import{S as pr,i as hr,s as fr,e as s,k as m,w as b,t as r,M as ur,c as n,d as t,m as p,a,x as y,h as i,b as l,N as gr,G as o,g as h,y as v,q as w,o as T,B as x,v as _r,L as aa}from"../../chunks/vendor-hf-doc-builder.js";import{T as mr}from"../../chunks/Tip-hf-doc-builder.js";import{D as I}from"../../chunks/Docstring-hf-doc-builder.js";import{C as ra}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as je}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as na}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";import{P as br}from"../../chunks/PipelineTag-hf-doc-builder.js";function yr(U){let f,j,_,u,$;return u=new ra({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> YolosConfig, YolosModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a YOLOS hustvl/yolos-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = YolosConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the hustvl/yolos-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = YolosModel(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=s("p"),j=r("Example:"),_=m(),b(u.$$.fragment)},l(c){f=n(c,"P",{});var g=a(f);j=i(g,"Example:"),g.forEach(t),_=p(c),y(u.$$.fragment,c)},m(c,g){h(c,f,g),o(f,j),h(c,_,g),v(u,c,g),$=!0},p:aa,i(c){$||(w(u.$$.fragment,c),$=!0)},o(c){T(u.$$.fragment,c),$=!1},d(c){c&&t(f),c&&t(_),x(u,c)}}}function vr(U){let f,j,_,u,$;return{c(){f=s("p"),j=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s("code"),u=r("Module"),$=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(c){f=n(c,"P",{});var g=a(f);j=i(g,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(g,"CODE",{});var z=a(_);u=i(z,"Module"),z.forEach(t),$=i(g,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),g.forEach(t)},m(c,g){h(c,f,g),o(f,j),o(f,_),o(_,u),o(f,$)},d(c){c&&t(f)}}}function wr(U){let f,j,_,u,$;return u=new ra({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, YolosModel
<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;hustvl/yolos-small&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = YolosModel.from_pretrained(<span class="hljs-string">&quot;hustvl/yolos-small&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">3401</span>, <span class="hljs-number">384</span>]`}}),{c(){f=s("p"),j=r("Example:"),_=m(),b(u.$$.fragment)},l(c){f=n(c,"P",{});var g=a(f);j=i(g,"Example:"),g.forEach(t),_=p(c),y(u.$$.fragment,c)},m(c,g){h(c,f,g),o(f,j),h(c,_,g),v(u,c,g),$=!0},p:aa,i(c){$||(w(u.$$.fragment,c),$=!0)},o(c){T(u.$$.fragment,c),$=!1},d(c){c&&t(f),c&&t(_),x(u,c)}}}function Tr(U){let f,j,_,u,$;return{c(){f=s("p"),j=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s("code"),u=r("Module"),$=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(c){f=n(c,"P",{});var g=a(f);j=i(g,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(g,"CODE",{});var z=a(_);u=i(z,"Module"),z.forEach(t),$=i(g,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),g.forEach(t)},m(c,g){h(c,f,g),o(f,j),o(f,_),o(_,u),o(f,$)},d(c){c&&t(f)}}}function xr(U){let f,j,_,u,$;return u=new ra({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, AutoModelForObjectDetection
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;hustvl/yolos-tiny&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(<span class="hljs-string">&quot;hustvl/yolos-tiny&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><span class="hljs-comment"># convert outputs (bounding boxes and class logits) to COCO API</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>target_sizes = torch.tensor([image.size[::-<span class="hljs-number">1</span>]])
<span class="hljs-meta">&gt;&gt;&gt; </span>results = image_processor.post_process_object_detection(outputs, threshold=<span class="hljs-number">0.9</span>, target_sizes=target_sizes)[
<span class="hljs-meta">... </span>    <span class="hljs-number">0</span>
<span class="hljs-meta">... </span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> score, label, box <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(results[<span class="hljs-string">&quot;scores&quot;</span>], results[<span class="hljs-string">&quot;labels&quot;</span>], results[<span class="hljs-string">&quot;boxes&quot;</span>]):
<span class="hljs-meta">... </span>    box = [<span class="hljs-built_in">round</span>(i, <span class="hljs-number">2</span>) <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> box.tolist()]
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(
<span class="hljs-meta">... </span>        <span class="hljs-string">f&quot;Detected <span class="hljs-subst">{model.config.id2label[label.item()]}</span> with confidence &quot;</span>
<span class="hljs-meta">... </span>        <span class="hljs-string">f&quot;<span class="hljs-subst">{<span class="hljs-built_in">round</span>(score.item(), <span class="hljs-number">3</span>)}</span> at location <span class="hljs-subst">{box}</span>&quot;</span>
<span class="hljs-meta">... </span>    )
Detected remote <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.994</span> at location [<span class="hljs-number">46.96</span>, <span class="hljs-number">72.61</span>, <span class="hljs-number">181.02</span>, <span class="hljs-number">119.73</span>]
Detected remote <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.975</span> at location [<span class="hljs-number">340.66</span>, <span class="hljs-number">79.19</span>, <span class="hljs-number">372.59</span>, <span class="hljs-number">192.65</span>]
Detected cat <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.984</span> at location [<span class="hljs-number">12.27</span>, <span class="hljs-number">54.25</span>, <span class="hljs-number">319.42</span>, <span class="hljs-number">470.99</span>]
Detected remote <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.922</span> at location [<span class="hljs-number">41.66</span>, <span class="hljs-number">71.96</span>, <span class="hljs-number">178.7</span>, <span class="hljs-number">120.33</span>]
Detected cat <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.914</span> at location [<span class="hljs-number">342.34</span>, <span class="hljs-number">21.48</span>, <span class="hljs-number">638.64</span>, <span class="hljs-number">372.46</span>]`}}),{c(){f=s("p"),j=r("Examples:"),_=m(),b(u.$$.fragment)},l(c){f=n(c,"P",{});var g=a(f);j=i(g,"Examples:"),g.forEach(t),_=p(c),y(u.$$.fragment,c)},m(c,g){h(c,f,g),o(f,j),h(c,_,g),v(u,c,g),$=!0},p:aa,i(c){$||(w(u.$$.fragment,c),$=!0)},o(c){T(u.$$.fragment,c),$=!1},d(c){c&&t(f),c&&t(_),x(u,c)}}}function $r(U){let f,j,_,u,$,c,g,z,Xt,lt,R,H,Po,Ye,Ht,Jo,Kt,ct,F,es,Me,os,ts,ro,ss,ns,dt,io,as,mt,lo,Fo,rs,pt,co,is,ht,mo,P,ls,po,cs,ds,ho,ms,ps,No,hs,fs,ft,K,ia,ut,ee,us,fo,gs,_s,gt,N,bs,Ce,ys,vs,Ee,ws,Ts,_t,S,oe,Do,Ie,xs,Ro,$s,bt,uo,js,yt,Ue,vt,te,L,Ys,go,Ms,Cs,ke,Es,Is,Us,_o,ks,bo,Os,wt,yo,zs,Tt,W,se,So,Oe,Ps,Lo,Js,xt,M,ze,Fs,A,Ns,vo,Ds,Rs,Pe,Ss,Ls,Ws,Z,As,wo,Zs,qs,To,Vs,Gs,Bs,ne,$t,q,ae,Wo,Je,Qs,Ao,Xs,jt,Y,Fe,Hs,Zo,Ks,en,re,Ne,on,qo,tn,sn,ie,De,nn,Vo,an,rn,le,Re,ln,Se,cn,xo,dn,mn,Yt,V,ce,Go,Le,pn,Bo,hn,Mt,C,We,fn,de,Ae,un,Qo,gn,_n,me,Ze,bn,Xo,yn,vn,pe,qe,wn,Ve,Tn,$o,xn,$n,Ct,G,he,Ho,Ge,jn,Ko,Yn,Et,J,Be,Mn,Qe,Cn,Xe,En,In,Un,k,He,kn,B,On,jo,zn,Pn,et,Jn,Fn,Nn,fe,Dn,ue,It,Q,ge,ot,Ke,Rn,tt,Sn,Ut,E,eo,Ln,st,Wn,An,oo,Zn,to,qn,Vn,Gn,O,so,Bn,X,Qn,Yo,Xn,Hn,nt,Kn,ea,oa,_e,ta,be,kt;return c=new je({}),Ye=new je({}),Ie=new je({}),Ue=new br({props:{pipeline:"object-detection"}}),Oe=new je({}),ze=new I({props:{name:"class transformers.YolosConfig",anchor:"transformers.YolosConfig",parameters:[{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.0"},{name:"attention_probs_dropout_prob",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"image_size",val:" = [512, 864]"},{name:"patch_size",val:" = 16"},{name:"num_channels",val:" = 3"},{name:"qkv_bias",val:" = True"},{name:"num_detection_tokens",val:" = 100"},{name:"use_mid_position_embeddings",val:" = True"},{name:"auxiliary_loss",val:" = False"},{name:"class_cost",val:" = 1"},{name:"bbox_cost",val:" = 5"},{name:"giou_cost",val:" = 2"},{name:"bbox_loss_coefficient",val:" = 5"},{name:"giou_loss_coefficient",val:" = 2"},{name:"eos_coefficient",val:" = 0.1"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.YolosConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.YolosConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.YolosConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.YolosConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.YolosConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.YolosConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.YolosConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.YolosConfig.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.YolosConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.YolosConfig.image_size",description:`<strong>image_size</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[512, 864]</code>) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.YolosConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to <code>16</code>) &#x2014;
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<li><code>&quot;channels_last&quot;</code> or <code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
<li><code>&quot;none&quot;</code> or <code>ChannelDimension.NONE</code>: image in (height, width) format.</li>
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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>
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<li><code>TensorType.PYTORCH</code> or <code>&apos;pt&apos;</code>: Return a batch of type <code>torch.Tensor</code>.</li>
<li><code>TensorType.NUMPY</code> or <code>&apos;np&apos;</code>: Return a batch of type <code>np.ndarray</code>.</li>
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Tensor of shape <code>(batch_size, 2)</code> or list of tuples (<code>Tuple[int, int]</code>) containing the target size
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`,returnType:`
<p><code>List[Dict]</code></p>
`}}),Ge=new je({}),Be=new I({props:{name:"class transformers.YolosModel",anchor:"transformers.YolosModel",parameters:[{name:"config",val:": YolosConfig"},{name:"add_pooling_layer",val:": bool = True"}],parametersDescription:[{anchor:"transformers.YolosModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</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/yolos/modeling_yolos.py#L597"}}),He=new I({props:{name:"forward",anchor:"transformers.YolosModel.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.YolosModel.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/mobilevit#transformers.MobileViTFeatureExtractor.__call__">YolosImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.YolosModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.YolosModel.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. See <code>attentions</code> under returned
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more detail.`,name:"output_hidden_states"},{anchor:"transformers.YolosModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</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/main/en/model_doc/yolos#transformers.YolosConfig"
>YolosConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \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 of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</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, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling"
>transformers.modeling_outputs.BaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),fe=new mr({props:{$$slots:{default:[vr]},$$scope:{ctx:U}}}),ue=new na({props:{anchor:"transformers.YolosModel.forward.example",$$slots:{default:[wr]},$$scope:{ctx:U}}}),Ke=new je({}),eo=new I({props:{name:"class transformers.YolosForObjectDetection",anchor:"transformers.YolosForObjectDetection",parameters:[{name:"config",val:": YolosConfig"}],parametersDescription:[{anchor:"transformers.YolosForObjectDetection.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
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Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.YolosForObjectDetection.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
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batch respectively). The class labels themselves should be a <code>torch.LongTensor</code> of len <code>(number of bounding boxes in the image,)</code> and the boxes a <code>torch.FloatTensor</code> of shape <code>(number of bounding boxes in the image, 4)</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/yolos/modeling_yolos.py#L732",returnDescription:`
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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/yolos#transformers.YolosConfig"
>YolosConfig</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> are provided)) \u2014 Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
scale-invariant IoU loss.</li>
<li><strong>loss_dict</strong> (<code>Dict</code>, <em>optional</em>) \u2014 A dictionary containing the individual losses. Useful for logging.</li>
<li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_queries, num_classes + 1)</code>) \u2014 Classification logits (including no-object) for all queries.</li>
<li><strong>pred_boxes</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_queries, 4)</code>) \u2014 Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
possible padding). You can use <code>post_process()</code> to retrieve the unnormalized bounding
boxes.</li>
<li><strong>auxiliary_outputs</strong> (<code>list[Dict]</code>, <em>optional</em>) \u2014 Optional, only returned when auxilary losses are activated (i.e. <code>config.auxiliary_loss</code> is set to <code>True</code>)
and labels are provided. It is a list of dictionaries containing the two above keys (<code>logits</code> and
<code>pred_boxes</code>) for each decoder layer.</li>
<li><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the decoder of the model.</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 layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states of
the model at the output of each layer plus the optional initial embedding outputs.</li>
<li><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, sequence_length, sequence_length)</code>. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.</li>
</ul>
`,returnType:`
<p><code>transformers.models.yolos.modeling_yolos.YolosObjectDetectionOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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