upload mmpretrain

configs/davit/davit-tiny_4xb256_in1k.py

+_base_ = [
+    '../_base_/models/davit/davit-tiny.py',
+    '../_base_/datasets/imagenet_bs256_davit_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# data settings
+train_dataloader = dict(batch_size=256)

configs/davit/metafile.yml

+Collections:
+  - Name: DaViT
+    Metadata:
+      Architecture:
+        - GELU
+        - Layer Normalization
+        - Multi-Head Attention
+        - Scaled Dot-Product Attention
+    Paper:
+      URL: https://arxiv.org/abs/2204.03645v1
+      Title: 'DaViT: Dual Attention Vision Transformers'
+    README: configs/davit/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmpretrain/blob/v1.0.0rc3/mmcls/models/backbones/davit.py
+      Version: v1.0.0rc3
+
+Models:
+  - Name: davit-tiny_3rdparty_in1k
+    In Collection: DaViT
+    Metadata:
+      FLOPs: 4539698688
+      Parameters: 28360168
+      Training Data:
+        - ImageNet-1k
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 82.24
+        Top 5 Accuracy: 96.13
+    Weights: https://download.openmmlab.com/mmclassification/v0/davit/davit-tiny_3rdparty_in1k_20221116-700fdf7d.pth
+    Converted From:
+      Weights: https://drive.google.com/file/d/1RSpi3lxKaloOL5-or20HuG975tbPwxRZ/view?usp=sharing
+      Code: https://github.com/dingmyu/davit/blob/main/mmdet/mmdet/models/backbones/davit.py#L355
+    Config: configs/davit/davit-tiny_4xb256_in1k.py
+  - Name: davit-small_3rdparty_in1k
+    In Collection: DaViT
+    Metadata:
+      FLOPs: 8799942144
+      Parameters: 49745896
+      Training Data:
+        - ImageNet-1k
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 83.61
+        Top 5 Accuracy: 96.75
+    Weights: https://download.openmmlab.com/mmclassification/v0/davit/davit-small_3rdparty_in1k_20221116-51a849a6.pth
+    Converted From:
+      Weights: https://drive.google.com/file/d/1q976ruj45mt0RhO9oxhOo6EP_cmj4ahQ/view?usp=sharing
+      Code: https://github.com/dingmyu/davit/blob/main/mmdet/mmdet/models/backbones/davit.py#L355
+    Config: configs/davit/davit-small_4xb256_in1k.py
+  - Name: davit-base_3rdparty_in1k
+    In Collection: DaViT
+    Metadata:
+      FLOPs: 15509702656
+      Parameters: 87954408
+      Training Data:
+        - ImageNet-1k
+    Results:
+    - Dataset: ImageNet-1k
+      Task: Image Classification
+      Metrics:
+        Top 1 Accuracy: 84.09
+        Top 5 Accuracy: 96.82
+    Weights: https://download.openmmlab.com/mmclassification/v0/davit/davit-base_3rdparty_in1k_20221116-19e0d956.pth
+    Converted From:
+      Weights: https://drive.google.com/file/d/1u9sDBEueB-YFuLigvcwf4b2YyA4MIVsZ/view?usp=sharing
+      Code: https://github.com/dingmyu/davit/blob/main/mmdet/mmdet/models/backbones/davit.py#L355
+    Config: configs/davit/davit-base_4xb256_in1k.py

configs/deit/README.md

+# DeiT
+
+> [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained with hundreds of millions of images using an expensive infrastructure, thereby limiting their adoption.   In this work, we produce a competitive convolution-free transformer by training on Imagenet only. We train them on a single computer in less than 3 days. Our reference vision transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evaluation) on ImageNet with no external data.   More importantly, we introduce a teacher-student strategy specific to transformers. It relies on a distillation token ensuring that the student learns from the teacher through attention. We show the interest of this token-based distillation, especially when using a convnet as a teacher. This leads us to report results competitive with convnets for both Imagenet (where we obtain up to 85.2% accuracy) and when transferring to other tasks. We share our code and models.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/26739999/143225703-c287c29e-82c9-4c85-a366-dfae30d198cd.png" width="40%"/>
+</div>
+
+## How to use it?
+
+<!-- [TABS-BEGIN] -->
+
+**Predict image**
+
+```python
+from mmpretrain import inference_model
+
+predict = inference_model('deit-tiny_4xb256_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('deit-tiny_4xb256_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))
+```
+
+**Train/Test Command**
+
+Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
+
+Train:
+
+```shell
+python tools/train.py configs/deit/deit-tiny_4xb256_in1k.py
+```
+
+Test:
+
+```shell
+python tools/test.py configs/deit/deit-tiny_4xb256_in1k.py https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth
+```
+
+<!-- [TABS-END] -->
+
+## Models and results
+
+### Image Classification on ImageNet-1k
+
+| Model                                             |   Pretrain   | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) |                       Config                       |                       Download                       |
+| :------------------------------------------------ | :----------: | :--------: | :-------: | :-------: | :-------: | :------------------------------------------------: | :--------------------------------------------------: |
+| `deit-tiny_4xb256_in1k`                           | From scratch |    5.72    |   1.26    |   74.50   |   92.24   |         [config](deit-tiny_4xb256_in1k.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.json) |
+| `deit-tiny-distilled_3rdparty_in1k`\*             | From scratch |    5.91    |   1.27    |   74.51   |   91.90   |    [config](deit-tiny-distilled_4xb256_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth) |
+| `deit-small_4xb256_in1k`                          | From scratch |   22.05    |   4.61    |   80.69   |   95.06   |        [config](deit-small_4xb256_in1k.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.json) |
+| `deit-small-distilled_3rdparty_in1k`\*            | From scratch |   22.44    |   4.63    |   81.17   |   95.40   |   [config](deit-small-distilled_4xb256_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth) |
+| `deit-base_16xb64_in1k`                           | From scratch |   86.57    |   17.58   |   81.76   |   95.81   |         [config](deit-base_16xb64_in1k.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.json) |
+| `deit-base_3rdparty_in1k`\*                       | From scratch |   86.57    |   17.58   |   81.79   |   95.59   |         [config](deit-base_16xb64_in1k.py)         | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_pt-16xb64_in1k_20211124-6f40c188.pth) |
+| `deit-base-distilled_3rdparty_in1k`\*             | From scratch |   87.34    |   17.67   |   83.33   |   96.49   |    [config](deit-base-distilled_16xb64_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211216-42891296.pth) |
+| `deit-base_224px-pre_3rdparty_in1k-384px`\*       |    224px     |   86.86    |   55.54   |   83.04   |   96.31   |      [config](deit-base_16xb32_in1k-384px.py)      | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth) |
+| `deit-base-distilled_224px-pre_3rdparty_in1k-384px`\* |    224px     |   87.63    |   55.65   |   85.55   |   97.35   | [config](deit-base-distilled_16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211216-e48d6000.pth) |
+
+*Models with * are converted from the [official repo](https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L168). The config files of these models are only for inference. We haven't reproduce the training results.*
+
+```{warning}
+MMPretrain doesn't support training the distilled version DeiT.
+And we provide distilled version checkpoints for inference only.
+```
+
+## Citation
+
+```bibtex
+@InProceedings{pmlr-v139-touvron21a,
+  title =     {Training data-efficient image transformers &amp; distillation through attention},
+  author =    {Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and Massa, Francisco and Sablayrolles, Alexandre and Jegou, Herve},
+  booktitle = {International Conference on Machine Learning},
+  pages =     {10347--10357},
+  year =      {2021},
+  volume =    {139},
+  month =     {July}
+}
+```

configs/deit/deit-base-distilled_16xb32_in1k-384px.py

+_base_ = [
+    '../_base_/datasets/imagenet_bs64_swin_384.py',
+    '../_base_/schedules/imagenet_bs4096_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='DistilledVisionTransformer',
+        arch='deit-base',
+        img_size=384,
+        patch_size=16,
+    ),
+    neck=None,
+    head=dict(
+        type='DeiTClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    # Change to the path of the pretrained model
+    # init_cfg=dict(type='Pretrained', checkpoint=''),
+)
+
+# dataset settings
+train_dataloader = dict(batch_size=32)
+
+# schedule settings
+optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR
+# based on the actual training batch size.
+# base_batch_size = (16 GPUs) x (32 samples per GPU)
+auto_scale_lr = dict(base_batch_size=512)

configs/deit/deit-base-distilled_16xb64_in1k.py

+_base_ = [
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='DistilledVisionTransformer',
+        arch='deit-base',
+        img_size=224,
+        patch_size=16),
+    neck=None,
+    head=dict(
+        type='DeiTClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=.02),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+    ],
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.8),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)
+
+# dataset settings
+train_dataloader = dict(batch_size=64)
+
+# schedule settings
+optim_wrapper = dict(
+    paramwise_cfg=dict(
+        norm_decay_mult=0.0,
+        bias_decay_mult=0.0,
+        custom_keys={
+            '.cls_token': dict(decay_mult=0.0),
+            '.pos_embed': dict(decay_mult=0.0)
+        }),
+    clip_grad=dict(max_norm=5.0),
+)

configs/deit/deit-base_16xb32_in1k-384px.py

+_base_ = [
+    '../_base_/datasets/imagenet_bs64_swin_384.py',
+    '../_base_/schedules/imagenet_bs4096_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VisionTransformer',
+        arch='deit-base',
+        img_size=384,
+        patch_size=16,
+    ),
+    neck=None,
+    head=dict(
+        type='VisionTransformerClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    # Change to the path of the pretrained model
+    # init_cfg=dict(type='Pretrained', checkpoint=''),
+)
+
+# dataset settings
+train_dataloader = dict(batch_size=32)
+
+# schedule settings
+optim_wrapper = dict(clip_grad=dict(max_norm=1.0))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR
+# based on the actual training batch size.
+# base_batch_size = (16 GPUs) x (32 samples per GPU)
+auto_scale_lr = dict(base_batch_size=512)

configs/deit/deit-base_16xb64_in1k.py

+_base_ = [
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VisionTransformer',
+        arch='deit-base',
+        img_size=224,
+        patch_size=16,
+        drop_path_rate=0.1),
+    neck=None,
+    head=dict(
+        type='VisionTransformerClsHead',
+        num_classes=1000,
+        in_channels=768,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=.02),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+    ],
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.8),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)
+
+# dataset settings
+train_dataloader = dict(batch_size=64)
+
+# schedule settings
+optim_wrapper = dict(
+    paramwise_cfg=dict(
+        norm_decay_mult=0.0,
+        bias_decay_mult=0.0,
+        custom_keys={
+            '.cls_token': dict(decay_mult=0.0),
+            '.pos_embed': dict(decay_mult=0.0)
+        }),
+    clip_grad=dict(max_norm=5.0),
+)
+
+# runtime settings
+custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]

configs/deit/deit-small-distilled_4xb256_in1k.py

+_base_ = [
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='DistilledVisionTransformer',
+        arch='deit-small',
+        img_size=224,
+        patch_size=16),
+    neck=None,
+    head=dict(
+        type='DeiTClsHead',
+        num_classes=1000,
+        in_channels=384,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=.02),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+    ],
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.8),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)
+
+# data settings
+train_dataloader = dict(batch_size=256)
+
+# schedule settings
+optim_wrapper = dict(
+    paramwise_cfg=dict(
+        norm_decay_mult=0.0,
+        bias_decay_mult=0.0,
+        custom_keys={
+            '.cls_token': dict(decay_mult=0.0),
+            '.pos_embed': dict(decay_mult=0.0)
+        }),
+    clip_grad=dict(max_norm=5.0),
+)

configs/deit/deit-small_4xb256_in1k.py

+# In small and tiny arch, remove drop path and EMA hook comparing with the
+# original config
+_base_ = [
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VisionTransformer',
+        arch='deit-small',
+        img_size=224,
+        patch_size=16),
+    neck=None,
+    head=dict(
+        type='VisionTransformerClsHead',
+        num_classes=1000,
+        in_channels=384,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=.02),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+    ],
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.8),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)
+
+# data settings
+train_dataloader = dict(batch_size=256)
+
+# schedule settings
+optim_wrapper = dict(
+    paramwise_cfg=dict(
+        norm_decay_mult=0.0,
+        bias_decay_mult=0.0,
+        custom_keys={
+            '.cls_token': dict(decay_mult=0.0),
+            '.pos_embed': dict(decay_mult=0.0)
+        }),
+    clip_grad=dict(max_norm=5.0),
+)

configs/deit/deit-tiny-distilled_4xb256_in1k.py

+# The distillation config is only for evaluation.
+_base_ = [
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='DistilledVisionTransformer',
+        arch='deit-tiny',
+        img_size=224,
+        patch_size=16),
+    neck=None,
+    head=dict(
+        type='DeiTClsHead',
+        num_classes=1000,
+        in_channels=192,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=.02),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+    ],
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.8),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)
+
+# data settings
+train_dataloader = dict(batch_size=256)
+
+# schedule settings
+optim_wrapper = dict(
+    paramwise_cfg=dict(
+        norm_decay_mult=0.0,
+        bias_decay_mult=0.0,
+        custom_keys={
+            '.cls_token': dict(decay_mult=0.0),
+            '.pos_embed': dict(decay_mult=0.0)
+        }),
+    clip_grad=dict(max_norm=5.0),
+)

configs/deit/deit-tiny_4xb256_in1k.py

+# In small and tiny arch, remove drop path and EMA hook comparing with the
+# original config
+_base_ = [
+    '../_base_/datasets/imagenet_bs64_swin_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='VisionTransformer',
+        arch='deit-tiny',
+        img_size=224,
+        patch_size=16),
+    neck=None,
+    head=dict(
+        type='VisionTransformerClsHead',
+        num_classes=1000,
+        in_channels=192,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+    ),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=.02),
+        dict(type='Constant', layer='LayerNorm', val=1., bias=0.),
+    ],
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.8),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)
+
+# data settings
+train_dataloader = dict(batch_size=256)
+
+# schedule settings
+optim_wrapper = dict(
+    paramwise_cfg=dict(
+        norm_decay_mult=0.0,
+        bias_decay_mult=0.0,
+        custom_keys={
+            '.cls_token': dict(decay_mult=0.0),
+            '.pos_embed': dict(decay_mult=0.0)
+        }),
+    clip_grad=dict(max_norm=5.0),
+)

configs/deit/metafile.yml

+Collections:
+  - Name: DeiT
+    Metadata:
+      Training Data: ImageNet-1k
+      Architecture:
+        - Layer Normalization
+        - Scaled Dot-Product Attention
+        - Attention Dropout
+        - Multi-Head Attention
+    Paper:
+      Title: Training data-efficient image transformers & distillation through attention
+      URL: https://arxiv.org/abs/2012.12877
+    README: configs/deit/README.md
+    Code:
+      URL: v0.19.0
+      Version: https://github.com/open-mmlab/mmpretrain/blob/v0.19.0/mmcls/models/backbones/deit.py
+
+Models:
+  - Name: deit-tiny_4xb256_in1k
+    Metadata:
+      FLOPs: 1258219200
+      Parameters: 5717416
+    In Collection: DeiT
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 74.5
+          Top 5 Accuracy: 92.24
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth
+    Config: configs/deit/deit-tiny_4xb256_in1k.py
+  - Name: deit-tiny-distilled_3rdparty_in1k
+    Metadata:
+      FLOPs: 1265371776
+      Parameters: 5910800
+    In Collection: DeiT
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 74.51
+          Top 5 Accuracy: 91.9
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth
+    Config: configs/deit/deit-tiny-distilled_4xb256_in1k.py
+    Converted From:
+      Weights: https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth
+      Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L108
+  - Name: deit-small_4xb256_in1k
+    Metadata:
+      FLOPs: 4607954304
+      Parameters: 22050664
+    In Collection: DeiT
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 80.69
+          Top 5 Accuracy: 95.06
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.pth
+    Config: configs/deit/deit-small_4xb256_in1k.py
+  - Name: deit-small-distilled_3rdparty_in1k
+    Metadata:
+      FLOPs: 4632876288
+      Parameters: 22436432
+    In Collection: DeiT
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 81.17
+          Top 5 Accuracy: 95.4
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth
+    Config: configs/deit/deit-small-distilled_4xb256_in1k.py
+    Converted From:
+      Weights: https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth
+      Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L123
+  - Name: deit-base_16xb64_in1k
+    Metadata:
+      FLOPs: 17581972224
+      Parameters: 86567656
+    In Collection: DeiT
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 81.76
+          Top 5 Accuracy: 95.81
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.pth
+    Config: configs/deit/deit-base_16xb64_in1k.py
+  - Name: deit-base_3rdparty_in1k
+    Metadata:
+      FLOPs: 17581972224
+      Parameters: 86567656
+    In Collection: DeiT
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 81.79
+          Top 5 Accuracy: 95.59
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_pt-16xb64_in1k_20211124-6f40c188.pth
+    Config: configs/deit/deit-base_16xb64_in1k.py
+    Converted From:
+      Weights: https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth
+      Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L93
+  - Name: deit-base-distilled_3rdparty_in1k
+    Metadata:
+      FLOPs: 17674283520
+      Parameters: 87338192
+    In Collection: DeiT
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 83.33
+          Top 5 Accuracy: 96.49
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211216-42891296.pth
+    Config: configs/deit/deit-base-distilled_16xb64_in1k.py
+    Converted From:
+      Weights: https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth
+      Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L138
+  - Name: deit-base_224px-pre_3rdparty_in1k-384px
+    Metadata:
+      FLOPs: 55538974464
+      Parameters: 86859496
+    In Collection: DeiT
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 83.04
+          Top 5 Accuracy: 96.31
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth
+    Config: configs/deit/deit-base_16xb32_in1k-384px.py
+    Converted From:
+      Weights: https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth
+      Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L153
+  - Name: deit-base-distilled_224px-pre_3rdparty_in1k-384px
+    Metadata:
+      FLOPs: 55645294080
+      Parameters: 87630032
+    In Collection: DeiT
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 85.55
+          Top 5 Accuracy: 97.35
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211216-e48d6000.pth
+    Config: configs/deit/deit-base-distilled_16xb32_in1k-384px.py
+    Converted From:
+      Weights: https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth
+      Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L168

configs/deit3/README.md

+# DeiT III: Revenge of the ViT
+
+> [DeiT III: Revenge of the ViT](https://arxiv.org/abs/2204.07118)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+A Vision Transformer (ViT) is a simple neural architecture amenable to serve several computer vision tasks. It has limited built-in architectural priors, in contrast to more recent architectures that incorporate priors either about the input data or of specific tasks. Recent works show that ViTs benefit from self-supervised pre-training, in particular BerT-like pre-training like BeiT. In this paper, we revisit the supervised training of ViTs. Our procedure builds upon and simplifies a recipe introduced for training ResNet-50. It includes a new simple data-augmentation procedure with only 3 augmentations, closer to the practice in self-supervised learning. Our evaluations on Image classification (ImageNet-1k with and without pre-training on ImageNet-21k), transfer learning and semantic segmentation show that our procedure outperforms by a large margin previous fully supervised training recipes for ViT. It also reveals that the performance of our ViT trained with supervision is comparable to that of more recent architectures. Our results could serve as better baselines for recent self-supervised approaches demonstrated on ViT.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/24734142/192964480-46726469-21d9-4e45-a06a-87c6a57c3367.png" width="90%"/>
+</div>
+
+## How to use it?
+
+<!-- [TABS-BEGIN] -->
+
+**Predict image**
+
+```python
+from mmpretrain import inference_model
+
+predict = inference_model('deit3-small-p16_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('deit3-small-p16_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/deit3/deit3-small-p16_64xb64_in1k.py https://download.openmmlab.com/mmclassification/v0/deit3/deit3-small-p16_3rdparty_in1k_20221008-0f7c70cf.pth
+```
+
+<!-- [TABS-END] -->
+
+## Models and results
+
+### Image Classification on ImageNet-1k
+
+| Model                                             |   Pretrain   | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) |                     Config                     |                         Download                         |
+| :------------------------------------------------ | :----------: | :--------: | :-------: | :-------: | :-------: | :--------------------------------------------: | :------------------------------------------------------: |
+| `deit3-small-p16_3rdparty_in1k`\*                 | From scratch |   22.06    |   4.61    |   81.35   |   95.31   |    [config](deit3-small-p16_64xb64_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-small-p16_3rdparty_in1k_20221008-0f7c70cf.pth) |
+| `deit3-small-p16_3rdparty_in1k-384px`\*           | From scratch |   22.21    |   15.52   |   83.43   |   96.68   | [config](deit3-small-p16_64xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-small-p16_3rdparty_in1k-384px_20221008-a2c1a0c7.pth) |
+| `deit3-small-p16_in21k-pre_3rdparty_in1k`\*       | ImageNet-21k |   22.06    |   4.61    |   83.06   |   96.77   |    [config](deit3-small-p16_64xb64_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-small-p16_in21k-pre_3rdparty_in1k_20221009-dcd90827.pth) |
+| `deit3-small-p16_in21k-pre_3rdparty_in1k-384px`\* | ImageNet-21k |   22.21    |   15.52   |   84.84   |   97.48   | [config](deit3-small-p16_64xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-small-p16_in21k-pre_3rdparty_in1k-384px_20221009-de116dd7.pth) |
+| `deit3-medium-p16_3rdparty_in1k`\*                | From scratch |   38.85    |   8.00    |   82.99   |   96.22   |   [config](deit3-medium-p16_64xb64_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-medium-p16_3rdparty_in1k_20221008-3b21284d.pth) |
+| `deit3-medium-p16_in21k-pre_3rdparty_in1k`\*      | ImageNet-21k |   38.85    |   8.00    |   84.56   |   97.19   |   [config](deit3-medium-p16_64xb64_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-medium-p16_in21k-pre_3rdparty_in1k_20221009-472f11e2.pth) |
+| `deit3-base-p16_3rdparty_in1k`\*                  | From scratch |   86.59    |   17.58   |   83.80   |   96.55   |    [config](deit3-base-p16_64xb64_in1k.py)     | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-base-p16_3rdparty_in1k_20221008-60b8c8bf.pth) |
+| `deit3-base-p16_3rdparty_in1k-384px`\*            | From scratch |   86.88    |   55.54   |   85.08   |   97.25   | [config](deit3-base-p16_64xb32_in1k-384px.py)  | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-base-p16_3rdparty_in1k-384px_20221009-e19e36d4.pth) |
+| `deit3-base-p16_in21k-pre_3rdparty_in1k`\*        | ImageNet-21k |   86.59    |   17.58   |   85.70   |   97.75   |    [config](deit3-base-p16_64xb64_in1k.py)     | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-base-p16_in21k-pre_3rdparty_in1k_20221009-87983ca1.pth) |
+| `deit3-base-p16_in21k-pre_3rdparty_in1k-384px`\*  | ImageNet-21k |   86.88    |   55.54   |   86.73   |   98.11   | [config](deit3-base-p16_64xb32_in1k-384px.py)  | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-base-p16_in21k-pre_3rdparty_in1k-384px_20221009-5e4e37b9.pth) |
+| `deit3-large-p16_3rdparty_in1k`\*                 | From scratch |   304.37   |   61.60   |   84.87   |   97.01   |    [config](deit3-large-p16_64xb64_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-large-p16_3rdparty_in1k_20221009-03b427ea.pth) |
+| `deit3-large-p16_3rdparty_in1k-384px`\*           | From scratch |   304.76   |  191.21   |   85.82   |   97.60   | [config](deit3-large-p16_64xb16_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-large-p16_3rdparty_in1k-384px_20221009-4317ce62.pth) |
+| `deit3-large-p16_in21k-pre_3rdparty_in1k`\*       | ImageNet-21k |   304.37   |   61.60   |   86.97   |   98.24   |    [config](deit3-large-p16_64xb64_in1k.py)    | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-large-p16_in21k-pre_3rdparty_in1k_20221009-d8d27084.pth) |
+| `deit3-large-p16_in21k-pre_3rdparty_in1k-384px`\* | ImageNet-21k |   304.76   |  191.21   |   87.73   |   98.51   | [config](deit3-large-p16_64xb16_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-large-p16_in21k-pre_3rdparty_in1k-384px_20221009-75fea03f.pth) |
+| `deit3-huge-p14_3rdparty_in1k`\*                  | From scratch |   632.13   |  167.40   |   85.21   |   97.36   |    [config](deit3-huge-p14_64xb32_in1k.py)     | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-huge-p14_3rdparty_in1k_20221009-e107bcb7.pth) |
+| `deit3-huge-p14_in21k-pre_3rdparty_in1k`\*        | ImageNet-21k |   632.13   |  167.40   |   87.19   |   98.26   |    [config](deit3-huge-p14_64xb32_in1k.py)     | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-huge-p14_in21k-pre_3rdparty_in1k_20221009-19b8a535.pth) |
+
+*Models with * are converted from the [official repo](https://github.com/facebookresearch/deit/blob/main/models_v2.py#L171). The config files of these models are only for inference. We haven't reproduce the training results.*
+
+## Citation
+
+```bibtex
+@article{Touvron2022DeiTIR,
+  title={DeiT III: Revenge of the ViT},
+  author={Hugo Touvron and Matthieu Cord and Herve Jegou},
+  journal={arXiv preprint arXiv:2204.07118},
+  year={2022},
+}
+```

configs/deit3/deit3-base-p16_64xb32_in1k-384px.py

+_base_ = [
+    '../_base_/models/deit3/deit3-base-p16-384.py',
+    '../_base_/datasets/imagenet_bs64_deit3_384.py',
+    '../_base_/schedules/imagenet_bs4096_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset setting
+train_dataloader = dict(batch_size=32)
+
+# schedule settings
+optim_wrapper = dict(optimizer=dict(lr=1e-5, weight_decay=0.1))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR
+# based on the actual training batch size.
+# base_batch_size = (64 GPUs) x (32 samples per GPU)
+auto_scale_lr = dict(base_batch_size=2048)

configs/deit3/deit3-base-p16_64xb64_in1k.py

+_base_ = [
+    '../_base_/models/deit3/deit3-base-p16-224.py',
+    '../_base_/datasets/imagenet_bs64_deit3_224.py',
+    '../_base_/schedules/imagenet_bs4096_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset setting
+train_dataloader = dict(batch_size=64)
+
+# schedule settings
+optim_wrapper = dict(optimizer=dict(lr=1e-5, weight_decay=0.1))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR
+# based on the actual training batch size.
+# base_batch_size = (64 GPUs) x (64 samples per GPU)
+auto_scale_lr = dict(base_batch_size=4096)

configs/deit3/deit3-huge-p14_64xb32_in1k.py

+_base_ = [
+    '../_base_/models/deit3/deit3-huge-p14-224.py',
+    '../_base_/datasets/imagenet_bs64_deit3_224.py',
+    '../_base_/schedules/imagenet_bs4096_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset setting
+train_dataloader = dict(batch_size=32)
+
+# schedule settings
+optim_wrapper = dict(optimizer=dict(lr=1e-5, weight_decay=0.1))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR
+# based on the actual training batch size.
+# base_batch_size = (64 GPUs) x (32 samples per GPU)
+auto_scale_lr = dict(base_batch_size=2048)

configs/deit3/deit3-large-p16_64xb16_in1k-384px.py

+_base_ = [
+    '../_base_/models/deit3/deit3-large-p16-384.py',
+    '../_base_/datasets/imagenet_bs64_deit3_384.py',
+    '../_base_/schedules/imagenet_bs4096_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset setting
+train_dataloader = dict(batch_size=16)
+
+# schedule settings
+optim_wrapper = dict(optimizer=dict(lr=1e-5, weight_decay=0.1))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR
+# based on the actual training batch size.
+# base_batch_size = (64 GPUs) x (16 samples per GPU)
+auto_scale_lr = dict(base_batch_size=1025)

configs/deit3/deit3-large-p16_64xb64_in1k.py

+_base_ = [
+    '../_base_/models/deit3/deit3-large-p16-224.py',
+    '../_base_/datasets/imagenet_bs64_deit3_224.py',
+    '../_base_/schedules/imagenet_bs4096_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset setting
+train_dataloader = dict(batch_size=64)
+
+# schedule settings
+optim_wrapper = dict(optimizer=dict(lr=1e-5, weight_decay=0.1))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR
+# based on the actual training batch size.
+# base_batch_size = (64 GPUs) x (64 samples per GPU)
+auto_scale_lr = dict(base_batch_size=4096)

configs/deit3/deit3-medium-p16_64xb64_in1k.py

+_base_ = [
+    '../_base_/models/deit3/deit3-medium-p16-224.py',
+    '../_base_/datasets/imagenet_bs64_deit3_224.py',
+    '../_base_/schedules/imagenet_bs4096_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset setting
+train_dataloader = dict(batch_size=64)
+
+# schedule settings
+optim_wrapper = dict(optimizer=dict(lr=1e-5, weight_decay=0.1))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR
+# based on the actual training batch size.
+# base_batch_size = (64 GPUs) x (64 samples per GPU)
+auto_scale_lr = dict(base_batch_size=4096)

configs/deit3/deit3-small-p16_64xb64_in1k-384px.py

+_base_ = [
+    '../_base_/models/deit3/deit3-small-p16-384.py',
+    '../_base_/datasets/imagenet_bs64_deit3_384.py',
+    '../_base_/schedules/imagenet_bs4096_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset setting
+train_dataloader = dict(batch_size=64)
+
+# schedule settings
+optim_wrapper = dict(optimizer=dict(lr=1e-5, weight_decay=0.1))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR
+# based on the actual training batch size.
+# base_batch_size = (64 GPUs) x (64 samples per GPU)
+auto_scale_lr = dict(base_batch_size=4096)