class transformers.ConvNextV2Configtransformers.ConvNextV2Confighttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/convnextv2/configuration_convnextv2.py#L25[{"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": "drop_path_rate", "val": " = 0.0"}, {"name": "image_size", "val": " = 224"}, {"name": "out_features", "val": " = None"}, {"name": "out_indices", "val": " = None"}, {"name": "**kwargs", "val": ""}]- **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.
- **drop_path_rate** (`float`, *optional*, defaults to 0.0) --
The drop rate for stochastic depth.
- **image_size** (`int`, *optional*, defaults to 224) --
The size (resolution) of each image.
- **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.0
This is the configuration class to store the configuration of a [ConvNextV2Model](/docs/transformers/v4.57.0/ja/model_doc/convnextv2#transformers.ConvNextV2Model). It is used to instantiate an
ConvNeXTV2 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 ConvNeXTV2
[facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) architecture.
Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.57.0/ja/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the
documentation from [PretrainedConfig](/docs/transformers/v4.57.0/ja/main_classes/configuration#transformers.PretrainedConfig) for more information.
Example:
```python
>>> from transformers import ConvNeXTV2Config, ConvNextV2Model
>>> # Initializing a ConvNeXTV2 convnextv2-tiny-1k-224 style configuration
>>> configuration = ConvNeXTV2Config()
>>> # Initializing a model (with random weights) from the convnextv2-tiny-1k-224 style configuration
>>> model = ConvNextV2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
class transformers.ConvNextV2Modeltransformers.ConvNextV2Modelhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/convnextv2/modeling_convnextv2.py#L285[{"name": "config", "val": ""}]- **config** ([ConvNextV2Model](/docs/transformers/v4.57.0/ja/model_doc/convnextv2#transformers.ConvNextV2Model)) --
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/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.0
The bare Convnextv2 Model outputting raw hidden-states without any specific head on top.
This model inherits from [PreTrainedModel](/docs/transformers/v4.57.0/ja/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.ConvNextV2Model.forwardhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/convnextv2/modeling_convnextv2.py#L299[{"name": "pixel_values", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}]- **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/v4.57.0/ja/model_doc/convnext#transformers.ConvNextImageProcessor). See [ConvNextImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details (`processor_class` uses
[ConvNextImageProcessor](/docs/transformers/v4.57.0/ja/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.0`transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention` or `tuple(torch.FloatTensor)`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 ([ConvNextV2Config](/docs/transformers/v4.57.0/ja/model_doc/convnextv2#transformers.ConvNextV2Config)) 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.
The [ConvNextV2Model](/docs/transformers/v4.57.0/ja/model_doc/convnextv2#transformers.ConvNextV2Model) 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.
Example:
```python
```
class transformers.ConvNextV2ForImageClassificationtransformers.ConvNextV2ForImageClassificationhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/convnextv2/modeling_convnextv2.py#L333[{"name": "config", "val": ""}]- **config** ([ConvNextV2ForImageClassification](/docs/transformers/v4.57.0/ja/model_doc/convnextv2#transformers.ConvNextV2ForImageClassification)) --
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/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.0
ConvNextV2 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/v4.57.0/ja/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.ConvNextV2ForImageClassification.forwardhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/convnextv2/modeling_convnextv2.py#L351[{"name": "pixel_values", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "labels", "val": ": typing.Optional[torch.LongTensor] = 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
[ConvNextImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/convnext#transformers.ConvNextImageProcessor). See [ConvNextImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details (`processor_class` uses
[ConvNextImageProcessor](/docs/transformers/v4.57.0/ja/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).0[transformers.modeling_outputs.ImageClassifierOutputWithNoAttention](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention) or `tuple(torch.FloatTensor)`A [transformers.modeling_outputs.ImageClassifierOutputWithNoAttention](/docs/transformers/v4.57.0/ja/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 ([ConvNextV2Config](/docs/transformers/v4.57.0/ja/model_doc/convnextv2#transformers.ConvNextV2Config)) 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.
The [ConvNextV2ForImageClassification](/docs/transformers/v4.57.0/ja/model_doc/convnextv2#transformers.ConvNextV2ForImageClassification) 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.
Example:
```python
>>> from transformers import AutoImageProcessor, ConvNextV2ForImageClassification
>>> import torch
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224")
>>> model = ConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-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])
...
```
class transformers.TFConvNextV2Modeltransformers.TFConvNextV2Modelhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/convnextv2/modeling_tf_convnextv2.py#L541[{"name": "config", "val": ": ConvNextV2Config"}, {"name": "*inputs", "val": ""}, {"name": "**kwargs", "val": ""}]- **config** ([ConvNextV2Config](/docs/transformers/v4.57.0/ja/model_doc/convnextv2#transformers.ConvNextV2Config)) -- 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/v4.57.0/ja/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.0
The bare ConvNextV2 model outputting raw features without any specific head on top.
This model inherits from [TFPreTrainedModel](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.TFPreTrainedModel). 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 [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `pixel_values` only and nothing else: `model(pixel_values)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
calltransformers.TFConvNextV2Model.callhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/convnextv2/modeling_tf_convnextv2.py#L546[{"name": "pixel_values", "val": ": TFModelInputType | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "training", "val": ": bool = False"}]- **pixel_values** (`np.ndarray`, `tf.Tensor`, `list[tf.Tensor]`, `dict[str, tf.Tensor]` or `dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`) --
Pixel values. Pixel values can be obtained using [AutoImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoImageProcessor). See
[ConvNextImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details.
- **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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
- **return_dict** (`bool`, *optional*) --
Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to `True`.0`transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndNoAttention` or `tuple(tf.Tensor)`A `transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndNoAttention` or a tuple of `tf.Tensor` (if
`return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
configuration ([ConvNextV2Config](/docs/transformers/v4.57.0/ja/model_doc/convnextv2#transformers.ConvNextV2Config)) and inputs.
- **last_hidden_state** (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`) -- Sequence of hidden-states at the output of the last layer of the model.
- **pooler_output** (`tf.Tensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state after a pooling operation on the spatial dimensions.
- **hidden_states** (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `tf.Tensor` (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.
The [TFConvNextV2Model](/docs/transformers/v4.57.0/ja/model_doc/convnextv2#transformers.TFConvNextV2Model) 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.
Example:
```python
>>> from transformers import AutoImageProcessor, TFConvNextV2Model
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224")
>>> model = TFConvNextV2Model.from_pretrained("facebook/convnextv2-tiny-1k-224")
>>> inputs = image_processor(image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 768, 7, 7]
```
class transformers.TFConvNextV2ForImageClassificationtransformers.TFConvNextV2ForImageClassificationhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/convnextv2/modeling_tf_convnextv2.py#L602[{"name": "config", "val": ": ConvNextV2Config"}, {"name": "*inputs", "val": ""}, {"name": "**kwargs", "val": ""}]- **config** ([ConvNextV2Config](/docs/transformers/v4.57.0/ja/model_doc/convnextv2#transformers.ConvNextV2Config)) -- 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/v4.57.0/ja/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.0
ConvNextV2 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 [TFPreTrainedModel](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.TFPreTrainedModel). 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 [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.
TensorFlow models and layers in `transformers` accept two formats as input:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional argument.
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:
- a single Tensor with `pixel_values` only and nothing else: `model(pixel_values)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})`
Note that when creating models and layers with
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
about any of this, as you can just pass inputs like you would to any other Python function!
calltransformers.TFConvNextV2ForImageClassification.callhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/convnextv2/modeling_tf_convnextv2.py#L617[{"name": "pixel_values", "val": ": TFModelInputType | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "labels", "val": ": np.ndarray | tf.Tensor | None = None"}, {"name": "training", "val": ": bool | None = False"}]- **pixel_values** (`np.ndarray`, `tf.Tensor`, `list[tf.Tensor]`, `dict[str, tf.Tensor]` or `dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`) --
Pixel values. Pixel values can be obtained using [AutoImageProcessor](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoImageProcessor). See
[ConvNextImageProcessor.__call__()](/docs/transformers/v4.57.0/ja/model_doc/detr#transformers.DetrFeatureExtractor.__call__) for details.
- **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. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.
- **return_dict** (`bool`, *optional*) --
Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to `True`.
- **labels** (`tf.Tensor` or `np.ndarray` 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).0`transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention` or `tuple(tf.Tensor)`A `transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention` or a tuple of `tf.Tensor` (if
`return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
configuration ([ConvNextV2Config](/docs/transformers/v4.57.0/ja/model_doc/convnextv2#transformers.ConvNextV2Config)) and inputs.
- **loss** (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Classification (or regression if config.num_labels==1) loss.
- **logits** (`tf.Tensor` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (before SoftMax).
- **hidden_states** (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `tf.Tensor` (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.
The [TFConvNextV2ForImageClassification](/docs/transformers/v4.57.0/ja/model_doc/convnextv2#transformers.TFConvNextV2ForImageClassification) 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.
Example:
```python
>>> from transformers import AutoImageProcessor, TFConvNextV2ForImageClassification
>>> import tensorflow as tf
>>> from datasets import load_dataset
>>> dataset = load_dataset("huggingface/cats-image"))
>>> image = dataset["test"]["image"][0]
>>> image_processor = AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224")
>>> model = TFConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224")
>>> inputs = image_processor(image, return_tensors="tf")
>>> logits = model(**inputs).logits
>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = int(tf.math.argmax(logits, axis=-1))
>>> print(model.config.id2label[predicted_label])
tabby, tabby cat
```
ConvNeXt V2 アーキテクチャ。 元の論文から抜粋。
このモデルは [adirik](https://huggingface.co/adirik) によって提供されました。元のコードは [こちら](https://github.com/facebookresearch/ConvNeXt-V2) にあります。
## Resources
ConvNeXt V2 の使用を開始するのに役立つ公式 Hugging Face およびコミュニティ (🌎 で示される) リソースのリスト。