import{S as cr,i as pr,s as fr,e as s,k as m,w as v,t as l,M as gr,c as r,d as o,m as u,a as n,x as $,h as i,b as c,G as e,g as _,y,q as N,o as T,B as R,v as hr,L as Jt}from"../../chunks/vendor-hf-doc-builder.js";import{T as xo}from"../../chunks/Tip-hf-doc-builder.js";import{D as X}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Qt}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ze}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Kt}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function mr(x){let d,b,h,f,w;return f=new Qt({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=s("p"),b=l("Example:"),h=m(),v(f.$$.fragment)},l(a){d=r(a,"P",{});var p=n(d);b=i(p,"Example:"),p.forEach(o),h=u(a),$(f.$$.fragment,a)},m(a,p){_(a,d,p),e(d,b),_(a,h,p),y(f,a,p),w=!0},p:Jt,i(a){w||(N(f.$$.fragment,a),w=!0)},o(a){T(f.$$.fragment,a),w=!1},d(a){a&&o(d),a&&o(h),R(f,a)}}}function ur(x){let d,b,h,f,w;return{c(){d=s("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),f=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(a){d=r(a,"P",{});var p=n(d);b=i(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(p,"CODE",{});var F=n(h);f=i(F,"Module"),F.forEach(o),w=i(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(a,p){_(a,d,p),e(d,b),e(d,h),e(h,f),e(d,w)},d(a){a&&o(d)}}}function _r(x){let d,b,h,f,w;return f=new Qt({props:{code:`from transformers import AutoFeatureExtractor, RegNetModel
import torch
from datasets import load_dataset

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

feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/regnet-y-040")
model = RegNetModel.from_pretrained("facebook/regnet-y-040")

inputs = feature_extractor(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> AutoFeatureExtractor, 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>feature_extractor = AutoFeatureExtractor.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 = feature_extractor(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=s("p"),b=l("Example:"),h=m(),v(f.$$.fragment)},l(a){d=r(a,"P",{});var p=n(d);b=i(p,"Example:"),p.forEach(o),h=u(a),$(f.$$.fragment,a)},m(a,p){_(a,d,p),e(d,b),_(a,h,p),y(f,a,p),w=!0},p:Jt,i(a){w||(N(f.$$.fragment,a),w=!0)},o(a){T(f.$$.fragment,a),w=!1},d(a){a&&o(d),a&&o(h),R(f,a)}}}function wr(x){let d,b,h,f,w;return{c(){d=s("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),f=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(a){d=r(a,"P",{});var p=n(d);b=i(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(p,"CODE",{});var F=n(h);f=i(F,"Module"),F.forEach(o),w=i(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(a,p){_(a,d,p),e(d,b),e(d,h),e(h,f),e(d,w)},d(a){a&&o(d)}}}function br(x){let d,b,h,f,w;return f=new Qt({props:{code:`from transformers import AutoFeatureExtractor, RegNetForImageClassification
import torch
from datasets import load_dataset

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

feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/regnet-y-040")
model = RegNetForImageClassification.from_pretrained("facebook/regnet-y-040")

inputs = feature_extractor(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> AutoFeatureExtractor, 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>feature_extractor = AutoFeatureExtractor.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 = feature_extractor(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=s("p"),b=l("Example:"),h=m(),v(f.$$.fragment)},l(a){d=r(a,"P",{});var p=n(d);b=i(p,"Example:"),p.forEach(o),h=u(a),$(f.$$.fragment,a)},m(a,p){_(a,d,p),e(d,b),_(a,h,p),y(f,a,p),w=!0},p:Jt,i(a){w||(N(f.$$.fragment,a),w=!0)},o(a){T(f.$$.fragment,a),w=!1},d(a){a&&o(d),a&&o(h),R(f,a)}}}function vr(x){let d,b,h,f,w;return{c(){d=s("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),f=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(a){d=r(a,"P",{});var p=n(d);b=i(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(p,"CODE",{});var F=n(h);f=i(F,"Module"),F.forEach(o),w=i(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(a,p){_(a,d,p),e(d,b),e(d,h),e(h,f),e(d,w)},d(a){a&&o(d)}}}function $r(x){let d,b,h,f,w;return f=new Qt({props:{code:`from transformers import AutoFeatureExtractor, TFRegNetModel
from datasets import load_dataset

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

feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/regnet-y-040")
model = TFRegNetModel.from_pretrained("facebook/regnet-y-040")

inputs = feature_extractor(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> AutoFeatureExtractor, 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>feature_extractor = AutoFeatureExtractor.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 = feature_extractor(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=s("p"),b=l("Example:"),h=m(),v(f.$$.fragment)},l(a){d=r(a,"P",{});var p=n(d);b=i(p,"Example:"),p.forEach(o),h=u(a),$(f.$$.fragment,a)},m(a,p){_(a,d,p),e(d,b),_(a,h,p),y(f,a,p),w=!0},p:Jt,i(a){w||(N(f.$$.fragment,a),w=!0)},o(a){T(f.$$.fragment,a),w=!1},d(a){a&&o(d),a&&o(h),R(f,a)}}}function yr(x){let d,b,h,f,w;return{c(){d=s("p"),b=l("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),f=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(a){d=r(a,"P",{});var p=n(d);b=i(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(p,"CODE",{});var F=n(h);f=i(F,"Module"),F.forEach(o),w=i(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(a,p){_(a,d,p),e(d,b),e(d,h),e(h,f),e(d,w)},d(a){a&&o(d)}}}function Nr(x){let d,b,h,f,w;return f=new Qt({props:{code:`from transformers import AutoFeatureExtractor, TFRegNetForImageClassification
import tensorflow as tf
from datasets import load_dataset

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

feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/regnet-y-040")
model = TFRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")

inputs = feature_extractor(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> AutoFeatureExtractor, 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>feature_extractor = AutoFeatureExtractor.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 = feature_extractor(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=s("p"),b=l("Example:"),h=m(),v(f.$$.fragment)},l(a){d=r(a,"P",{});var p=n(d);b=i(p,"Example:"),p.forEach(o),h=u(a),$(f.$$.fragment,a)},m(a,p){_(a,d,p),e(d,b),_(a,h,p),y(f,a,p),w=!0},p:Jt,i(a){w||(N(f.$$.fragment,a),w=!0)},o(a){T(f.$$.fragment,a),w=!1},d(a){a&&o(d),a&&o(h),R(f,a)}}}function Tr(x){let d,b,h,f,w,a,p,F,Fo,Xt,D,Y,$t,ye,Eo,yt,ko,Yt,Z,jo,Ne,Co,Ao,Zt,et,Mo,eo,tt,Io,to,ot,Nt,Po,oo,at,qo,ao,ee,Te,zo,st,Oo,Do,So,te,Lo,Re,Wo,Uo,xe,Ho,so,E,Vo,Fe,Bo,Go,Ee,Ko,Jo,ke,Qo,Xo,je,Yo,Zo,ro,S,oe,Tt,Ce,ea,Rt,ta,no,k,Ae,oa,L,aa,rt,sa,ra,Me,na,la,ia,W,da,nt,ca,pa,lt,fa,ga,ha,ae,lo,U,se,xt,Ie,ma,Ft,ua,io,P,Pe,_a,qe,wa,ze,ba,va,$a,C,Oe,ya,H,Na,it,Ta,Ra,Et,xa,Fa,Ea,re,ka,ne,co,V,le,kt,De,ja,jt,Ca,po,j,Se,Aa,Ct,Ma,Ia,Le,Pa,We,qa,za,Oa,A,Ue,Da,B,Sa,dt,La,Wa,At,Ua,Ha,Va,ie,Ba,de,fo,G,ce,Mt,He,Ga,It,Ka,go,q,Ve,Ja,Pt,Qa,Xa,M,Be,Ya,K,Za,ct,es,ts,qt,os,as,ss,pe,rs,fe,ho,J,ge,zt,Ge,ns,Ot,ls,mo,z,Ke,is,Dt,ds,cs,I,Je,ps,Q,fs,pt,gs,hs,St,ms,us,_s,he,ws,me,uo;return a=new Ze({}),ye=new Ze({}),Ce=new Ze({}),Ae=new X({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/vr_19907/src/transformers/models/regnet/configuration_regnet.py#L28"}}),ae=new Kt({props:{anchor:"transformers.RegNetConfig.example",$$slots:{default:[mr]},$$scope:{ctx:x}}}),Ie=new Ze({}),Pe=new X({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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/regnet/modeling_regnet.py#L332"}}),Oe=new X({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/pr_19907/en/model_doc/auto#transformers.AutoFeatureExtractor">AutoFeatureExtractor</a>. See
<code>AutoFeatureExtractor.__call__()</code> 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/pr_19907/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_19907/src/transformers/models/regnet/modeling_regnet.py#L342",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/pr_19907/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>
`}}),re=new xo({props:{$$slots:{default:[ur]},$$scope:{ctx:x}}}),ne=new Kt({props:{anchor:"transformers.RegNetModel.forward.example",$$slots:{default:[_r]},$$scope:{ctx:x}}}),De=new Ze({}),Se=new X({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/pr_19907/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/pr_19907/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/vr_19907/src/transformers/models/regnet/modeling_regnet.py#L387"}}),Ue=new X({props:{name:"forward",anchor:"transformers.RegNetForImageClassification.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.FloatTensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.RegNetForImageClassification.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_19907/en/model_doc/auto#transformers.AutoFeatureExtractor">AutoFeatureExtractor</a>. See
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<p>A <a
  href="/docs/transformers/pr_19907/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/pr_19907/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/pr_19907/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
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`}}),ie=new xo({props:{$$slots:{default:[wr]},$$scope:{ctx:x}}}),de=new Kt({props:{anchor:"transformers.RegNetForImageClassification.forward.example",$$slots:{default:[br]},$$scope:{ctx:x}}}),He=new Ze({}),Ve=new X({props:{name:"class transformers.TFRegNetModel",anchor:"transformers.TFRegNetModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRegNetModel.This",description:`<strong>This</strong> model is a Tensorflow &#x2014;
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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/pr_19907/en/model_doc/auto#transformers.AutoFeatureExtractor">AutoFeatureExtractor</a>. See
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<p>A <code>transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndNoAttention</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_19907/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>
`}}),pe=new xo({props:{$$slots:{default:[vr]},$$scope:{ctx:x}}}),fe=new Kt({props:{anchor:"transformers.TFRegNetModel.call.example",$$slots:{default:[$r]},$$scope:{ctx:x}}}),Ge=new Ze({}),Ke=new X({props:{name:"class transformers.TFRegNetForImageClassification",anchor:"transformers.TFRegNetForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRegNetForImageClassification.This",description:`<strong>This</strong> model is a Tensorflow &#x2014;
[tf.keras.layers.Layer](https &#x2014;//<a href="http://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer" rel="nofollow">www.tensorflow.org/api_docs/python/tf/keras/layers/Layer</a>) sub-class. Use it as a`,name:"This"},{anchor:"transformers.TFRegNetForImageClassification.regular",description:`<strong>regular</strong> Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and &#x2014;
behavior. &#x2014;
config (<a href="/docs/transformers/pr_19907/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a>): 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/pr_19907/en/model_doc/auto#transformers.AutoFeatureExtractor">AutoFeatureExtractor</a>. See
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Labels for computing the image classification/regression 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/vr_19907/src/transformers/models/regnet/modeling_tf_regnet.py#L477",returnDescription:`
<p>A <a
  href="/docs/transformers/pr_19907/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
configuration (<a
  href="/docs/transformers/pr_19907/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
<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>) \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/pr_19907/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
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