ConvNeXT

## [](#overview)Overview

The ConvNeXT model was proposed in [A ConvNet for the 2020s](https://arxiv.org/abs/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_The “Roaring 20s” of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.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._

Tips:

-   See the code examples below each model regarding usage.

![drawing](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnext_architecture.jpg) ConvNeXT architecture. Taken from the [original paper](https://arxiv.org/abs/2201.03545).

This model was contributed by [nielsr](https://huggingface.co/nielsr). TensorFlow version of the model was contributed by [ariG23498](https://github.com/ariG23498), [gante](https://github.com/gante), and [sayakpaul](https://github.com/sayakpaul) (equal contribution). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt).

## [](#resources)Resources

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

-   [ConvNextForImageClassification](/docs/transformers/v4.30.0/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.

## [](#transformers.ConvNextConfig)ConvNextConfig

### class transformers.ConvNextConfig

[](#transformers.ConvNextConfig)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/convnext/configuration_convnext.py#L36)

( num\_channels = 3patch\_size = 4num\_stages = 4hidden\_sizes = Nonedepths = Nonehidden\_act = 'gelu'initializer\_range = 0.02layer\_norm\_eps = 1e-12layer\_scale\_init\_value = 1e-06drop\_path\_rate = 0.0image\_size = 224out\_features = Noneout\_indices = None\*\*kwargs )

This is the configuration class to store the configuration of a [ConvNextModel](/docs/transformers/v4.30.0/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/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) for more information.

Example:

```
>>> from transformers import ConvNextConfig, ConvNextModel

>>> 
>>> configuration = ConvNextConfig()

>>> 
>>> model = ConvNextModel(configuration)

>>> 
>>> configuration = model.config```

## [](#transformers.ConvNextFeatureExtractor)ConvNextFeatureExtractor

## [](#transformers.ConvNextImageProcessor)ConvNextImageProcessor

### class transformers.ConvNextImageProcessor

[](#transformers.ConvNextImageProcessor)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/convnext/image_processing_convnext.py#L50)

( do\_resize: bool = Truesize: typing.Dict\[str, int\] = Nonecrop\_pct: float = Noneresample: Resampling = <Resampling.BILINEAR: 2>do\_rescale: bool = Truerescale\_factor: typing.Union\[int, float\] = 0.00392156862745098do\_normalize: bool = Trueimage\_mean: typing.Union\[float, typing.List\[float\], NoneType\] = Noneimage\_std: typing.Union\[float, typing.List\[float\], NoneType\] = None\*\*kwargs )

Constructs a ConvNeXT image processor.

#### preprocess

[](#transformers.ConvNextImageProcessor.preprocess)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/convnext/image_processing_convnext.py#L204)

( images: typing.Union\[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List\[ForwardRef('PIL.Image.Image')\], typing.List\[numpy.ndarray\], typing.List\[ForwardRef('torch.Tensor')\]\]do\_resize: bool = Nonesize: typing.Dict\[str, int\] = Nonecrop\_pct: float = Noneresample: Resampling = Nonedo\_rescale: bool = Nonerescale\_factor: float = Nonedo\_normalize: bool = Noneimage\_mean: typing.Union\[float, typing.List\[float\], NoneType\] = Noneimage\_std: typing.Union\[float, typing.List\[float\], NoneType\] = Nonereturn\_tensors: typing.Union\[str, transformers.utils.generic.TensorType, NoneType\] = Nonedata\_format: ChannelDimension = <ChannelDimension.FIRST: 'channels\_first'>\*\*kwargs )

Preprocess an image or batch of images.

## [](#transformers.ConvNextModel)ConvNextModel

### class transformers.ConvNextModel

[](#transformers.ConvNextModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/convnext/modeling_convnext.py#L333)

( config )

Parameters

-   [](#transformers.ConvNextModel.config)**config** ([ConvNextConfig](/docs/transformers/v4.30.0/en/model_doc/convnext#transformers.ConvNextConfig)) — 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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare ConvNext model outputting raw features without any specific head on top. This model is 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.

#### forward

[](#transformers.ConvNextModel.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/convnext/modeling_convnext.py#L347)

( pixel\_values: FloatTensor = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → `transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention` or `tuple(torch.FloatTensor)`

The [ConvNextModel](/docs/transformers/v4.30.0/en/model_doc/convnext#transformers.ConvNextModel) 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:

```
>>> from transformers import AutoImageProcessor, ConvNextModel
>>> 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 = ConvNextModel.from_pretrained("facebook/convnext-tiny-224")

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

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

>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 768, 7, 7]```

## [](#transformers.ConvNextForImageClassification)ConvNextForImageClassification

### class transformers.ConvNextForImageClassification

[](#transformers.ConvNextForImageClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/convnext/modeling_convnext.py#L399)

( config )

Parameters

-   [](#transformers.ConvNextForImageClassification.config)**config** ([ConvNextConfig](/docs/transformers/v4.30.0/en/model_doc/convnext#transformers.ConvNextConfig)) — 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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

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

This model is 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.

The [ConvNextForImageClassification](/docs/transformers/v4.30.0/en/model_doc/convnext#transformers.ConvNextForImageClassification) 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:

```
>>> 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

>>> 
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
tabby, tabby cat```

## [](#transformers.TFConvNextModel)TFConvNextModel

### class transformers.TFConvNextModel

[](#transformers.TFConvNextModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/convnext/modeling_tf_convnext.py#L416)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFConvNextModel.config)**config** ([ConvNextConfig](/docs/transformers/v4.30.0/en/model_doc/convnext#transformers.ConvNextConfig)) — 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/v4.30.0/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.

The bare ConvNext model outputting raw features without any specific head on top. This model inherits from [TFPreTrainedModel](/docs/transformers/v4.30.0/en/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 [tf.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!

The [TFConvNextModel](/docs/transformers/v4.30.0/en/model_doc/convnext#transformers.TFConvNextModel) 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.

Examples:

```
>>> from transformers import AutoImageProcessor, TFConvNextModel
>>> from PIL import Image
>>> import requests

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

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

>>> inputs = image_processor(images=image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state```

## [](#transformers.TFConvNextForImageClassification)TFConvNextForImageClassification

### class transformers.TFConvNextForImageClassification

[](#transformers.TFConvNextForImageClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/convnext/modeling_tf_convnext.py#L483)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFConvNextForImageClassification.config)**config** ([ConvNextConfig](/docs/transformers/v4.30.0/en/model_doc/convnext#transformers.ConvNextConfig)) — 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/v4.30.0/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.

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 [TFPreTrainedModel](/docs/transformers/v4.30.0/en/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 [tf.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!

The [TFConvNextForImageClassification](/docs/transformers/v4.30.0/en/model_doc/convnext#transformers.TFConvNextForImageClassification) 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.

Examples:

```
>>> from transformers import AutoImageProcessor, TFConvNextForImageClassification
>>> import tensorflow as tf
>>> from PIL import Image
>>> import requests

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

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

>>> inputs = image_processor(images=image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> 
>>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0]
>>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)])```