import{s as wo,f as yo,o as $o,n as Le}from"../chunks/scheduler.9bc65507.js";import{S as Co,i as No,g as n,s as d,u as _,A as Vo,h as s,f as t,c,j as u,v,y as f,k as m,o as l,a as r,w as M,d as b,t as T,x as w}from"../chunks/index.78c82d43.js";import{T as To}from"../chunks/Tip.87d55b76.js";import{D as Ae}from"../chunks/Docstring.f3d04bd2.js";import{C as Pt}from"../chunks/CodeBlock.48618ec2.js";import{I as We}from"../chunks/IconCopyLink.bedaa44d.js";import{E as Bt}from"../chunks/ExampleCodeBlock.872b014d.js";import{P as xo}from"../chunks/PipelineTag.75b0f707.js";function So(V){let i,y="Example:",p,h,g;return h=new Pt({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> ViTMSNModel, ViTMSNConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a ViT MSN vit-msn-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = ViTConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the vit-msn-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViTMSNModel(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`}}),{c(){i=n("p"),i.textContent=y,p=d(),_(h.$$.fragment)},l(a){i=s(a,"P",{"data-svelte-h":!0}),f(i)!=="svelte-11lpom8"&&(i.textContent=y),p=c(a),v(h.$$.fragment,a)},m(a,$){r(a,i,$),r(a,p,$),M(h,a,$),g=!0},p:Le,i(a){g||(b(h.$$.fragment,a),g=!0)},o(a){T(h.$$.fragment,a),g=!1},d(a){a&&(t(i),t(p)),w(h,a)}}}function ko(V){let i,y=`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(){i=n("p"),i.innerHTML=y},l(p){i=s(p,"P",{"data-svelte-h":!0}),f(i)!=="svelte-fincs2"&&(i.innerHTML=y)},m(p,h){r(p,i,h)},p:Le,d(p){p&&t(i)}}}function jo(V){let i,y="Examples:",p,h,g;return h=new Pt({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, ViTMSNModel
<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/vit-msn-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViTMSNModel.from_pretrained(<span class="hljs-string">&quot;facebook/vit-msn-small&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><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`}}),{c(){i=n("p"),i.textContent=y,p=d(),_(h.$$.fragment)},l(a){i=s(a,"P",{"data-svelte-h":!0}),f(i)!=="svelte-kvfsh7"&&(i.textContent=y),p=c(a),v(h.$$.fragment,a)},m(a,$){r(a,i,$),r(a,p,$),M(h,a,$),g=!0},p:Le,i(a){g||(b(h.$$.fragment,a),g=!0)},o(a){T(h.$$.fragment,a),g=!1},d(a){a&&(t(i),t(p)),w(h,a)}}}function Jo(V){let i,y=`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(){i=n("p"),i.innerHTML=y},l(p){i=s(p,"P",{"data-svelte-h":!0}),f(i)!=="svelte-fincs2"&&(i.innerHTML=y)},m(p,h){r(p,i,h)},p:Le,d(p){p&&t(i)}}}function Io(V){let i,y="Examples:",p,h,g;return h=new Pt({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, ViTMSNForImageClassification
<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">2</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>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/vit-msn-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViTMSNForImageClassification.from_pretrained(<span class="hljs-string">&quot;facebook/vit-msn-small&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><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])
Kerry blue terrier`}}),{c(){i=n("p"),i.textContent=y,p=d(),_(h.$$.fragment)},l(a){i=s(a,"P",{"data-svelte-h":!0}),f(i)!=="svelte-kvfsh7"&&(i.textContent=y),p=c(a),v(h.$$.fragment,a)},m(a,$){r(a,i,$),r(a,p,$),M(h,a,$),g=!0},p:Le,i(a){g||(b(h.$$.fragment,a),g=!0)},o(a){T(h.$$.fragment,a),g=!1},d(a){a&&(t(i),t(p)),w(h,a)}}}function Fo(V){let i,y,p,h,g,a,$,O,Mt,Te,Ht="ViTMSN",qe,j,Z,Ze,D,bt,we,Et="Overview",Ye,K,Gt=`The ViTMSN model was proposed in <a href="https://arxiv.org/abs/2204.07141" rel="nofollow">Masked Siamese Networks for Label-Efficient Learning</a> by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes,
Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas. The paper presents a joint-embedding architecture to match the prototypes
of masked patches with that of the unmasked patches. With this setup, their method yields excellent performance in the low-shot and extreme low-shot
regimes.`,Xe,ee,Rt="The abstract from the paper is the following:",Qe,te,At=`<em>We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our
approach matches the representation of an image view containing randomly masked patches to the representation of the original
unmasked image. This self-supervised pre-training strategy is particularly scalable when applied to Vision Transformers since only the
unmasked patches are processed by the network. As a result, MSNs improve the scalability of joint-embedding architectures,
while producing representations of a high semantic level that perform competitively on low-shot image classification. For instance,
on ImageNet-1K, with only 5,000 annotated images, our base MSN model achieves 72.4% top-1 accuracy,
and with 1% of ImageNet-1K labels, we achieve 75.7% top-1 accuracy, setting a new state-of-the-art for self-supervised learning on this benchmark.</em>`,Oe,oe,Lt="Tips:",De,ne,qt=`<li>MSN (masked siamese networks) is a method for self-supervised pre-training of Vision Transformers (ViTs). The pre-training
objective is to match the prototypes assigned to the unmasked views of the images to that of the masked views of the same images.</li> <li>The authors have only released pre-trained weights of the backbone (ImageNet-1k pre-training). So, to use that on your own image classification dataset,
use the <a href="/docs/transformers/v4.33.3/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification">ViTMSNForImageClassification</a> class which is initialized from <a href="/docs/transformers/v4.33.3/en/model_doc/vit_msn#transformers.ViTMSNModel">ViTMSNModel</a>. Follow
<a href="https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb" rel="nofollow">this notebook</a> for a detailed tutorial on fine-tuning.</li> <li>MSN is particularly useful in the low-shot and extreme low-shot regimes. Notably, it achieves 75.7% top-1 accuracy with only 1% of ImageNet-1K
labels when fine-tuned.</li>`,Ke,U,Yt,et,se,Xt='MSN architecture. Taken from the <a href="https://arxiv.org/abs/2204.07141">original paper.</a>',tt,ae,Qt='This model was contributed by <a href="https://huggingface.co/sayakpaul" rel="nofollow">sayakpaul</a>. The original code can be found <a href="https://github.com/facebookresearch/msn" rel="nofollow">here</a>.',ot,J,z,Ue,re,Tt,ye,Ot="Resources",nt,ie,Dt="A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViT MSN.",st,le,at,de,Kt='<li><a href="/docs/transformers/v4.33.3/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification">ViTMSNForImageClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb" rel="nofollow">notebook</a>.</li> <li>See also: <a href="../tasks/image_classification">Image classification task guide</a></li>',rt,ce,eo="If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.",it,I,B,ze,me,wt,$e,to="ViTMSNConfig",lt,C,pe,yt,Ce,oo=`This is the configuration class to store the configuration of a <a href="/docs/transformers/v4.33.3/en/model_doc/vit_msn#transformers.ViTMSNModel">ViTMSNModel</a>. It is used to instantiate an ViT
MSN 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 ViT
<a href="https://huggingface.co/facebook/vit_msn_base" rel="nofollow">facebook/vit_msn_base</a> architecture.`,$t,Ne,no=`Configuration objects inherit from <a href="/docs/transformers/v4.33.3/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/v4.33.3/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Ct,P,dt,F,H,Be,he,Nt,Ve,so="ViTMSNModel",ct,k,fe,Vt,xe,ao=`The bare ViTMSN Model 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.`,xt,x,ue,St,Se,ro='The <a href="/docs/transformers/v4.33.3/en/model_doc/vit_msn#transformers.ViTMSNModel">ViTMSNModel</a> forward method, overrides the <code>__call__</code> special method.',kt,E,jt,G,mt,W,R,Pe,ge,Jt,ke,io="ViTMSNForImageClassification",pt,N,_e,It,je,lo="ViTMSN Model with an image classification head on top e.g. for ImageNet.",Ft,Je,co=`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.`,Wt,S,ve,Zt,Ie,mo='The <a href="/docs/transformers/v4.33.3/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification">ViTMSNForImageClassification</a> forward method, overrides the <code>__call__</code> special method.',Ut,A,zt,L,ht,He,ft;return O=new We({}),D=new We({}),re=new We({}),le=new xo({props:{pipeline:"image-classification"}}),me=new We({}),pe=new Ae({props:{name:"class transformers.ViTMSNConfig",anchor:"transformers.ViTMSNConfig",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-06"},{name:"image_size",val:" = 224"},{name:"patch_size",val:" = 16"},{name:"num_channels",val:" = 3"},{name:"qkv_bias",val:" = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ViTMSNConfig.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.ViTMSNConfig.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.ViTMSNConfig.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.ViTMSNConfig.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.ViTMSNConfig.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.ViTMSNConfig.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.ViTMSNConfig.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.ViTMSNConfig.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.ViTMSNConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-06) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.ViTMSNConfig.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.ViTMSNConfig.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.ViTMSNConfig.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.ViTMSNConfig.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"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/vit_msn/configuration_vit_msn.py#L30"}}),P=new Bt({props:{anchor:"transformers.ViTMSNConfig.example",$$slots:{default:[So]},$$scope:{ctx:V}}}),he=new We({}),fe=new Ae({props:{name:"class transformers.ViTMSNModel",anchor:"transformers.ViTMSNModel",parameters:[{name:"config",val:": ViTMSNConfig"},{name:"use_mask_token",val:": bool = False"}],parametersDescription:[{anchor:"transformers.ViTMSNModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.33.3/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</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
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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.33.3/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.33.3/en/model_doc/mobilevit#transformers.MobileViTFeatureExtractor.__call__">ViTImageProcessor.<strong>call</strong>()</a>
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Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
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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/v4.33.3/src/transformers/models/vit_msn/modeling_vit_msn.py#L516",returnDescription:`
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  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</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/v4.33.3/en/model_doc/vit_msn#transformers.ViTMSNConfig"
>ViTMSNConfig</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>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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.33.3/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.33.3/en/model_doc/mobilevit#transformers.MobileViTFeatureExtractor.__call__">ViTImageProcessor.<strong>call</strong>()</a>
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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>
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  href="/docs/transformers/v4.33.3/en/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/v4.33.3/en/model_doc/vit_msn#transformers.ViTMSNConfig"
>ViTMSNConfig</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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutput"
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