import{s as Ke,f as et,o as tt,n as ve}from"../chunks/scheduler.9bc65507.js";import{S as ot,i as nt,g as d,s as l,r as C,A as st,h as m,f as s,c as i,j as ie,u as x,x as b,k as K,y as g,a as r,v as y,d as $,t as M,w as T}from"../chunks/index.707bf1b6.js";import{T as Re}from"../chunks/Tip.c2ecdbf4.js";import{D as ue}from"../chunks/Docstring.86474e80.js";import{C as Le}from"../chunks/CodeBlock.54a9f38d.js";import{F as at,M as Oe}from"../chunks/Markdown.8ab98a13.js";import{E as qe}from"../chunks/ExampleCodeBlock.034f3a73.js";import{P as rt}from"../chunks/PipelineTag.44585822.js";import{H as $e,E as lt}from"../chunks/EditOnGithub.922df6ba.js";function it(Z){let t,u="Example:",o,a,_;return a=new Le({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`,wrap:!1}}),{c(){t=d("p"),t.textContent=u,o=l(),C(a.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),b(t)!=="svelte-11lpom8"&&(t.textContent=u),o=i(n),x(a.$$.fragment,n)},m(n,v){r(n,t,v),r(n,o,v),y(a,n,v),_=!0},p:ve,i(n){_||($(a.$$.fragment,n),_=!0)},o(n){M(a.$$.fragment,n),_=!1},d(n){n&&(s(t),s(o)),T(a,n)}}}function ct(Z){let t,u=`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(){t=d("p"),t.innerHTML=u},l(o){t=m(o,"P",{"data-svelte-h":!0}),b(t)!=="svelte-fincs2"&&(t.innerHTML=u)},m(o,a){r(o,t,a)},p:ve,d(o){o&&s(t)}}}function dt(Z){let t,u="Example:",o,a,_;return a=new Le({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, ConvNextModel
<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 = ConvNextModel.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>    outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">768</span>, <span class="hljs-number">7</span>, <span class="hljs-number">7</span>]`,wrap:!1}}),{c(){t=d("p"),t.textContent=u,o=l(),C(a.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),b(t)!=="svelte-11lpom8"&&(t.textContent=u),o=i(n),x(a.$$.fragment,n)},m(n,v){r(n,t,v),r(n,o,v),y(a,n,v),_=!0},p:ve,i(n){_||($(a.$$.fragment,n),_=!0)},o(n){M(a.$$.fragment,n),_=!1},d(n){n&&(s(t),s(o)),T(a,n)}}}function mt(Z){let t,u=`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(){t=d("p"),t.innerHTML=u},l(o){t=m(o,"P",{"data-svelte-h":!0}),b(t)!=="svelte-fincs2"&&(t.innerHTML=u)},m(o,a){r(o,t,a)},p:ve,d(o){o&&s(t)}}}function pt(Z){let t,u="Example:",o,a,_;return a=new Le({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])
tabby, tabby cat`,wrap:!1}}),{c(){t=d("p"),t.textContent=u,o=l(),C(a.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),b(t)!=="svelte-11lpom8"&&(t.textContent=u),o=i(n),x(a.$$.fragment,n)},m(n,v){r(n,t,v),r(n,o,v),y(a,n,v),_=!0},p:ve,i(n){_||($(a.$$.fragment,n),_=!0)},o(n){M(a.$$.fragment,n),_=!1},d(n){n&&(s(t),s(o)),T(a,n)}}}function ft(Z){let t,u,o,a,_,n,v=`The bare ConvNext model outputting raw features without any specific head on top.
This model is 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.`,S,N,k,A,I,R='The <a href="/docs/transformers/pr_31208/ja/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> forward method, overrides the <code>__call__</code> special method.',W,c,z,B,ee,re,D,J,G,ce,q,Me=`ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.`,de,U,te=`This model is 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.`,oe,L,ne,se,Y,me='The <a href="/docs/transformers/pr_31208/ja/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> forward method, overrides the <code>__call__</code> special method.',be,E,le,ae,O;return t=new $e({props:{title:"ConvNextModel",local:"transformers.ConvNextModel",headingTag:"h2"}}),a=new ue({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_31208/ja/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</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_31208/ja/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_31208/src/transformers/models/convnext/modeling_convnext.py#L319"}}),k=new ue({props:{name:"forward",anchor:"transformers.ConvNextModel.forward",parameters:[{name:"pixel_values",val:": FloatTensor = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.ConvNextModel.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/pr_31208/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/pr_31208/ja/model_doc/deit#transformers.DeiTFeatureExtractor.__call__">ConvNextImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.ConvNextModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.ConvNextModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31208/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/convnext/modeling_convnext.py#L337",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.modeling_outputs.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_31208/ja/model_doc/convnext#transformers.ConvNextConfig"
>ConvNextConfig</a>) and inputs.</p>
<ul>
<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>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),c=new Re({props:{$$slots:{default:[ct]},$$scope:{ctx:Z}}}),B=new qe({props:{anchor:"transformers.ConvNextModel.forward.example",$$slots:{default:[dt]},$$scope:{ctx:Z}}}),re=new $e({props:{title:"ConvNextForImageClassification",local:"transformers.ConvNextForImageClassification",headingTag:"h2"}}),G=new ue({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_31208/ja/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</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_31208/ja/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_31208/src/transformers/models/convnext/modeling_convnext.py#L382"}}),ne=new ue({props:{name:"forward",anchor:"transformers.ConvNextForImageClassification.forward",parameters:[{name:"pixel_values",val:": FloatTensor = None"},{name:"labels",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.ConvNextForImageClassification.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/pr_31208/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/pr_31208/ja/model_doc/deit#transformers.DeiTFeatureExtractor.__call__">ConvNextImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.ConvNextForImageClassification.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.ConvNextForImageClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31208/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{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_31208/src/transformers/models/convnext/modeling_convnext.py#L404",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_31208/ja/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.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_31208/ja/model_doc/convnext#transformers.ConvNextConfig"
>ConvNextConfig</a>) and inputs.</p>
<ul>
<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>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_31208/ja/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),E=new Re({props:{$$slots:{default:[mt]},$$scope:{ctx:Z}}}),ae=new qe({props:{anchor:"transformers.ConvNextForImageClassification.forward.example",$$slots:{default:[pt]},$$scope:{ctx:Z}}}),{c(){C(t.$$.fragment),u=l(),o=d("div"),C(a.$$.fragment),_=l(),n=d("p"),n.innerHTML=v,S=l(),N=d("div"),C(k.$$.fragment),A=l(),I=d("p"),I.innerHTML=R,W=l(),C(c.$$.fragment),z=l(),C(B.$$.fragment),ee=l(),C(re.$$.fragment),D=l(),J=d("div"),C(G.$$.fragment),ce=l(),q=d("p"),q.textContent=Me,de=l(),U=d("p"),U.innerHTML=te,oe=l(),L=d("div"),C(ne.$$.fragment),se=l(),Y=d("p"),Y.innerHTML=me,be=l(),C(E.$$.fragment),le=l(),C(ae.$$.fragment),this.h()},l(p){x(t.$$.fragment,p),u=i(p),o=m(p,"DIV",{class:!0});var w=ie(o);x(a.$$.fragment,w),_=i(w),n=m(w,"P",{"data-svelte-h":!0}),b(n)!=="svelte-22827l"&&(n.innerHTML=v),S=i(w),N=m(w,"DIV",{class:!0});var F=ie(N);x(k.$$.fragment,F),A=i(F),I=m(F,"P",{"data-svelte-h":!0}),b(I)!=="svelte-f8irvi"&&(I.innerHTML=R),W=i(F),x(c.$$.fragment,F),z=i(F),x(B.$$.fragment,F),F.forEach(s),w.forEach(s),ee=i(p),x(re.$$.fragment,p),D=i(p),J=m(p,"DIV",{class:!0});var P=ie(J);x(G.$$.fragment,P),ce=i(P),q=m(P,"P",{"data-svelte-h":!0}),b(q)!=="svelte-xy24s5"&&(q.textContent=Me),de=i(P),U=m(P,"P",{"data-svelte-h":!0}),b(U)!=="svelte-1gjh92c"&&(U.innerHTML=te),oe=i(P),L=m(P,"DIV",{class:!0});var H=ie(L);x(ne.$$.fragment,H),se=i(H),Y=m(H,"P",{"data-svelte-h":!0}),b(Y)!=="svelte-fbp2u8"&&(Y.innerHTML=me),be=i(H),x(E.$$.fragment,H),le=i(H),x(ae.$$.fragment,H),H.forEach(s),P.forEach(s),this.h()},h(){K(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),K(o,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),K(L,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),K(J,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(p,w){y(t,p,w),r(p,u,w),r(p,o,w),y(a,o,null),g(o,_),g(o,n),g(o,S),g(o,N),y(k,N,null),g(N,A),g(N,I),g(N,W),y(c,N,null),g(N,z),y(B,N,null),r(p,ee,w),y(re,p,w),r(p,D,w),r(p,J,w),y(G,J,null),g(J,ce),g(J,q),g(J,de),g(J,U),g(J,oe),g(J,L),y(ne,L,null),g(L,se),g(L,Y),g(L,be),y(E,L,null),g(L,le),y(ae,L,null),O=!0},p(p,w){const F={};w&2&&(F.$$scope={dirty:w,ctx:p}),c.$set(F);const P={};w&2&&(P.$$scope={dirty:w,ctx:p}),B.$set(P);const H={};w&2&&(H.$$scope={dirty:w,ctx:p}),E.$set(H);const pe={};w&2&&(pe.$$scope={dirty:w,ctx:p}),ae.$set(pe)},i(p){O||($(t.$$.fragment,p),$(a.$$.fragment,p),$(k.$$.fragment,p),$(c.$$.fragment,p),$(B.$$.fragment,p),$(re.$$.fragment,p),$(G.$$.fragment,p),$(ne.$$.fragment,p),$(E.$$.fragment,p),$(ae.$$.fragment,p),O=!0)},o(p){M(t.$$.fragment,p),M(a.$$.fragment,p),M(k.$$.fragment,p),M(c.$$.fragment,p),M(B.$$.fragment,p),M(re.$$.fragment,p),M(G.$$.fragment,p),M(ne.$$.fragment,p),M(E.$$.fragment,p),M(ae.$$.fragment,p),O=!1},d(p){p&&(s(u),s(o),s(ee),s(D),s(J)),T(t,p),T(a),T(k),T(c),T(B),T(re,p),T(G),T(ne),T(E),T(ae)}}}function ut(Z){let t,u;return t=new Oe({props:{$$slots:{default:[ft]},$$scope:{ctx:Z}}}),{c(){C(t.$$.fragment)},l(o){x(t.$$.fragment,o)},m(o,a){y(t,o,a),u=!0},p(o,a){const _={};a&2&&(_.$$scope={dirty:a,ctx:o}),t.$set(_)},i(o){u||($(t.$$.fragment,o),u=!0)},o(o){M(t.$$.fragment,o),u=!1},d(o){T(t,o)}}}function ht(Z){let t,u="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",o,a,_="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",n,v,S=`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 <code>model.fit()</code> things should “just work” for you - just
pass your inputs and labels in any format that <code>model.fit()</code> supports! If, however, you want to use the second
format outside of Keras methods like <code>fit()</code> and <code>predict()</code>, such as when creating your own layers or models with
the Keras <code>Functional</code> API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`,N,k,A=`<li>a single Tensor with <code>pixel_values</code> only and nothing else: <code>model(pixel_values)</code></li> <li>a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
<code>model([pixel_values, attention_mask])</code> or <code>model([pixel_values, attention_mask, token_type_ids])</code></li> <li>a dictionary with one or several input Tensors associated to the input names given in the docstring:
<code>model({&quot;pixel_values&quot;: pixel_values, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,I,R,W=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> 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!`;return{c(){t=d("p"),t.innerHTML=u,o=l(),a=d("ul"),a.innerHTML=_,n=l(),v=d("p"),v.innerHTML=S,N=l(),k=d("ul"),k.innerHTML=A,I=l(),R=d("p"),R.innerHTML=W},l(c){t=m(c,"P",{"data-svelte-h":!0}),b(t)!=="svelte-1ajbfxg"&&(t.innerHTML=u),o=i(c),a=m(c,"UL",{"data-svelte-h":!0}),b(a)!=="svelte-qm1t26"&&(a.innerHTML=_),n=i(c),v=m(c,"P",{"data-svelte-h":!0}),b(v)!=="svelte-1v9qsc5"&&(v.innerHTML=S),N=i(c),k=m(c,"UL",{"data-svelte-h":!0}),b(k)!=="svelte-99h8aq"&&(k.innerHTML=A),I=i(c),R=m(c,"P",{"data-svelte-h":!0}),b(R)!=="svelte-1an3odd"&&(R.innerHTML=W)},m(c,z){r(c,t,z),r(c,o,z),r(c,a,z),r(c,n,z),r(c,v,z),r(c,N,z),r(c,k,z),r(c,I,z),r(c,R,z)},p:ve,d(c){c&&(s(t),s(o),s(a),s(n),s(v),s(N),s(k),s(I),s(R))}}}function gt(Z){let t,u=`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(){t=d("p"),t.innerHTML=u},l(o){t=m(o,"P",{"data-svelte-h":!0}),b(t)!=="svelte-fincs2"&&(t.innerHTML=u)},m(o,a){r(o,t,a)},p:ve,d(o){o&&s(t)}}}function _t(Z){let t,u="Examples:",o,a,_;return a=new Le({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, TFConvNextModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

<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 = TFConvNextModel.from_pretrained(<span class="hljs-string">&quot;facebook/convnext-tiny-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`,wrap:!1}}),{c(){t=d("p"),t.textContent=u,o=l(),C(a.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),b(t)!=="svelte-kvfsh7"&&(t.textContent=u),o=i(n),x(a.$$.fragment,n)},m(n,v){r(n,t,v),r(n,o,v),y(a,n,v),_=!0},p:ve,i(n){_||($(a.$$.fragment,n),_=!0)},o(n){M(a.$$.fragment,n),_=!1},d(n){n&&(s(t),s(o)),T(a,n)}}}function vt(Z){let t,u="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",o,a,_="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",n,v,S=`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 <code>model.fit()</code> things should “just work” for you - just
pass your inputs and labels in any format that <code>model.fit()</code> supports! If, however, you want to use the second
format outside of Keras methods like <code>fit()</code> and <code>predict()</code>, such as when creating your own layers or models with
the Keras <code>Functional</code> API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`,N,k,A=`<li>a single Tensor with <code>pixel_values</code> only and nothing else: <code>model(pixel_values)</code></li> <li>a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
<code>model([pixel_values, attention_mask])</code> or <code>model([pixel_values, attention_mask, token_type_ids])</code></li> <li>a dictionary with one or several input Tensors associated to the input names given in the docstring:
<code>model({&quot;pixel_values&quot;: pixel_values, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,I,R,W=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> 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!`;return{c(){t=d("p"),t.innerHTML=u,o=l(),a=d("ul"),a.innerHTML=_,n=l(),v=d("p"),v.innerHTML=S,N=l(),k=d("ul"),k.innerHTML=A,I=l(),R=d("p"),R.innerHTML=W},l(c){t=m(c,"P",{"data-svelte-h":!0}),b(t)!=="svelte-1ajbfxg"&&(t.innerHTML=u),o=i(c),a=m(c,"UL",{"data-svelte-h":!0}),b(a)!=="svelte-qm1t26"&&(a.innerHTML=_),n=i(c),v=m(c,"P",{"data-svelte-h":!0}),b(v)!=="svelte-1v9qsc5"&&(v.innerHTML=S),N=i(c),k=m(c,"UL",{"data-svelte-h":!0}),b(k)!=="svelte-99h8aq"&&(k.innerHTML=A),I=i(c),R=m(c,"P",{"data-svelte-h":!0}),b(R)!=="svelte-1an3odd"&&(R.innerHTML=W)},m(c,z){r(c,t,z),r(c,o,z),r(c,a,z),r(c,n,z),r(c,v,z),r(c,N,z),r(c,k,z),r(c,I,z),r(c,R,z)},p:ve,d(c){c&&(s(t),s(o),s(a),s(n),s(v),s(N),s(k),s(I),s(R))}}}function bt(Z){let t,u=`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(){t=d("p"),t.innerHTML=u},l(o){t=m(o,"P",{"data-svelte-h":!0}),b(t)!=="svelte-fincs2"&&(t.innerHTML=u)},m(o,a){r(o,t,a)},p:ve,d(o){o&&s(t)}}}function Ct(Z){let t,u="Examples:",o,a,_;return a=new Le({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, TFConvNextForImageClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

<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 = TFConvNextForImageClassification.from_pretrained(<span class="hljs-string">&quot;facebook/convnext-tiny-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.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_class_idx = tf.math.argmax(logits, axis=-<span class="hljs-number">1</span>)[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Predicted class:&quot;</span>, model.config.id2label[<span class="hljs-built_in">int</span>(predicted_class_idx)])`,wrap:!1}}),{c(){t=d("p"),t.textContent=u,o=l(),C(a.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),b(t)!=="svelte-kvfsh7"&&(t.textContent=u),o=i(n),x(a.$$.fragment,n)},m(n,v){r(n,t,v),r(n,o,v),y(a,n,v),_=!0},p:ve,i(n){_||($(a.$$.fragment,n),_=!0)},o(n){M(a.$$.fragment,n),_=!1},d(n){n&&(s(t),s(o)),T(a,n)}}}function xt(Z){let t,u,o,a,_,n,v=`The bare ConvNext model outputting raw features without any specific head on top.
This model inherits from <a href="/docs/transformers/pr_31208/ja/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</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.)`,S,N,k=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">keras.Model</a> 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.`,A,I,R,W,c,z,B,ee='The <a href="/docs/transformers/pr_31208/ja/model_doc/convnext#transformers.TFConvNextModel">TFConvNextModel</a> forward method, overrides the <code>__call__</code> special method.',re,D,J,G,ce,q,Me="## TFConvNextForImageClassification[[transformers.TFConvNextForImageClassification]]",de,U,te,oe,L,ne=`ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.`,se,Y,me=`This model inherits from <a href="/docs/transformers/pr_31208/ja/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</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.)`,be,E,le=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">keras.Model</a> 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.`,ae,O,p,w,F,P,H,pe='The <a href="/docs/transformers/pr_31208/ja/model_doc/convnext#transformers.TFConvNextForImageClassification">TFConvNextForImageClassification</a> forward method, overrides the <code>__call__</code> special method.',Ze,fe,Ce,he,Te;return t=new $e({props:{title:"TFConvNextModel",local:"transformers.TFConvNextModel",headingTag:"h2"}}),a=new ue({props:{name:"class transformers.TFConvNextModel",anchor:"transformers.TFConvNextModel",parameters:[{name:"config",val:""},{name:"*inputs",val:""},{name:"add_pooling_layer",val:" = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFConvNextModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31208/ja/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</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_31208/ja/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/convnext/modeling_tf_convnext.py#L491"}}),I=new Re({props:{$$slots:{default:[ht]},$$scope:{ctx:Z}}}),c=new ue({props:{name:"call",anchor:"transformers.TFConvNextModel.call",parameters:[{name:"pixel_values",val:": TFModelInputType | None = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFConvNextModel.call.pixel_values",description:`<strong>pixel_values</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/pr_31208/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/pr_31208/ja/model_doc/deit#transformers.DeiTFeatureExtractor.__call__">ConvNextImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFConvNextModel.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> 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.`,name:"output_hidden_states"},{anchor:"transformers.TFConvNextModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31208/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/convnext/modeling_tf_convnext.py#L500",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_31208/ja/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or a tuple of <code>tf.Tensor</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_31208/ja/model_doc/convnext#transformers.ConvNextConfig"
>ConvNextConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, hidden_size)</code>) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
<p>This output is usually <em>not</em> a good summary of the semantic content of the input, you’re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_31208/ja/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or <code>tuple(tf.Tensor)</code></p>
`}}),D=new Re({props:{$$slots:{default:[gt]},$$scope:{ctx:Z}}}),G=new qe({props:{anchor:"transformers.TFConvNextModel.call.example",$$slots:{default:[_t]},$$scope:{ctx:Z}}}),te=new ue({props:{name:"class transformers.TFConvNextForImageClassification",anchor:"transformers.TFConvNextForImageClassification",parameters:[{name:"config",val:": ConvNextConfig"},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFConvNextForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31208/ja/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</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_31208/ja/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/convnext/modeling_tf_convnext.py#L563"}}),O=new Re({props:{$$slots:{default:[vt]},$$scope:{ctx:Z}}}),F=new ue({props:{name:"call",anchor:"transformers.TFConvNextForImageClassification.call",parameters:[{name:"pixel_values",val:": TFModelInputType | None = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"labels",val:": np.ndarray | tf.Tensor | None = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFConvNextForImageClassification.call.pixel_values",description:`<strong>pixel_values</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/pr_31208/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/pr_31208/ja/model_doc/deit#transformers.DeiTFeatureExtractor.__call__">ConvNextImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFConvNextForImageClassification.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> 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.`,name:"output_hidden_states"},{anchor:"transformers.TFConvNextForImageClassification.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31208/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFConvNextForImageClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> or <code>np.ndarray</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_31208/src/transformers/models/convnext/modeling_tf_convnext.py#L586",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_31208/ja/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or a tuple of <code>tf.Tensor</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_31208/ja/model_doc/convnext#transformers.ConvNextConfig"
>ConvNextConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, )</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</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>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_31208/ja/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),fe=new Re({props:{$$slots:{default:[bt]},$$scope:{ctx:Z}}}),he=new qe({props:{anchor:"transformers.TFConvNextForImageClassification.call.example",$$slots:{default:[Ct]},$$scope:{ctx:Z}}}),{c(){C(t.$$.fragment),u=l(),o=d("div"),C(a.$$.fragment),_=l(),n=d("p"),n.innerHTML=v,S=l(),N=d("p"),N.innerHTML=k,A=l(),C(I.$$.fragment),R=l(),W=d("div"),C(c.$$.fragment),z=l(),B=d("p"),B.innerHTML=ee,re=l(),C(D.$$.fragment),J=l(),C(G.$$.fragment),ce=l(),q=d("p"),q.textContent=Me,de=l(),U=d("div"),C(te.$$.fragment),oe=l(),L=d("p"),L.textContent=ne,se=l(),Y=d("p"),Y.innerHTML=me,be=l(),E=d("p"),E.innerHTML=le,ae=l(),C(O.$$.fragment),p=l(),w=d("div"),C(F.$$.fragment),P=l(),H=d("p"),H.innerHTML=pe,Ze=l(),C(fe.$$.fragment),Ce=l(),C(he.$$.fragment),this.h()},l(f){x(t.$$.fragment,f),u=i(f),o=m(f,"DIV",{class:!0});var j=ie(o);x(a.$$.fragment,j),_=i(j),n=m(j,"P",{"data-svelte-h":!0}),b(n)!=="svelte-18yuo51"&&(n.innerHTML=v),S=i(j),N=m(j,"P",{"data-svelte-h":!0}),b(N)!=="svelte-1be7e3c"&&(N.innerHTML=k),A=i(j),x(I.$$.fragment,j),R=i(j),W=m(j,"DIV",{class:!0});var V=ie(W);x(c.$$.fragment,V),z=i(V),B=m(V,"P",{"data-svelte-h":!0}),b(B)!=="svelte-lsioc"&&(B.innerHTML=ee),re=i(V),x(D.$$.fragment,V),J=i(V),x(G.$$.fragment,V),V.forEach(s),j.forEach(s),ce=i(f),q=m(f,"P",{"data-svelte-h":!0}),b(q)!=="svelte-fgselu"&&(q.textContent=Me),de=i(f),U=m(f,"DIV",{class:!0});var X=ie(U);x(te.$$.fragment,X),oe=i(X),L=m(X,"P",{"data-svelte-h":!0}),b(L)!=="svelte-xy24s5"&&(L.textContent=ne),se=i(X),Y=m(X,"P",{"data-svelte-h":!0}),b(Y)!=="svelte-189ythm"&&(Y.innerHTML=me),be=i(X),E=m(X,"P",{"data-svelte-h":!0}),b(E)!=="svelte-1be7e3c"&&(E.innerHTML=le),ae=i(X),x(O.$$.fragment,X),p=i(X),w=m(X,"DIV",{class:!0});var Q=ie(w);x(F.$$.fragment,Q),P=i(Q),H=m(Q,"P",{"data-svelte-h":!0}),b(H)!=="svelte-i77zuc"&&(H.innerHTML=pe),Ze=i(Q),x(fe.$$.fragment,Q),Ce=i(Q),x(he.$$.fragment,Q),Q.forEach(s),X.forEach(s),this.h()},h(){K(W,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),K(o,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),K(w,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),K(U,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(f,j){y(t,f,j),r(f,u,j),r(f,o,j),y(a,o,null),g(o,_),g(o,n),g(o,S),g(o,N),g(o,A),y(I,o,null),g(o,R),g(o,W),y(c,W,null),g(W,z),g(W,B),g(W,re),y(D,W,null),g(W,J),y(G,W,null),r(f,ce,j),r(f,q,j),r(f,de,j),r(f,U,j),y(te,U,null),g(U,oe),g(U,L),g(U,se),g(U,Y),g(U,be),g(U,E),g(U,ae),y(O,U,null),g(U,p),g(U,w),y(F,w,null),g(w,P),g(w,H),g(w,Ze),y(fe,w,null),g(w,Ce),y(he,w,null),Te=!0},p(f,j){const V={};j&2&&(V.$$scope={dirty:j,ctx:f}),I.$set(V);const X={};j&2&&(X.$$scope={dirty:j,ctx:f}),D.$set(X);const Q={};j&2&&(Q.$$scope={dirty:j,ctx:f}),G.$set(Q);const xe={};j&2&&(xe.$$scope={dirty:j,ctx:f}),O.$set(xe);const je={};j&2&&(je.$$scope={dirty:j,ctx:f}),fe.$set(je);const ye={};j&2&&(ye.$$scope={dirty:j,ctx:f}),he.$set(ye)},i(f){Te||($(t.$$.fragment,f),$(a.$$.fragment,f),$(I.$$.fragment,f),$(c.$$.fragment,f),$(D.$$.fragment,f),$(G.$$.fragment,f),$(te.$$.fragment,f),$(O.$$.fragment,f),$(F.$$.fragment,f),$(fe.$$.fragment,f),$(he.$$.fragment,f),Te=!0)},o(f){M(t.$$.fragment,f),M(a.$$.fragment,f),M(I.$$.fragment,f),M(c.$$.fragment,f),M(D.$$.fragment,f),M(G.$$.fragment,f),M(te.$$.fragment,f),M(O.$$.fragment,f),M(F.$$.fragment,f),M(fe.$$.fragment,f),M(he.$$.fragment,f),Te=!1},d(f){f&&(s(u),s(o),s(ce),s(q),s(de),s(U)),T(t,f),T(a),T(I),T(c),T(D),T(G),T(te),T(O),T(F),T(fe),T(he)}}}function yt(Z){let t,u;return t=new Oe({props:{$$slots:{default:[xt]},$$scope:{ctx:Z}}}),{c(){C(t.$$.fragment)},l(o){x(t.$$.fragment,o)},m(o,a){y(t,o,a),u=!0},p(o,a){const _={};a&2&&(_.$$scope={dirty:a,ctx:o}),t.$set(_)},i(o){u||($(t.$$.fragment,o),u=!0)},o(o){M(t.$$.fragment,o),u=!1},d(o){T(t,o)}}}function $t(Z){let t,u,o,a,_,n,v,S,N,k=`ConvNeXT モデルは、<a href="https://arxiv.org/abs/2201.03545" rel="nofollow">A ConvNet for the 2020s</a> で Zhuang Liu、Hanzi Mao、Chao-Yuan Wu、Christoph Feichtenhofer、Trevor Darrell、Saining Xie によって提案されました。
ConvNeXT は、ビジョン トランスフォーマーの設計からインスピレーションを得た純粋な畳み込みモデル (ConvNet) であり、ビジョン トランスフォーマーよりも優れたパフォーマンスを発揮すると主張しています。`,A,I,R="論文の要約は次のとおりです。",W,c,z=`<em>視覚認識の「狂騒の 20 年代」は、最先端の画像分類モデルとして ConvNet にすぐに取って代わられた Vision Transformers (ViT) の導入から始まりました。
一方、バニラ ViT は、オブジェクト検出やセマンティック セグメンテーションなどの一般的なコンピューター ビジョン タスクに適用すると困難に直面します。階層型トランスフォーマーです
(Swin Transformers など) は、いくつかの ConvNet の以前の機能を再導入し、Transformers を汎用ビジョン バックボーンとして実用的に可能にし、幅広い環境で顕著なパフォーマンスを実証しました。
さまざまな視覚タスク。ただし、このようなハイブリッド アプローチの有効性は、依然として、固有の誘導性ではなく、トランスフォーマーの本質的な優位性によるところが大きいと考えられています。
畳み込みのバイアス。この作業では、設計空間を再検討し、純粋な ConvNet が達成できる限界をテストします。標準 ResNet を設計に向けて徐々に「最新化」します。
ビジョン Transformer の概要を確認し、途中でパフォーマンスの違いに寄与するいくつかの重要なコンポーネントを発見します。この調査の結果は、純粋な ConvNet モデルのファミリーです。
ConvNextと呼ばれます。 ConvNeXts は完全に標準の ConvNet モジュールから構築されており、精度と拡張性の点で Transformers と有利に競合し、87.8% の ImageNet トップ 1 精度を達成しています。
標準 ConvNet のシンプルさと効率を維持しながら、COCO 検出と ADE20K セグメンテーションでは Swin Transformers よりも優れたパフォーマンスを発揮します。</em>`,B,ee,re,D,J,G='ConvNeXT アーキテクチャ。 <a href="https://arxiv.org/abs/2201.03545">元の論文</a>から抜粋。',ce,q,Me=`このモデルは、<a href="https://huggingface.co/nielsr" rel="nofollow">nielsr</a> によって提供されました。 TensorFlow バージョンのモデルは <a href="https://github.com/ariG23498" rel="nofollow">ariG23498</a> によって提供されました。
<a href="https://github.com/gante" rel="nofollow">gante</a>、および <a href="https://github.com/sayakpaul" rel="nofollow">sayakpaul</a> (同等の貢献)。元のコードは <a href="https://github.com/facebookresearch/ConvNeXt" rel="nofollow">こちら</a> にあります。`,de,U,te,oe,L="ConvNeXT の使用を開始するのに役立つ公式 Hugging Face およびコミュニティ (🌎 で示される) リソースのリスト。",ne,se,Y,me,be='<li><a href="/docs/transformers/pr_31208/ja/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> は、この <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification" rel="nofollow">サンプル スクリプト</a> および <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb" rel="nofollow">ノートブック</a>。</li> <li>参照: <a href="../tasks/image_classification">画像分類タスク ガイド</a></li>',E,le,ae="ここに含めるリソースの送信に興味がある場合は、お気軽にプル リクエストを開いてください。審査させていただきます。リソースは、既存のリソースを複製するのではなく、何か新しいものを示すことが理想的です。",O,p,w,F,P,H,pe,Ze=`This is the configuration class to store the configuration of a <a href="/docs/transformers/pr_31208/ja/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a>. 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
<a href="https://huggingface.co/facebook/convnext-tiny-224" rel="nofollow">facebook/convnext-tiny-224</a> architecture.`,fe,Ce,he=`Configuration objects inherit from <a href="/docs/transformers/pr_31208/ja/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
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The non-linear activation function (function or string) in each block. If string, <code>&quot;gelu&quot;</code>, <code>&quot;relu&quot;</code>,
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<li><code>&quot;channels_last&quot;</code> or <code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
<li>Unset: Use the channel dimension format of the input image.</li>
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The channel dimension format for the input image. If unset, the channel dimension format is inferred
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<li><code>&quot;channels_last&quot;</code> or <code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
<li><code>&quot;none&quot;</code> or <code>ChannelDimension.NONE</code>: image in (height, width) format.</li>
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