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On this page
  • ConvNeXt V2
  • Overview
  • Resources
  • ConvNextV2Config
  • ConvNextV2Model
  • ConvNextV2ForImageClassification
  1. API
  2. MODELS
  3. VISION MODELS

ConvNeXTV2

PreviousConvNeXTNextCvT

Last updated 1 year ago

ConvNeXt V2

Overview

The ConvNeXt V2 model was proposed in 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 .

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.

Tips:

  • See the code examples below each model regarding usage.

Resources

A list of official BOINC AI and community (indicated by 🌎) resources to help you get started with ConvNeXt V2.

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.

ConvNextV2Config

class transformers.ConvNextV2Config

( num_channels = 3patch_size = 4num_stages = 4hidden_sizes = Nonedepths = Nonehidden_act = 'gelu'initializer_range = 0.02layer_norm_eps = 1e-12drop_path_rate = 0.0image_size = 224out_features = Noneout_indices = None**kwargs )

Parameters

  • 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.

  • 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.

  • 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.

Example:

Copied

>>> 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

ConvNextV2Model

class transformers.ConvNextV2Model

( config )

Parameters

forward

( pixel_values: FloatTensor = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention or tuple(torch.FloatTensor)

Parameters

  • 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.

Returns

transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention or tuple(torch.FloatTensor)

  • 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.

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:

Copied

>>> 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]

ConvNextV2ForImageClassification

class transformers.ConvNextV2ForImageClassification

( config )

Parameters

ConvNextV2 Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet.

forward

Parameters

  • 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.

  • 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).

Returns

  • 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.

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:

Copied

>>> 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

ConvNeXt V2 architecture. Taken from the .

This model was contributed by . The original code can be found .

is supported by this and .

This is the configuration class to store the configuration of a . 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 architecture.

Configuration objects inherit from and can be used to control the model outputs. Read the documentation from for more information.

config () — 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 method to load the model weights.

The bare ConvNextV2 model outputting raw features without any specific head on top. This model is a PyTorch 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 (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using . See for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

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 () and inputs.

The forward method, overrides the __call__ special method.

config () — 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 method to load the model weights.

This model is a PyTorch 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 = Nonelabels: typing.Optional[torch.LongTensor] = Noneoutput_hidden_states: typing.Optional[bool] = Nonereturn_dict: typing.Optional[bool] = None ) → or tuple(torch.FloatTensor)

pixel_values (torch.FloatTensor of shape (batch_size, num_channels, height, width)) — Pixel values. Pixel values can be obtained using . See for details.

return_dict (bool, optional) — Whether or not to return a instead of a plain tuple.

or tuple(torch.FloatTensor)

A 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 () and inputs.

The forward method, overrides the __call__ special method.

🌍
🌍
🌍
original paper
adirik
here
ConvNextV2ForImageClassification
example script
notebook
<source>
ConvNextV2Model
facebook/convnextv2-tiny-1k-224
PretrainedConfig
PretrainedConfig
<source>
ConvNextV2Config
from_pretrained()
torch.nn.Module
<source>
ConvNextImageProcessor
ConvNextImageProcessor.call()
ModelOutput
ConvNextV2Config
ConvNextV2Model
<source>
ConvNextV2Config
from_pretrained()
torch.nn.Module
<source>
transformers.modeling_outputs.ImageClassifierOutputWithNoAttention
ConvNextImageProcessor
ConvNextImageProcessor.call()
ModelOutput
transformers.modeling_outputs.ImageClassifierOutputWithNoAttention
transformers.modeling_outputs.ImageClassifierOutputWithNoAttention
ConvNextV2Config
ConvNextV2ForImageClassification
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
ConvNeXT