# Swin Transformer V2 [[swin-transformer-v2]]

## 개요 [[overview]]

Swin Transformer V2는  Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo가 제안한 논문 [Swin Transformer V2: Scaling Up Capacity and Resolution](https://huggingface.co/papers/2111.09883)에서 소개되었습니다.

논문의 초록은 다음과 같습니다:

*대규모 NLP 모델들은 언어 작업에서의 성능을 크게 향상하며, 성능이 포화하는 징후를 보이지 않습니다. 또한, 사람과 유사한 few-shot 학습 능력을 보여줍니다. 이 논문은 대규모 모델을 컴퓨터 비전 분야에서 탐구하고자 합니다. 대형 비전 모델을 훈련하고 적용하는 데 있어 세 가지 주요 문제를 다룹니다: 훈련 불안정성, 사전 학습과 파인튜닝 간의 해상도 차이, 그리고 레이블이 달린 데이터에 대한 높은 요구입니다. 세 가지 주요 기법을 제안합니다: 1) 훈련 안정성을 개선하기 위한 residual-post-norm 방법과 cosine attention의 결합; 2) 저해상도 이미지로 사전 학습된 모델을 고해상도 입력으로 전이할 수 있는 log-spaced continuous position bias 방법; 3) 레이블이 달린 방대한 이미지의 필요성을 줄이기 위한 self-supervised 사전 학습 방법인 SimMIM입니다. 이러한 기법들을 통해 30억 개의 파라미터를 가진 Swin Transformer V2 모델을 성공적으로 훈련하였으며, 이는 현재까지 가장 크고 고밀도의 비전 모델로, 최대 1,536×1,536 해상도의 이미지를 다룰 수 있습니다. 이 모델은 ImageNet-V2 이미지 분류, COCO 객체 탐지, ADE20K 의미론적 분할, Kinetics-400 비디오 행동 분류 등 네 가지 대표적인 비전 작업에서 새로운 성능 기록을 세웠습니다. 또한, 우리의 훈련은 Google의 billion-level 비전 모델과 비교해 40배 적은 레이블이 달린 데이터와 40배 적은 훈련 시간으로 이루어졌다는 점에서 훨씬 더 효율적입니다.*

이 모델은 [nandwalritik](https://huggingface.co/nandwalritik)이 기여하였습니다.
원본 코드는 [여기](https://github.com/microsoft/Swin-Transformer)에서 확인할 수 있습니다.

## 리소스 [[resources]]

Swin Transformer v2의 사용을 도울 수 있는 Hugging Face 및 커뮤니티(🌎로 표시)의 공식 자료 목록입니다.

- [Swinv2ForImageClassification](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2ForImageClassification)은 이 [예제 스크립트](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification)와 [노트북](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb)을 통해 지원됩니다.
- 관련 자료: [이미지 분류 작업 가이드](../tasks/image_classification)

또한:

- [Swinv2ForMaskedImageModeling](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling)는 이 [예제 스크립트](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining)를 통해 지원됩니다.

새로운 자료를 추가하고 싶으시다면, 언제든지 Pull Request를 열어주세요! 저희가 검토해 드릴게요. 이때, 추가하는 자료는 기존 자료와 중복되지 않고 새로운 내용을 보여주는 자료여야 합니다.

## Swinv2Config [[transformers.Swinv2Config]][[transformers.Swinv2Config]]

#### transformers.Swinv2Config[[transformers.Swinv2Config]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.0/src/transformers/models/swinv2/configuration_swinv2.py#L25)

This is the configuration class to store the configuration of a Swinv2Model. It is used to instantiate a Swinv2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256)

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.5.0/ko/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/v5.5.0/ko/main_classes/configuration#transformers.PreTrainedConfig) for more information.

Example:

```python
>>> from transformers import Swinv2Config, Swinv2Model

>>> # Initializing a Swinv2 microsoft/swinv2-tiny-patch4-window8-256 style configuration
>>> configuration = Swinv2Config()

>>> # Initializing a model (with random weights) from the microsoft/swinv2-tiny-patch4-window8-256 style configuration
>>> model = Swinv2Model(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

**Parameters:**

image_size (`Union[int, list[int], tuple[int, int]]`, *optional*, defaults to `224`) : The size (resolution) of each image.

patch_size (`Union[int, list[int], tuple[int, int]]`, *optional*, defaults to `4`) : The size (resolution) of each patch.

num_channels (`int`, *optional*, defaults to `3`) : The number of input channels.

embed_dim (`int`, *optional*, defaults to `96`) : Dimensionality of the embeddings and hidden states.

depths (`Union[list[int], tuple[int, ...]]`, *optional*, defaults to `(2, 2, 6, 2)`) : Depth of each layer in the Transformer.

num_heads (`Union[list[int], tuple[int, ...]]`, *optional*, defaults to `(3, 6, 12, 24)`) : Number of attention heads for each attention layer in the Transformer decoder.

window_size (`int`, *optional*, defaults to 7) : Size of windows.

pretrained_window_sizes (`list(int)`, *optional*, defaults to `[0, 0, 0, 0]`) : Size of windows during pretraining.

mlp_ratio (`float`, *optional*, defaults to `4.0`) : Ratio of the MLP hidden dim to the embedding dim.

qkv_bias (`bool`, *optional*, defaults to `True`) : Whether to add a bias to the queries, keys and values.

hidden_dropout_prob (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

attention_probs_dropout_prob (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout ratio for the attention probabilities.

drop_path_rate (`Union[float, int]`, *optional*, defaults to `0.1`) : Drop path rate for the patch fusion.

hidden_act (`str`, *optional*, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc.

use_absolute_embeddings (`bool`, *optional*, defaults to `False`) : Whether to use absolute position embeddings.

initializer_range (`float`, *optional*, defaults to `0.02`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

layer_norm_eps (`float`, *optional*, defaults to `1e-05`) : The epsilon used by the layer normalization layers.

encoder_stride (`int`, *optional*, defaults to 32) : Factor to increase the spatial resolution by in the decoder head for masked image modeling.

## Swinv2Model [[transformers.Swinv2Model]][[transformers.Swinv2Model]]

#### transformers.Swinv2Model[[transformers.Swinv2Model]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.0/src/transformers/models/swinv2/modeling_swinv2.py#L907)

The bare Swinv2 Model outputting raw hidden-states without any specific head on top.

This model inherits from [PreTrainedModel](/docs/transformers/v5.5.0/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.Swinv2Model.forwardhttps://github.com/huggingface/transformers/blob/v5.5.0/src/transformers/models/swinv2/modeling_swinv2.py#L932[{"name": "pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "bool_masked_pos", "val": ": torch.BoolTensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [ViTImageProcessor](/docs/transformers/v5.5.0/ko/model_doc/vit#transformers.ViTImageProcessor). See `ViTImageProcessor.__call__()` for details (`processor_class` uses
  [ViTImageProcessor](/docs/transformers/v5.5.0/ko/model_doc/vit#transformers.ViTImageProcessor) for processing images).
- **bool_masked_pos** (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*) --
  Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **interpolate_pos_encoding** (`bool`, *optional*, defaults to `False`) --
  Whether to interpolate the pre-trained position encodings.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/v5.5.0/ko/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0`Swinv2ModelOutput` or `tuple(torch.FloatTensor)`A `Swinv2ModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Swinv2Config](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2Config)) and inputs.
The [Swinv2Model](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2Model) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*, defaults to `None`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed) -- Average pooling of the last layer hidden-state.
- **hidden_states** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.
- **reshaped_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
  shape `(batch_size, hidden_size, height, width)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
  include the spatial dimensions.

Example:

```python
```

**Parameters:**

config ([Swinv2Model](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2Model)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.5.0/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

add_pooling_layer (`bool`, *optional*, defaults to `True`) : Whether or not to apply pooling layer.

use_mask_token (`bool`, *optional*, defaults to `False`) : Whether or not to create and apply mask tokens in the embedding layer.

**Returns:**

``Swinv2ModelOutput` or `tuple(torch.FloatTensor)``

A `Swinv2ModelOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Swinv2Config](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2Config)) and inputs.

## Swinv2ForMaskedImageModeling [[transformers.Swinv2ForMaskedImageModeling]][[transformers.Swinv2ForMaskedImageModeling]]

#### transformers.Swinv2ForMaskedImageModeling[[transformers.Swinv2ForMaskedImageModeling]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.0/src/transformers/models/swinv2/modeling_swinv2.py#L1004)

Swinv2 Model with a decoder on top for masked image modeling, as proposed in
[SimMIM](https://huggingface.co/papers/2111.09886).

Note that we provide a script to pre-train this model on custom data in our [examples
directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).

This model inherits from [PreTrainedModel](/docs/transformers/v5.5.0/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.Swinv2ForMaskedImageModeling.forwardhttps://github.com/huggingface/transformers/blob/v5.5.0/src/transformers/models/swinv2/modeling_swinv2.py#L1021[{"name": "pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "bool_masked_pos", "val": ": torch.BoolTensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [ViTImageProcessor](/docs/transformers/v5.5.0/ko/model_doc/vit#transformers.ViTImageProcessor). See `ViTImageProcessor.__call__()` for details (`processor_class` uses
  [ViTImageProcessor](/docs/transformers/v5.5.0/ko/model_doc/vit#transformers.ViTImageProcessor) for processing images).
- **bool_masked_pos** (`torch.BoolTensor` of shape `(batch_size, num_patches)`) --
  Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **interpolate_pos_encoding** (`bool`, *optional*, defaults to `False`) --
  Whether to interpolate the pre-trained position encodings.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/v5.5.0/ko/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0`Swinv2MaskedImageModelingOutput` or `tuple(torch.FloatTensor)`A `Swinv2MaskedImageModelingOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Swinv2Config](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2Config)) and inputs.
The [Swinv2ForMaskedImageModeling](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided) -- Masked image modeling (MLM) loss.
- **reconstruction** (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) -- Reconstructed pixel values.
- **hidden_states** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.
- **reshaped_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
  shape `(batch_size, hidden_size, height, width)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
  include the spatial dimensions.

Examples:
```python
>>> from transformers import AutoImageProcessor, Swinv2ForMaskedImageModeling
>>> import torch
>>> from PIL import Image
>>> import httpx
>>> from io import BytesIO

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))

>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256")
>>> model = Swinv2ForMaskedImageModeling.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256")

>>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
>>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
>>> # create random boolean mask of shape (batch_size, num_patches)
>>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()

>>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
>>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
>>> list(reconstructed_pixel_values.shape)
[1, 3, 256, 256]
```

**Parameters:**

config ([Swinv2ForMaskedImageModeling](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.5.0/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

``Swinv2MaskedImageModelingOutput` or `tuple(torch.FloatTensor)``

A `Swinv2MaskedImageModelingOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Swinv2Config](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2Config)) and inputs.

## Swinv2ForImageClassification [[transformers.Swinv2ForImageClassification]][[transformers.Swinv2ForImageClassification]]

#### transformers.Swinv2ForImageClassification[[transformers.Swinv2ForImageClassification]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.0/src/transformers/models/swinv2/modeling_swinv2.py#L1123)

Swinv2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state
of the [CLS] token) e.g. for ImageNet.

Note that it's possible to fine-tune SwinV2 on higher resolution images than the ones it has been trained on, by
setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained
position embeddings to the higher resolution.

This model inherits from [PreTrainedModel](/docs/transformers/v5.5.0/ko/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.

forwardtransformers.Swinv2ForImageClassification.forwardhttps://github.com/huggingface/transformers/blob/v5.5.0/src/transformers/models/swinv2/modeling_swinv2.py#L1138[{"name": "pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "labels", "val": ": torch.LongTensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool = False"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) --
  The tensors corresponding to the input images. Pixel values can be obtained using
  [ViTImageProcessor](/docs/transformers/v5.5.0/ko/model_doc/vit#transformers.ViTImageProcessor). See `ViTImageProcessor.__call__()` for details (`processor_class` uses
  [ViTImageProcessor](/docs/transformers/v5.5.0/ko/model_doc/vit#transformers.ViTImageProcessor) for processing images).
- **labels** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
  config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
  `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **interpolate_pos_encoding** (`bool`, *optional*, defaults to `False`) --
  Whether to interpolate the pre-trained position encodings.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/v5.5.0/ko/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0`Swinv2ImageClassifierOutput` or `tuple(torch.FloatTensor)`A `Swinv2ImageClassifierOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Swinv2Config](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2Config)) and inputs.
The [Swinv2ForImageClassification](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2ForImageClassification) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Classification (or regression if config.num_labels==1) loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (before SoftMax).
- **hidden_states** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
  one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
- **attentions** (`tuple[torch.FloatTensor, ...]`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
  heads.
- **reshaped_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
  shape `(batch_size, hidden_size, height, width)`.

  Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
  include the spatial dimensions.

Example:

```python
>>> from transformers import AutoImageProcessor, Swinv2ForImageClassification
>>> import torch
>>> from datasets import load_dataset

>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]

>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256")
>>> model = Swinv2ForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256")

>>> inputs = image_processor(image, return_tensors="pt")

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
...
```

**Parameters:**

config ([Swinv2ForImageClassification](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2ForImageClassification)) : Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from_pretrained()](/docs/transformers/v5.5.0/ko/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

**Returns:**

``Swinv2ImageClassifierOutput` or `tuple(torch.FloatTensor)``

A `Swinv2ImageClassifierOutput` or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([Swinv2Config](/docs/transformers/v5.5.0/ko/model_doc/swinv2#transformers.Swinv2Config)) and inputs.