transformers/pr_43265/en/_app/immutable/nodes/163.eb45caa8.js

import{s as mn,b as dn,o as pn,n as Be}from"../chunks/scheduler.31fdf58d.js";import{S as gn,i as fn,e as c,s as o,c as g,h as hn,a as m,d as n,b as s,f as F,j as x,g as f,k as M,l,m as r,n as h,t as u,o as v,p as _}from"../chunks/index.2f76fdf0.js";import{T as cn}from"../chunks/Tip.8d349121.js";import{C as un}from"../chunks/CopyLLMTxtMenu.53b607bf.js";import{D as R}from"../chunks/Docstring.7acc6835.js";import{C as Ht}from"../chunks/CodeBlock.e52df5d6.js";import{E as Wt}from"../chunks/ExampleCodeBlock.f9704f52.js";import{P as vn}from"../chunks/PipelineTag.37119c44.js";import{H as A,E as _n}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.08750ec0.js";function xn(P){let a,b="Example:",d,p,C;return p=new Ht({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ConvNextConfig, ConvNextModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a ConvNext convnext-tiny-224 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = ConvNextConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the convnext-tiny-224 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ConvNextModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`,lang:"python",wrap:!1}}),{c(){a=c("p"),a.textContent=b,d=o(),g(p.$$.fragment)},l(i){a=m(i,"P",{"data-svelte-h":!0}),x(a)!=="svelte-11lpom8"&&(a.textContent=b),d=s(i),f(p.$$.fragment,i)},m(i,$){r(i,a,$),r(i,d,$),h(p,i,$),C=!0},p:Be,i(i){C||(u(p.$$.fragment,i),C=!0)},o(i){v(p.$$.fragment,i),C=!1},d(i){i&&(n(a),n(d)),_(p,i)}}}function Cn(P){let a,b=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){a=c("p"),a.innerHTML=b},l(d){a=m(d,"P",{"data-svelte-h":!0}),x(a)!=="svelte-fincs2"&&(a.innerHTML=b)},m(d,p){r(d,a,p)},p:Be,d(d){d&&n(a)}}}function bn(P){let a,b="Example:",d,p,C;return p=new Ht({props:{code:"",highlighted:"",lang:"python",wrap:!1}}),{c(){a=c("p"),a.textContent=b,d=o(),g(p.$$.fragment)},l(i){a=m(i,"P",{"data-svelte-h":!0}),x(a)!=="svelte-11lpom8"&&(a.textContent=b),d=s(i),f(p.$$.fragment,i)},m(i,$){r(i,a,$),r(i,d,$),h(p,i,$),C=!0},p:Be,i(i){C||(u(p.$$.fragment,i),C=!0)},o(i){v(p.$$.fragment,i),C=!1},d(i){i&&(n(a),n(d)),_(p,i)}}}function $n(P){let a,b=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){a=c("p"),a.innerHTML=b},l(d){a=m(d,"P",{"data-svelte-h":!0}),x(a)!=="svelte-fincs2"&&(a.innerHTML=b)},m(d,p){r(d,a,p)},p:Be,d(d){d&&n(a)}}}function yn(P){let a,b="Example:",d,p,C;return p=new Ht({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, ConvNextForImageClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/convnext-tiny-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ConvNextForImageClassification.from_pretrained(<span class="hljs-string">&quot;facebook/convnext-tiny-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># model predicts one of the 1000 ImageNet classes</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(model.config.id2label[predicted_label])
...`,lang:"python",wrap:!1}}),{c(){a=c("p"),a.textContent=b,d=o(),g(p.$$.fragment)},l(i){a=m(i,"P",{"data-svelte-h":!0}),x(a)!=="svelte-11lpom8"&&(a.textContent=b),d=s(i),f(p.$$.fragment,i)},m(i,$){r(i,a,$),r(i,d,$),h(p,i,$),C=!0},p:Be,i(i){C||(u(p.$$.fragment,i),C=!0)},o(i){v(p.$$.fragment,i),C=!1},d(i){i&&(n(a),n(d)),_(p,i)}}}function wn(P){let a,b,d,p,C,i="<em>This model was published in HF papers on 2022-01-10 and contributed to Hugging Face Transformers on 2022-02-07.</em>",$,S,Ee,q,De,Y,Ge,Q,Jt=`The ConvNeXT model was proposed in <a href="https://huggingface.co/papers/2201.03545" rel="nofollow">A ConvNet for the 2020s</a> 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.`,Xe,K,Rt="The abstract from the paper is the following:",Ve,O,Bt=`<em>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.</em>`,Ae,B,Et,Se,ee,Dt='ConvNeXT architecture. Taken from the <a href="https://huggingface.co/papers/2201.03545">original paper</a>.',qe,te,Gt='This model was contributed by <a href="https://huggingface.co/nielsr" rel="nofollow">nielsr</a>. The original code can be found <a href="https://github.com/facebookresearch/ConvNeXt" rel="nofollow">here</a>.',Ye,ne,Qe,oe,Xt="A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ConvNeXT.",Ke,se,Oe,re,Vt='<li><a href="/docs/transformers/pr_43265/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb" rel="nofollow">notebook</a>.</li> <li>See also: <a href="../tasks/image_classification">Image classification task guide</a></li>',et,ae,At="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.",tt,ie,nt,I,le,ft,$e,St=`This is the configuration class to store the configuration of a ConvNextModel. It is used to instantiate a 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 <a href="https://huggingface.co/facebook/convnext-tiny-224" rel="nofollow">facebook/convnext-tiny-224</a>`,ht,ye,qt=`Configuration objects inherit from <a href="/docs/transformers/pr_43265/en/main_classes/configuration#transformers.PreTrainedConfig">PreTrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/pr_43265/en/main_classes/configuration#transformers.PreTrainedConfig">PreTrainedConfig</a> for more information.`,ut,E,ot,ce,st,k,me,vt,we,Yt="Constructs a ConvNextImageProcessor image processor.",_t,Te,de,rt,pe,at,U,ge,xt,Ne,Qt="Constructs a ConvNextImageProcessor image processor.",Ct,Me,fe,it,he,lt,y,ue,bt,Ie,Kt="The bare Convnext Model outputting raw hidden-states without any specific head on top.",$t,Pe,Ot=`This model inherits from <a href="/docs/transformers/pr_43265/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. 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.)`,yt,ke,en=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,wt,T,ve,Tt,Ue,tn='The <a href="/docs/transformers/pr_43265/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> forward method, overrides the <code>__call__</code> special method.',Nt,D,Mt,je,nn=`<li><p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p></li> <li><p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) — Last layer hidden-state after a pooling operation on the spatial dimensions.</p></li> <li><p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, num_channels, height, width)</code>.</p> <p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p></li>`,It,G,ct,_e,mt,w,xe,Pt,Fe,on=`ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.`,kt,Le,sn=`This model inherits from <a href="/docs/transformers/pr_43265/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. 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.)`,Ut,Ze,rn=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,jt,N,Ce,Ft,ze,an='The <a href="/docs/transformers/pr_43265/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> forward method, overrides the <code>__call__</code> special method.',Lt,X,Zt,We,ln=`<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Classification (or regression if config.num_labels==1) loss.</li> <li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Classification (or regression if config.num_labels==1) scores (before SoftMax).</li> <li><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape <code>(batch_size, num_channels, height, width)</code>. Hidden-states (also
called feature maps) of the model at the output of each stage.</li>`,zt,V,dt,be,pt,He,gt;return S=new un({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),q=new A({props:{title:"ConvNeXT",local:"convnext",headingTag:"h1"}}),Y=new A({props:{title:"Overview",local:"overview",headingTag:"h2"}}),ne=new A({props:{title:"Resources",local:"resources",headingTag:"h2"}}),se=new vn({props:{pipeline:"image-classification"}}),ie=new A({props:{title:"ConvNextConfig",local:"transformers.ConvNextConfig",headingTag:"h2"}}),le=new R({props:{name:"class transformers.ConvNextConfig",anchor:"transformers.ConvNextConfig",parameters:[{name:"transformers_version",val:": str | None = None"},{name:"architectures",val:": list[str] | None = None"},{name:"output_hidden_states",val:": bool | None = False"},{name:"return_dict",val:": bool | None = True"},{name:"dtype",val:": typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None"},{name:"chunk_size_feed_forward",val:": int = 0"},{name:"is_encoder_decoder",val:": bool = False"},{name:"id2label",val:": dict[int, str] | dict[str, str] | None = None"},{name:"label2id",val:": dict[str, int] | dict[str, str] | None = None"},{name:"problem_type",val:": typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None"},{name:"num_channels",val:": int = 3"},{name:"patch_size",val:": int | list[int] | tuple[int, int] = 4"},{name:"num_stages",val:": int = 4"},{name:"hidden_sizes",val:": list[int] | tuple[int, ...] | None = (96, 192, 384, 768)"},{name:"depths",val:": list[int] | tuple[int, ...] | None = (3, 3, 9, 3)"},{name:"hidden_act",val:": str = 'gelu'"},{name:"initializer_range",val:": float = 0.02"},{name:"layer_norm_eps",val:": float = 1e-12"},{name:"layer_scale_init_value",val:": float = 1e-06"},{name:"drop_path_rate",val:": float | int = 0.0"},{name:"image_size",val:": int | list[int] | tuple[int, int] = 224"},{name:"_out_features",val:": list[str] | None = None"},{name:"_out_indices",val:": list[int] | None = None"}],parametersDescription:[{anchor:"transformers.ConvNextConfig.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to <code>3</code>) &#x2014;
The number of input channels.`,name:"num_channels"},{anchor:"transformers.ConvNextConfig.patch_size",description:`<strong>patch_size</strong> (<code>Union[int, list[int], tuple[int, int]]</code>, <em>optional</em>, defaults to <code>4</code>) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.ConvNextConfig.num_stages",description:`<strong>num_stages</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The number of stages in the model.`,name:"num_stages"},{anchor:"transformers.ConvNextConfig.hidden_sizes",description:`<strong>hidden_sizes</strong> (<code>Union[list[int], tuple[int, ...]]</code>, <em>optional</em>, defaults to <code>(96, 192, 384, 768)</code>) &#x2014;
Dimensionality (hidden size) at each stage of the model.`,name:"hidden_sizes"},{anchor:"transformers.ConvNextConfig.depths",description:`<strong>depths</strong> (<code>Union[list[int], tuple[int, ...]]</code>, <em>optional</em>, defaults to <code>(3, 3, 9, 3)</code>) &#x2014;
Depth of each layer in the Transformer.`,name:"depths"},{anchor:"transformers.ConvNextConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code>, <em>optional</em>, defaults to <code>gelu</code>) &#x2014;
The non-linear activation function (function or string) in the decoder. For example, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code>, etc.`,name:"hidden_act"},{anchor:"transformers.ConvNextConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to <code>0.02</code>) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.ConvNextConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to <code>1e-12</code>) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.ConvNextConfig.layer_scale_init_value",description:`<strong>layer_scale_init_value</strong> (<code>float</code>, <em>optional</em>, defaults to <code>1e-06</code>) &#x2014;
Scale to use in the self-attention layers. 0.1 for base, 1e-6 for large. Set 0 to disable layer scale.`,name:"layer_scale_init_value"},{anchor:"transformers.ConvNextConfig.drop_path_rate",description:`<strong>drop_path_rate</strong> (<code>Union[float, int]</code>, <em>optional</em>, defaults to <code>0.0</code>) &#x2014;
Drop path rate for the patch fusion.`,name:"drop_path_rate"},{anchor:"transformers.ConvNextConfig.image_size",description:`<strong>image_size</strong> (<code>Union[int, list[int], tuple[int, int]]</code>, <em>optional</em>, defaults to <code>224</code>) &#x2014;
The size (resolution) of each image.`,name:"image_size"}],source:"https://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/convnext/configuration_convnext.py#L25"}}),E=new Wt({props:{anchor:"transformers.ConvNextConfig.example",$$slots:{default:[xn]},$$scope:{ctx:P}}}),ce=new A({props:{title:"ConvNextImageProcessor",local:"transformers.ConvNextImageProcessor",headingTag:"h2"}}),me=new R({props:{name:"class transformers.ConvNextImageProcessor",anchor:"transformers.ConvNextImageProcessor",parameters:[{name:"**kwargs",val:": typing_extensions.Unpack[transformers.models.convnext.image_processing_convnext.ConvNextImageProcessorKwargs]"}],parametersDescription:[{anchor:"transformers.ConvNextImageProcessor.crop_pct",description:`<strong>crop_pct</strong> (<code>float</code>, <em>kwargs</em>, <em>optional</em>, defaults to <code>self.crop_pct</code>) &#x2014;
Percentage of the image to crop. Only has an effect if size &lt; 384.`,name:"crop_pct"},{anchor:"transformers.ConvNextImageProcessor.*kwargs",description:`*<strong>*kwargs</strong> (<a href="/docs/transformers/pr_43265/en/main_classes/processors#transformers.ImagesKwargs">ImagesKwargs</a>, <em>optional</em>) &#x2014;
Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class
for the complete list of supported arguments.`,name:"*kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/convnext/image_processing_convnext.py#L43"}}),de=new R({props:{name:"preprocess",anchor:"transformers.ConvNextImageProcessor.preprocess",parameters:[{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:"*args",val:""},{name:"**kwargs",val:": typing_extensions.Unpack[transformers.processing_utils.ImagesKwargs]"}],parametersDescription:[{anchor:"transformers.ConvNextImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]</code>) &#x2014;
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 <code>do_rescale=False</code>.`,name:"images"},{anchor:"transformers.ConvNextImageProcessor.preprocess.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_43265/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) &#x2014;
Returns stacked tensors if set to <code>&apos;pt&apos;</code>, otherwise returns a list of tensors.`,name:"return_tensors"},{anchor:"transformers.ConvNextImageProcessor.preprocess.*kwargs",description:`*<strong>*kwargs</strong> (<a href="/docs/transformers/pr_43265/en/main_classes/processors#transformers.ImagesKwargs">ImagesKwargs</a>, <em>optional</em>) &#x2014;
Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class
for the complete list of supported arguments.`,name:"*kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_43265/src/transformers/image_processing_utils.py#L382",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><strong>data</strong> (<code>dict</code>) — Dictionary of lists/arrays/tensors returned by the <strong>call</strong> method (‘pixel_values’, etc.).</li>
<li><strong>tensor_type</strong> (<code>Union[None, str, TensorType]</code>, <em>optional</em>) — You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at
initialization.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>~image_processing_base.BatchFeature</code></p>
`}}),pe=new A({props:{title:"ConvNextImageProcessorPil",local:"transformers.ConvNextImageProcessorPil",headingTag:"h2"}}),ge=new R({props:{name:"class transformers.ConvNextImageProcessorPil",anchor:"transformers.ConvNextImageProcessorPil",parameters:[{name:"**kwargs",val:": typing_extensions.Unpack[transformers.models.convnext.image_processing_pil_convnext.ConvNextImageProcessorKwargs]"}],parametersDescription:[{anchor:"transformers.ConvNextImageProcessorPil.crop_pct",description:`<strong>crop_pct</strong> (<code>float</code>, <em>kwargs</em>, <em>optional</em>, defaults to <code>self.crop_pct</code>) &#x2014;
Percentage of the image to crop. Only has an effect if size &lt; 384.`,name:"crop_pct"},{anchor:"transformers.ConvNextImageProcessorPil.*kwargs",description:`*<strong>*kwargs</strong> (<a href="/docs/transformers/pr_43265/en/main_classes/processors#transformers.ImagesKwargs">ImagesKwargs</a>, <em>optional</em>) &#x2014;
Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class
for the complete list of supported arguments.`,name:"*kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/convnext/image_processing_pil_convnext.py#L43"}}),fe=new R({props:{name:"preprocess",anchor:"transformers.ConvNextImageProcessorPil.preprocess",parameters:[{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:"*args",val:""},{name:"**kwargs",val:": typing_extensions.Unpack[transformers.processing_utils.ImagesKwargs]"}],parametersDescription:[{anchor:"transformers.ConvNextImageProcessorPil.preprocess.images",description:`<strong>images</strong> (<code>Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]</code>) &#x2014;
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 <code>do_rescale=False</code>.`,name:"images"},{anchor:"transformers.ConvNextImageProcessorPil.preprocess.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <a href="/docs/transformers/pr_43265/en/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) &#x2014;
Returns stacked tensors if set to <code>&apos;pt&apos;</code>, otherwise returns a list of tensors.`,name:"return_tensors"},{anchor:"transformers.ConvNextImageProcessorPil.preprocess.*kwargs",description:`*<strong>*kwargs</strong> (<a href="/docs/transformers/pr_43265/en/main_classes/processors#transformers.ImagesKwargs">ImagesKwargs</a>, <em>optional</em>) &#x2014;
Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class
for the complete list of supported arguments.`,name:"*kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_43265/src/transformers/image_processing_utils.py#L382",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<ul>
<li><strong>data</strong> (<code>dict</code>) — Dictionary of lists/arrays/tensors returned by the <strong>call</strong> method (‘pixel_values’, etc.).</li>
<li><strong>tensor_type</strong> (<code>Union[None, str, TensorType]</code>, <em>optional</em>) — You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at
initialization.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>~image_processing_base.BatchFeature</code></p>
`}}),he=new A({props:{title:"ConvNextModel",local:"transformers.ConvNextModel",headingTag:"h2"}}),ue=new R({props:{name:"class transformers.ConvNextModel",anchor:"transformers.ConvNextModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.ConvNextModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_43265/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a>) &#x2014;
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
<a href="/docs/transformers/pr_43265/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/convnext/modeling_convnext.py#L251"}}),ve=new R({props:{name:"forward",anchor:"transformers.ConvNextModel.forward",parameters:[{name:"pixel_values",val:": torch.FloatTensor | None = None"},{name:"**kwargs",val:": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}],parametersDescription:[{anchor:"transformers.ConvNextModel.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, image_size, image_size)</code>, <em>optional</em>) &#x2014;
The tensors corresponding to the input images. Pixel values can be obtained using
<a href="/docs/transformers/pr_43265/en/model_doc/convnext#transformers.ConvNextImageProcessor">ConvNextImageProcessor</a>. See <code>ConvNextImageProcessor.__call__()</code> for details (<code>processor_class</code> uses
<a href="/docs/transformers/pr_43265/en/model_doc/convnext#transformers.ConvNextImageProcessor">ConvNextImageProcessor</a> for processing images).`,name:"pixel_values"}],source:"https://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/convnext/modeling_convnext.py#L265",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>BaseModelOutputWithPoolingAndNoAttention</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_43265/en/model_doc/convnext#transformers.ConvNextConfig"
>ConvNextConfig</a>) and inputs.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>BaseModelOutputWithPoolingAndNoAttention</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),D=new cn({props:{$$slots:{default:[Cn]},$$scope:{ctx:P}}}),G=new Wt({props:{anchor:"transformers.ConvNextModel.forward.example",$$slots:{default:[bn]},$$scope:{ctx:P}}}),_e=new A({props:{title:"ConvNextForImageClassification",local:"transformers.ConvNextForImageClassification",headingTag:"h2"}}),xe=new R({props:{name:"class transformers.ConvNextForImageClassification",anchor:"transformers.ConvNextForImageClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.ConvNextForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_43265/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a>) &#x2014;
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
<a href="/docs/transformers/pr_43265/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/convnext/modeling_convnext.py#L293"}}),Ce=new R({props:{name:"forward",anchor:"transformers.ConvNextForImageClassification.forward",parameters:[{name:"pixel_values",val:": torch.FloatTensor | None = None"},{name:"labels",val:": torch.LongTensor | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ConvNextForImageClassification.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, image_size, image_size)</code>, <em>optional</em>) &#x2014;
The tensors corresponding to the input images. Pixel values can be obtained using
<a href="/docs/transformers/pr_43265/en/model_doc/convnext#transformers.ConvNextImageProcessor">ConvNextImageProcessor</a>. See <code>ConvNextImageProcessor.__call__()</code> for details (<code>processor_class</code> uses
<a href="/docs/transformers/pr_43265/en/model_doc/convnext#transformers.ConvNextImageProcessor">ConvNextImageProcessor</a> for processing images).`,name:"pixel_values"},{anchor:"transformers.ConvNextForImageClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the image classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/vr_43265/src/transformers/models/convnext/modeling_convnext.py#L311",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_43265/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>ImageClassifierOutputWithNoAttention</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_43265/en/model_doc/convnext#transformers.ConvNextConfig"
>ConvNextConfig</a>) and inputs.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_43265/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>ImageClassifierOutputWithNoAttention</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),X=new cn({props:{$$slots:{default:[$n]},$$scope:{ctx:P}}}),V=new Wt({props:{anchor:"transformers.ConvNextForImageClassification.forward.example",$$slots:{default:[yn]},$$scope:{ctx:P}}}),be=new 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