The ConvNeXt V2 model was proposed in ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie. ConvNeXt V2 is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, and a successor of ConvNeXT.
The abstract from the paper is the following:
Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt, have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE). However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.
ConvNeXt V2 architecture. Taken from the original paper. This model was contributed by adirik. The original code can be found here.
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ConvNeXt V2.
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.
( num_channels = 3 patch_size = 4 num_stages = 4 hidden_sizes = None depths = None hidden_act = 'gelu' initializer_range = 0.02 layer_norm_eps = 1e-12 drop_path_rate = 0.0 image_size = 224 out_features = None out_indices = None **kwargs )
Parameters
int, optional, defaults to 3) —
The number of input channels. int, optional, defaults to 4) —
Patch size to use in the patch embedding layer. int, optional, defaults to 4) —
The number of stages in the model. List[int], optional, defaults to [96, 192, 384, 768]) —
Dimensionality (hidden size) at each stage. List[int], optional, defaults to [3, 3, 9, 3]) —
Depth (number of blocks) for each stage. 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. float, optional, defaults to 0.02) —
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. float, optional, defaults to 1e-12) —
The epsilon used by the layer normalization layers. float, optional, defaults to 0.0) —
The drop rate for stochastic depth. 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. 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. This is the configuration class to store the configuration of a 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 architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
Example:
>>> 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( config )
Parameters
The bare ConvNextV2 model outputting raw features without any specific head on top. This model is a PyTorch 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.
( pixel_values: FloatTensor = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using ConvNextImageProcessor. See
ConvNextImageProcessor.call() for details. bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. Returns
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) 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 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, ConvNextV2Model
>>> 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 = ConvNextV2Model.from_pretrained("facebook/convnextv2-tiny-1k-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]( config )
Parameters
ConvNextV2 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 subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
( pixel_values: FloatTensor = None labels: Optional = None output_hidden_states: Optional = None return_dict: Optional = None ) → transformers.modeling_outputs.ImageClassifierOutputWithNoAttention or tuple(torch.FloatTensor)
Parameters
torch.FloatTensor of shape (batch_size, num_channels, height, width)) —
Pixel values. Pixel values can be obtained using ConvNextImageProcessor. See
ConvNextImageProcessor.call() for details. bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail. bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple. 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). Returns
transformers.modeling_outputs.ImageClassifierOutputWithNoAttention or tuple(torch.FloatTensor)
A 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) and inputs.
torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).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 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, 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])
tabby, tabby cat( config: ConvNextV2Config *inputs **kwargs )
Parameters
The bare ConvNextV2 model outputting raw features without any specific head on top. This model inherits from 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 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:
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:
pixel_values only and nothing else: model(pixel_values)model([pixel_values, attention_mask]) or model([pixel_values, attention_mask, token_type_ids])model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})Note that when creating models and layers with 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!
( pixel_values: TFModelInputType | None = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: bool = False ) → transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndNoAttention or tuple(tf.Tensor)
Parameters
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. See
ConvNextImageProcessor.call() for details. 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. bool, optional) —
Whether or not to return a 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. Returns
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) 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 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, 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]( config: ConvNextV2Config *inputs **kwargs )
Parameters
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. 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 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:
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:
pixel_values only and nothing else: model(pixel_values)model([pixel_values, attention_mask]) or model([pixel_values, attention_mask, token_type_ids])model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})Note that when creating models and layers with 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!
( pixel_values: TFModelInputType | None = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None training: Optional[bool] = False ) → transformers.modeling_tf_outputs.TFImageClassifierOutputWithNoAttention or tuple(tf.Tensor)
Parameters
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. See
ConvNextImageProcessor.call() for details. 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. bool, optional) —
Whether or not to return a 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. 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). Returns
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) and inputs.
tf.Tensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.tf.Tensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).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 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, 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