32xb128_in1k_20230406-2b9d1abf.pth) |
+| riformer-s12_384_in1k |  384x384   |   11.92   |   5.36   |   78.29   |   93.93   | [config](./riformer-s12_8xb128_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k-384px_20230406-145eda4c.pth) |
+| riformer-s24_384_in1k |  384x384   |   21.39   |  10.03   |   81.36   |   95.40   | [config](./riformer-s24_8xb128_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s24_32xb128_in1k-384px_20230406-bafae7ab.pth) |
+| riformer-s36_384_in1k |  384x384   |   30.86   |  14.70   |   82.22   |   95.95   | [config](./riformer-s36_8xb64_in1k-384px.py)  | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s36_32xb128_in1k-384px_20230406-017ed3c4.pth) |
+| riformer-m36_384_in1k |  384x384   |   56.17   |  25.87   |   83.39   |   96.40   | [config](./riformer-m36_8xb64_in1k-384px.py)  | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m36_32xb128_in1k-384px_20230406-66a6f764.pth) |
+| riformer-m48_384_in1k |  384x384   |   73.47   |  34.06   |   83.70   |   96.60   | [config](./riformer-m48_8xb64_in1k-384px.py)  | [model](https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m48_32xb128_in1k-384px_20230406-2e874826.pth) |
+
+The config files of these models are only for inference.
+
+## Citation
+
+```bibtex
+@inproceedings{wang2023riformer,
+  title={RIFormer: Keep Your Vision Backbone Effective But Removing Token Mixer},
+  author={Wang, Jiahao and Zhang, Songyang and Liu, Yong and Wu, Taiqiang and Yang, Yujiu and Liu, Xihui and Chen, Kai and Luo, Ping and Lin, Dahua},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  year={2023}
+}
+```

configs/riformer/deploy/riformer-m36-deploy_8xb128_in1k.py

+_base_ = '../riformer-m36_8xb128_in1k.py'
+
+model = dict(backbone=dict(deploy=True))

configs/riformer/deploy/riformer-m36-deploy_8xb64_in1k-384px.py

+_base_ = '../riformer-m36_8xb64_in1k-384px.py'
+
+model = dict(backbone=dict(deploy=True))

configs/riformer/deploy/riformer-m48-deploy_8xb64_in1k-384px.py

+_base_ = '../riformer-m48_8xb64_in1k-384px.py'
+
+model = dict(backbone=dict(deploy=True))

configs/riformer/deploy/riformer-m48-deploy_8xb64_in1k.py

+_base_ = '../riformer-m48_8xb64_in1k.py'
+
+model = dict(backbone=dict(deploy=True))

configs/riformer/deploy/riformer-s12-deploy_8xb128_in1k-384px.py

+_base_ = '../riformer-s12_8xb128_in1k-384px.py'
+
+model = dict(backbone=dict(deploy=True))

configs/riformer/deploy/riformer-s12-deploy_8xb128_in1k.py

+_base_ = '../riformer-s12_8xb128_in1k.py'
+
+model = dict(backbone=dict(deploy=True))

configs/riformer/deploy/riformer-s24-deploy_8xb128_in1k-384px.py

+_base_ = '../riformer-s24_8xb128_in1k-384px.py'
+
+model = dict(backbone=dict(deploy=True))

configs/riformer/deploy/riformer-s24-deploy_8xb128_in1k.py

+_base_ = '../riformer-s24_8xb128_in1k.py'
+
+model = dict(backbone=dict(deploy=True))

configs/riformer/deploy/riformer-s36-deploy_8xb128_in1k.py

+_base_ = '../riformer-s36_8xb128_in1k.py'
+
+model = dict(backbone=dict(deploy=True))

configs/riformer/deploy/riformer-s36-deploy_8xb64_in1k-384px.py

+_base_ = '../riformer-s36_8xb64_in1k-384px.py'
+
+model = dict(backbone=dict(deploy=True))

configs/riformer/metafile.yml

+Collections:
+  - Name: RIFormer
+    Metadata:
+      Training Data: ImageNet-1k
+      Training Resources: 8x A100 GPUs
+      Architecture:
+        - Affine
+        - 1x1 Convolution
+        - LayerScale
+    Paper:
+      URL: https://arxiv.org/abs/xxxx.xxxxx
+      Title: "RIFormer: Keep Your Vision Backbone Effective But Removing Token Mixer"
+    README: configs/riformer/README.md
+    Code:
+      Version: v1.0.0rc7
+      URL: null
+
+Models:
+  - Name: riformer-s12_in1k
+    Metadata:
+      FLOPs: 1822000000
+      Parameters: 11915000
+    In Collection: RIFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 76.90
+          Top 5 Accuracy: 93.06
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k_20230406-6741ce71.pth
+    Config: configs/riformer/riformer-s12_8xb128_in1k.py
+  - Name: riformer-s24_in1k
+    Metadata:
+      Training Data: ImageNet-1k
+      FLOPs: 3412000000
+      Parameters: 21389000
+    In Collection: RIFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 80.28
+          Top 5 Accuracy: 94.80
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s24_32xb128_in1k_20230406-fdab072a.pth
+    Config: configs/riformer/riformer-s24_8xb128_in1k.py
+  - Name: riformer-s36_in1k
+    Metadata:
+      FLOPs: 5003000000
+      Parameters: 30863000
+    In Collection: RIFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 81.29
+          Top 5 Accuracy: 95.41
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s36_32xb128_in1k_20230406-fdfcd3b0.pth
+    Config: configs/riformer/riformer-s36_8xb128_in1k.py
+  - Name: riformer-m36_in1k
+    Metadata:
+      Training Data: ImageNet-1k
+      FLOPs: 8801000000
+      Parameters: 56173000
+    In Collection: RIFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 82.57
+          Top 5 Accuracy: 95.99
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m36_32xb128_in1k_20230406-2fcb9d9b.pth
+    Config: configs/riformer/riformer-m36_8xb128_in1k.py
+  - Name: riformer-m48_in1k
+    Metadata:
+      FLOPs: 11590000000
+      Parameters: 73473000
+    In Collection: RIFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 82.75
+          Top 5 Accuracy: 96.11
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m48_32xb128_in1k_20230406-2b9d1abf.pth
+    Config: configs/riformer/riformer-m48_8xb64_in1k.py
+  - Name: riformer-s12_in1k-384
+    Metadata:
+      FLOPs: 5355000000
+      Parameters: 11915000
+    In Collection: RIFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.29
+          Top 5 Accuracy: 93.93
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s12_32xb128_in1k-384px_20230406-145eda4c.pth
+    Config: configs/riformer/riformer-s12_8xb128_in1k-384px.py
+  - Name: riformer-s24_in1k-384
+    Metadata:
+      Training Data: ImageNet-1k
+      FLOPs: 10028000000
+      Parameters: 21389000
+    In Collection: RIFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 81.36
+          Top 5 Accuracy: 95.40
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s24_32xb128_in1k-384px_20230406-bafae7ab.pth
+    Config: configs/riformer/riformer-s24_8xb128_in1k-384px.py
+  - Name: riformer-s36_in1k-384
+    Metadata:
+      FLOPs: 14702000000
+      Parameters: 30863000
+    In Collection: RIFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 82.22
+          Top 5 Accuracy: 95.95
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-s36_32xb128_in1k-384px_20230406-017ed3c4.pth
+    Config: configs/riformer/riformer-s36_8xb64_in1k-384px.py
+  - Name: riformer-m36_in1k-384
+    Metadata:
+      Training Data: ImageNet-1k
+      FLOPs: 25865000000
+      Parameters: 56173000
+    In Collection: RIFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 83.39
+          Top 5 Accuracy: 96.40
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m36_32xb128_in1k-384px_20230406-66a6f764.pth
+    Config: configs/riformer/riformer-m36_8xb64_in1k-384px.py
+  - Name: riformer-m48_in1k-384
+    Metadata:
+      FLOPs: 34060000000
+      Parameters: 73473000
+    In Collection: RIFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 83.70
+          Top 5 Accuracy: 96.60
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v1/riformer/riformer-m48_32xb128_in1k-384px_20230406-2e874826.pth
+    Config: configs/riformer/riformer-m48_8xb64_in1k-384px.py