import{S as rc,i as ac,s as sc,e as r,k as d,w as v,t as n,M as nc,c as a,d as o,m,a as s,x as y,h as i,b as l,G as e,g as h,y as M,q as w,o as N,B as T,v as ic,L as pa}from"../../chunks/vendor-hf-doc-builder.js";import{T as yi}from"../../chunks/Tip-hf-doc-builder.js";import{D as P}from"../../chunks/Docstring-hf-doc-builder.js";import{C as ha}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as ne}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as ma}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";import{P as lc}from"../../chunks/PipelineTag-hf-doc-builder.js";function cc($){let f,V,_,g,b;return g=new ha({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> MobileNetV2Config, MobileNetV2Model

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a &quot;mobilenet_v2_1.0_224&quot; style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = MobileNetV2Config()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the &quot;mobilenet_v2_1.0_224&quot; style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileNetV2Model(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(){f=r("p"),V=n("Example:"),_=d(),v(g.$$.fragment)},l(c){f=a(c,"P",{});var u=s(f);V=i(u,"Example:"),u.forEach(o),_=m(c),y(g.$$.fragment,c)},m(c,u){h(c,f,u),e(f,V),h(c,_,u),M(g,c,u),b=!0},p:pa,i(c){b||(w(g.$$.fragment,c),b=!0)},o(c){N(g.$$.fragment,c),b=!1},d(c){c&&o(f),c&&o(_),T(g,c)}}}function dc($){let f,V,_,g,b;return{c(){f=r("p"),V=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r("code"),g=n("Module"),b=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(c){f=a(c,"P",{});var u=s(f);V=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(u,"CODE",{});var E=s(_);g=i(E,"Module"),E.forEach(o),b=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(o)},m(c,u){h(c,f,u),e(f,V),e(f,_),e(_,g),e(f,b)},d(c){c&&o(f)}}}function mc($){let f,V,_,g,b;return g=new ha({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, MobileNetV2Model
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

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

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;google/mobilenet_v2_1.0_224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileNetV2Model.from_pretrained(<span class="hljs-string">&quot;google/mobilenet_v2_1.0_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">1280</span>, <span class="hljs-number">7</span>, <span class="hljs-number">7</span>]`}}),{c(){f=r("p"),V=n("Example:"),_=d(),v(g.$$.fragment)},l(c){f=a(c,"P",{});var u=s(f);V=i(u,"Example:"),u.forEach(o),_=m(c),y(g.$$.fragment,c)},m(c,u){h(c,f,u),e(f,V),h(c,_,u),M(g,c,u),b=!0},p:pa,i(c){b||(w(g.$$.fragment,c),b=!0)},o(c){N(g.$$.fragment,c),b=!1},d(c){c&&o(f),c&&o(_),T(g,c)}}}function pc($){let f,V,_,g,b;return{c(){f=r("p"),V=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r("code"),g=n("Module"),b=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(c){f=a(c,"P",{});var u=s(f);V=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(u,"CODE",{});var E=s(_);g=i(E,"Module"),E.forEach(o),b=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(o)},m(c,u){h(c,f,u),e(f,V),e(f,_),e(_,g),e(f,b)},d(c){c&&o(f)}}}function hc($){let f,V,_,g,b;return g=new ha({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, MobileNetV2ForImageClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

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

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;google/mobilenet_v2_1.0_224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileNetV2ForImageClassification.from_pretrained(<span class="hljs-string">&quot;google/mobilenet_v2_1.0_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`}}),{c(){f=r("p"),V=n("Example:"),_=d(),v(g.$$.fragment)},l(c){f=a(c,"P",{});var u=s(f);V=i(u,"Example:"),u.forEach(o),_=m(c),y(g.$$.fragment,c)},m(c,u){h(c,f,u),e(f,V),h(c,_,u),M(g,c,u),b=!0},p:pa,i(c){b||(w(g.$$.fragment,c),b=!0)},o(c){N(g.$$.fragment,c),b=!1},d(c){c&&o(f),c&&o(_),T(g,c)}}}function fc($){let f,V,_,g,b;return{c(){f=r("p"),V=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r("code"),g=n("Module"),b=n(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(c){f=a(c,"P",{});var u=s(f);V=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(u,"CODE",{});var E=s(_);g=i(E,"Module"),E.forEach(o),b=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(o)},m(c,u){h(c,f,u),e(f,V),e(f,_),e(_,g),e(f,b)},d(c){c&&o(f)}}}function gc($){let f,V,_,g,b;return g=new ha({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9JbWFnZVByb2Nlc3NvciUyQyUyME1vYmlsZU5ldFYyRm9yU2VtYW50aWNTZWdtZW50YXRpb24lMEFmcm9tJTIwUElMJTIwaW1wb3J0JTIwSW1hZ2UlMEFpbXBvcnQlMjByZXF1ZXN0cyUwQSUwQXVybCUyMCUzRCUyMCUyMmh0dHAlM0ElMkYlMkZpbWFnZXMuY29jb2RhdGFzZXQub3JnJTJGdmFsMjAxNyUyRjAwMDAwMDAzOTc2OS5qcGclMjIlMEFpbWFnZSUyMCUzRCUyMEltYWdlLm9wZW4ocmVxdWVzdHMuZ2V0KHVybCUyQyUyMHN0cmVhbSUzRFRydWUpLnJhdyklMEElMEFpbWFnZV9wcm9jZXNzb3IlMjAlM0QlMjBBdXRvSW1hZ2VQcm9jZXNzb3IuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZSUyRmRlZXBsYWJ2M19tb2JpbGVuZXRfdjJfMS4wXzUxMyUyMiklMEFtb2RlbCUyMCUzRCUyME1vYmlsZU5ldFYyRm9yU2VtYW50aWNTZWdtZW50YXRpb24uZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZSUyRmRlZXBsYWJ2M19tb2JpbGVuZXRfdjJfMS4wXzUxMyUyMiklMEElMEFpbnB1dHMlMjAlM0QlMjBpbWFnZV9wcm9jZXNzb3IoaW1hZ2VzJTNEaW1hZ2UlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKSUwQSUwQXdpdGglMjB0b3JjaC5ub19ncmFkKCklM0ElMEElMjAlMjAlMjAlMjBvdXRwdXRzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMpJTBBJTBBJTIzJTIwbG9naXRzJTIwYXJlJTIwb2YlMjBzaGFwZSUyMChiYXRjaF9zaXplJTJDJTIwbnVtX2xhYmVscyUyQyUyMGhlaWdodCUyQyUyMHdpZHRoKSUwQWxvZ2l0cyUyMCUzRCUyMG91dHB1dHMubG9naXRz",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, MobileNetV2ForSemanticSegmentation
<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;google/deeplabv3_mobilenet_v2_1.0_513&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileNetV2ForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;google/deeplabv3_mobilenet_v2_1.0_513&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><span class="hljs-comment"># logits are of shape (batch_size, num_labels, height, width)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){f=r("p"),V=n("Examples:"),_=d(),v(g.$$.fragment)},l(c){f=a(c,"P",{});var u=s(f);V=i(u,"Examples:"),u.forEach(o),_=m(c),y(g.$$.fragment,c)},m(c,u){h(c,f,u),e(f,V),h(c,_,u),M(g,c,u),b=!0},p:pa,i(c){b||(w(g.$$.fragment,c),b=!0)},o(c){N(g.$$.fragment,c),b=!1},d(c){c&&o(f),c&&o(_),T(g,c)}}}function uc($){let f,V,_,g,b,c,u,E,fa,pr,D,ie,ho,Re,ga,fo,ua,hr,le,_a,We,ba,va,fr,Ft,ya,gr,St,go,Ma,ur,Pt,uo,wa,_r,jt,Na,br,I,_o,x,Ta,ce,Va,bo,$a,Ia,vo,Ea,xa,yo,za,Ca,Mo,Fa,Sa,wo,Pa,ja,Ua,No,To,ka,Ra,Vo,Je,Wa,Ut,Ja,Aa,Za,$o,Ae,La,Ze,Ba,Da,Ga,Io,G,Oa,Le,Xa,Ya,Be,qa,Qa,Ha,Eo,O,Ka,kt,es,ts,xo,os,rs,vr,Rt,as,yr,j,zo,De,ss,Wt,ns,is,ls,Co,Fo,cs,ds,So,Ge,ms,Po,ps,hs,fs,jo,Oe,gs,Jt,us,_s,Mr,U,bs,Xe,vs,ys,Ye,Ms,ws,qe,Ns,Ts,wr,X,de,Uo,Qe,Vs,ko,$s,Nr,At,Is,Tr,He,Vr,me,L,Zt,Es,xs,Ke,zs,Cs,et,Fs,Ss,Ps,Lt,js,Bt,Us,$r,Dt,Ro,ks,Ir,Gt,Wo,Ot,Rs,Er,Xt,Ws,xr,Y,pe,Jo,tt,Js,Ao,As,zr,z,ot,Zs,q,Ls,Yt,Bs,Ds,rt,Gs,Os,Xs,Q,Ys,qt,qs,Qs,Qt,Hs,Ks,en,he,Cr,H,fe,Zo,at,tn,Lo,on,Fr,A,st,rn,ge,nt,an,Bo,sn,nn,ue,it,ln,lt,cn,Ht,dn,mn,Sr,K,_e,Do,ct,pn,Go,hn,Pr,C,dt,fn,Oo,gn,un,be,mt,_n,Xo,bn,vn,ve,pt,yn,ht,Mn,Kt,wn,Nn,jr,ee,ye,Yo,ft,Tn,qo,Vn,Ur,Z,gt,$n,ut,In,_t,En,xn,zn,k,bt,Cn,te,Fn,eo,Sn,Pn,Qo,jn,Un,kn,Me,Rn,we,kr,oe,Ne,Ho,vt,Wn,Ko,Jn,Rr,F,yt,An,er,Zn,Ln,Mt,Bn,wt,Dn,Gn,On,R,Nt,Xn,re,Yn,to,qn,Qn,tr,Hn,Kn,ei,Te,ti,Ve,Wr,ae,$e,or,Tt,oi,rr,ri,Jr,S,Vt,ai,ar,si,ni,$t,ii,It,li,ci,di,W,Et,mi,se,pi,oo,hi,fi,sr,gi,ui,_i,Ie,bi,Ee,Ar;return c=new ne({}),Re=new ne({}),Qe=new ne({}),He=new lc({props:{pipeline:"image-classification"}}),tt=new ne({}),ot=new P({props:{name:"class transformers.MobileNetV2Config",anchor:"transformers.MobileNetV2Config",parameters:[{name:"num_channels",val:" = 3"},{name:"image_size",val:" = 224"},{name:"depth_multiplier",val:" = 1.0"},{name:"depth_divisible_by",val:" = 8"},{name:"min_depth",val:" = 8"},{name:"expand_ratio",val:" = 6"},{name:"output_stride",val:" = 32"},{name:"first_layer_is_expansion",val:" = True"},{name:"finegrained_output",val:" = True"},{name:"hidden_act",val:" = 'relu6'"},{name:"tf_padding",val:" = True"},{name:"classifier_dropout_prob",val:" = 0.8"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 0.001"},{name:"semantic_loss_ignore_index",val:" = 255"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MobileNetV2Config.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.MobileNetV2Config.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.MobileNetV2Config.depth_multiplier",description:`<strong>depth_multiplier</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Shrinks or expands the number of channels in each layer. Default is 1.0, which starts the network with 32
channels. This is sometimes also called &#x201C;alpha&#x201D; or &#x201C;width multiplier&#x201D;.`,name:"depth_multiplier"},{anchor:"transformers.MobileNetV2Config.depth_divisible_by",description:`<strong>depth_divisible_by</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
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<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>
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Scale factor to use if rescaling the image. Can be overridden by the <code>rescale_factor</code> parameter in the
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Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
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<li><code>&quot;channels_last&quot;</code> or <code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
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`,returnType:`
<p><code>List[torch.Tensor]</code></p>
`}}),ft=new ne({}),gt=new P({props:{name:"class transformers.MobileNetV2Model",anchor:"transformers.MobileNetV2Model",parameters:[{name:"config",val:": MobileNetV2Config"},{name:"add_pooling_layer",val:": bool = True"}],parametersDescription:[{anchor:"transformers.MobileNetV2Model.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</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/v4.31.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py#L502"}}),bt=new P({props:{name:"forward",anchor:"transformers.MobileNetV2Model.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MobileNetV2Model.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/v4.31.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.31.0/en/model_doc/perceiver#transformers.PerceiverFeatureExtractor.__call__">MobileNetV2ImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.MobileNetV2Model.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.MobileNetV2Model.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/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py#L566",returnDescription:`
<p>A <code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.31.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config"
>MobileNetV2Config</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) \u2014 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>) \u2014 Last layer hidden-state after a pooling operation on the spatial dimensions.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, num_channels, height, width)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Me=new yi({props:{$$slots:{default:[dc]},$$scope:{ctx:$}}}),we=new ma({props:{anchor:"transformers.MobileNetV2Model.forward.example",$$slots:{default:[mc]},$$scope:{ctx:$}}}),vt=new ne({}),yt=new P({props:{name:"class transformers.MobileNetV2ForImageClassification",anchor:"transformers.MobileNetV2ForImageClassification",parameters:[{name:"config",val:": MobileNetV2Config"}],parametersDescription:[{anchor:"transformers.MobileNetV2ForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</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/v4.31.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py#L622"}}),Nt=new P({props:{name:"forward",anchor:"transformers.MobileNetV2ForImageClassification.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MobileNetV2ForImageClassification.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/v4.31.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.31.0/en/model_doc/perceiver#transformers.PerceiverFeatureExtractor.__call__">MobileNetV2ImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.MobileNetV2ForImageClassification.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.MobileNetV2ForImageClassification.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/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.MobileNetV2ForImageClassification.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/v4.31.0/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py#L638",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.31.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config"
>MobileNetV2Config</a>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</li>
<li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</li>
<li><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape <code>(batch_size, num_channels, height, width)</code>. Hidden-states (also
called feature maps) of the model at the output of each stage.</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Te=new yi({props:{$$slots:{default:[pc]},$$scope:{ctx:$}}}),Ve=new ma({props:{anchor:"transformers.MobileNetV2ForImageClassification.forward.example",$$slots:{default:[hc]},$$scope:{ctx:$}}}),Tt=new ne({}),Vt=new P({props:{name:"class transformers.MobileNetV2ForSemanticSegmentation",anchor:"transformers.MobileNetV2ForSemanticSegmentation",parameters:[{name:"config",val:": MobileNetV2Config"}],parametersDescription:[{anchor:"transformers.MobileNetV2ForSemanticSegmentation.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</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/v4.31.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py#L781"}}),Et=new P({props:{name:"forward",anchor:"transformers.MobileNetV2ForSemanticSegmentation.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MobileNetV2ForSemanticSegmentation.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/v4.31.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.31.0/en/model_doc/perceiver#transformers.PerceiverFeatureExtractor.__call__">MobileNetV2ImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.MobileNetV2ForSemanticSegmentation.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.MobileNetV2ForSemanticSegmentation.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/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.MobileNetV2ForSemanticSegmentation.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, height, width)</code>, <em>optional</em>) &#x2014;
Ground truth semantic segmentation maps for computing the loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels &gt; 1</code>, a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py#L792",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.modeling_outputs.SemanticSegmenterOutput"
>transformers.modeling_outputs.SemanticSegmenterOutput</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/v4.31.0/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config"
>MobileNetV2Config</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) \u2014 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, logits_height, logits_width)</code>) \u2014 Classification scores for each pixel.</p>
<Tip warning={true}>
<p>The logits returned do not necessarily have the same size as the <code>pixel_values</code> passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.</p>
</Tip>
</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>) \u2014 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, patch_size, 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>) \u2014 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/v4.31.0/en/main_classes/output#transformers.modeling_outputs.SemanticSegmenterOutput"
>transformers.modeling_outputs.SemanticSegmenterOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ie=new yi({props:{$$slots:{default:[fc]},$$scope:{ctx:$}}}),Ee=new ma({props:{anchor:"transformers.MobileNetV2ForSemanticSegmentation.forward.example",$$slots:{default:[gc]},$$scope:{ctx:$}}}),{c(){f=r("meta"),V=d(),_=r("h1"),g=r("a"),b=r("span"),v(c.$$.fragment),u=d(),E=r("span"),fa=n("MobileNet V2"),pr=d(),D=r("h2"),ie=r("a"),ho=r("span"),v(Re.$$.fragment),ga=d(),fo=r("span"),ua=n("Overview"),hr=d(),le=r("p"),_a=n("The MobileNet model was proposed in "),We=r("a"),ba=n("MobileNetV2: Inverted Residuals and Linear Bottlenecks"),va=n(" by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen."),fr=d(),Ft=r("p"),ya=n("The abstract from the paper is the following:"),gr=d(),St=r("p"),go=r("em"),Ma=n("In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. 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MobileNetV2 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 MobileNetV2
`),rt=r("a"),Gs=n("google/mobilenet_v2_1.0_224"),Os=n(" architecture."),Xs=d(),Q=r("p"),Ys=n("Configuration objects inherit from "),qt=r("a"),qs=n("PretrainedConfig"),Qs=n(` and can be used to control the model outputs. Read the
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This model is a PyTorch `),_t=r("a"),En=n("torch.nn.Module"),xn=n(` subclass. Use it
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