import{S as da,i as ua,s as ga,e as n,k as m,w as b,t as s,M as _a,c as a,d as o,m as h,a as r,x as w,h as i,b as l,N as va,G as t,g as p,y,q as $,o as N,B as S,v as ba,L as ha}from"../../chunks/vendor-hf-doc-builder.js";import{T as wa}from"../../chunks/Tip-hf-doc-builder.js";import{D as Bt}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Xt}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ze}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as ma}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function ya(H){let d,k,g,u,v;return u=new Xt({props:{code:`from transformers import UperNetConfig, UperNetForSemanticSegmentation

# Initializing a configuration
configuration = UperNetConfig()

# Initializing a model (with random weights) from the configuration
model = UperNetForSemanticSegmentation(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> UperNetConfig, UperNetForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = UperNetConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = UperNetForSemanticSegmentation(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=n("p"),k=s("Examples:"),g=m(),b(u.$$.fragment)},l(f){d=a(f,"P",{});var _=r(d);k=i(_,"Examples:"),_.forEach(o),g=h(f),w(u.$$.fragment,f)},m(f,_){p(f,d,_),t(d,k),p(f,g,_),y(u,f,_),v=!0},p:ha,i(f){v||($(u.$$.fragment,f),v=!0)},o(f){N(u.$$.fragment,f),v=!1},d(f){f&&o(d),f&&o(g),S(u,f)}}}function $a(H){let d,k,g,u,v;return{c(){d=n("p"),k=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n("code"),u=s("Module"),v=s(`
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(f){d=a(f,"P",{});var _=r(d);k=i(_,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=a(_,"CODE",{});var W=r(g);u=i(W,"Module"),W.forEach(o),v=i(_,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),_.forEach(o)},m(f,_){p(f,d,_),t(d,k),t(d,g),t(g,u),t(d,v)},d(f){f&&o(d)}}}function Na(H){let d,k,g,u,v;return u=new Xt({props:{code:`from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
from PIL import Image
from huggingface_hub import hf_hub_download

image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny")
model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny")

filepath = hf_hub_download(
    repo_id="hf-internal-testing/fixtures_ade20k", filename="ADE_val_00000001.jpg", repo_type="dataset"
)
image = Image.open(filepath).convert("RGB")

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

outputs = model(**inputs)

logits = outputs.logits  # shape (batch_size, num_labels, height, width)
list(logits.shape)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, UperNetForSemanticSegmentation
<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">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;openmmlab/upernet-convnext-tiny&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = UperNetForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;openmmlab/upernet-convnext-tiny&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>filepath = hf_hub_download(
<span class="hljs-meta">... </span>    repo_id=<span class="hljs-string">&quot;hf-internal-testing/fixtures_ade20k&quot;</span>, filename=<span class="hljs-string">&quot;ADE_val_00000001.jpg&quot;</span>, repo_type=<span class="hljs-string">&quot;dataset&quot;</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(filepath).convert(<span class="hljs-string">&quot;RGB&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits  <span class="hljs-comment"># shape (batch_size, num_labels, height, width)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(logits.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">150</span>, <span class="hljs-number">512</span>, <span class="hljs-number">512</span>]`}}),{c(){d=n("p"),k=s("Examples:"),g=m(),b(u.$$.fragment)},l(f){d=a(f,"P",{});var _=r(d);k=i(_,"Examples:"),_.forEach(o),g=h(f),w(u.$$.fragment,f)},m(f,_){p(f,d,_),t(d,k),p(f,g,_),y(u,f,_),v=!0},p:ha,i(f){v||($(u.$$.fragment,f),v=!0)},o(f){N(u.$$.fragment,f),v=!1},d(f){f&&o(d),f&&o(g),S(u,f)}}}function Sa(H){let d,k,g,u,v,f,_,W,Gt,dt,F,B,Ke,re,Jt,Qe,Yt,ut,x,Vt,se,Zt,Kt,Ue,Qt,eo,xe,to,oo,gt,Pe,no,_t,Ee,et,ao,vt,X,yn,bt,G,ro,Ce,so,io,wt,C,lo,ie,co,po,le,fo,mo,yt,q,J,tt,ce,ho,ot,uo,$t,je,go,Nt,j,pe,_o,fe,vo,bo,wo,T,Te,yo,$o,me,No,So,he,ko,Uo,xo,Ae,Po,Fe,Eo,St,qe,Co,kt,I,Y,nt,de,jo,at,To,Ut,Ie,Ao,xt,ue,Pt,V,Fo,ze,qo,Io,Et,ge,Ct,De,zo,jt,z,Z,rt,_e,Do,st,Oo,Tt,U,ve,Lo,D,Mo,Oe,Ro,Ho,be,Wo,Bo,Xo,O,Go,Le,Jo,Yo,Me,Vo,Zo,Ko,K,Qo,Q,we,en,L,tn,Re,on,nn,it,an,rn,At,M,ee,lt,ye,sn,ct,ln,Ft,E,$e,cn,pt,pn,fn,P,Ne,mn,R,hn,He,dn,un,ft,gn,_n,vn,te,bn,oe,qt;return f=new Ze({}),re=new Ze({}),ce=new Ze({}),de=new Ze({}),ue=new Xt({props:{code:`from transformers import SwinConfig, UperNetConfig, UperNetForSemanticSegmentation

backbone_config = SwinConfig(out_features=["stage1", "stage2", "stage3", "stage4"])

config = UperNetConfig(backbone_config=backbone_config)
model = UperNetForSemanticSegmentation(config)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> SwinConfig, UperNetConfig, UperNetForSemanticSegmentation

backbone_config = SwinConfig(out_features=[<span class="hljs-string">&quot;stage1&quot;</span>, <span class="hljs-string">&quot;stage2&quot;</span>, <span class="hljs-string">&quot;stage3&quot;</span>, <span class="hljs-string">&quot;stage4&quot;</span>])

config = UperNetConfig(backbone_config=backbone_config)
model = UperNetForSemanticSegmentation(config)`}}),ge=new Xt({props:{code:`from transformers import ConvNextConfig, UperNetConfig, UperNetForSemanticSegmentation

backbone_config = ConvNextConfig(out_features=["stage1", "stage2", "stage3", "stage4"])

config = UperNetConfig(backbone_config=backbone_config)
model = UperNetForSemanticSegmentation(config)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ConvNextConfig, UperNetConfig, UperNetForSemanticSegmentation

backbone_config = ConvNextConfig(out_features=[<span class="hljs-string">&quot;stage1&quot;</span>, <span class="hljs-string">&quot;stage2&quot;</span>, <span class="hljs-string">&quot;stage3&quot;</span>, <span class="hljs-string">&quot;stage4&quot;</span>])

config = UperNetConfig(backbone_config=backbone_config)
model = UperNetForSemanticSegmentation(config)`}}),_e=new Ze({}),ve=new Bt({props:{name:"class transformers.UperNetConfig",anchor:"transformers.UperNetConfig",parameters:[{name:"backbone_config",val:" = None"},{name:"hidden_size",val:" = 512"},{name:"initializer_range",val:" = 0.02"},{name:"pool_scales",val:" = [1, 2, 3, 6]"},{name:"use_auxiliary_head",val:" = True"},{name:"auxiliary_loss_weight",val:" = 0.4"},{name:"auxiliary_in_channels",val:" = 384"},{name:"auxiliary_channels",val:" = 256"},{name:"auxiliary_num_convs",val:" = 1"},{name:"auxiliary_concat_input",val:" = False"},{name:"loss_ignore_index",val:" = 255"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.UperNetConfig.backbone_config",description:`<strong>backbone_config</strong> (<code>PretrainedConfig</code> or <code>dict</code>, <em>optional</em>, defaults to <code>ResNetConfig()</code>) &#x2014;
The configuration of the backbone model.`,name:"backbone_config"},{anchor:"transformers.UperNetConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The number of hidden units in the convolutional layers.`,name:"hidden_size"},{anchor:"transformers.UperNetConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.UperNetConfig.pool_scales",description:`<strong>pool_scales</strong> (<code>Tuple[int]</code>, <em>optional</em>, defaults to <code>[1, 2, 3, 6]</code>) &#x2014;
Pooling scales used in Pooling Pyramid Module applied on the last feature map.`,name:"pool_scales"},{anchor:"transformers.UperNetConfig.use_auxiliary_head",description:`<strong>use_auxiliary_head</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to use an auxiliary head during training.`,name:"use_auxiliary_head"},{anchor:"transformers.UperNetConfig.auxiliary_loss_weight",description:`<strong>auxiliary_loss_weight</strong> (<code>float</code>, <em>optional</em>, defaults to 0.4) &#x2014;
Weight of the cross-entropy loss of the auxiliary head.`,name:"auxiliary_loss_weight"},{anchor:"transformers.UperNetConfig.auxiliary_channels",description:`<strong>auxiliary_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
Number of channels to use in the auxiliary head.`,name:"auxiliary_channels"},{anchor:"transformers.UperNetConfig.auxiliary_num_convs",description:`<strong>auxiliary_num_convs</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Number of convolutional layers to use in the auxiliary head.`,name:"auxiliary_num_convs"},{anchor:"transformers.UperNetConfig.auxiliary_concat_input",description:`<strong>auxiliary_concat_input</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to concatenate the output of the auxiliary head with the input before the classification layer.`,name:"auxiliary_concat_input"},{anchor:"transformers.UperNetConfig.loss_ignore_index",description:`<strong>loss_ignore_index</strong> (<code>int</code>, <em>optional</em>, defaults to 255) &#x2014;
The index that is ignored by the loss function.`,name:"loss_ignore_index"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/upernet/configuration_upernet.py#L27"}}),K=new ma({props:{anchor:"transformers.UperNetConfig.example",$$slots:{default:[ya]},$$scope:{ctx:H}}}),we=new Bt({props:{name:"to_dict",anchor:"transformers.UperNetConfig.to_dict",parameters:[],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/upernet/configuration_upernet.py#L112"}}),ye=new Ze({}),$e=new Bt({props:{name:"class transformers.UperNetForSemanticSegmentation",anchor:"transformers.UperNetForSemanticSegmentation",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.UperNetForSemanticSegmentation.This",description:"<strong>This</strong> model is a PyTorch [torch.nn.Module](https &#x2014;//pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use",name:"This"},{anchor:"transformers.UperNetForSemanticSegmentation.it",description:`<strong>it</strong> as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and &#x2014;
behavior. &#x2014;
config (<a href="/docs/transformers/v4.29.0/en/model_doc/upernet#transformers.UperNetConfig">UperNetConfig</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
configuration. Check out the <a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"it"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/upernet/modeling_upernet.py#L351"}}),Ne=new Bt({props:{name:"forward",anchor:"transformers.UperNetForSemanticSegmentation.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = 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.UperNetForSemanticSegmentation.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. Padding will be ignored by default should you provide it. Pixel values can be obtained using
<a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.29.0/en/model_doc/segformer#transformers.SegformerFeatureExtractor.__call__">SegformerImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.UperNetForSemanticSegmentation.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
<code>attentions</code> under returned tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.UperNetForSemanticSegmentation.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 of the backbone. See <code>hidden_states</code> under
returned tensors for more detail.`,name:"output_hidden_states"},{anchor:"transformers.UperNetForSemanticSegmentation.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/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.UperNetForSemanticSegmentation.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/v4.29.0/src/transformers/models/upernet/modeling_upernet.py#L364",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.29.0/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/v4.29.0/en/model_doc/upernet#transformers.UperNetConfig"
>UperNetConfig</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/v4.29.0/en/main_classes/output#transformers.modeling_outputs.SemanticSegmenterOutput"
>transformers.modeling_outputs.SemanticSegmenterOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),te=new wa({props:{$$slots:{default:[$a]},$$scope:{ctx:H}}}),oe=new ma({props:{anchor:"transformers.UperNetForSemanticSegmentation.forward.example",$$slots:{default:[Na]},$$scope:{ctx:H}}}),{c(){d=n("meta"),k=m(),g=n("h1"),u=n("a"),v=n("span"),b(f.$$.fragment),_=m(),W=n("span"),Gt=s("UPerNet"),dt=m(),F=n("h2"),B=n("a"),Ke=n("span"),b(re.$$.fragment),Jt=m(),Qe=n("span"),Yt=s("Overview"),ut=m(),x=n("p"),Vt=s("The UPerNet model was proposed in "),se=n("a"),Zt=s("Unified Perceptual Parsing for Scene Understanding"),Kt=s(`
by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun. UPerNet is a general framework to effectively segment
a wide range of concepts from images, leveraging any vision backbone like `),Ue=n("a"),Qt=s("ConvNeXt"),eo=s(" or "),xe=n("a"),to=s("Swin"),oo=s("."),gt=m(),Pe=n("p"),no=s("The abstract from the paper is the following:"),_t=m(),Ee=n("p"),et=n("em"),ao=s("Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image. A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations. We benchmark our framework on Unified Perceptual Parsing and show that it is able to effectively segment a wide range of concepts from images. The trained networks are further applied to discover visual knowledge in natural scenes."),vt=m(),X=n("img"),bt=m(),G=n("small"),ro=s("UPerNet framework. Taken from the "),Ce=n("a"),so=s("original paper"),io=s("."),wt=m(),C=n("p"),lo=s("This model was contributed by "),ie=n("a"),co=s("nielsr"),po=s(". The original code is based on OpenMMLab\u2019s mmsegmentation "),le=n("a"),fo=s("here"),mo=s("."),yt=m(),q=n("h2"),J=n("a"),tt=n("span"),b(ce.$$.fragment),ho=m(),ot=n("span"),uo=s("Resources"),$t=m(),je=n("p"),go=s("A list of official Hugging Face and community (indicated by \u{1F30E}) resources to help you get started with UPerNet."),Nt=m(),j=n("ul"),pe=n("li"),_o=s("Demo notebooks for UPerNet can be found "),fe=n("a"),vo=s("here"),bo=s("."),wo=m(),T=n("li"),Te=n("a"),yo=s("UperNetForSemanticSegmentation"),$o=s(" is supported by this "),me=n("a"),No=s("example script"),So=s(" and "),he=n("a"),ko=s("notebook"),Uo=s("."),xo=m(),Ae=n("li"),Po=s("See also: "),Fe=n("a"),Eo=s("Semantic segmentation task guide"),St=m(),qe=n("p"),Co=s("If you\u2019re interested in submitting a resource to be included here, please feel free to open a Pull Request and we\u2019ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource."),kt=m(),I=n("h2"),Y=n("a"),nt=n("span"),b(de.$$.fragment),jo=m(),at=n("span"),To=s("Usage"),Ut=m(),Ie=n("p"),Ao=s("UPerNet is a general framework for semantic segmentation. It can be used with any vision backbone, like so:"),xt=m(),b(ue.$$.fragment),Pt=m(),V=n("p"),Fo=s("To use another vision backbone, like "),ze=n("a"),qo=s("ConvNeXt"),Io=s(", simply instantiate the model with the appropriate backbone:"),Et=m(),b(ge.$$.fragment),Ct=m(),De=n("p"),zo=s("Note that this will randomly initialize all the weights of the model."),jt=m(),z=n("h2"),Z=n("a"),rt=n("span"),b(_e.$$.fragment),Do=m(),st=n("span"),Oo=s("UperNetConfig"),Tt=m(),U=n("div"),b(ve.$$.fragment),Lo=m(),D=n("p"),Mo=s("This is the configuration class to store the configuration of an "),Oe=n("a"),Ro=s("UperNetForSemanticSegmentation"),Ho=s(`. It is used to
instantiate an UperNet 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 UperNet
`),be=n("a"),Wo=s("openmmlab/upernet-convnext-tiny"),Bo=s(" architecture."),Xo=m(),O=n("p"),Go=s("Configuration objects inherit from "),Le=n("a"),Jo=s("PretrainedConfig"),Yo=s(` and can be used to control the model outputs. Read the
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