import{s as ut,o as _t,n as Te}from"../chunks/scheduler.9bc65507.js";import{S as Tt,i as bt,g as p,s as l,r as g,A as Mt,h,f as r,c as i,j as L,u,x as $,k as H,y as c,a as m,v as _,d as T,t as b,w as M}from"../chunks/index.707bf1b6.js";import{T as dt}from"../chunks/Tip.c2ecdbf4.js";import{D as ae}from"../chunks/Docstring.570ddb64.js";import{C as mt}from"../chunks/CodeBlock.54a9f38d.js";import{F as yt,M as gt}from"../chunks/Markdown.8ab98a13.js";import{E as ct}from"../chunks/ExampleCodeBlock.bc2be6bc.js";import{P as wt}from"../chunks/PipelineTag.44585822.js";import{H as Ue,E as $t}from"../chunks/EditOnGithub.922df6ba.js";function vt(F){let e,f="Example:",a,n,y;return n=new mt({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> DeiTConfig, DeiTModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a DeiT deit-base-distilled-patch16-224 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = DeiTConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the deit-base-distilled-patch16-224 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DeiTModel(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(){e=p("p"),e.textContent=f,a=l(),g(n.$$.fragment)},l(s){e=h(s,"P",{"data-svelte-h":!0}),$(e)!=="svelte-11lpom8"&&(e.textContent=f),a=i(s),u(n.$$.fragment,s)},m(s,j){m(s,e,j),m(s,a,j),_(n,s,j),y=!0},p:Te,i(s){y||(T(n.$$.fragment,s),y=!0)},o(s){b(n.$$.fragment,s),y=!1},d(s){s&&(r(e),r(a)),M(n,s)}}}function jt(F){let e,f=`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(){e=p("p"),e.innerHTML=f},l(a){e=h(a,"P",{"data-svelte-h":!0}),$(e)!=="svelte-fincs2"&&(e.innerHTML=f)},m(a,n){m(a,e,n)},p:Te,d(a){a&&r(e)}}}function Ft(F){let e,f="Example:",a,n,y;return n=new mt({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, DeiTModel
<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>, trust_remote_code=<span class="hljs-literal">True</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/deit-base-distilled-patch16-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DeiTModel.from_pretrained(<span class="hljs-string">&quot;facebook/deit-base-distilled-patch16-224&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">198</span>, <span class="hljs-number">768</span>]`,wrap:!1}}),{c(){e=p("p"),e.textContent=f,a=l(),g(n.$$.fragment)},l(s){e=h(s,"P",{"data-svelte-h":!0}),$(e)!=="svelte-11lpom8"&&(e.textContent=f),a=i(s),u(n.$$.fragment,s)},m(s,j){m(s,e,j),m(s,a,j),_(n,s,j),y=!0},p:Te,i(s){y||(T(n.$$.fragment,s),y=!0)},o(s){b(n.$$.fragment,s),y=!1},d(s){s&&(r(e),r(a)),M(n,s)}}}function It(F){let e,f=`Note that we provide a script to pre-train this model on custom data in our <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining" rel="nofollow">examples
directory</a>.`;return{c(){e=p("p"),e.innerHTML=f},l(a){e=h(a,"P",{"data-svelte-h":!0}),$(e)!=="svelte-7i3y9o"&&(e.innerHTML=f)},m(a,n){m(a,e,n)},p:Te,d(a){a&&r(e)}}}function Ct(F){let e,f=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=p("p"),e.innerHTML=f},l(a){e=h(a,"P",{"data-svelte-h":!0}),$(e)!=="svelte-fincs2"&&(e.innerHTML=f)},m(a,n){m(a,e,n)},p:Te,d(a){a&&r(e)}}}function Wt(F){let e,f="Examples:",a,n,y;return n=new mt({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, DeiTForMaskedImageModeling
<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> 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/deit-base-distilled-patch16-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DeiTForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;facebook/deit-base-distilled-patch16-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>num_patches = (model.config.image_size // model.config.patch_size) ** <span class="hljs-number">2</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pixel_values = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).pixel_values
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># create random boolean mask of shape (batch_size, num_patches)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>bool_masked_pos = torch.randint(low=<span class="hljs-number">0</span>, high=<span class="hljs-number">2</span>, size=(<span class="hljs-number">1</span>, num_patches)).<span class="hljs-built_in">bool</span>()

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(reconstructed_pixel_values.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">3</span>, <span class="hljs-number">224</span>, <span class="hljs-number">224</span>]`,wrap:!1}}),{c(){e=p("p"),e.textContent=f,a=l(),g(n.$$.fragment)},l(s){e=h(s,"P",{"data-svelte-h":!0}),$(e)!=="svelte-kvfsh7"&&(e.textContent=f),a=i(s),u(n.$$.fragment,s)},m(s,j){m(s,e,j),m(s,a,j),_(n,s,j),y=!0},p:Te,i(s){y||(T(n.$$.fragment,s),y=!0)},o(s){b(n.$$.fragment,s),y=!1},d(s){s&&(r(e),r(a)),M(n,s)}}}function kt(F){let e,f=`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(){e=p("p"),e.innerHTML=f},l(a){e=h(a,"P",{"data-svelte-h":!0}),$(e)!=="svelte-fincs2"&&(e.innerHTML=f)},m(a,n){m(a,e,n)},p:Te,d(a){a&&r(e)}}}function xt(F){let e,f="Examples:",a,n,y;return n=new mt({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9JbWFnZVByb2Nlc3NvciUyQyUyMERlaVRGb3JJbWFnZUNsYXNzaWZpY2F0aW9uJTBBaW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwUElMJTIwaW1wb3J0JTIwSW1hZ2UlMEFpbXBvcnQlMjByZXF1ZXN0cyUwQSUwQXRvcmNoLm1hbnVhbF9zZWVkKDMpJTBBdXJsJTIwJTNEJTIwJTIyaHR0cCUzQSUyRiUyRmltYWdlcy5jb2NvZGF0YXNldC5vcmclMkZ2YWwyMDE3JTJGMDAwMDAwMDM5NzY5LmpwZyUyMiUwQWltYWdlJTIwJTNEJTIwSW1hZ2Uub3BlbihyZXF1ZXN0cy5nZXQodXJsJTJDJTIwc3RyZWFtJTNEVHJ1ZSkucmF3KSUwQSUwQSUyMyUyMG5vdGUlM0ElMjB3ZSUyMGFyZSUyMGxvYWRpbmclMjBhJTIwRGVpVEZvckltYWdlQ2xhc3NpZmljYXRpb25XaXRoVGVhY2hlciUyMGZyb20lMjB0aGUlMjBodWIlMjBoZXJlJTJDJTBBJTIzJTIwc28lMjB0aGUlMjBoZWFkJTIwd2lsbCUyMGJlJTIwcmFuZG9tbHklMjBpbml0aWFsaXplZCUyQyUyMGhlbmNlJTIwdGhlJTIwcHJlZGljdGlvbnMlMjB3aWxsJTIwYmUlMjByYW5kb20lMEFpbWFnZV9wcm9jZXNzb3IlMjAlM0QlMjBBdXRvSW1hZ2VQcm9jZXNzb3IuZnJvbV9wcmV0cmFpbmVkKCUyMmZhY2Vib29rJTJGZGVpdC1iYXNlLWRpc3RpbGxlZC1wYXRjaDE2LTIyNCUyMiklMEFtb2RlbCUyMCUzRCUyMERlaVRGb3JJbWFnZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJmYWNlYm9vayUyRmRlaXQtYmFzZS1kaXN0aWxsZWQtcGF0Y2gxNi0yMjQlMjIpJTBBJTBBaW5wdXRzJTIwJTNEJTIwaW1hZ2VfcHJvY2Vzc29yKGltYWdlcyUzRGltYWdlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiklMEFvdXRwdXRzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMpJTBBbG9naXRzJTIwJTNEJTIwb3V0cHV0cy5sb2dpdHMlMEElMjMlMjBtb2RlbCUyMHByZWRpY3RzJTIwb25lJTIwb2YlMjB0aGUlMjAxMDAwJTIwSW1hZ2VOZXQlMjBjbGFzc2VzJTBBcHJlZGljdGVkX2NsYXNzX2lkeCUyMCUzRCUyMGxvZ2l0cy5hcmdtYXgoLTEpLml0ZW0oKSUwQXByaW50KCUyMlByZWRpY3RlZCUyMGNsYXNzJTNBJTIyJTJDJTIwbW9kZWwuY29uZmlnLmlkMmxhYmVsJTVCcHJlZGljdGVkX2NsYXNzX2lkeCU1RCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, DeiTForImageClassification
<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> 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>torch.manual_seed(<span class="hljs-number">3</span>)
<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><span class="hljs-comment"># note: we are loading a DeiTForImageClassificationWithTeacher from the hub here,</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># so the head will be randomly initialized, hence the predictions will be random</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/deit-base-distilled-patch16-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DeiTForImageClassification.from_pretrained(<span class="hljs-string">&quot;facebook/deit-base-distilled-patch16-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;pt&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 = logits.argmax(-<span class="hljs-number">1</span>).item()
<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[predicted_class_idx])
Predicted <span class="hljs-keyword">class</span>: Polaroid camera, Polaroid Land camera`,wrap:!1}}),{c(){e=p("p"),e.textContent=f,a=l(),g(n.$$.fragment)},l(s){e=h(s,"P",{"data-svelte-h":!0}),$(e)!=="svelte-kvfsh7"&&(e.textContent=f),a=i(s),u(n.$$.fragment,s)},m(s,j){m(s,e,j),m(s,a,j),_(n,s,j),y=!0},p:Te,i(s){y||(T(n.$$.fragment,s),y=!0)},o(s){b(n.$$.fragment,s),y=!1},d(s){s&&(r(e),r(a)),M(n,s)}}}function Jt(F){let e,f=`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(){e=p("p"),e.innerHTML=f},l(a){e=h(a,"P",{"data-svelte-h":!0}),$(e)!=="svelte-fincs2"&&(e.innerHTML=f)},m(a,n){m(a,e,n)},p:Te,d(a){a&&r(e)}}}function Dt(F){let e,f="Example:",a,n,y;return n=new mt({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, DeiTForImageClassificationWithTeacher
<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>, trust_remote_code=<span class="hljs-literal">True</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/deit-base-distilled-patch16-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DeiTForImageClassificationWithTeacher.from_pretrained(<span class="hljs-string">&quot;facebook/deit-base-distilled-patch16-224&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(){e=p("p"),e.textContent=f,a=l(),g(n.$$.fragment)},l(s){e=h(s,"P",{"data-svelte-h":!0}),$(e)!=="svelte-11lpom8"&&(e.textContent=f),a=i(s),u(n.$$.fragment,s)},m(s,j){m(s,e,j),m(s,a,j),_(n,s,j),y=!0},p:Te,i(s){y||(T(n.$$.fragment,s),y=!0)},o(s){b(n.$$.fragment,s),y=!1},d(s){s&&(r(e),r(a)),M(n,s)}}}function Zt(F){let e,f,a,n,y,s,j=`The bare DeiT Model transformer outputting raw hidden-states 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.`,Xe,W,he,Ye,K,pt='The <a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTModel">DeiTModel</a> forward method, overrides the <code>__call__</code> special method.',Ee,D,tt,S,be,fe,ze,I,ne,Ie,je,ot='DeiT Model with a decoder on top for masked image modeling, as proposed in <a href="https://arxiv.org/abs/2111.09886" rel="nofollow">SimMIM</a>.',Ce,J,le,Be,Ne=`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.`,Ge,ee,z,st,re,We='The <a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> forward method, overrides the <code>__call__</code> special method.',Re,ge,Z,ie,Ve,q,at,Q,A,Qe,N,V=`DeiT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
the [CLS] token) e.g. for ImageNet.`,ke,Me,Pe=`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.`,nt,X,de,Ae,G,Le='The <a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> forward method, overrides the <code>__call__</code> special method.',ce,ue,k,R,He,Y,xe,U,te,it,Je,ye=`DeiT Model transformer with image classification heads on top (a linear layer on top of the final hidden state of
the [CLS] token and a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet.`,Oe,O,we=".. warning::",rt,$e,_e=`This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet
supported.`,De,me,ve=`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.`,lt,E,pe,Se,B,qe='The <a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a> forward method, overrides the <code>__call__</code> special method.',d,v,oe,P,t;return e=new Ue({props:{title:"DeiTModel",local:"transformers.DeiTModel",headingTag:"h2"}}),n=new ae({props:{name:"class transformers.DeiTModel",anchor:"transformers.DeiTModel",parameters:[{name:"config",val:": DeiTConfig"},{name:"add_pooling_layer",val:": bool = True"},{name:"use_mask_token",val:": bool = False"}],parametersDescription:[{anchor:"transformers.DeiTModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTConfig">DeiTConfig</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_30950/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_30950/src/transformers/models/deit/modeling_deit.py#L540"}}),he=new ae({props:{name:"forward",anchor:"transformers.DeiTModel.forward",parameters:[{name:"pixel_values",val:": Optional = None"},{name:"bool_masked_pos",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"},{name:"interpolate_pos_encoding",val:": bool = False"}],parametersDescription:[{anchor:"transformers.DeiTModel.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_30950/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/pr_30950/ja/model_doc/deformable_detr#transformers.DeformableDetrFeatureExtractor.__call__">DeiTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.DeiTModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DeiTModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DeiTModel.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.DeiTModel.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_30950/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DeiTModel.forward.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.DeiTModel.forward.bool_masked_pos",description:`<strong>bool_masked_pos</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, num_patches)</code>, <em>optional</em>) &#x2014;
Boolean masked positions. Indicates which patches are masked (1) and which aren&#x2019;t (0).`,name:"bool_masked_pos"}],source:"https://github.com/huggingface/transformers/blob/vr_30950/src/transformers/models/deit/modeling_deit.py#L569",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30950/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</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_30950/ja/model_doc/deit#transformers.DeiTConfig"
>DeiTConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</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>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) — Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</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, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</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_30950/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),D=new dt({props:{$$slots:{default:[jt]},$$scope:{ctx:F}}}),S=new ct({props:{anchor:"transformers.DeiTModel.forward.example",$$slots:{default:[Ft]},$$scope:{ctx:F}}}),fe=new Ue({props:{title:"DeiTForMaskedImageModeling",local:"transformers.DeiTForMaskedImageModeling",headingTag:"h2"}}),ne=new ae({props:{name:"class transformers.DeiTForMaskedImageModeling",anchor:"transformers.DeiTForMaskedImageModeling",parameters:[{name:"config",val:": DeiTConfig"}],parametersDescription:[{anchor:"transformers.DeiTForMaskedImageModeling.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTConfig">DeiTConfig</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_30950/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_30950/src/transformers/models/deit/modeling_deit.py#L655"}}),J=new dt({props:{$$slots:{default:[It]},$$scope:{ctx:F}}}),z=new ae({props:{name:"forward",anchor:"transformers.DeiTForMaskedImageModeling.forward",parameters:[{name:"pixel_values",val:": Optional = None"},{name:"bool_masked_pos",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"},{name:"interpolate_pos_encoding",val:": bool = False"}],parametersDescription:[{anchor:"transformers.DeiTForMaskedImageModeling.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_30950/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/pr_30950/ja/model_doc/deformable_detr#transformers.DeformableDetrFeatureExtractor.__call__">DeiTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.DeiTForMaskedImageModeling.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DeiTForMaskedImageModeling.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DeiTForMaskedImageModeling.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.DeiTForMaskedImageModeling.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_30950/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DeiTForMaskedImageModeling.forward.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.DeiTForMaskedImageModeling.forward.bool_masked_pos",description:`<strong>bool_masked_pos</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, num_patches)</code>) &#x2014;
Boolean masked positions. Indicates which patches are masked (1) and which aren&#x2019;t (0).`,name:"bool_masked_pos"}],source:"https://github.com/huggingface/transformers/blob/vr_30950/src/transformers/models/deit/modeling_deit.py#L685",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.modeling_outputs.MaskedImageModelingOutput</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_30950/ja/model_doc/deit#transformers.DeiTConfig"
>DeiTConfig</a>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>bool_masked_pos</code> is provided) — Reconstruction loss.</li>
<li><strong>reconstruction</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) — Reconstructed / completed images.</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</li>
<li><strong>when</strong> <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, sequence_length, hidden_size)</code>. Hidden-states
(also called feature maps) of the model at the output of each stage.</li>
<li><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when</li>
<li><strong><code>config.output_attentions=True</code>):</strong>
Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, patch_size, sequence_length)</code>. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.modeling_outputs.MaskedImageModelingOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ge=new dt({props:{$$slots:{default:[Ct]},$$scope:{ctx:F}}}),ie=new ct({props:{anchor:"transformers.DeiTForMaskedImageModeling.forward.example",$$slots:{default:[Wt]},$$scope:{ctx:F}}}),q=new Ue({props:{title:"DeiTForImageClassification",local:"transformers.DeiTForImageClassification",headingTag:"h2"}}),A=new ae({props:{name:"class transformers.DeiTForImageClassification",anchor:"transformers.DeiTForImageClassification",parameters:[{name:"config",val:": DeiTConfig"}],parametersDescription:[{anchor:"transformers.DeiTForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTConfig">DeiTConfig</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_30950/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_30950/src/transformers/models/deit/modeling_deit.py#L774"}}),de=new ae({props:{name:"forward",anchor:"transformers.DeiTForImageClassification.forward",parameters:[{name:"pixel_values",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"},{name:"interpolate_pos_encoding",val:": bool = False"}],parametersDescription:[{anchor:"transformers.DeiTForImageClassification.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_30950/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/pr_30950/ja/model_doc/deformable_detr#transformers.DeformableDetrFeatureExtractor.__call__">DeiTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.DeiTForImageClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DeiTForImageClassification.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DeiTForImageClassification.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.DeiTForImageClassification.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_30950/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DeiTForImageClassification.forward.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.DeiTForImageClassification.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_30950/src/transformers/models/deit/modeling_deit.py#L794",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30950/ja/main_classes/output#transformers.modeling_outputs.ImageClassifierOutput"
>transformers.modeling_outputs.ImageClassifierOutput</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_30950/ja/model_doc/deit#transformers.DeiTConfig"
>DeiTConfig</a>) and inputs.</p>
<ul>
<li>
<p><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.</p>
</li>
<li>
<p><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).</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 stage) of shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states
(also called feature maps) of the model at the output of each stage.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, patch_size, 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_30950/ja/main_classes/output#transformers.modeling_outputs.ImageClassifierOutput"
>transformers.modeling_outputs.ImageClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ue=new dt({props:{$$slots:{default:[kt]},$$scope:{ctx:F}}}),R=new ct({props:{anchor:"transformers.DeiTForImageClassification.forward.example",$$slots:{default:[xt]},$$scope:{ctx:F}}}),Y=new Ue({props:{title:"DeiTForImageClassificationWithTeacher",local:"transformers.DeiTForImageClassificationWithTeacher",headingTag:"h2"}}),te=new ae({props:{name:"class transformers.DeiTForImageClassificationWithTeacher",anchor:"transformers.DeiTForImageClassificationWithTeacher",parameters:[{name:"config",val:": DeiTConfig"}],parametersDescription:[{anchor:"transformers.DeiTForImageClassificationWithTeacher.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTConfig">DeiTConfig</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_30950/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_30950/src/transformers/models/deit/modeling_deit.py#L921"}}),pe=new ae({props:{name:"forward",anchor:"transformers.DeiTForImageClassificationWithTeacher.forward",parameters:[{name:"pixel_values",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"},{name:"interpolate_pos_encoding",val:": bool = False"}],parametersDescription:[{anchor:"transformers.DeiTForImageClassificationWithTeacher.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_30950/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/pr_30950/ja/model_doc/deformable_detr#transformers.DeformableDetrFeatureExtractor.__call__">DeiTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.DeiTForImageClassificationWithTeacher.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DeiTForImageClassificationWithTeacher.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DeiTForImageClassificationWithTeacher.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.DeiTForImageClassificationWithTeacher.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_30950/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DeiTForImageClassificationWithTeacher.forward.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"}],source:"https://github.com/huggingface/transformers/blob/vr_30950/src/transformers/models/deit/modeling_deit.py#L951",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.deit.modeling_deit.DeiTForImageClassificationWithTeacherOutput</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_30950/ja/model_doc/deit#transformers.DeiTConfig"
>DeiTConfig</a>) and inputs.</p>
<ul>
<li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Prediction scores as the average of the cls_logits and distillation logits.</li>
<li><strong>cls_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Prediction scores of the classification head (i.e. the linear layer on top of the final hidden state of the
class token).</li>
<li><strong>distillation_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Prediction scores of the distillation head (i.e. the linear layer on top of the final hidden state of the
distillation token).</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 + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states of the model at the output of each layer
plus the initial embedding outputs.</li>
<li><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.deit.modeling_deit.DeiTForImageClassificationWithTeacherOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),v=new dt({props:{$$slots:{default:[Jt]},$$scope:{ctx:F}}}),P=new ct({props:{anchor:"transformers.DeiTForImageClassificationWithTeacher.forward.example",$$slots:{default:[Dt]},$$scope:{ctx:F}}}),{c(){g(e.$$.fragment),f=l(),a=p("div"),g(n.$$.fragment),y=l(),s=p("p"),s.innerHTML=j,Xe=l(),W=p("div"),g(he.$$.fragment),Ye=l(),K=p("p"),K.innerHTML=pt,Ee=l(),g(D.$$.fragment),tt=l(),g(S.$$.fragment),be=l(),g(fe.$$.fragment),ze=l(),I=p("div"),g(ne.$$.fragment),Ie=l(),je=p("p"),je.innerHTML=ot,Ce=l(),g(J.$$.fragment),le=l(),Be=p("p"),Be.innerHTML=Ne,Ge=l(),ee=p("div"),g(z.$$.fragment),st=l(),re=p("p"),re.innerHTML=We,Re=l(),g(ge.$$.fragment),Z=l(),g(ie.$$.fragment),Ve=l(),g(q.$$.fragment),at=l(),Q=p("div"),g(A.$$.fragment),Qe=l(),N=p("p"),N.textContent=V,ke=l(),Me=p("p"),Me.innerHTML=Pe,nt=l(),X=p("div"),g(de.$$.fragment),Ae=l(),G=p("p"),G.innerHTML=Le,ce=l(),g(ue.$$.fragment),k=l(),g(R.$$.fragment),He=l(),g(Y.$$.fragment),xe=l(),U=p("div"),g(te.$$.fragment),it=l(),Je=p("p"),Je.textContent=ye,Oe=l(),O=p("p"),O.textContent=we,rt=l(),$e=p("p"),$e.textContent=_e,De=l(),me=p("p"),me.innerHTML=ve,lt=l(),E=p("div"),g(pe.$$.fragment),Se=l(),B=p("p"),B.innerHTML=qe,d=l(),g(v.$$.fragment),oe=l(),g(P.$$.fragment),this.h()},l(o){u(e.$$.fragment,o),f=i(o),a=h(o,"DIV",{class:!0});var w=L(a);u(n.$$.fragment,w),y=i(w),s=h(w,"P",{"data-svelte-h":!0}),$(s)!=="svelte-1g5epvj"&&(s.innerHTML=j),Xe=i(w),W=h(w,"DIV",{class:!0});var C=L(W);u(he.$$.fragment,C),Ye=i(C),K=h(C,"P",{"data-svelte-h":!0}),$(K)!=="svelte-13mu6au"&&(K.innerHTML=pt),Ee=i(C),u(D.$$.fragment,C),tt=i(C),u(S.$$.fragment,C),C.forEach(r),w.forEach(r),be=i(o),u(fe.$$.fragment,o),ze=i(o),I=h(o,"DIV",{class:!0});var x=L(I);u(ne.$$.fragment,x),Ie=i(x),je=h(x,"P",{"data-svelte-h":!0}),$(je)!=="svelte-1lv61v1"&&(je.innerHTML=ot),Ce=i(x),u(J.$$.fragment,x),le=i(x),Be=h(x,"P",{"data-svelte-h":!0}),$(Be)!=="svelte-1gjh92c"&&(Be.innerHTML=Ne),Ge=i(x),ee=h(x,"DIV",{class:!0});var se=L(ee);u(z.$$.fragment,se),st=i(se),re=h(se,"P",{"data-svelte-h":!0}),$(re)!=="svelte-1k5logi"&&(re.innerHTML=We),Re=i(se),u(ge.$$.fragment,se),Z=i(se),u(ie.$$.fragment,se),se.forEach(r),x.forEach(r),Ve=i(o),u(q.$$.fragment,o),at=i(o),Q=h(o,"DIV",{class:!0});var Fe=L(Q);u(A.$$.fragment,Fe),Qe=i(Fe),N=h(Fe,"P",{"data-svelte-h":!0}),$(N)!=="svelte-1azp76l"&&(N.textContent=V),ke=i(Fe),Me=h(Fe,"P",{"data-svelte-h":!0}),$(Me)!=="svelte-1gjh92c"&&(Me.innerHTML=Pe),nt=i(Fe),X=h(Fe,"DIV",{class:!0});var Ke=L(X);u(de.$$.fragment,Ke),Ae=i(Ke),G=h(Ke,"P",{"data-svelte-h":!0}),$(G)!=="svelte-1n3wgfu"&&(G.innerHTML=Le),ce=i(Ke),u(ue.$$.fragment,Ke),k=i(Ke),u(R.$$.fragment,Ke),Ke.forEach(r),Fe.forEach(r),He=i(o),u(Y.$$.fragment,o),xe=i(o),U=h(o,"DIV",{class:!0});var Ze=L(U);u(te.$$.fragment,Ze),it=i(Ze),Je=h(Ze,"P",{"data-svelte-h":!0}),$(Je)!=="svelte-17o4rh5"&&(Je.textContent=ye),Oe=i(Ze),O=h(Ze,"P",{"data-svelte-h":!0}),$(O)!=="svelte-zri3pk"&&(O.textContent=we),rt=i(Ze),$e=h(Ze,"P",{"data-svelte-h":!0}),$($e)!=="svelte-1gp6z48"&&($e.textContent=_e),De=i(Ze),me=h(Ze,"P",{"data-svelte-h":!0}),$(me)!=="svelte-1gjh92c"&&(me.innerHTML=ve),lt=i(Ze),E=h(Ze,"DIV",{class:!0});var et=L(E);u(pe.$$.fragment,et),Se=i(et),B=h(et,"P",{"data-svelte-h":!0}),$(B)!=="svelte-e1fic2"&&(B.innerHTML=qe),d=i(et),u(v.$$.fragment,et),oe=i(et),u(P.$$.fragment,et),et.forEach(r),Ze.forEach(r),this.h()},h(){H(W,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(a,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(ee,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(Q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(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(o,w){_(e,o,w),m(o,f,w),m(o,a,w),_(n,a,null),c(a,y),c(a,s),c(a,Xe),c(a,W),_(he,W,null),c(W,Ye),c(W,K),c(W,Ee),_(D,W,null),c(W,tt),_(S,W,null),m(o,be,w),_(fe,o,w),m(o,ze,w),m(o,I,w),_(ne,I,null),c(I,Ie),c(I,je),c(I,Ce),_(J,I,null),c(I,le),c(I,Be),c(I,Ge),c(I,ee),_(z,ee,null),c(ee,st),c(ee,re),c(ee,Re),_(ge,ee,null),c(ee,Z),_(ie,ee,null),m(o,Ve,w),_(q,o,w),m(o,at,w),m(o,Q,w),_(A,Q,null),c(Q,Qe),c(Q,N),c(Q,ke),c(Q,Me),c(Q,nt),c(Q,X),_(de,X,null),c(X,Ae),c(X,G),c(X,ce),_(ue,X,null),c(X,k),_(R,X,null),m(o,He,w),_(Y,o,w),m(o,xe,w),m(o,U,w),_(te,U,null),c(U,it),c(U,Je),c(U,Oe),c(U,O),c(U,rt),c(U,$e),c(U,De),c(U,me),c(U,lt),c(U,E),_(pe,E,null),c(E,Se),c(E,B),c(E,d),_(v,E,null),c(E,oe),_(P,E,null),t=!0},p(o,w){const C={};w&2&&(C.$$scope={dirty:w,ctx:o}),D.$set(C);const x={};w&2&&(x.$$scope={dirty:w,ctx:o}),S.$set(x);const se={};w&2&&(se.$$scope={dirty:w,ctx:o}),J.$set(se);const Fe={};w&2&&(Fe.$$scope={dirty:w,ctx:o}),ge.$set(Fe);const Ke={};w&2&&(Ke.$$scope={dirty:w,ctx:o}),ie.$set(Ke);const Ze={};w&2&&(Ze.$$scope={dirty:w,ctx:o}),ue.$set(Ze);const et={};w&2&&(et.$$scope={dirty:w,ctx:o}),R.$set(et);const ht={};w&2&&(ht.$$scope={dirty:w,ctx:o}),v.$set(ht);const ft={};w&2&&(ft.$$scope={dirty:w,ctx:o}),P.$set(ft)},i(o){t||(T(e.$$.fragment,o),T(n.$$.fragment,o),T(he.$$.fragment,o),T(D.$$.fragment,o),T(S.$$.fragment,o),T(fe.$$.fragment,o),T(ne.$$.fragment,o),T(J.$$.fragment,o),T(z.$$.fragment,o),T(ge.$$.fragment,o),T(ie.$$.fragment,o),T(q.$$.fragment,o),T(A.$$.fragment,o),T(de.$$.fragment,o),T(ue.$$.fragment,o),T(R.$$.fragment,o),T(Y.$$.fragment,o),T(te.$$.fragment,o),T(pe.$$.fragment,o),T(v.$$.fragment,o),T(P.$$.fragment,o),t=!0)},o(o){b(e.$$.fragment,o),b(n.$$.fragment,o),b(he.$$.fragment,o),b(D.$$.fragment,o),b(S.$$.fragment,o),b(fe.$$.fragment,o),b(ne.$$.fragment,o),b(J.$$.fragment,o),b(z.$$.fragment,o),b(ge.$$.fragment,o),b(ie.$$.fragment,o),b(q.$$.fragment,o),b(A.$$.fragment,o),b(de.$$.fragment,o),b(ue.$$.fragment,o),b(R.$$.fragment,o),b(Y.$$.fragment,o),b(te.$$.fragment,o),b(pe.$$.fragment,o),b(v.$$.fragment,o),b(P.$$.fragment,o),t=!1},d(o){o&&(r(f),r(a),r(be),r(ze),r(I),r(Ve),r(at),r(Q),r(He),r(xe),r(U)),M(e,o),M(n),M(he),M(D),M(S),M(fe,o),M(ne),M(J),M(z),M(ge),M(ie),M(q,o),M(A),M(de),M(ue),M(R),M(Y,o),M(te),M(pe),M(v),M(P)}}}function Ut(F){let e,f;return e=new gt({props:{$$slots:{default:[Zt]},$$scope:{ctx:F}}}),{c(){g(e.$$.fragment)},l(a){u(e.$$.fragment,a)},m(a,n){_(e,a,n),f=!0},p(a,n){const y={};n&2&&(y.$$scope={dirty:n,ctx:a}),e.$set(y)},i(a){f||(T(e.$$.fragment,a),f=!0)},o(a){b(e.$$.fragment,a),f=!1},d(a){M(e,a)}}}function zt(F){let e,f=`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(){e=p("p"),e.innerHTML=f},l(a){e=h(a,"P",{"data-svelte-h":!0}),$(e)!=="svelte-fincs2"&&(e.innerHTML=f)},m(a,n){m(a,e,n)},p:Te,d(a){a&&r(e)}}}function Nt(F){let e,f="Example:",a,n,y;return n=new mt({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, TFDeiTModel
<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>, trust_remote_code=<span class="hljs-literal">True</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/deit-base-distilled-patch16-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDeiTModel.from_pretrained(<span class="hljs-string">&quot;facebook/deit-base-distilled-patch16-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(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
<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">198</span>, <span class="hljs-number">768</span>]`,wrap:!1}}),{c(){e=p("p"),e.textContent=f,a=l(),g(n.$$.fragment)},l(s){e=h(s,"P",{"data-svelte-h":!0}),$(e)!=="svelte-11lpom8"&&(e.textContent=f),a=i(s),u(n.$$.fragment,s)},m(s,j){m(s,e,j),m(s,a,j),_(n,s,j),y=!0},p:Te,i(s){y||(T(n.$$.fragment,s),y=!0)},o(s){b(n.$$.fragment,s),y=!1},d(s){s&&(r(e),r(a)),M(n,s)}}}function Gt(F){let e,f=`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(){e=p("p"),e.innerHTML=f},l(a){e=h(a,"P",{"data-svelte-h":!0}),$(e)!=="svelte-fincs2"&&(e.innerHTML=f)},m(a,n){m(a,e,n)},p:Te,d(a){a&&r(e)}}}function Rt(F){let e,f="Examples:",a,n,y;return n=new mt({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, TFDeiTForMaskedImageModeling
<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/deit-base-distilled-patch16-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDeiTForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;facebook/deit-base-distilled-patch16-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>num_patches = (model.config.image_size // model.config.patch_size) ** <span class="hljs-number">2</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pixel_values = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>).pixel_values
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># create random boolean mask of shape (batch_size, num_patches)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>bool_masked_pos = tf.cast(tf.random.uniform((<span class="hljs-number">1</span>, num_patches), minval=<span class="hljs-number">0</span>, maxval=<span class="hljs-number">2</span>, dtype=tf.int32), tf.<span class="hljs-built_in">bool</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(reconstructed_pixel_values.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">3</span>, <span class="hljs-number">224</span>, <span class="hljs-number">224</span>]`,wrap:!1}}),{c(){e=p("p"),e.textContent=f,a=l(),g(n.$$.fragment)},l(s){e=h(s,"P",{"data-svelte-h":!0}),$(e)!=="svelte-kvfsh7"&&(e.textContent=f),a=i(s),u(n.$$.fragment,s)},m(s,j){m(s,e,j),m(s,a,j),_(n,s,j),y=!0},p:Te,i(s){y||(T(n.$$.fragment,s),y=!0)},o(s){b(n.$$.fragment,s),y=!1},d(s){s&&(r(e),r(a)),M(n,s)}}}function Bt(F){let e,f=`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(){e=p("p"),e.innerHTML=f},l(a){e=h(a,"P",{"data-svelte-h":!0}),$(e)!=="svelte-fincs2"&&(e.innerHTML=f)},m(a,n){m(a,e,n)},p:Te,d(a){a&&r(e)}}}function Vt(F){let e,f="Examples:",a,n,y;return n=new mt({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, TFDeiTForImageClassification
<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>keras.utils.set_random_seed(<span class="hljs-number">3</span>)
<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><span class="hljs-comment"># note: we are loading a TFDeiTForImageClassificationWithTeacher from the hub here,</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># so the head will be randomly initialized, hence the predictions will be random</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/deit-base-distilled-patch16-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDeiTForImageClassification.from_pretrained(<span class="hljs-string">&quot;facebook/deit-base-distilled-patch16-224&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)])
Predicted <span class="hljs-keyword">class</span>: little blue heron, Egretta caerulea`,wrap:!1}}),{c(){e=p("p"),e.textContent=f,a=l(),g(n.$$.fragment)},l(s){e=h(s,"P",{"data-svelte-h":!0}),$(e)!=="svelte-kvfsh7"&&(e.textContent=f),a=i(s),u(n.$$.fragment,s)},m(s,j){m(s,e,j),m(s,a,j),_(n,s,j),y=!0},p:Te,i(s){y||(T(n.$$.fragment,s),y=!0)},o(s){b(n.$$.fragment,s),y=!1},d(s){s&&(r(e),r(a)),M(n,s)}}}function Xt(F){let e,f=`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(){e=p("p"),e.innerHTML=f},l(a){e=h(a,"P",{"data-svelte-h":!0}),$(e)!=="svelte-fincs2"&&(e.innerHTML=f)},m(a,n){m(a,e,n)},p:Te,d(a){a&&r(e)}}}function Yt(F){let e,f="Example:",a,n,y;return n=new mt({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, TFDeiTForImageClassificationWithTeacher
<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> 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>, trust_remote_code=<span class="hljs-literal">True</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/deit-base-distilled-patch16-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDeiTForImageClassificationWithTeacher.from_pretrained(<span class="hljs-string">&quot;facebook/deit-base-distilled-patch16-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </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 = <span class="hljs-built_in">int</span>(tf.math.argmax(logits, axis=-<span class="hljs-number">1</span>))
<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(){e=p("p"),e.textContent=f,a=l(),g(n.$$.fragment)},l(s){e=h(s,"P",{"data-svelte-h":!0}),$(e)!=="svelte-11lpom8"&&(e.textContent=f),a=i(s),u(n.$$.fragment,s)},m(s,j){m(s,e,j),m(s,a,j),_(n,s,j),y=!0},p:Te,i(s){y||(T(n.$$.fragment,s),y=!0)},o(s){b(n.$$.fragment,s),y=!1},d(s){s&&(r(e),r(a)),M(n,s)}}}function Et(F){let e,f,a,n,y,s,j=`The bare DeiT Model transformer outputting raw hidden-states without any specific head on top.
This model is a TensorFlow
<a href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer" rel="nofollow">keras.layers.Layer</a>. Use it as a regular
TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.`,Xe,W,he,Ye,K,pt='The <a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.TFDeiTModel">TFDeiTModel</a> forward method, overrides the <code>__call__</code> special method.',Ee,D,tt,S,be,fe,ze,I,ne,Ie,je,ot=`DeiT Model with a decoder on top for masked image modeling, as proposed in <a href="https://arxiv.org/abs/2111.09886" rel="nofollow">SimMIM</a>.
This model is a TensorFlow
<a href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer" rel="nofollow">keras.layers.Layer</a>. Use it as a regular
TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.`,Ce,J,le,Be,Ne,Ge='The <a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.TFDeiTForMaskedImageModeling">TFDeiTForMaskedImageModeling</a> forward method, overrides the <code>__call__</code> special method.',ee,z,st,re,We,Re,ge,Z,ie,Ve,q,at=`DeiT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
the [CLS] token) e.g. for ImageNet.`,Q,A,Qe=`This model is a TensorFlow
<a href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer" rel="nofollow">keras.layers.Layer</a>. Use it as a regular
TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.`,N,V,ke,Me,Pe,nt='The <a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.TFDeiTForImageClassification">TFDeiTForImageClassification</a> forward method, overrides the <code>__call__</code> special method.',X,de,Ae,G,Le,ce,ue,k,R,He,Y,xe=`DeiT Model transformer with image classification heads on top (a linear layer on top of the final hidden state of
the [CLS] token and a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet.`,U,te,it=".. warning::",Je,ye,Oe=`This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet
supported.`,O,we,rt=`This model is a TensorFlow
<a href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer" rel="nofollow">keras.layers.Layer</a>. Use it as a regular
TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.`,$e,_e,De,me,ve,lt='The <a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.TFDeiTForImageClassificationWithTeacher">TFDeiTForImageClassificationWithTeacher</a> forward method, overrides the <code>__call__</code> special method.',E,pe,Se,B,qe;return e=new Ue({props:{title:"TFDeiTModel",local:"transformers.TFDeiTModel",headingTag:"h2"}}),n=new ae({props:{name:"class transformers.TFDeiTModel",anchor:"transformers.TFDeiTModel",parameters:[{name:"config",val:": DeiTConfig"},{name:"add_pooling_layer",val:": bool = True"},{name:"use_mask_token",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDeiTModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTConfig">DeiTConfig</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_30950/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_30950/src/transformers/models/deit/modeling_tf_deit.py#L755"}}),he=new ae({props:{name:"call",anchor:"transformers.TFDeiTModel.call",parameters:[{name:"pixel_values",val:": tf.Tensor | None = None"},{name:"bool_masked_pos",val:": tf.Tensor | None = None"},{name:"head_mask",val:": tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"interpolate_pos_encoding",val:": bool = False"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFDeiTModel.call.pixel_values",description:`<strong>pixel_values</strong> (<code>tf.Tensor</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_30950/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/pr_30950/ja/model_doc/deformable_detr#transformers.DeformableDetrFeatureExtractor.__call__">DeiTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFDeiTModel.call.head_mask",description:`<strong>head_mask</strong> (<code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDeiTModel.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.TFDeiTModel.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.`,name:"output_hidden_states"},{anchor:"transformers.TFDeiTModel.call.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.TFDeiTModel.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_30950/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_30950/src/transformers/models/deit/modeling_tf_deit.py#L769",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30950/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_30950/ja/model_doc/deit#transformers.DeiTConfig"
>DeiTConfig</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_30950/ja/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or <code>tuple(tf.Tensor)</code></p>
`}}),D=new dt({props:{$$slots:{default:[zt]},$$scope:{ctx:F}}}),S=new ct({props:{anchor:"transformers.TFDeiTModel.call.example",$$slots:{default:[Nt]},$$scope:{ctx:F}}}),fe=new Ue({props:{title:"TFDeiTForMaskedImageModeling",local:"transformers.TFDeiTForMaskedImageModeling",headingTag:"h2"}}),ne=new ae({props:{name:"class transformers.TFDeiTForMaskedImageModeling",anchor:"transformers.TFDeiTForMaskedImageModeling",parameters:[{name:"config",val:": DeiTConfig"}],parametersDescription:[{anchor:"transformers.TFDeiTForMaskedImageModeling.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTConfig">DeiTConfig</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_30950/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_30950/src/transformers/models/deit/modeling_tf_deit.py#L893"}}),le=new ae({props:{name:"call",anchor:"transformers.TFDeiTForMaskedImageModeling.call",parameters:[{name:"pixel_values",val:": tf.Tensor | None = None"},{name:"bool_masked_pos",val:": tf.Tensor | None = None"},{name:"head_mask",val:": tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"interpolate_pos_encoding",val:": bool = False"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFDeiTForMaskedImageModeling.call.pixel_values",description:`<strong>pixel_values</strong> (<code>tf.Tensor</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_30950/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/pr_30950/ja/model_doc/deformable_detr#transformers.DeformableDetrFeatureExtractor.__call__">DeiTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFDeiTForMaskedImageModeling.call.head_mask",description:`<strong>head_mask</strong> (<code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDeiTForMaskedImageModeling.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.TFDeiTForMaskedImageModeling.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.`,name:"output_hidden_states"},{anchor:"transformers.TFDeiTForMaskedImageModeling.call.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.TFDeiTForMaskedImageModeling.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_30950/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.TFDeiTForMaskedImageModeling.call.bool_masked_pos",description:`<strong>bool_masked_pos</strong> (<code>tf.Tensor</code> of type bool and shape <code>(batch_size, num_patches)</code>) &#x2014;
Boolean masked positions. Indicates which patches are masked (1) and which aren&#x2019;t (0).`,name:"bool_masked_pos"}],source:"https://github.com/huggingface/transformers/blob/vr_30950/src/transformers/models/deit/modeling_tf_deit.py#L905",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.modeling_tf_outputs.TFMaskedImageModelingOutput</code> 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_30950/ja/model_doc/deit#transformers.DeiTConfig"
>DeiTConfig</a>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>bool_masked_pos</code> is provided) — Reconstruction loss.</li>
<li><strong>reconstruction</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) — Reconstructed / completed images.</li>
<li><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when</li>
<li><strong><code>config.output_hidden_states=True</code>):</strong>
Tuple of <code>tf.Tensor</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, sequence_length, hidden_size)</code>. Hidden-states (also called
feature maps) of the model at the output of each stage.</li>
<li><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when</li>
<li><strong><code>config.output_attentions=True</code>):</strong>
Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, patch_size, sequence_length)</code>.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.modeling_tf_outputs.TFMaskedImageModelingOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),z=new dt({props:{$$slots:{default:[Gt]},$$scope:{ctx:F}}}),re=new ct({props:{anchor:"transformers.TFDeiTForMaskedImageModeling.call.example",$$slots:{default:[Rt]},$$scope:{ctx:F}}}),Re=new Ue({props:{title:"TFDeiTForImageClassification",local:"transformers.TFDeiTForImageClassification",headingTag:"h2"}}),ie=new ae({props:{name:"class transformers.TFDeiTForImageClassification",anchor:"transformers.TFDeiTForImageClassification",parameters:[{name:"config",val:": DeiTConfig"}],parametersDescription:[{anchor:"transformers.TFDeiTForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTConfig">DeiTConfig</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_30950/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_30950/src/transformers/models/deit/modeling_tf_deit.py#L1019"}}),ke=new ae({props:{name:"call",anchor:"transformers.TFDeiTForImageClassification.call",parameters:[{name:"pixel_values",val:": tf.Tensor | None = None"},{name:"head_mask",val:": tf.Tensor | None = None"},{name:"labels",val:": tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"interpolate_pos_encoding",val:": bool = False"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFDeiTForImageClassification.call.pixel_values",description:`<strong>pixel_values</strong> (<code>tf.Tensor</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_30950/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/pr_30950/ja/model_doc/deformable_detr#transformers.DeformableDetrFeatureExtractor.__call__">DeiTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFDeiTForImageClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDeiTForImageClassification.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.TFDeiTForImageClassification.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.`,name:"output_hidden_states"},{anchor:"transformers.TFDeiTForImageClassification.call.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.TFDeiTForImageClassification.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_30950/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.TFDeiTForImageClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</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_30950/src/transformers/models/deit/modeling_tf_deit.py#L1041",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.modeling_tf_outputs.TFImageClassifierOutput</code> 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_30950/ja/model_doc/deit#transformers.DeiTConfig"
>DeiTConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</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.</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, if the model has an embedding layer, + one for
the output of each stage) of shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states (also called
feature maps) of the model at the output of each stage.</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, patch_size, 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><code>transformers.modeling_tf_outputs.TFImageClassifierOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),de=new dt({props:{$$slots:{default:[Bt]},$$scope:{ctx:F}}}),G=new ct({props:{anchor:"transformers.TFDeiTForImageClassification.call.example",$$slots:{default:[Vt]},$$scope:{ctx:F}}}),ce=new Ue({props:{title:"TFDeiTForImageClassificationWithTeacher",local:"transformers.TFDeiTForImageClassificationWithTeacher",headingTag:"h2"}}),R=new ae({props:{name:"class transformers.TFDeiTForImageClassificationWithTeacher",anchor:"transformers.TFDeiTForImageClassificationWithTeacher",parameters:[{name:"config",val:": DeiTConfig"}],parametersDescription:[{anchor:"transformers.TFDeiTForImageClassificationWithTeacher.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTConfig">DeiTConfig</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_30950/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_30950/src/transformers/models/deit/modeling_tf_deit.py#L1130"}}),De=new ae({props:{name:"call",anchor:"transformers.TFDeiTForImageClassificationWithTeacher.call",parameters:[{name:"pixel_values",val:": tf.Tensor | None = None"},{name:"head_mask",val:": tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"interpolate_pos_encoding",val:": bool = False"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFDeiTForImageClassificationWithTeacher.call.pixel_values",description:`<strong>pixel_values</strong> (<code>tf.Tensor</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_30950/ja/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/pr_30950/ja/model_doc/deformable_detr#transformers.DeformableDetrFeatureExtractor.__call__">DeiTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFDeiTForImageClassificationWithTeacher.call.head_mask",description:`<strong>head_mask</strong> (<code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDeiTForImageClassificationWithTeacher.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.TFDeiTForImageClassificationWithTeacher.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.`,name:"output_hidden_states"},{anchor:"transformers.TFDeiTForImageClassificationWithTeacher.call.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.TFDeiTForImageClassificationWithTeacher.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_30950/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_30950/src/transformers/models/deit/modeling_tf_deit.py#L1162",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.deit.modeling_tf_deit.TFDeiTForImageClassificationWithTeacherOutput</code> 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_30950/ja/model_doc/deit#transformers.DeiTConfig"
>DeiTConfig</a>) and inputs.</p>
<ul>
<li><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Prediction scores as the average of the cls_logits and distillation logits.</li>
<li><strong>cls_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Prediction scores of the classification head (i.e. the linear layer on top of the final hidden state of the
class token).</li>
<li><strong>distillation_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Prediction scores of the distillation head (i.e. the linear layer on top of the final hidden state of the
distillation token).</li>
<li><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>. Hidden-states of the model at the output of each layer plus
the initial embedding outputs.</li>
<li><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>. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.deit.modeling_tf_deit.TFDeiTForImageClassificationWithTeacherOutput</code> or <code>tuple(tf.Tensor)</code></p>
`}}),pe=new dt({props:{$$slots:{default:[Xt]},$$scope:{ctx:F}}}),B=new ct({props:{anchor:"transformers.TFDeiTForImageClassificationWithTeacher.call.example",$$slots:{default:[Yt]},$$scope:{ctx:F}}}),{c(){g(e.$$.fragment),f=l(),a=p("div"),g(n.$$.fragment),y=l(),s=p("p"),s.innerHTML=j,Xe=l(),W=p("div"),g(he.$$.fragment),Ye=l(),K=p("p"),K.innerHTML=pt,Ee=l(),g(D.$$.fragment),tt=l(),g(S.$$.fragment),be=l(),g(fe.$$.fragment),ze=l(),I=p("div"),g(ne.$$.fragment),Ie=l(),je=p("p"),je.innerHTML=ot,Ce=l(),J=p("div"),g(le.$$.fragment),Be=l(),Ne=p("p"),Ne.innerHTML=Ge,ee=l(),g(z.$$.fragment),st=l(),g(re.$$.fragment),We=l(),g(Re.$$.fragment),ge=l(),Z=p("div"),g(ie.$$.fragment),Ve=l(),q=p("p"),q.textContent=at,Q=l(),A=p("p"),A.innerHTML=Qe,N=l(),V=p("div"),g(ke.$$.fragment),Me=l(),Pe=p("p"),Pe.innerHTML=nt,X=l(),g(de.$$.fragment),Ae=l(),g(G.$$.fragment),Le=l(),g(ce.$$.fragment),ue=l(),k=p("div"),g(R.$$.fragment),He=l(),Y=p("p"),Y.textContent=xe,U=l(),te=p("p"),te.textContent=it,Je=l(),ye=p("p"),ye.textContent=Oe,O=l(),we=p("p"),we.innerHTML=rt,$e=l(),_e=p("div"),g(De.$$.fragment),me=l(),ve=p("p"),ve.innerHTML=lt,E=l(),g(pe.$$.fragment),Se=l(),g(B.$$.fragment),this.h()},l(d){u(e.$$.fragment,d),f=i(d),a=h(d,"DIV",{class:!0});var v=L(a);u(n.$$.fragment,v),y=i(v),s=h(v,"P",{"data-svelte-h":!0}),$(s)!=="svelte-12cbu7j"&&(s.innerHTML=j),Xe=i(v),W=h(v,"DIV",{class:!0});var oe=L(W);u(he.$$.fragment,oe),Ye=i(oe),K=h(oe,"P",{"data-svelte-h":!0}),$(K)!=="svelte-1a6zo9y"&&(K.innerHTML=pt),Ee=i(oe),u(D.$$.fragment,oe),tt=i(oe),u(S.$$.fragment,oe),oe.forEach(r),v.forEach(r),be=i(d),u(fe.$$.fragment,d),ze=i(d),I=h(d,"DIV",{class:!0});var P=L(I);u(ne.$$.fragment,P),Ie=i(P),je=h(P,"P",{"data-svelte-h":!0}),$(je)!=="svelte-1phjouv"&&(je.innerHTML=ot),Ce=i(P),J=h(P,"DIV",{class:!0});var t=L(J);u(le.$$.fragment,t),Be=i(t),Ne=h(t,"P",{"data-svelte-h":!0}),$(Ne)!=="svelte-1dd8fma"&&(Ne.innerHTML=Ge),ee=i(t),u(z.$$.fragment,t),st=i(t),u(re.$$.fragment,t),t.forEach(r),P.forEach(r),We=i(d),u(Re.$$.fragment,d),ge=i(d),Z=h(d,"DIV",{class:!0});var o=L(Z);u(ie.$$.fragment,o),Ve=i(o),q=h(o,"P",{"data-svelte-h":!0}),$(q)!=="svelte-1azp76l"&&(q.textContent=at),Q=i(o),A=h(o,"P",{"data-svelte-h":!0}),$(A)!=="svelte-1jzqql0"&&(A.innerHTML=Qe),N=i(o),V=h(o,"DIV",{class:!0});var w=L(V);u(ke.$$.fragment,w),Me=i(w),Pe=h(w,"P",{"data-svelte-h":!0}),$(Pe)!=="svelte-1e7i6qy"&&(Pe.innerHTML=nt),X=i(w),u(de.$$.fragment,w),Ae=i(w),u(G.$$.fragment,w),w.forEach(r),o.forEach(r),Le=i(d),u(ce.$$.fragment,d),ue=i(d),k=h(d,"DIV",{class:!0});var C=L(k);u(R.$$.fragment,C),He=i(C),Y=h(C,"P",{"data-svelte-h":!0}),$(Y)!=="svelte-17o4rh5"&&(Y.textContent=xe),U=i(C),te=h(C,"P",{"data-svelte-h":!0}),$(te)!=="svelte-zri3pk"&&(te.textContent=it),Je=i(C),ye=h(C,"P",{"data-svelte-h":!0}),$(ye)!=="svelte-1gp6z48"&&(ye.textContent=Oe),O=i(C),we=h(C,"P",{"data-svelte-h":!0}),$(we)!=="svelte-1jzqql0"&&(we.innerHTML=rt),$e=i(C),_e=h(C,"DIV",{class:!0});var x=L(_e);u(De.$$.fragment,x),me=i(x),ve=h(x,"P",{"data-svelte-h":!0}),$(ve)!=="svelte-c8pgkq"&&(ve.innerHTML=lt),E=i(x),u(pe.$$.fragment,x),Se=i(x),u(B.$$.fragment,x),x.forEach(r),C.forEach(r),this.h()},h(){H(W,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(a,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(J,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(Z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(_e,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(k,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(d,v){_(e,d,v),m(d,f,v),m(d,a,v),_(n,a,null),c(a,y),c(a,s),c(a,Xe),c(a,W),_(he,W,null),c(W,Ye),c(W,K),c(W,Ee),_(D,W,null),c(W,tt),_(S,W,null),m(d,be,v),_(fe,d,v),m(d,ze,v),m(d,I,v),_(ne,I,null),c(I,Ie),c(I,je),c(I,Ce),c(I,J),_(le,J,null),c(J,Be),c(J,Ne),c(J,ee),_(z,J,null),c(J,st),_(re,J,null),m(d,We,v),_(Re,d,v),m(d,ge,v),m(d,Z,v),_(ie,Z,null),c(Z,Ve),c(Z,q),c(Z,Q),c(Z,A),c(Z,N),c(Z,V),_(ke,V,null),c(V,Me),c(V,Pe),c(V,X),_(de,V,null),c(V,Ae),_(G,V,null),m(d,Le,v),_(ce,d,v),m(d,ue,v),m(d,k,v),_(R,k,null),c(k,He),c(k,Y),c(k,U),c(k,te),c(k,Je),c(k,ye),c(k,O),c(k,we),c(k,$e),c(k,_e),_(De,_e,null),c(_e,me),c(_e,ve),c(_e,E),_(pe,_e,null),c(_e,Se),_(B,_e,null),qe=!0},p(d,v){const oe={};v&2&&(oe.$$scope={dirty:v,ctx:d}),D.$set(oe);const P={};v&2&&(P.$$scope={dirty:v,ctx:d}),S.$set(P);const t={};v&2&&(t.$$scope={dirty:v,ctx:d}),z.$set(t);const o={};v&2&&(o.$$scope={dirty:v,ctx:d}),re.$set(o);const w={};v&2&&(w.$$scope={dirty:v,ctx:d}),de.$set(w);const C={};v&2&&(C.$$scope={dirty:v,ctx:d}),G.$set(C);const x={};v&2&&(x.$$scope={dirty:v,ctx:d}),pe.$set(x);const se={};v&2&&(se.$$scope={dirty:v,ctx:d}),B.$set(se)},i(d){qe||(T(e.$$.fragment,d),T(n.$$.fragment,d),T(he.$$.fragment,d),T(D.$$.fragment,d),T(S.$$.fragment,d),T(fe.$$.fragment,d),T(ne.$$.fragment,d),T(le.$$.fragment,d),T(z.$$.fragment,d),T(re.$$.fragment,d),T(Re.$$.fragment,d),T(ie.$$.fragment,d),T(ke.$$.fragment,d),T(de.$$.fragment,d),T(G.$$.fragment,d),T(ce.$$.fragment,d),T(R.$$.fragment,d),T(De.$$.fragment,d),T(pe.$$.fragment,d),T(B.$$.fragment,d),qe=!0)},o(d){b(e.$$.fragment,d),b(n.$$.fragment,d),b(he.$$.fragment,d),b(D.$$.fragment,d),b(S.$$.fragment,d),b(fe.$$.fragment,d),b(ne.$$.fragment,d),b(le.$$.fragment,d),b(z.$$.fragment,d),b(re.$$.fragment,d),b(Re.$$.fragment,d),b(ie.$$.fragment,d),b(ke.$$.fragment,d),b(de.$$.fragment,d),b(G.$$.fragment,d),b(ce.$$.fragment,d),b(R.$$.fragment,d),b(De.$$.fragment,d),b(pe.$$.fragment,d),b(B.$$.fragment,d),qe=!1},d(d){d&&(r(f),r(a),r(be),r(ze),r(I),r(We),r(ge),r(Z),r(Le),r(ue),r(k)),M(e,d),M(n),M(he),M(D),M(S),M(fe,d),M(ne),M(le),M(z),M(re),M(Re,d),M(ie),M(ke),M(de),M(G),M(ce,d),M(R),M(De),M(pe),M(B)}}}function Pt(F){let e,f;return e=new gt({props:{$$slots:{default:[Et]},$$scope:{ctx:F}}}),{c(){g(e.$$.fragment)},l(a){u(e.$$.fragment,a)},m(a,n){_(e,a,n),f=!0},p(a,n){const y={};n&2&&(y.$$scope={dirty:n,ctx:a}),e.$set(y)},i(a){f||(T(e.$$.fragment,a),f=!0)},o(a){b(e.$$.fragment,a),f=!1},d(a){M(e,a)}}}function Lt(F){let e,f,a,n,y,s,j,Xe,W,he=`DeiT モデルは、Hugo Touvron、Matthieu Cord、Matthijs Douze、Francisco Massa、Alexandre
Sablayrolles, Hervé Jégou.によって <a href="https://arxiv.org/abs/2012.12877" rel="nofollow">Training data-efficient image Transformers &amp; distillation through attention</a> で提案されました。
サブレイロール、エルヴェ・ジェグー。 <a href="https://arxiv.org/abs/2010.11929" rel="nofollow">Dosovitskiy et al., 2020</a> で紹介された <a href="vit">Vision Transformer (ViT)</a> は、既存の畳み込みニューラルと同等、またはそれを上回るパフォーマンスを発揮できることを示しました。
Transformer エンコーダ (BERT のような) を使用したネットワーク。ただし、その論文で紹介された ViT モデルには、次のトレーニングが必要でした。
外部データを使用して、数週間にわたる高価なインフラストラクチャ。 DeiT (データ効率の高い画像変換器) はさらに優れています
画像分類用に効率的にトレーニングされたトランスフォーマーにより、必要なデータとコンピューティング リソースがはるかに少なくなります。
オリジナルの ViT モデルとの比較。`,Ye,K,pt="論文の要約は次のとおりです。",Ee,D,tt=`<em>最近、純粋に注意に基づくニューラル ネットワークが、画像などの画像理解タスクに対処できることが示されました。
分類。ただし、これらのビジュアル トランスフォーマーは、
インフラストラクチャが高価であるため、その採用が制限されています。この作業では、コンボリューションフリーの競争力のあるゲームを作成します。
Imagenet のみでトレーニングしてトランスフォーマーを作成します。 1 台のコンピューターで 3 日以内にトレーニングを行います。私たちの基準となるビジョン
トランス (86M パラメータ) は、外部なしで ImageNet 上で 83.1% (単一クロップ評価) のトップ 1 の精度を達成します。
データ。さらに重要なのは、トランスフォーマーに特有の教師と生徒の戦略を導入することです。蒸留に依存している
学生が注意を払って教師から学ぶことを保証するトークン。私たちはこのトークンベースに興味を示します
特に convnet を教師として使用する場合。これにより、convnet と競合する結果を報告できるようになります。
Imagenet (最大 85.2% の精度が得られます) と他のタスクに転送するときの両方で。私たちはコードを共有し、
モデル。</em>`,S,be,fe='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。このモデルの TensorFlow バージョンは、<a href="https://huggingface.co/amyeroberts" rel="nofollow">amyeroberts</a> によって追加されました。',ze,I,ne,Ie,je=`<li>ViT と比較して、DeiT モデルはいわゆる蒸留トークンを使用して教師から効果的に学習します (これは、
DeiT 論文は、ResNet のようなモデルです)。蒸留トークンは、バックプロパゲーションを通じて、と対話することによって学習されます。
セルフアテンション層を介したクラス ([CLS]) とパッチ トークン。</li> <li>抽出されたモデルを微調整するには 2 つの方法があります。(1) 上部に予測ヘッドを配置するだけの古典的な方法。
クラス トークンの最終的な非表示状態を抽出し、蒸留シグナルを使用しない、または (2) 両方の
予測ヘッドはクラス トークンの上と蒸留トークンの上にあります。その場合、[CLS] 予測は
head は、head の予測とグラウンド トゥルース ラベル間の通常のクロスエントロピーを使用してトレーニングされます。
蒸留予測ヘッドは、硬蒸留 (予測と予測の間のクロスエントロピー) を使用してトレーニングされます。
蒸留ヘッドと教師が予測したラベル）。推論時に、平均予測を取得します。
最終的な予測として両頭の間で。 (2) は「蒸留による微調整」とも呼ばれます。
下流のデータセットですでに微調整されている教師。モデル的には (1) に相当します。
<a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> と (2) に対応します。
<a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a>。</li> <li>著者らは (2) についてもソフト蒸留を試みたことに注意してください (この場合、蒸留予測ヘッドは
教師のソフトマックス出力に一致するように KL ダイバージェンスを使用してトレーニングしました）が、ハード蒸留が最良の結果をもたらしました。</li> <li>リリースされたすべてのチェックポイントは、ImageNet-1k のみで事前トレーニングおよび微調整されました。外部データは使用されませんでした。これは
JFT-300M データセット/Imagenet-21k などの外部データを使用した元の ViT モデルとは対照的です。
事前トレーニング。</li> <li>DeiT の作者は、より効率的にトレーニングされた ViT モデルもリリースしました。これは、直接プラグインできます。
<code>ViTModel</code> または <code>ViTForImageClassification</code>。データなどのテクニック
はるかに大規模なデータセットでのトレーニングをシミュレートするために、拡張、最適化、正則化が使用されました。
(ただし、事前トレーニングには ImageNet-1k のみを使用します)。 4 つのバリエーション (3 つの異なるサイズ) が利用可能です。
<em>facebook/deit-tiny-patch16-224</em>、<em>facebook/deit-small-patch16-224</em>、<em>facebook/deit-base-patch16-224</em> および
<em>facebook/deit-base-patch16-384</em>。以下を行うには <a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTImageProcessor">DeiTImageProcessor</a> を使用する必要があることに注意してください。
モデル用の画像を準備します。</li>`,ot,Ce,J,le,Be="DeiT を始めるのに役立つ公式 Hugging Face およびコミュニティ (🌎 で示されている) リソースのリスト。",Ne,Ge,ee,z,st='<li><a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</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>',re,We,Re="それに加えて:",ge,Z,ie='<li><a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> は、この <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining" rel="nofollow">サンプル スクリプト</a> でサポートされています。</li>',Ve,q,at="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。",Q,A,Qe,N,V,ke,Me,Pe=`This is the configuration class to store the configuration of a <a href="/docs/transformers/pr_30950/ja/model_doc/deit#transformers.DeiTModel">DeiTModel</a>. It is used to instantiate an DeiT
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 DeiT
<a href="https://huggingface.co/facebook/deit-base-distilled-patch16-224" rel="nofollow">facebook/deit-base-distilled-patch16-224</a>
architecture.`,nt,X,de=`Configuration objects inherit from <a href="/docs/transformers/pr_30950/ja/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_30950/ja/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Ae,G,Le,ce,ue,k,R,He,Y,xe,U,te,it="Preprocess an image or a batch of images.",Je,ye,Oe,O,we,rt,$e,_e="Constructs a DeiT image processor.",De,me,ve,lt,E,pe="Preprocess an image or batch of images.",Se,B,qe,d,v,oe,P;return y=new Ue({props:{title:"DeiT",local:"deit",headingTag:"h1"}}),j=new Ue({props:{title:"Overview",local:"overview",headingTag:"h2"}}),I=new Ue({props:{title:"Usage tips",local:"usage-tips",headingTag:"h2"}}),Ce=new Ue({props:{title:"Resources",local:"resources",headingTag:"h2"}}),Ge=new wt({props:{pipeline:"image-classification"}}),A=new Ue({props:{title:"DeiTConfig",local:"transformers.DeiTConfig",headingTag:"h2"}}),V=new ae({props:{name:"class transformers.DeiTConfig",anchor:"transformers.DeiTConfig",parameters:[{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.0"},{name:"attention_probs_dropout_prob",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"image_size",val:" = 224"},{name:"patch_size",val:" = 16"},{name:"num_channels",val:" = 3"},{name:"qkv_bias",val:" = True"},{name:"encoder_stride",val:" = 16"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DeiTConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.DeiTConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.DeiTConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.DeiTConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.DeiTConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.DeiTConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.DeiTConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.DeiTConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.DeiTConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.DeiTConfig.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 224) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.DeiTConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.DeiTConfig.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The number of input channels.`,name:"num_channels"},{anchor:"transformers.DeiTConfig.qkv_bias",description:`<strong>qkv_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to add a bias to the queries, keys and values.`,name:"qkv_bias"},{anchor:"transformers.DeiTConfig.encoder_stride",description:`<strong>encoder_stride</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Factor to increase the spatial resolution by in the decoder head for masked image modeling.`,name:"encoder_stride"}],source:"https://github.com/huggingface/transformers/blob/vr_30950/src/transformers/models/deit/configuration_deit.py#L30"}}),G=new ct({props:{anchor:"transformers.DeiTConfig.example",$$slots:{default:[vt]},$$scope:{ctx:F}}}),ce=new Ue({props:{title:"DeiTFeatureExtractor",local:"transformers.DeiTFeatureExtractor",headingTag:"h2"}}),R=new ae({props:{name:"class transformers.DeiTFeatureExtractor",anchor:"transformers.DeiTFeatureExtractor",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_30950/src/transformers/models/deit/feature_extraction_deit.py#L26"}}),xe=new ae({props:{name:"__call__",anchor:"transformers.DeiTFeatureExtractor.__call__",parameters:[{name:"images",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_30950/src/transformers/image_processing_utils.py#L39"}}),ye=new Ue({props:{title:"DeiTImageProcessor",local:"transformers.DeiTImageProcessor",headingTag:"h2"}}),we=new ae({props:{name:"class transformers.DeiTImageProcessor",anchor:"transformers.DeiTImageProcessor",parameters:[{name:"do_resize",val:": bool = True"},{name:"size",val:": Dict = None"},{name:"resample",val:": Resampling = 3"},{name:"do_center_crop",val:": bool = True"},{name:"crop_size",val:": Dict = None"},{name:"rescale_factor",val:": Union = 0.00392156862745098"},{name:"do_rescale",val:": bool = True"},{name:"do_normalize",val:": bool = True"},{name:"image_mean",val:": Union = None"},{name:"image_std",val:": Union = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DeiTImageProcessor.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to resize the image&#x2019;s (height, width) dimensions to the specified <code>size</code>. Can be overridden by
<code>do_resize</code> in <code>preprocess</code>.`,name:"do_resize"},{anchor:"transformers.DeiTImageProcessor.size",description:`<strong>size</strong> (<code>Dict[str, int]</code> <em>optional</em>, defaults to <code>{&quot;height&quot; -- 256, &quot;width&quot;: 256}</code>):
Size of the image after <code>resize</code>. Can be overridden by <code>size</code> in <code>preprocess</code>.`,name:"size"},{anchor:"transformers.DeiTImageProcessor.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code> filter, <em>optional</em>, defaults to <code>Resampling.BICUBIC</code>) &#x2014;
Resampling filter to use if resizing the image. Can be overridden by <code>resample</code> in <code>preprocess</code>.`,name:"resample"},{anchor:"transformers.DeiTImageProcessor.do_center_crop",description:`<strong>do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to center crop the image. If the input size is smaller than <code>crop_size</code> along any edge, the image
is padded with 0&#x2019;s and then center cropped. Can be overridden by <code>do_center_crop</code> in <code>preprocess</code>.`,name:"do_center_crop"},{anchor:"transformers.DeiTImageProcessor.crop_size",description:`<strong>crop_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>{&quot;height&quot; -- 224, &quot;width&quot;: 224}</code>):
Desired output size when applying center-cropping. Can be overridden by <code>crop_size</code> in <code>preprocess</code>.`,name:"crop_size"},{anchor:"transformers.DeiTImageProcessor.rescale_factor",description:`<strong>rescale_factor</strong> (<code>int</code> or <code>float</code>, <em>optional</em>, defaults to <code>1/255</code>) &#x2014;
Scale factor to use if rescaling the image. Can be overridden by the <code>rescale_factor</code> parameter in the
<code>preprocess</code> method.`,name:"rescale_factor"},{anchor:"transformers.DeiTImageProcessor.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overridden by the <code>do_rescale</code>
parameter in the <code>preprocess</code> method.`,name:"do_rescale"},{anchor:"transformers.DeiTImageProcessor.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to normalize the image. Can be overridden by the <code>do_normalize</code> parameter in the <code>preprocess</code>
method.`,name:"do_normalize"},{anchor:"transformers.DeiTImageProcessor.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>IMAGENET_STANDARD_MEAN</code>) &#x2014;
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the <code>image_mean</code> parameter in the <code>preprocess</code> method.`,name:"image_mean"},{anchor:"transformers.DeiTImageProcessor.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>IMAGENET_STANDARD_STD</code>) &#x2014;
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the <code>image_std</code> parameter in the <code>preprocess</code> method.`,name:"image_std"}],source:"https://github.com/huggingface/transformers/blob/vr_30950/src/transformers/models/deit/image_processing_deit.py#L46"}}),ve=new ae({props:{name:"preprocess",anchor:"transformers.DeiTImageProcessor.preprocess",parameters:[{name:"images",val:": Union"},{name:"do_resize",val:": bool = None"},{name:"size",val:": Dict = None"},{name:"resample",val:" = None"},{name:"do_center_crop",val:": bool = None"},{name:"crop_size",val:": Dict = None"},{name:"do_rescale",val:": bool = None"},{name:"rescale_factor",val:": float = None"},{name:"do_normalize",val:": bool = None"},{name:"image_mean",val:": Union = None"},{name:"image_std",val:": Union = None"},{name:"return_tensors",val:": Union = None"},{name:"data_format",val:": ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>"},{name:"input_data_format",val:": Union = None"}],parametersDescription:[{anchor:"transformers.DeiTImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</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.DeiTImageProcessor.preprocess.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_resize</code>) &#x2014;
Whether to resize the image.`,name:"do_resize"},{anchor:"transformers.DeiTImageProcessor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.size</code>) &#x2014;
Size of the image after <code>resize</code>.`,name:"size"},{anchor:"transformers.DeiTImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>self.resample</code>) &#x2014;
PILImageResampling filter to use if resizing the image Only has an effect if <code>do_resize</code> is set to
<code>True</code>.`,name:"resample"},{anchor:"transformers.DeiTImageProcessor.preprocess.do_center_crop",description:`<strong>do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_center_crop</code>) &#x2014;
Whether to center crop the image.`,name:"do_center_crop"},{anchor:"transformers.DeiTImageProcessor.preprocess.crop_size",description:`<strong>crop_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.crop_size</code>) &#x2014;
Size of the image after center crop. If one edge the image is smaller than <code>crop_size</code>, it will be
padded with zeros and then cropped`,name:"crop_size"},{anchor:"transformers.DeiTImageProcessor.preprocess.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_rescale</code>) &#x2014;
Whether to rescale the image values between [0 - 1].`,name:"do_rescale"},{anchor:"transformers.DeiTImageProcessor.preprocess.rescale_factor",description:`<strong>rescale_factor</strong> (<code>float</code>, <em>optional</em>, defaults to <code>self.rescale_factor</code>) &#x2014;
Rescale factor to rescale the image by if <code>do_rescale</code> is set to <code>True</code>.`,name:"rescale_factor"},{anchor:"transformers.DeiTImageProcessor.preprocess.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_normalize</code>) &#x2014;
Whether to normalize the image.`,name:"do_normalize"},{anchor:"transformers.DeiTImageProcessor.preprocess.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_mean</code>) &#x2014;
Image mean.`,name:"image_mean"},{anchor:"transformers.DeiTImageProcessor.preprocess.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_std</code>) &#x2014;
Image standard deviation.`,name:"image_std"},{anchor:"transformers.DeiTImageProcessor.preprocess.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <code>TensorType</code>, <em>optional</em>) &#x2014;
The type of tensors to return. Can be one of:<ul>
<li><code>None</code>: Return a list of <code>np.ndarray</code>.</li>
<li><code>TensorType.TENSORFLOW</code> or <code>&apos;tf&apos;</code>: Return a batch of type <code>tf.Tensor</code>.</li>
<li><code>TensorType.PYTORCH</code> or <code>&apos;pt&apos;</code>: Return a batch of type <code>torch.Tensor</code>.</li>
<li><code>TensorType.NUMPY</code> or <code>&apos;np&apos;</code>: Return a batch of type <code>np.ndarray</code>.</li>
<li><code>TensorType.JAX</code> or <code>&apos;jax&apos;</code>: Return a batch of type <code>jax.numpy.ndarray</code>.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.DeiTImageProcessor.preprocess.data_format",description:`<strong>data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>, defaults to <code>ChannelDimension.FIRST</code>) &#x2014;
The channel dimension format for the output image. Can be one of:<ul>
<li><code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
</ul>`,name:"data_format"},{anchor:"transformers.DeiTImageProcessor.preprocess.input_data_format",description:`<strong>input_data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>) &#x2014;
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:<ul>
<li><code>&quot;channels_first&quot;</code> or <code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<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>
</ul>`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/vr_30950/src/transformers/models/deit/image_processing_deit.py#L162"}}),B=new yt({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Pt],pytorch:[Ut]},$$scope:{ctx:F}}}),d=new $t({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/ja/model_doc/deit.md"}}),{c(){e=p("meta"),f=l(),a=p("p"),n=l(),g(y.$$.fragment),s=l(),g(j.$$.fragment),Xe=l(),W=p("p"),W.innerHTML=he,Ye=l(),K=p("p"),K.textContent=pt,Ee=l(),D=p("p"),D.innerHTML=tt,S=l(),be=p("p"),be.innerHTML=fe,ze=l(),g(I.$$.fragment),ne=l(),Ie=p("ul"),Ie.innerHTML=je,ot=l(),g(Ce.$$.fragment),J=l(),le=p("p"),le.textContent=Be,Ne=l(),g(Ge.$$.fragment),ee=l(),z=p("ul"),z.innerHTML=st,re=l(),We=p("p"),We.textContent=Re,ge=l(),Z=p("ul"),Z.innerHTML=ie,Ve=l(),q=p("p"),q.textContent=at,Q=l(),g(A.$$.fragment),Qe=l(),N=p("div"),g(V.$$.fragment),ke=l(),Me=p("p"),Me.innerHTML=Pe,nt=l(),X=p("p"),X.innerHTML=de,Ae=l(),g(G.$$.fragment),Le=l(),g(ce.$$.fragment),ue=l(),k=p("div"),g(R.$$.fragment),He=l(),Y=p("div"),g(xe.$$.fragment),U=l(),te=p("p"),te.textContent=it,Je=l(),g(ye.$$.fragment),Oe=l(),O=p("div"),g(we.$$.fragment),rt=l(),$e=p("p"),$e.textContent=_e,De=l(),me=p("div"),g(ve.$$.fragment),lt=l(),E=p("p"),E.textContent=pe,Se=l(),g(B.$$.fragment),qe=l(),g(d.$$.fragment),v=l(),oe=p("p"),this.h()},l(t){const o=Mt("svelte-u9bgzb",document.head);e=h(o,"META",{name:!0,content:!0}),o.forEach(r),f=i(t),a=h(t,"P",{}),L(a).forEach(r),n=i(t),u(y.$$.fragment,t),s=i(t),u(j.$$.fragment,t),Xe=i(t),W=h(t,"P",{"data-svelte-h":!0}),$(W)!=="svelte-mafcr4"&&(W.innerHTML=he),Ye=i(t),K=h(t,"P",{"data-svelte-h":!0}),$(K)!=="svelte-1cv3nri"&&(K.textContent=pt),Ee=i(t),D=h(t,"P",{"data-svelte-h":!0}),$(D)!=="svelte-19kjohr"&&(D.innerHTML=tt),S=i(t),be=h(t,"P",{"data-svelte-h":!0}),$(be)!=="svelte-nvgx1x"&&(be.innerHTML=fe),ze=i(t),u(I.$$.fragment,t),ne=i(t),Ie=h(t,"UL",{"data-svelte-h":!0}),$(Ie)!=="svelte-1k7ghyf"&&(Ie.innerHTML=je),ot=i(t),u(Ce.$$.fragment,t),J=i(t),le=h(t,"P",{"data-svelte-h":!0}),$(le)!=="svelte-5xp01e"&&(le.textContent=Be),Ne=i(t),u(Ge.$$.fragment,t),ee=i(t),z=h(t,"UL",{"data-svelte-h":!0}),$(z)!=="svelte-xaku06"&&(z.innerHTML=st),re=i(t),We=h(t,"P",{"data-svelte-h":!0}),$(We)!=="svelte-1ix811i"&&(We.textContent=Re),ge=i(t),Z=h(t,"UL",{"data-svelte-h":!0}),$(Z)!=="svelte-1gkmub7"&&(Z.innerHTML=ie),Ve=i(t),q=h(t,"P",{"data-svelte-h":!0}),$(q)!=="svelte-17ytafw"&&(q.textContent=at),Q=i(t),u(A.$$.fragment,t),Qe=i(t),N=h(t,"DIV",{class:!0});var w=L(N);u(V.$$.fragment,w),ke=i(w),Me=h(w,"P",{"data-svelte-h":!0}),$(Me)!=="svelte-w2kb1k"&&(Me.innerHTML=Pe),nt=i(w),X=h(w,"P",{"data-svelte-h":!0}),$(X)!=="svelte-1b2n3nn"&&(X.innerHTML=de),Ae=i(w),u(G.$$.fragment,w),w.forEach(r),Le=i(t),u(ce.$$.fragment,t),ue=i(t),k=h(t,"DIV",{class:!0});var C=L(k);u(R.$$.fragment,C),He=i(C),Y=h(C,"DIV",{class:!0});var x=L(Y);u(xe.$$.fragment,x),U=i(x),te=h(x,"P",{"data-svelte-h":!0}),$(te)!=="svelte-khengj"&&(te.textContent=it),x.forEach(r),C.forEach(r),Je=i(t),u(ye.$$.fragment,t),Oe=i(t),O=h(t,"DIV",{class:!0});var se=L(O);u(we.$$.fragment,se),rt=i(se),$e=h(se,"P",{"data-svelte-h":!0}),$($e)!=="svelte-19jpkyi"&&($e.textContent=_e),De=i(se),me=h(se,"DIV",{class:!0});var Fe=L(me);u(ve.$$.fragment,Fe),lt=i(Fe),E=h(Fe,"P",{"data-svelte-h":!0}),$(E)!=="svelte-1x3yxsa"&&(E.textContent=pe),Fe.forEach(r),se.forEach(r),Se=i(t),u(B.$$.fragment,t),qe=i(t),u(d.$$.fragment,t),v=i(t),oe=h(t,"P",{}),L(oe).forEach(r),this.h()},h(){H(e,"name","hf:doc:metadata"),H(e,"content",Ht),H(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(Y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(k,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(me,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),H(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(t,o){c(document.head,e),m(t,f,o),m(t,a,o),m(t,n,o),_(y,t,o),m(t,s,o),_(j,t,o),m(t,Xe,o),m(t,W,o),m(t,Ye,o),m(t,K,o),m(t,Ee,o),m(t,D,o),m(t,S,o),m(t,be,o),m(t,ze,o),_(I,t,o),m(t,ne,o),m(t,Ie,o),m(t,ot,o),_(Ce,t,o),m(t,J,o),m(t,le,o),m(t,Ne,o),_(Ge,t,o),m(t,ee,o),m(t,z,o),m(t,re,o),m(t,We,o),m(t,ge,o),m(t,Z,o),m(t,Ve,o),m(t,q,o),m(t,Q,o),_(A,t,o),m(t,Qe,o),m(t,N,o),_(V,N,null),c(N,ke),c(N,Me),c(N,nt),c(N,X),c(N,Ae),_(G,N,null),m(t,Le,o),_(ce,t,o),m(t,ue,o),m(t,k,o),_(R,k,null),c(k,He),c(k,Y),_(xe,Y,null),c(Y,U),c(Y,te),m(t,Je,o),_(ye,t,o),m(t,Oe,o),m(t,O,o),_(we,O,null),c(O,rt),c(O,$e),c(O,De),c(O,me),_(ve,me,null),c(me,lt),c(me,E),m(t,Se,o),_(B,t,o),m(t,qe,o),_(d,t,o),m(t,v,o),m(t,oe,o),P=!0},p(t,[o]){const w={};o&2&&(w.$$scope={dirty:o,ctx:t}),G.$set(w);const C={};o&2&&(C.$$scope={dirty:o,ctx:t}),B.$set(C)},i(t){P||(T(y.$$.fragment,t),T(j.$$.fragment,t),T(I.$$.fragment,t),T(Ce.$$.fragment,t),T(Ge.$$.fragment,t),T(A.$$.fragment,t),T(V.$$.fragment,t),T(G.$$.fragment,t),T(ce.$$.fragment,t),T(R.$$.fragment,t),T(xe.$$.fragment,t),T(ye.$$.fragment,t),T(we.$$.fragment,t),T(ve.$$.fragment,t),T(B.$$.fragment,t),T(d.$$.fragment,t),P=!0)},o(t){b(y.$$.fragment,t),b(j.$$.fragment,t),b(I.$$.fragment,t),b(Ce.$$.fragment,t),b(Ge.$$.fragment,t),b(A.$$.fragment,t),b(V.$$.fragment,t),b(G.$$.fragment,t),b(ce.$$.fragment,t),b(R.$$.fragment,t),b(xe.$$.fragment,t),b(ye.$$.fragment,t),b(we.$$.fragment,t),b(ve.$$.fragment,t),b(B.$$.fragment,t),b(d.$$.fragment,t),P=!1},d(t){t&&(r(f),r(a),r(n),r(s),r(Xe),r(W),r(Ye),r(K),r(Ee),r(D),r(S),r(be),r(ze),r(ne),r(Ie),r(ot),r(J),r(le),r(Ne),r(ee),r(z),r(re),r(We),r(ge),r(Z),r(Ve),r(q),r(Q),r(Qe),r(N),r(Le),r(ue),r(k),r(Je),r(Oe),r(O),r(Se),r(qe),r(v),r(oe)),r(e),M(y,t),M(j,t),M(I,t),M(Ce,t),M(Ge,t),M(A,t),M(V),M(G),M(ce,t),M(R),M(xe),M(ye,t),M(we),M(ve),M(B,t),M(d,t)}}}const Ht='{"title":"DeiT","local":"deit","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"Usage tips","local":"usage-tips","sections":[],"depth":2},{"title":"Resources","local":"resources","sections":[],"depth":2},{"title":"DeiTConfig","local":"transformers.DeiTConfig","sections":[],"depth":2},{"title":"DeiTFeatureExtractor","local":"transformers.DeiTFeatureExtractor","sections":[],"depth":2},{"title":"DeiTImageProcessor","local":"transformers.DeiTImageProcessor","sections":[],"depth":2},{"title":"DeiTModel","local":"transformers.DeiTModel","sections":[],"depth":2},{"title":"DeiTForMaskedImageModeling","local":"transformers.DeiTForMaskedImageModeling","sections":[],"depth":2},{"title":"DeiTForImageClassification","local":"transformers.DeiTForImageClassification","sections":[],"depth":2},{"title":"DeiTForImageClassificationWithTeacher","local":"transformers.DeiTForImageClassificationWithTeacher","sections":[],"depth":2},{"title":"TFDeiTModel","local":"transformers.TFDeiTModel","sections":[],"depth":2},{"title":"TFDeiTForMaskedImageModeling","local":"transformers.TFDeiTForMaskedImageModeling","sections":[],"depth":2},{"title":"TFDeiTForImageClassification","local":"transformers.TFDeiTForImageClassification","sections":[],"depth":2},{"title":"TFDeiTForImageClassificationWithTeacher","local":"transformers.TFDeiTForImageClassificationWithTeacher","sections":[],"depth":2}],"depth":1}';function St(F){return _t(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ao extends Tt{constructor(e){super(),bt(this,e,St,Lt,ut,{})}}export{ao as component};
