transformers/pr_33962/en/model_doc/convnext.md

# ConvNeXT

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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

The ConvNeXT model was proposed in [A ConvNet for the 2020s](https://huggingface.co/papers/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them.

The abstract from the paper is the following:

*The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.
A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers
(e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide
variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive
biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design
of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models
dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy
and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.*

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnext_architecture.jpg"
alt="drawing" width="600"/>

<small> ConvNeXT architecture. Taken from the <a href="https://huggingface.co/papers/2201.03545">original paper</a>.</small>

This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt).

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ConvNeXT.

<PipelineTag pipeline="image-classification"/>

- [ConvNextForImageClassification](/docs/transformers/pr_33962/en/model_doc/convnext#transformers.ConvNextForImageClassification) is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
- See also: [Image classification task guide](../tasks/image_classification)

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

## ConvNextConfig[[transformers.ConvNextConfig]]

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<docstring><name>class transformers.ConvNextConfig</name><anchor>transformers.ConvNextConfig</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/convnext/configuration_convnext.py#L31</source><parameters>[{"name": "num_channels", "val": " = 3"}, {"name": "patch_size", "val": " = 4"}, {"name": "num_stages", "val": " = 4"}, {"name": "hidden_sizes", "val": " = None"}, {"name": "depths", "val": " = None"}, {"name": "hidden_act", "val": " = 'gelu'"}, {"name": "initializer_range", "val": " = 0.02"}, {"name": "layer_norm_eps", "val": " = 1e-12"}, {"name": "layer_scale_init_value", "val": " = 1e-06"}, {"name": "drop_path_rate", "val": " = 0.0"}, {"name": "image_size", "val": " = 224"}, {"name": "out_features", "val": " = None"}, {"name": "out_indices", "val": " = None"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **num_channels** (`int`, *optional*, defaults to 3) --
  The number of input channels.
- **patch_size** (`int`, *optional*, defaults to 4) --
  Patch size to use in the patch embedding layer.
- **num_stages** (`int`, *optional*, defaults to 4) --
  The number of stages in the model.
- **hidden_sizes** (`list[int]`, *optional*, defaults to [96, 192, 384, 768]) --
  Dimensionality (hidden size) at each stage.
- **depths** (`list[int]`, *optional*, defaults to [3, 3, 9, 3]) --
  Depth (number of blocks) for each stage.
- **hidden_act** (`str` or `function`, *optional*, defaults to `"gelu"`) --
  The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
  `"selu"` and `"gelu_new"` are supported.
- **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-12) --
  The epsilon used by the layer normalization layers.
- **layer_scale_init_value** (`float`, *optional*, defaults to 1e-6) --
  The initial value for the layer scale.
- **drop_path_rate** (`float`, *optional*, defaults to 0.0) --
  The drop rate for stochastic depth.
- **out_features** (`list[str]`, *optional*) --
  If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
  (depending on how many stages the model has). If unset and `out_indices` is set, will default to the
  corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
  same order as defined in the `stage_names` attribute.
- **out_indices** (`list[int]`, *optional*) --
  If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
  many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
  If unset and `out_features` is unset, will default to the last stage. Must be in the
  same order as defined in the `stage_names` attribute.</paramsdesc><paramgroups>0</paramgroups></docstring>

This is the configuration class to store the configuration of a [ConvNextModel](/docs/transformers/pr_33962/en/model_doc/convnext#transformers.ConvNextModel). It is used to instantiate an
ConvNeXT 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 ConvNeXT
[facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) architecture.

Configuration objects inherit from [PreTrainedConfig](/docs/transformers/pr_33962/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the
documentation from [PreTrainedConfig](/docs/transformers/pr_33962/en/main_classes/configuration#transformers.PreTrainedConfig) for more information.



<ExampleCodeBlock anchor="transformers.ConvNextConfig.example">

Example:
```python
>>> from transformers import ConvNextConfig, ConvNextModel

>>> # Initializing a ConvNext convnext-tiny-224 style configuration
>>> configuration = ConvNextConfig()

>>> # Initializing a model (with random weights) from the convnext-tiny-224 style configuration
>>> model = ConvNextModel(configuration)

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

</ExampleCodeBlock>

</div>

## ConvNextImageProcessor[[transformers.ConvNextImageProcessor]]

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<docstring><name>class transformers.ConvNextImageProcessor</name><anchor>transformers.ConvNextImageProcessor</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/convnext/image_processing_convnext.py#L64</source><parameters>[{"name": "do_resize", "val": ": bool = True"}, {"name": "size", "val": ": typing.Optional[dict[str, int]] = None"}, {"name": "crop_pct", "val": ": typing.Optional[float] = None"}, {"name": "resample", "val": ": Resampling = <Resampling.BILINEAR: 2>"}, {"name": "do_rescale", "val": ": bool = True"}, {"name": "rescale_factor", "val": ": typing.Union[int, float] = 0.00392156862745098"}, {"name": "do_normalize", "val": ": bool = True"}, {"name": "image_mean", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "image_std", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **do_resize** (`bool`, *optional*, defaults to `True`) --
  Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden
  by `do_resize` in the `preprocess` method.
- **size** (`dict[str, int]` *optional*, defaults to `{"shortest_edge" -- 384}`):
  Resolution of the output image after `resize` is applied. If `size["shortest_edge"]` >= 384, the image is
  resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the image will
  be matched to `int(size["shortest_edge"]/crop_pct)`, after which the image is cropped to
  `(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`. Can
  be overridden by `size` in the `preprocess` method.
- **crop_pct** (`float` *optional*, defaults to 224 / 256) --
  Percentage of the image to crop. Only has an effect if `do_resize` is `True` and size < 384. Can be
  overridden by `crop_pct` in the `preprocess` method.
- **resample** (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`) --
  Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
- **do_rescale** (`bool`, *optional*, defaults to `True`) --
  Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
  the `preprocess` method.
- **rescale_factor** (`int` or `float`, *optional*, defaults to `1/255`) --
  Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
  method.
- **do_normalize** (`bool`, *optional*, defaults to `True`) --
  Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
  method.
- **image_mean** (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`) --
  Mean to use if normalizing the image. This is a float or list of floats the length of the number of
  channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
- **image_std** (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`) --
  Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
  number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.</paramsdesc><paramgroups>0</paramgroups></docstring>

Constructs a ConvNeXT image processor.





<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>preprocess</name><anchor>transformers.ConvNextImageProcessor.preprocess</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/convnext/image_processing_convnext.py#L200</source><parameters>[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "do_resize", "val": ": typing.Optional[bool] = None"}, {"name": "size", "val": ": typing.Optional[dict[str, int]] = None"}, {"name": "crop_pct", "val": ": typing.Optional[float] = None"}, {"name": "resample", "val": ": typing.Optional[PIL.Image.Resampling] = None"}, {"name": "do_rescale", "val": ": typing.Optional[bool] = None"}, {"name": "rescale_factor", "val": ": typing.Optional[float] = None"}, {"name": "do_normalize", "val": ": typing.Optional[bool] = None"}, {"name": "image_mean", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "image_std", "val": ": typing.Union[float, list[float], NoneType] = None"}, {"name": "return_tensors", "val": ": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"}, {"name": "data_format", "val": ": ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>"}, {"name": "input_data_format", "val": ": typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None"}]</parameters><paramsdesc>- **images** (`ImageInput`) --
  Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
  passing in images with pixel values between 0 and 1, set `do_rescale=False`.
- **do_resize** (`bool`, *optional*, defaults to `self.do_resize`) --
  Whether to resize the image.
- **size** (`dict[str, int]`, *optional*, defaults to `self.size`) --
  Size of the output image after `resize` has been applied. If `size["shortest_edge"]` >= 384, the image
  is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the
  image will be matched to `int(size["shortest_edge"]/ crop_pct)`, after which the image is cropped to
  `(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`.
- **crop_pct** (`float`, *optional*, defaults to `self.crop_pct`) --
  Percentage of the image to crop if size < 384.
- **resample** (`int`, *optional*, defaults to `self.resample`) --
  Resampling filter to use if resizing the image. This can be one of `PILImageResampling`, filters. Only
  has an effect if `do_resize` is set to `True`.
- **do_rescale** (`bool`, *optional*, defaults to `self.do_rescale`) --
  Whether to rescale the image values between [0 - 1].
- **rescale_factor** (`float`, *optional*, defaults to `self.rescale_factor`) --
  Rescale factor to rescale the image by if `do_rescale` is set to `True`.
- **do_normalize** (`bool`, *optional*, defaults to `self.do_normalize`) --
  Whether to normalize the image.
- **image_mean** (`float` or `list[float]`, *optional*, defaults to `self.image_mean`) --
  Image mean.
- **image_std** (`float` or `list[float]`, *optional*, defaults to `self.image_std`) --
  Image standard deviation.
- **return_tensors** (`str` or `TensorType`, *optional*) --
  The type of tensors to return. Can be one of:
  - Unset: Return a list of `np.ndarray`.
  - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
  - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- **data_format** (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`) --
  The channel dimension format for the output image. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
  - Unset: Use the channel dimension format of the input image.
- **input_data_format** (`ChannelDimension` or `str`, *optional*) --
  The channel dimension format for the input image. If unset, the channel dimension format is inferred
  from the input image. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
  - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.</paramsdesc><paramgroups>0</paramgroups></docstring>

Preprocess an image or batch of images.




</div></div>

## ConvNextImageProcessorFast[[transformers.ConvNextImageProcessorFast]]

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<docstring><name>class transformers.ConvNextImageProcessorFast</name><anchor>transformers.ConvNextImageProcessorFast</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/convnext/image_processing_convnext_fast.py#L45</source><parameters>[{"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.convnext.image_processing_convnext.ConvNextImageProcessorKwargs]"}]</parameters></docstring>

Constructs a fast Convnext image processor.



<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>preprocess</name><anchor>transformers.ConvNextImageProcessorFast.preprocess</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/convnext/image_processing_convnext_fast.py#L60</source><parameters>[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.convnext.image_processing_convnext.ConvNextImageProcessorKwargs]"}]</parameters><paramsdesc>- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]`) --
  Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
  passing in images with pixel values between 0 and 1, set `do_rescale=False`.
- **do_convert_rgb** (`bool`, *optional*) --
  Whether to convert the image to RGB.
- **do_resize** (`bool`, *optional*) --
  Whether to resize the image.
- **size** (`Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]`) --
  Describes the maximum input dimensions to the model.
- **crop_size** (`Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]`) --
  Size of the output image after applying `center_crop`.
- **resample** (`Annotated[Union[PILImageResampling, int, NoneType], None]`) --
  Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
  has an effect if `do_resize` is set to `True`.
- **do_rescale** (`bool`, *optional*) --
  Whether to rescale the image.
- **rescale_factor** (`float`, *optional*) --
  Rescale factor to rescale the image by if `do_rescale` is set to `True`.
- **do_normalize** (`bool`, *optional*) --
  Whether to normalize the image.
- **image_mean** (`Union[float, list[float], tuple[float, ...], NoneType]`) --
  Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
- **image_std** (`Union[float, list[float], tuple[float, ...], NoneType]`) --
  Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
  `True`.
- **do_pad** (`bool`, *optional*) --
  Whether to pad the image. Padding is done either to the largest size in the batch
  or to a fixed square size per image. The exact padding strategy depends on the model.
- **pad_size** (`Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]`) --
  The size in `{"height": int, "width" int}` to pad the images to. Must be larger than any image size
  provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
  height and width in the batch. Applied only when `do_pad=True.`
- **do_center_crop** (`bool`, *optional*) --
  Whether to center crop the image.
- **data_format** (`Union[str, ~image_utils.ChannelDimension, NoneType]`) --
  Only `ChannelDimension.FIRST` is supported. Added for compatibility with slow processors.
- **input_data_format** (`Union[str, ~image_utils.ChannelDimension, NoneType]`) --
  The channel dimension format for the input image. If unset, the channel dimension format is inferred
  from the input image. Can be one of:
  - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
  - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
  - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
- **device** (`Annotated[str, None]`, *optional*) --
  The device to process the images on. If unset, the device is inferred from the input images.
- **return_tensors** (`Annotated[Union[str, ~utils.generic.TensorType, NoneType], None]`) --
  Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
- **disable_grouping** (`bool`, *optional*) --
  Whether to disable grouping of images by size to process them individually and not in batches.
  If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on
  empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157
- **crop_pct** (`float`, *optional*) --
  Percentage of the image to crop. Only has an effect if size < 384. Can be
  overridden by `crop_pct` in the`preprocess` method.</paramsdesc><paramgroups>0</paramgroups><rettype>`<class 'transformers.image_processing_base.BatchFeature'>`</rettype><retdesc>- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.).
- **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at
  initialization.</retdesc></docstring>







</div></div>

## ConvNextModel[[transformers.ConvNextModel]]

<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>class transformers.ConvNextModel</name><anchor>transformers.ConvNextModel</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/convnext/modeling_convnext.py#L257</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([ConvNextModel](/docs/transformers/pr_33962/en/model_doc/convnext#transformers.ConvNextModel)) --
  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/pr_33962/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

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

This model inherits from [PreTrainedModel](/docs/transformers/pr_33962/en/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.





<div class="docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8">


<docstring><name>forward</name><anchor>transformers.ConvNextModel.forward</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/convnext/modeling_convnext.py#L271</source><parameters>[{"name": "pixel_values", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}]</parameters><paramsdesc>- **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
  [ConvNextImageProcessor](/docs/transformers/pr_33962/en/model_doc/convnext#transformers.ConvNextImageProcessor). See [ConvNextImageProcessor.__call__()](/docs/transformers/pr_33962/en/model_doc/fuyu#transformers.FuyuImageProcessor.__call__) for details (`processor_class` uses
  [ConvNextImageProcessor](/docs/transformers/pr_33962/en/model_doc/convnext#transformers.ConvNextImageProcessor) for processing images).
- **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.</paramsdesc><paramgroups>0</paramgroups><rettype>`transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention` or `tuple(torch.FloatTensor)`</rettype><retdesc>A `transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention` 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 ([ConvNextConfig](/docs/transformers/pr_33962/en/model_doc/convnext#transformers.ConvNextConfig)) and inputs.

- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state after a pooling operation on the spatial dimensions.
- **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, num_channels, height, width)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</retdesc></docstring>
The [ConvNextModel](/docs/transformers/pr_33962/en/model_doc/convnext#transformers.ConvNextModel) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







<ExampleCodeBlock anchor="transformers.ConvNextModel.forward.example">

Example:

```python
```

</ExampleCodeBlock>


</div></div>

## ConvNextForImageClassification[[transformers.ConvNextForImageClassification]]

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<docstring><name>class transformers.ConvNextForImageClassification</name><anchor>transformers.ConvNextForImageClassification</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/convnext/modeling_convnext.py#L304</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([ConvNextForImageClassification](/docs/transformers/pr_33962/en/model_doc/convnext#transformers.ConvNextForImageClassification)) --
  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/pr_33962/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.

This model inherits from [PreTrainedModel](/docs/transformers/pr_33962/en/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.





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<docstring><name>forward</name><anchor>transformers.ConvNextForImageClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/vr_33962/src/transformers/models/convnext/modeling_convnext.py#L322</source><parameters>[{"name": "pixel_values", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "labels", "val": ": typing.Optional[torch.LongTensor] = None"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **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
  [ConvNextImageProcessor](/docs/transformers/pr_33962/en/model_doc/convnext#transformers.ConvNextImageProcessor). See [ConvNextImageProcessor.__call__()](/docs/transformers/pr_33962/en/model_doc/fuyu#transformers.FuyuImageProcessor.__call__) for details (`processor_class` uses
  [ConvNextImageProcessor](/docs/transformers/pr_33962/en/model_doc/convnext#transformers.ConvNextImageProcessor) 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).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.ImageClassifierOutputWithNoAttention](/docs/transformers/pr_33962/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.ImageClassifierOutputWithNoAttention](/docs/transformers/pr_33962/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention) 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 ([ConvNextConfig](/docs/transformers/pr_33962/en/model_doc/convnext#transformers.ConvNextConfig)) and inputs.

- **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 stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also
  called feature maps) of the model at the output of each stage.</retdesc></docstring>
The [ConvNextForImageClassification](/docs/transformers/pr_33962/en/model_doc/convnext#transformers.ConvNextForImageClassification) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







<ExampleCodeBlock anchor="transformers.ConvNextForImageClassification.forward.example">

Example:

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

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

>>> image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224")
>>> model = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224")

>>> 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])
...
```

</ExampleCodeBlock>


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