import{S as Jr,i as Qr,s as Xr,e as a,k as m,w as v,t as l,M as Yr,c as r,d as o,m as h,a as n,x as $,h as i,b as c,G as t,g as u,y,q as N,o as T,B as R,v as Zr,L as co}from"../../chunks/vendor-hf-doc-builder.js";import{T as Bo}from"../../chunks/Tip-hf-doc-builder.js";import{D as Z}from"../../chunks/Docstring-hf-doc-builder.js";import{C as po}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Re}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as io}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";import{P as en}from"../../chunks/PipelineTag-hf-doc-builder.js";function tn(k){let d,b,_,g,w;return g=new po({props:{code:`from transformers import RegNetConfig, RegNetModel

# Initializing a RegNet regnet-y-40 style configuration
configuration = RegNetConfig()
# Initializing a model from the regnet-y-40 style configuration
model = RegNetModel(configuration)
# Accessing the model configuration
configuration = model.config`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> RegNetConfig, RegNetModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a RegNet regnet-y-40 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = RegNetConfig()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the regnet-y-40 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RegNetModel(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(){d=a("p"),b=l("Example:"),_=m(),v(g.$$.fragment)},l(s){d=r(s,"P",{});var f=n(d);b=i(f,"Example:"),f.forEach(o),_=h(s),$(g.$$.fragment,s)},m(s,f){u(s,d,f),t(d,b),u(s,_,f),y(g,s,f),w=!0},p:co,i(s){w||(N(g.$$.fragment,s),w=!0)},o(s){T(g.$$.fragment,s),w=!1},d(s){s&&o(d),s&&o(_),R(g,s)}}}function on(k){let d,b,_,g,w;return{c(){d=a("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),g=l("Module"),w=l(`
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(s){d=r(s,"P",{});var f=n(d);b=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r(f,"CODE",{});var F=n(_);g=i(F,"Module"),F.forEach(o),w=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(o)},m(s,f){u(s,d,f),t(d,b),t(d,_),t(_,g),t(d,w)},d(s){s&&o(d)}}}function sn(k){let d,b,_,g,w;return g=new po({props:{code:`from transformers import AutoImageProcessor, RegNetModel
import torch
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

image_processor = AutoImageProcessor.from_pretrained("facebook/regnet-y-040")
model = RegNetModel.from_pretrained("facebook/regnet-y-040")

inputs = image_processor(image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state
list(last_hidden_states.shape)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, RegNetModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

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

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/regnet-y-040&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RegNetModel.from_pretrained(<span class="hljs-string">&quot;facebook/regnet-y-040&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">1088</span>, <span class="hljs-number">7</span>, <span class="hljs-number">7</span>]`}}),{c(){d=a("p"),b=l("Example:"),_=m(),v(g.$$.fragment)},l(s){d=r(s,"P",{});var f=n(d);b=i(f,"Example:"),f.forEach(o),_=h(s),$(g.$$.fragment,s)},m(s,f){u(s,d,f),t(d,b),u(s,_,f),y(g,s,f),w=!0},p:co,i(s){w||(N(g.$$.fragment,s),w=!0)},o(s){T(g.$$.fragment,s),w=!1},d(s){s&&o(d),s&&o(_),R(g,s)}}}function an(k){let d,b,_,g,w;return{c(){d=a("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),g=l("Module"),w=l(`
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(s){d=r(s,"P",{});var f=n(d);b=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r(f,"CODE",{});var F=n(_);g=i(F,"Module"),F.forEach(o),w=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(o)},m(s,f){u(s,d,f),t(d,b),t(d,_),t(_,g),t(d,w)},d(s){s&&o(d)}}}function rn(k){let d,b,_,g,w;return g=new po({props:{code:`from transformers import AutoImageProcessor, RegNetForImageClassification
import torch
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

image_processor = AutoImageProcessor.from_pretrained("facebook/regnet-y-040")
model = RegNetForImageClassification.from_pretrained("facebook/regnet-y-040")

inputs = image_processor(image, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, RegNetForImageClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

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

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/regnet-y-040&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RegNetForImageClassification.from_pretrained(<span class="hljs-string">&quot;facebook/regnet-y-040&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(){d=a("p"),b=l("Example:"),_=m(),v(g.$$.fragment)},l(s){d=r(s,"P",{});var f=n(d);b=i(f,"Example:"),f.forEach(o),_=h(s),$(g.$$.fragment,s)},m(s,f){u(s,d,f),t(d,b),u(s,_,f),y(g,s,f),w=!0},p:co,i(s){w||(N(g.$$.fragment,s),w=!0)},o(s){T(g.$$.fragment,s),w=!1},d(s){s&&o(d),s&&o(_),R(g,s)}}}function nn(k){let d,b,_,g,w;return{c(){d=a("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),g=l("Module"),w=l(`
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(s){d=r(s,"P",{});var f=n(d);b=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r(f,"CODE",{});var F=n(_);g=i(F,"Module"),F.forEach(o),w=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(o)},m(s,f){u(s,d,f),t(d,b),t(d,_),t(_,g),t(d,w)},d(s){s&&o(d)}}}function ln(k){let d,b,_,g,w;return g=new po({props:{code:`from transformers import AutoImageProcessor, TFRegNetModel
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

image_processor = AutoImageProcessor.from_pretrained("facebook/regnet-y-040")
model = TFRegNetModel.from_pretrained("facebook/regnet-y-040")

inputs = image_processor(image, return_tensors="tf")
outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state
list(last_hidden_states.shape)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, TFRegNetModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

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

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/regnet-y-040&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRegNetModel.from_pretrained(<span class="hljs-string">&quot;facebook/regnet-y-040&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">1088</span>, <span class="hljs-number">7</span>, <span class="hljs-number">7</span>]`}}),{c(){d=a("p"),b=l("Example:"),_=m(),v(g.$$.fragment)},l(s){d=r(s,"P",{});var f=n(d);b=i(f,"Example:"),f.forEach(o),_=h(s),$(g.$$.fragment,s)},m(s,f){u(s,d,f),t(d,b),u(s,_,f),y(g,s,f),w=!0},p:co,i(s){w||(N(g.$$.fragment,s),w=!0)},o(s){T(g.$$.fragment,s),w=!1},d(s){s&&o(d),s&&o(_),R(g,s)}}}function dn(k){let d,b,_,g,w;return{c(){d=a("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a("code"),g=l("Module"),w=l(`
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(s){d=r(s,"P",{});var f=n(d);b=i(f,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r(f,"CODE",{});var F=n(_);g=i(F,"Module"),F.forEach(o),w=i(f,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),f.forEach(o)},m(s,f){u(s,d,f),t(d,b),t(d,_),t(_,g),t(d,w)},d(s){s&&o(d)}}}function cn(k){let d,b,_,g,w;return g=new po({props:{code:`from transformers import AutoImageProcessor, TFRegNetForImageClassification
import tensorflow as tf
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

image_processor = AutoImageProcessor.from_pretrained("facebook/regnet-y-040")
model = TFRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")

inputs = image_processor(image, return_tensors="tf")
logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = int(tf.math.argmax(logits, axis=-1))
print(model.config.id2label[predicted_label])`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, TFRegNetForImageClassification
<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>)
<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/regnet-y-040&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRegNetForImageClassification.from_pretrained(<span class="hljs-string">&quot;facebook/regnet-y-040&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`}}),{c(){d=a("p"),b=l("Example:"),_=m(),v(g.$$.fragment)},l(s){d=r(s,"P",{});var f=n(d);b=i(f,"Example:"),f.forEach(o),_=h(s),$(g.$$.fragment,s)},m(s,f){u(s,d,f),t(d,b),u(s,_,f),y(g,s,f),w=!0},p:co,i(s){w||(N(g.$$.fragment,s),w=!0)},o(s){T(g.$$.fragment,s),w=!1},d(s){s&&o(d),s&&o(_),R(g,s)}}}function pn(k){let d,b,_,g,w,s,f,F,Go,fo,D,ee,Pt,ke,Ko,At,Jo,go,te,Qo,Fe,Xo,Yo,mo,it,Zo,ho,dt,es,uo,ct,Mt,ts,_o,pt,os,wo,oe,je,ss,ft,as,rs,ns,se,ls,xe,is,ds,Ee,cs,bo,j,ps,Ie,fs,gs,Ce,ms,hs,Pe,us,_s,Ae,ws,bs,vo,L,ae,qt,Me,vs,zt,$s,$o,gt,ys,yo,qe,No,mt,O,ht,Ns,Ts,ze,Rs,ks,Oe,Fs,js,To,ut,xs,Ro,W,re,Ot,Se,Es,St,Is,ko,x,De,Cs,H,Ps,_t,As,Ms,Le,qs,zs,Os,U,Ss,wt,Ds,Ls,bt,Ws,Hs,Us,ne,Fo,V,le,Dt,We,Vs,Lt,Bs,jo,M,He,Gs,Ue,Ks,Ve,Js,Qs,Xs,I,Be,Ys,B,Zs,vt,ea,ta,Wt,oa,sa,aa,ie,ra,de,xo,G,ce,Ht,Ge,na,Ut,la,Eo,E,Ke,ia,Vt,da,ca,Je,pa,Qe,fa,ga,ma,C,Xe,ha,K,ua,$t,_a,wa,Bt,ba,va,$a,pe,ya,fe,Io,J,ge,Gt,Ye,Na,Kt,Ta,Co,q,Ze,Ra,Jt,ka,Fa,P,et,ja,Q,xa,yt,Ea,Ia,Qt,Ca,Pa,Aa,me,Ma,he,Po,X,ue,Xt,tt,qa,Yt,za,Ao,z,ot,Oa,Zt,Sa,Da,A,st,La,Y,Wa,Nt,Ha,Ua,eo,Va,Ba,Ga,_e,Ka,we,Mo;return s=new Re({}),ke=new Re({}),Me=new Re({}),qe=new en({props:{pipeline:"image-classification"}}),Se=new Re({}),De=new Z({props:{name:"class transformers.RegNetConfig",anchor:"transformers.RegNetConfig",parameters:[{name:"num_channels",val:" = 3"},{name:"embedding_size",val:" = 32"},{name:"hidden_sizes",val:" = [128, 192, 512, 1088]"},{name:"depths",val:" = [2, 6, 12, 2]"},{name:"groups_width",val:" = 64"},{name:"layer_type",val:" = 'y'"},{name:"hidden_act",val:" = 'relu'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RegNetConfig.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.RegNetConfig.embedding_size",description:`<strong>embedding_size</strong> (<code>int</code>, <em>optional</em>, defaults to 64) &#x2014;
Dimensionality (hidden size) for the embedding layer.`,name:"embedding_size"},{anchor:"transformers.RegNetConfig.hidden_sizes",description:`<strong>hidden_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[256, 512, 1024, 2048]</code>) &#x2014;
Dimensionality (hidden size) at each stage.`,name:"hidden_sizes"},{anchor:"transformers.RegNetConfig.depths",description:`<strong>depths</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[3, 4, 6, 3]</code>) &#x2014;
Depth (number of layers) for each stage.`,name:"depths"},{anchor:"transformers.RegNetConfig.layer_type",description:`<strong>layer_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;y&quot;</code>) &#x2014;
The layer to use, it can be either <code>&quot;x&quot; or </code>&#x201C;y&#x201D;<code>. An </code>x<code>layer is a ResNet&apos;s BottleNeck layer with</code>reduction<code>fixed to</code>1<code>. While a </code>y<code>layer is a</code>x\` but with squeeze and excitation. Please refer to the
paper for a detailed explanation of how these layers were constructed.`,name:"layer_type"},{anchor:"transformers.RegNetConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;relu&quot;</code>) &#x2014;
The non-linear activation function in each block. 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.RegNetConfig.downsample_in_first_stage",description:`<strong>downsample_in_first_stage</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>True</code>, the first stage will downsample the inputs using a <code>stride</code> of 2.`,name:"downsample_in_first_stage"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/regnet/configuration_regnet.py#L28"}}),ne=new io({props:{anchor:"transformers.RegNetConfig.example",$$slots:{default:[tn]},$$scope:{ctx:k}}}),We=new Re({}),He=new Z({props:{name:"class transformers.RegNetModel",anchor:"transformers.RegNetModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RegNetModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</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/main/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/main/src/transformers/models/regnet/modeling_regnet.py#L331"}}),Be=new Z({props:{name:"forward",anchor:"transformers.RegNetModel.forward",parameters:[{name:"pixel_values",val:": Tensor"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.RegNetModel.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/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/main/en/model_doc/poolformer#transformers.PoolFormerFeatureExtractor.__call__">ConvNextImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.RegNetModel.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.RegNetModel.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/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/regnet/modeling_regnet.py#L341",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/main/en/model_doc/regnet#transformers.RegNetConfig"
>RegNetConfig</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>
`}}),ie=new Bo({props:{$$slots:{default:[on]},$$scope:{ctx:k}}}),de=new io({props:{anchor:"transformers.RegNetModel.forward.example",$$slots:{default:[sn]},$$scope:{ctx:k}}}),Ge=new Re({}),Ke=new Z({props:{name:"class transformers.RegNetForImageClassification",anchor:"transformers.RegNetForImageClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RegNetForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
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<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
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<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/main/en/model_doc/regnet#transformers.RegNetConfig"
>RegNetConfig</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/main/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
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configuration (<a
  href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig"
>RegNetConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</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>tf.Tensor</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(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>) \u2014 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 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_tf_outputs.TFBaseModelOutputWithPoolingAndNoAttention</code> or <code>tuple(tf.Tensor)</code></p>
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config (<a href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a>): Model configuration class with all the parameters of the model.
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  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
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  href="/docs/transformers/main/en/model_doc/regnet#transformers.RegNetConfig"
>RegNetConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, )</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 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>) \u2014 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>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
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<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>) \u2014 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:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
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