transformers/pr_37082/en/model_doc/convnext.md

ConvNeXT

Overview

The ConvNeXT model was proposed in A ConvNet for the 2020s 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The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.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.

ConvNeXT architecture. Taken from the original paper.

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

Resources

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

• ConvNextForImageClassification is supported by this example script and notebook.
• See also: Image classification task guide

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]]

transformers.ConvNextConfig[[transformers.ConvNextConfig]]

Source

This is the configuration class to store the configuration of a 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 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 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

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.

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.

ConvNextImageProcessor[[transformers.ConvNextImageProcessor]]

transformers.ConvNextImageProcessor[[transformers.ConvNextImageProcessor]]

Source

Constructs a ConvNeXT image processor.

preprocesstransformers.ConvNextImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/vr_37082/src/transformers/models/convnext/image_processing_convnext.py#L200[{"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 = "}, {"name": "input_data_format", "val": ": typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None"}]- 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 0

Preprocess an image or batch of images.

Parameters:

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 1a classification loss is computed (Cross-Entropy).0[transformers.modeling_outputs.ImageClassifierOutputWithNoAttention](/docs/transformers/pr_37082/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention) ortuple(torch.FloatTensor)A [transformers.modeling_outputs.ImageClassifierOutputWithNoAttention](/docs/transformers/pr_37082/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention) or a tuple of torch.FloatTensor(ifreturn_dict=Falseis passed or whenconfig.return_dict=False`) comprising various elements depending on the configuration (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. The 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

>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
...

Parameters:

config (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() method to load the model weights.

Returns:

[transformers.modeling_outputs.ImageClassifierOutputWithNoAttention](/docs/transformers/pr_37082/en/main_classes/output#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 (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.