import{S as Oa,i as Ma,s as Na,e as n,k as m,w,t as r,M as La,c as s,d as o,m as h,a,x as y,h as i,b as d,N as Sa,G as e,g as p,y as D,q as T,o as x,B as $,v as Ra,L as qs}from"../../chunks/vendor-hf-doc-builder.js";import{T as Fa}from"../../chunks/Tip-hf-doc-builder.js";import{D as X}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Fs}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ge}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as As}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Ua(z){let f,v,_,u,b;return u=new Fs({props:{code:`from transformers import DetaConfig, DetaModel

# Initializing a DETA SenseTime/deformable-detr style configuration
configuration = DetaConfig()

# Initializing a model (with random weights) from the SenseTime/deformable-detr style configuration
model = DetaModel(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> DetaConfig, DetaModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a DETA SenseTime/deformable-detr style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = DetaConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the SenseTime/deformable-detr style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DetaModel(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=n("p"),v=r("Examples:"),_=m(),w(u.$$.fragment)},l(c){f=s(c,"P",{});var g=a(f);v=i(g,"Examples:"),g.forEach(o),_=h(c),y(u.$$.fragment,c)},m(c,g){p(c,f,g),e(f,v),p(c,_,g),D(u,c,g),b=!0},p:qs,i(c){b||(T(u.$$.fragment,c),b=!0)},o(c){x(u.$$.fragment,c),b=!1},d(c){c&&o(f),c&&o(_),$(u,c)}}}function Ha(z){let f,v,_,u,b;return{c(){f=n("p"),v=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),u=r("Module"),b=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=s(c,"P",{});var g=a(f);v=i(g,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(g,"CODE",{});var A=a(_);u=i(A,"Module"),A.forEach(o),b=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(o)},m(c,g){p(c,f,g),e(f,v),e(f,_),e(_,u),e(f,b)},d(c){c&&o(f)}}}function Wa(z){let f,v,_,u,b;return u=new Fs({props:{code:`from transformers import AutoImageProcessor, DetaModel
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = AutoImageProcessor.from_pretrained("jozhang97/deta-swin-large-o365")
model = DetaModel.from_pretrained("jozhang97/deta-swin-large-o365", two_stage=False)

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

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, DetaModel
<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;jozhang97/deta-swin-large-o365&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DetaModel.from_pretrained(<span class="hljs-string">&quot;jozhang97/deta-swin-large-o365&quot;</span>, two_stage=<span class="hljs-literal">False</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>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">900</span>, <span class="hljs-number">256</span>]`}}),{c(){f=n("p"),v=r("Examples:"),_=m(),w(u.$$.fragment)},l(c){f=s(c,"P",{});var g=a(f);v=i(g,"Examples:"),g.forEach(o),_=h(c),y(u.$$.fragment,c)},m(c,g){p(c,f,g),e(f,v),p(c,_,g),D(u,c,g),b=!0},p:qs,i(c){b||(T(u.$$.fragment,c),b=!0)},o(c){x(u.$$.fragment,c),b=!1},d(c){c&&o(f),c&&o(_),$(u,c)}}}function Va(z){let f,v,_,u,b;return{c(){f=n("p"),v=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n("code"),u=r("Module"),b=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=s(c,"P",{});var g=a(f);v=i(g,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=s(g,"CODE",{});var A=a(_);u=i(A,"Module"),A.forEach(o),b=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(o)},m(c,g){p(c,f,g),e(f,v),e(f,_),e(_,u),e(f,b)},d(c){c&&o(f)}}}function Ba(z){let f,v,_,u,b;return u=new Fs({props:{code:`from transformers import AutoImageProcessor, DetaForObjectDetection
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = AutoImageProcessor.from_pretrained("jozhang97/deta-swin-large")
model = DetaForObjectDetection.from_pretrained("jozhang97/deta-swin-large")

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

# convert outputs (bounding boxes and class logits) to COCO API
target_sizes = torch.tensor([image.size[::-1]])
results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[
    0
]
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    box = [round(i, 2) for i in box.tolist()]
    print(
        f"Detected {model.config.id2label[label.item()]} with confidence "
        f"{round(score.item(), 3)} at location {box}"
    )`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, DetaForObjectDetection
<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;jozhang97/deta-swin-large&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DetaForObjectDetection.from_pretrained(<span class="hljs-string">&quot;jozhang97/deta-swin-large&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">0.5</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 cat <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.683</span> at location [<span class="hljs-number">345.85</span>, <span class="hljs-number">23.68</span>, <span class="hljs-number">639.86</span>, <span class="hljs-number">372.83</span>]
Detected cat <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.683</span> at location [<span class="hljs-number">8.8</span>, <span class="hljs-number">52.49</span>, <span class="hljs-number">316.93</span>, <span class="hljs-number">473.45</span>]
Detected remote <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.568</span> at location [<span class="hljs-number">40.02</span>, <span class="hljs-number">73.75</span>, <span class="hljs-number">175.96</span>, <span class="hljs-number">117.33</span>]
Detected remote <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.546</span> at location [<span class="hljs-number">333.68</span>, <span class="hljs-number">77.13</span>, <span class="hljs-number">370.12</span>, <span class="hljs-number">187.51</span>]`}}),{c(){f=n("p"),v=r("Examples:"),_=m(),w(u.$$.fragment)},l(c){f=s(c,"P",{});var g=a(f);v=i(g,"Examples:"),g.forEach(o),_=h(c),y(u.$$.fragment,c)},m(c,g){p(c,f,g),e(f,v),p(c,_,g),D(u,c,g),b=!0},p:qs,i(c){b||(T(u.$$.fragment,c),b=!0)},o(c){x(u.$$.fragment,c),b=!1},d(c){c&&o(f),c&&o(_),$(u,c)}}}function Ja(z){let f,v,_,u,b,c,g,A,Co,Vt,L,K,Dt,_e,Io,Tt,Ao,Bt,q,qo,be,Fo,Oo,Ze,Mo,No,Jt,Xe,Lo,Gt,Ke,xt,So,Zt,Ye,Ro,Xt,Qe,ve,Uo,et,Ho,Wo,Kt,Y,Os,Yt,Q,Vo,tt,Bo,Jo,Qt,F,Go,we,Zo,Xo,ye,Ko,Yo,eo,S,ee,$t,De,Qo,jt,en,to,ot,tn,oo,te,Te,on,xe,nn,sn,an,nt,rn,st,dn,no,at,cn,so,R,oe,Pt,$e,ln,Et,mn,ao,j,je,hn,U,pn,rt,fn,un,Pe,gn,_n,bn,H,vn,it,wn,yn,dt,Dn,Tn,xn,ne,$n,se,Ee,jn,W,Pn,ct,En,kn,kt,zn,Cn,ro,V,ae,zt,ke,In,Ct,An,io,k,ze,qn,It,Fn,On,re,Ce,Mn,At,Nn,Ln,ie,Ie,Sn,Ae,Rn,lt,Un,Hn,co,B,de,qt,qe,Wn,Ft,Vn,lo,P,Fe,Bn,Ot,Jn,Gn,Oe,Zn,mt,Xn,Kn,Yn,Me,Qn,Ne,es,ts,os,C,Le,ns,J,ss,ht,as,rs,Mt,is,ds,cs,ce,ls,le,mo,G,me,Nt,Se,ms,Lt,hs,ho,E,Re,ps,St,fs,us,Ue,gs,pt,_s,bs,vs,He,ws,We,ys,Ds,Ts,I,Ve,xs,Z,$s,ft,js,Ps,Rt,Es,ks,zs,he,Cs,pe,po;return c=new Ge({}),_e=new Ge({}),De=new Ge({}),$e=new Ge({}),je=new X({props:{name:"class transformers.DetaConfig",anchor:"transformers.DetaConfig",parameters:[{name:"backbone_config",val:" = None"},{name:"num_queries",val:" = 900"},{name:"max_position_embeddings",val:" = 2048"},{name:"encoder_layers",val:" = 6"},{name:"encoder_ffn_dim",val:" = 2048"},{name:"encoder_attention_heads",val:" = 8"},{name:"decoder_layers",val:" = 6"},{name:"decoder_ffn_dim",val:" = 1024"},{name:"decoder_attention_heads",val:" = 8"},{name:"encoder_layerdrop",val:" = 0.0"},{name:"is_encoder_decoder",val:" = True"},{name:"activation_function",val:" = 'relu'"},{name:"d_model",val:" = 256"},{name:"dropout",val:" = 0.1"},{name:"attention_dropout",val:" = 0.0"},{name:"activation_dropout",val:" = 0.0"},{name:"init_std",val:" = 0.02"},{name:"init_xavier_std",val:" = 1.0"},{name:"return_intermediate",val:" = True"},{name:"auxiliary_loss",val:" = False"},{name:"position_embedding_type",val:" = 'sine'"},{name:"num_feature_levels",val:" = 5"},{name:"encoder_n_points",val:" = 4"},{name:"decoder_n_points",val:" = 4"},{name:"two_stage",val:" = True"},{name:"two_stage_num_proposals",val:" = 300"},{name:"with_box_refine",val:" = True"},{name:"assign_first_stage",val:" = True"},{name:"class_cost",val:" = 1"},{name:"bbox_cost",val:" = 5"},{name:"giou_cost",val:" = 2"},{name:"mask_loss_coefficient",val:" = 1"},{name:"dice_loss_coefficient",val:" = 1"},{name:"bbox_loss_coefficient",val:" = 5"},{name:"giou_loss_coefficient",val:" = 2"},{name:"eos_coefficient",val:" = 0.1"},{name:"focal_alpha",val:" = 0.25"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DetaConfig.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.DetaConfig.num_queries",description:`<strong>num_queries</strong> (<code>int</code>, <em>optional</em>, defaults to 900) &#x2014;
Number of object queries, i.e. detection slots. This is the maximal number of objects <a href="/docs/transformers/v4.29.0/en/model_doc/deta#transformers.DetaModel">DetaModel</a> can
detect in a single image. In case <code>two_stage</code> is set to <code>True</code>, we use <code>two_stage_num_proposals</code> instead.`,name:"num_queries"},{anchor:"transformers.DetaConfig.d_model",description:`<strong>d_model</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
Dimension of the layers.`,name:"d_model"},{anchor:"transformers.DetaConfig.encoder_layers",description:`<strong>encoder_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 6) &#x2014;
Number of encoder layers.`,name:"encoder_layers"},{anchor:"transformers.DetaConfig.decoder_layers",description:`<strong>decoder_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 6) &#x2014;
Number of decoder layers.`,name:"decoder_layers"},{anchor:"transformers.DetaConfig.encoder_attention_heads",description:`<strong>encoder_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"encoder_attention_heads"},{anchor:"transformers.DetaConfig.decoder_attention_heads",description:`<strong>decoder_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of attention heads for each attention layer in the Transformer decoder.`,name:"decoder_attention_heads"},{anchor:"transformers.DetaConfig.decoder_ffn_dim",description:`<strong>decoder_ffn_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
Dimension of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in decoder.`,name:"decoder_ffn_dim"},{anchor:"transformers.DetaConfig.encoder_ffn_dim",description:`<strong>encoder_ffn_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
Dimension of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in decoder.`,name:"encoder_ffn_dim"},{anchor:"transformers.DetaConfig.activation_function",description:`<strong>activation_function</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;relu&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;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"activation_function"},{anchor:"transformers.DetaConfig.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.DetaConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.DetaConfig.activation_dropout",description:`<strong>activation_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for activations inside the fully connected layer.`,name:"activation_dropout"},{anchor:"transformers.DetaConfig.init_std",description:`<strong>init_std</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:"init_std"},{anchor:"transformers.DetaConfig.init_xavier_std",description:`<strong>init_xavier_std</strong> (<code>float</code>, <em>optional</em>, defaults to 1) &#x2014;
The scaling factor used for the Xavier initialization gain in the HM Attention map module.
encoder_layerdrop &#x2014; (<code>float</code>, <em>optional</em>, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see <a href="https://arxiv.org/abs/1909.11556" rel="nofollow">https://arxiv.org/abs/1909.11556</a>)
for more details.`,name:"init_xavier_std"},{anchor:"transformers.DetaConfig.auxiliary_loss",description:`<strong>auxiliary_loss</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether auxiliary decoding losses (loss at each decoder layer) are to be used.`,name:"auxiliary_loss"},{anchor:"transformers.DetaConfig.position_embedding_type",description:`<strong>position_embedding_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;sine&quot;</code>) &#x2014;
Type of position embeddings to be used on top of the image features. One of <code>&quot;sine&quot;</code> or <code>&quot;learned&quot;</code>.`,name:"position_embedding_type"},{anchor:"transformers.DetaConfig.class_cost",description:`<strong>class_cost</strong> (<code>float</code>, <em>optional</em>, defaults to 1) &#x2014;
Relative weight of the classification error in the Hungarian matching cost.`,name:"class_cost"},{anchor:"transformers.DetaConfig.bbox_cost",description:`<strong>bbox_cost</strong> (<code>float</code>, <em>optional</em>, defaults to 5) &#x2014;
Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.`,name:"bbox_cost"},{anchor:"transformers.DetaConfig.giou_cost",description:`<strong>giou_cost</strong> (<code>float</code>, <em>optional</em>, defaults to 2) &#x2014;
Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.`,name:"giou_cost"},{anchor:"transformers.DetaConfig.mask_loss_coefficient",description:`<strong>mask_loss_coefficient</strong> (<code>float</code>, <em>optional</em>, defaults to 1) &#x2014;
Relative weight of the Focal loss in the panoptic segmentation loss.`,name:"mask_loss_coefficient"},{anchor:"transformers.DetaConfig.dice_loss_coefficient",description:`<strong>dice_loss_coefficient</strong> (<code>float</code>, <em>optional</em>, defaults to 1) &#x2014;
Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.`,name:"dice_loss_coefficient"},{anchor:"transformers.DetaConfig.bbox_loss_coefficient",description:`<strong>bbox_loss_coefficient</strong> (<code>float</code>, <em>optional</em>, defaults to 5) &#x2014;
Relative weight of the L1 bounding box loss in the object detection loss.`,name:"bbox_loss_coefficient"},{anchor:"transformers.DetaConfig.giou_loss_coefficient",description:`<strong>giou_loss_coefficient</strong> (<code>float</code>, <em>optional</em>, defaults to 2) &#x2014;
Relative weight of the generalized IoU loss in the object detection loss.`,name:"giou_loss_coefficient"},{anchor:"transformers.DetaConfig.eos_coefficient",description:`<strong>eos_coefficient</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
Relative classification weight of the &#x2018;no-object&#x2019; class in the object detection loss.`,name:"eos_coefficient"},{anchor:"transformers.DetaConfig.num_feature_levels",description:`<strong>num_feature_levels</strong> (<code>int</code>, <em>optional</em>, defaults to 5) &#x2014;
The number of input feature levels.`,name:"num_feature_levels"},{anchor:"transformers.DetaConfig.encoder_n_points",description:`<strong>encoder_n_points</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The number of sampled keys in each feature level for each attention head in the encoder.`,name:"encoder_n_points"},{anchor:"transformers.DetaConfig.decoder_n_points",description:`<strong>decoder_n_points</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The number of sampled keys in each feature level for each attention head in the decoder.`,name:"decoder_n_points"},{anchor:"transformers.DetaConfig.two_stage",description:`<strong>two_stage</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of
DETA, which are further fed into the decoder for iterative bounding box refinement.`,name:"two_stage"},{anchor:"transformers.DetaConfig.two_stage_num_proposals",description:`<strong>two_stage_num_proposals</strong> (<code>int</code>, <em>optional</em>, defaults to 300) &#x2014;
The number of region proposals to be generated, in case <code>two_stage</code> is set to <code>True</code>.`,name:"two_stage_num_proposals"},{anchor:"transformers.DetaConfig.with_box_refine",description:`<strong>with_box_refine</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to apply iterative bounding box refinement, where each decoder layer refines the bounding boxes
based on the predictions from the previous layer.`,name:"with_box_refine"},{anchor:"transformers.DetaConfig.focal_alpha",description:`<strong>focal_alpha</strong> (<code>float</code>, <em>optional</em>, defaults to 0.25) &#x2014;
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Resampling filter to use if resizing the image.`,name:"resample"},{anchor:"transformers.DetaImageProcessor.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
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Scale factor to use if rescaling the image. Can be overridden by the <code>rescale_factor</code> parameter in the
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do_normalize &#x2014;
Controls whether to normalize the image. Can be overridden by the <code>do_normalize</code> parameter in the
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Mean values to use when normalizing the image. Can be a single value or a list of values, one for each
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Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one
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Controls whether to pad the image to the largest image in a batch and create a pixel mask. Can be
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List of annotations associated with the image or batch of images. If annotionation is for object
detection, the annotations should be a dictionary with the following keys:<ul>
<li>&#x201C;image_id&#x201D; (<code>int</code>): The image id.</li>
<li>&#x201C;annotations&#x201D; (<code>List[Dict]</code>): List of annotations for an image. Each annotation should be a
dictionary. An image can have no annotations, in which case the list should be empty.
If annotionation is for segmentation, the annotations should be a dictionary with the following keys:</li>
<li>&#x201C;image_id&#x201D; (<code>int</code>): The image id.</li>
<li>&#x201C;segments_info&#x201D; (<code>List[Dict]</code>): List of segments for an image. Each segment should be a dictionary.
An image can have no segments, in which case the list should be empty.</li>
<li>&#x201C;file_name&#x201D; (<code>str</code>): The file name of the image.</li>
</ul>`,name:"annotations"},{anchor:"transformers.DetaImageProcessor.preprocess.return_segmentation_masks",description:`<strong>return_segmentation_masks</strong> (<code>bool</code>, <em>optional</em>, defaults to self.return_segmentation_masks) &#x2014;
Whether to return segmentation masks.`,name:"return_segmentation_masks"},{anchor:"transformers.DetaImageProcessor.preprocess.masks_path",description:`<strong>masks_path</strong> (<code>str</code> or <code>pathlib.Path</code>, <em>optional</em>) &#x2014;
Path to the directory containing the segmentation masks.`,name:"masks_path"},{anchor:"transformers.DetaImageProcessor.preprocess.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to self.do_resize) &#x2014;
Whether to resize the image.`,name:"do_resize"},{anchor:"transformers.DetaImageProcessor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to self.size) &#x2014;
Size of the image after resizing.`,name:"size"},{anchor:"transformers.DetaImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to self.resample) &#x2014;
Resampling filter to use when resizing the image.`,name:"resample"},{anchor:"transformers.DetaImageProcessor.preprocess.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to self.do_rescale) &#x2014;
Whether to rescale the image.`,name:"do_rescale"},{anchor:"transformers.DetaImageProcessor.preprocess.rescale_factor",description:`<strong>rescale_factor</strong> (<code>float</code>, <em>optional</em>, defaults to self.rescale_factor) &#x2014;
Rescale factor to use when rescaling the image.`,name:"rescale_factor"},{anchor:"transformers.DetaImageProcessor.preprocess.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to self.do_normalize) &#x2014;
Whether to normalize the image.`,name:"do_normalize"},{anchor:"transformers.DetaImageProcessor.preprocess.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to self.image_mean) &#x2014;
Mean to use when normalizing the image.`,name:"image_mean"},{anchor:"transformers.DetaImageProcessor.preprocess.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to self.image_std) &#x2014;
Standard deviation to use when normalizing the image.`,name:"image_std"},{anchor:"transformers.DetaImageProcessor.preprocess.do_pad",description:`<strong>do_pad</strong> (<code>bool</code>, <em>optional</em>, defaults to self.do_pad) &#x2014;
Whether to pad the image.`,name:"do_pad"},{anchor:"transformers.DetaImageProcessor.preprocess.format",description:`<strong>format</strong> (<code>str</code> or <code>AnnotionFormat</code>, <em>optional</em>, defaults to self.format) &#x2014;
Format of the annotations.`,name:"format"},{anchor:"transformers.DetaImageProcessor.preprocess.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <code>TensorType</code>, <em>optional</em>, defaults to self.return_tensors) &#x2014;
Type of tensors to return. If <code>None</code>, will return the list of images.`,name:"return_tensors"},{anchor:"transformers.DetaImageProcessor.preprocess.data_format",description:`<strong>data_format</strong> (<code>str</code> or <code>ChannelDimension</code>, <em>optional</em>, defaults to self.data_format) &#x2014;
The channel dimension format of the image. If not provided, it will be the same as the input image.`,name:"data_format"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/deta/image_processing_deta.py#L739"}}),Ie=new X({props:{name:"post_process_object_detection",anchor:"transformers.DetaImageProcessor.post_process_object_detection",parameters:[{name:"outputs",val:""},{name:"threshold",val:": float = 0.5"},{name:"target_sizes",val:": typing.Union[transformers.utils.generic.TensorType, typing.List[typing.Tuple]] = None"},{name:"nms_threshold",val:": float = 0.7"}],parametersDescription:[{anchor:"transformers.DetaImageProcessor.post_process_object_detection.outputs",description:`<strong>outputs</strong> (<code>DetrObjectDetectionOutput</code>) &#x2014;
Raw outputs of the model.`,name:"outputs"},{anchor:"transformers.DetaImageProcessor.post_process_object_detection.threshold",description:`<strong>threshold</strong> (<code>float</code>, <em>optional</em>, defaults to 0.5) &#x2014;
Score threshold to keep object detection predictions.`,name:"threshold"},{anchor:"transformers.DetaImageProcessor.post_process_object_detection.target_sizes",description:`<strong>target_sizes</strong> (<code>torch.Tensor</code> or <code>List[Tuple[int, int]]</code>, <em>optional</em>) &#x2014;
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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NMS threshold.`,name:"nms_threshold"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/deta/image_processing_deta.py#L936",returnDescription:`
<p>A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
in the batch as predicted by the model.</p>
`,returnType:`
<p><code>List[Dict]</code></p>
`}}),qe=new Ge({}),Fe=new X({props:{name:"class transformers.DetaModel",anchor:"transformers.DetaModel",parameters:[{name:"config",val:": DetaConfig"}],parametersDescription:[{anchor:"transformers.DetaModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a>) &#x2014;
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
<a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/deta/modeling_deta.py#L1359"}}),Le=new X({props:{name:"forward",anchor:"transformers.DetaModel.forward",parameters:[{name:"pixel_values",val:""},{name:"pixel_mask",val:" = None"},{name:"decoder_attention_mask",val:" = None"},{name:"encoder_outputs",val:" = None"},{name:"inputs_embeds",val:" = None"},{name:"decoder_inputs_embeds",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = None"}],parametersDescription:[{anchor:"transformers.DetaModel.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.</p>
<p>Pixel values can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <code>AutoImageProcessor.__call__()</code> for details.`,name:"pixel_values"},{anchor:"transformers.DetaModel.forward.pixel_mask",description:`<strong>pixel_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, height, width)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"pixel_mask"},{anchor:"transformers.DetaModel.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_queries)</code>, <em>optional</em>) &#x2014;
Not used by default. Can be used to mask object queries.`,name:"decoder_attention_mask"},{anchor:"transformers.DetaModel.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"transformers.DetaModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
can choose to directly pass a flattened representation of an image.`,name:"inputs_embeds"},{anchor:"transformers.DetaModel.forward.decoder_inputs_embeds",description:`<strong>decoder_inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_queries, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
embedded representation.`,name:"decoder_inputs_embeds"},{anchor:"transformers.DetaModel.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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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.DetaModel.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"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/deta/modeling_deta.py#L1524",returnDescription:`
<p>A <code>transformers.models.deta.modeling_deta.DetaModelOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/deta#transformers.DetaConfig"
>DetaConfig</a>) and inputs.</p>
<ul>
<li><strong>init_reference_points</strong> (<code>torch.FloatTensor</code> of shape  <code>(batch_size, num_queries, 4)</code>) \u2014 Initial reference points sent through the Transformer decoder.</li>
<li><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_queries, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the decoder of the model.</li>
<li><strong>intermediate_hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.decoder_layers, num_queries, hidden_size)</code>) \u2014 Stacked intermediate hidden states (output of each layer of the decoder).</li>
<li><strong>intermediate_reference_points</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.decoder_layers, num_queries, 4)</code>) \u2014 Stacked intermediate reference points (reference points of each layer of the decoder).</li>
<li><strong>decoder_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 + one for the output of each layer) of
shape <code>(batch_size, num_queries, hidden_size)</code>. Hidden-states of the decoder at the output of each layer
plus the initial embedding outputs.</li>
<li><strong>decoder_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, num_queries, num_queries)</code>. Attentions weights of the decoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.</li>
<li><strong>cross_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_queries, num_heads, 4, 4)</code>.
Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</li>
<li><strong>encoder_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 encoder of the model.</li>
<li><strong>encoder_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 + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.</li>
<li><strong>encoder_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_queries, num_heads, 4, 4)</code>.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</li>
<li><strong>enc_outputs_class</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>, <em>optional</em>, returned when <code>config.with_box_refine=True</code> and <code>config.two_stage=True</code>) \u2014 Predicted bounding boxes scores where the top <code>config.two_stage_num_proposals</code> scoring bounding boxes are
picked as region proposals in the first stage. Output of bounding box binary classification (i.e.
foreground and background).</li>
<li><strong>enc_outputs_coord_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, 4)</code>, <em>optional</em>, returned when <code>config.with_box_refine=True</code> and <code>config.two_stage=True</code>) \u2014 Logits of predicted bounding boxes coordinates in the first stage.</li>
</ul>
`,returnType:`
<p><code>transformers.models.deta.modeling_deta.DetaModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ce=new Fa({props:{$$slots:{default:[Ha]},$$scope:{ctx:z}}}),le=new As({props:{anchor:"transformers.DetaModel.forward.example",$$slots:{default:[Wa]},$$scope:{ctx:z}}}),Se=new Ge({}),Re=new X({props:{name:"class transformers.DetaForObjectDetection",anchor:"transformers.DetaForObjectDetection",parameters:[{name:"config",val:": DetaConfig"}],parametersDescription:[{anchor:"transformers.DetaForObjectDetection.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.29.0/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a>) &#x2014;
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
<a href="/docs/transformers/v4.29.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/deta/modeling_deta.py#L1776"}}),Ve=new X({props:{name:"forward",anchor:"transformers.DetaForObjectDetection.forward",parameters:[{name:"pixel_values",val:""},{name:"pixel_mask",val:" = None"},{name:"decoder_attention_mask",val:" = None"},{name:"encoder_outputs",val:" = None"},{name:"inputs_embeds",val:" = None"},{name:"decoder_inputs_embeds",val:" = None"},{name:"labels",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = None"}],parametersDescription:[{anchor:"transformers.DetaForObjectDetection.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.</p>
<p>Pixel values can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <code>AutoImageProcessor.__call__()</code> for details.`,name:"pixel_values"},{anchor:"transformers.DetaForObjectDetection.forward.pixel_mask",description:`<strong>pixel_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, height, width)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"pixel_mask"},{anchor:"transformers.DetaForObjectDetection.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_queries)</code>, <em>optional</em>) &#x2014;
Not used by default. Can be used to mask object queries.`,name:"decoder_attention_mask"},{anchor:"transformers.DetaForObjectDetection.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.`,name:"encoder_outputs"},{anchor:"transformers.DetaForObjectDetection.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
can choose to directly pass a flattened representation of an image.`,name:"inputs_embeds"},{anchor:"transformers.DetaForObjectDetection.forward.decoder_inputs_embeds",description:`<strong>decoder_inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_queries, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
embedded representation.`,name:"decoder_inputs_embeds"},{anchor:"transformers.DetaForObjectDetection.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
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DetaForObjectDetection.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.DetaForObjectDetection.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.DetaForObjectDetection.forward.labels",description:`<strong>labels</strong> (<code>List[Dict]</code> of len <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
following 2 keys: &#x2018;class_labels&#x2019; and &#x2018;boxes&#x2019; (the class labels and bounding boxes of an image in the 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/v4.29.0/src/transformers/models/deta/modeling_deta.py#L1829",returnDescription:`
<p>A <code>transformers.models.deta.modeling_deta.DetaObjectDetectionOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/deta#transformers.DetaConfig"
>DetaConfig</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>~DetaProcessor.post_process_object_detection</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, num_queries, 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>decoder_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 + one for the output of each layer) of
shape <code>(batch_size, num_queries, hidden_size)</code>. Hidden-states of the decoder at the output of each layer
plus the initial embedding outputs.</li>
<li><strong>decoder_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, num_queries, num_queries)</code>. Attentions weights of the decoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.</li>
<li><strong>cross_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_queries, num_heads, 4, 4)</code>.
Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</li>
<li><strong>encoder_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 encoder of the model.</li>
<li><strong>encoder_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 + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.</li>
<li><strong>encoder_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, sequence_length, num_heads, 4, 4)</code>. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average
in the self-attention heads.</li>
<li><strong>intermediate_hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.decoder_layers, num_queries, hidden_size)</code>) \u2014 Stacked intermediate hidden states (output of each layer of the decoder).</li>
<li><strong>intermediate_reference_points</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.decoder_layers, num_queries, 4)</code>) \u2014 Stacked intermediate reference points (reference points of each layer of the decoder).</li>
<li><strong>init_reference_points</strong> (<code>torch.FloatTensor</code> of shape  <code>(batch_size, num_queries, 4)</code>) \u2014 Initial reference points sent through the Transformer decoder.</li>
<li><strong>enc_outputs_class</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>, <em>optional</em>, returned when <code>config.with_box_refine=True</code> and <code>config.two_stage=True</code>) \u2014 Predicted bounding boxes scores where the top <code>config.two_stage_num_proposals</code> scoring bounding boxes are
picked as region proposals in the first stage. Output of bounding box binary classification (i.e.
foreground and background).</li>
<li><strong>enc_outputs_coord_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, 4)</code>, <em>optional</em>, returned when <code>config.with_box_refine=True</code> and <code>config.two_stage=True</code>) \u2014 Logits of predicted bounding boxes coordinates in the first stage.</li>
</ul>
`,returnType:`
<p><code>transformers.models.deta.modeling_deta.DetaObjectDetectionOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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The original code can be found `),ye=s(gt,"A",{href:!0,rel:!0});var Xs=a(ye);Ko=i(Xs,"here"),Xs.forEach(o),Yo=i(gt,"."),gt.forEach(o),eo=h(t),S=s(t,"H2",{class:!0});var go=a(S);ee=s(go,"A",{id:!0,class:!0,href:!0});var Ks=a(ee);$t=s(Ks,"SPAN",{});var Ys=a($t);y(De.$$.fragment,Ys),Ys.forEach(o),Ks.forEach(o),Qo=h(go),jt=s(go,"SPAN",{});var Qs=a(jt);en=i(Qs,"Resources"),Qs.forEach(o),go.forEach(o),to=h(t),ot=s(t,"P",{});var ea=a(ot);tn=i(ea,"A list of official Hugging Face and community (indicated by \u{1F30E}) resources to help you get started with DETA."),ea.forEach(o),oo=h(t),te=s(t,"UL",{});var _o=a(te);Te=s(_o,"LI",{});var bo=a(Te);on=i(bo,"Demo notebooks for DETA can be found "),xe=s(bo,"A",{href:!0,rel:!0});var ta=a(xe);nn=i(ta,"here"),ta.forEach(o),sn=i(bo,"."),bo.forEach(o),an=h(_o),nt=s(_o,"LI",{});var Is=a(nt);rn=i(Is,"See also: "),st=s(Is,"A",{href:!0});var oa=a(st);dn=i(oa,"Object detection task guide"),oa.forEach(o),Is.forEach(o),_o.forEach(o),no=h(t),at=s(t,"P",{});var na=a(at);cn=i(na,"If you\u2019re interested in submitting a resource to be included here, please feel free to open a Pull Request and we\u2019ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource."),na.forEach(o),so=h(t),R=s(t,"H2",{class:!0});var vo=a(R);oe=s(vo,"A",{id:!0,class:!0,href:!0});var sa=a(oe);Pt=s(sa,"SPAN",{});var aa=a(Pt);y($e.$$.fragment,aa),aa.forEach(o),sa.forEach(o),ln=h(vo),Et=s(vo,"SPAN",{});var ra=a(Et);mn=i(ra,"DetaConfig"),ra.forEach(o),vo.forEach(o),ao=h(t),j=s(t,"DIV",{class:!0});var O=a(j);y(je.$$.fragment,O),hn=h(O),U=s(O,"P",{});var _t=a(U);pn=i(_t,"This is the configuration class to store the configuration of a "),rt=s(_t,"A",{href:!0});var ia=a(rt);fn=i(ia,"DetaModel"),ia.forEach(o),un=i(_t,`. It is used to instantiate a DETA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the DETA
`),Pe=s(_t,"A",{href:!0,rel:!0});var da=a(Pe);gn=i(da,"SenseTime/deformable-detr"),da.forEach(o),_n=i(_t," architecture."),_t.forEach(o),bn=h(O),H=s(O,"P",{});var bt=a(H);vn=i(bt,"Configuration objects inherit from "),it=s(bt,"A",{href:!0});var ca=a(it);wn=i(ca,"PretrainedConfig"),ca.forEach(o),yn=i(bt,` and can be used to control the model outputs. Read the
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`),kt=s(vt,"CODE",{});var ha=a(kt);zn=i(ha,"Dict[str, any]"),ha.forEach(o),Cn=i(vt,": Dictionary of all the attributes that make up this configuration instance,"),vt.forEach(o),wo.forEach(o),O.forEach(o),ro=h(t),V=s(t,"H2",{class:!0});var yo=a(V);ae=s(yo,"A",{id:!0,class:!0,href:!0});var pa=a(ae);zt=s(pa,"SPAN",{});var fa=a(zt);y(ke.$$.fragment,fa),fa.forEach(o),pa.forEach(o),In=h(yo),Ct=s(yo,"SPAN",{});var ua=a(Ct);An=i(ua,"DetaImageProcessor"),ua.forEach(o),yo.forEach(o),io=h(t),k=s(t,"DIV",{class:!0});var fe=a(k);y(ze.$$.fragment,fe),qn=h(fe),It=s(fe,"P",{});var ga=a(It);Fn=i(ga,"Constructs a Deformable DETR image processor."),ga.forEach(o),On=h(fe),re=s(fe,"DIV",{class:!0});var Do=a(re);y(Ce.$$.fragment,Do),Mn=h(Do),At=s(Do,"P",{});var _a=a(At);Nn=i(_a,"Preprocess an image or a batch of images so that it can be used by the model."),_a.forEach(o),Do.forEach(o),Ln=h(fe),ie=s(fe,"DIV",{class:!0});var To=a(ie);y(Ie.$$.fragment,To),Sn=h(To),Ae=s(To,"P",{});var xo=a(Ae);Rn=i(xo,"Converts the output of "),lt=s(xo,"A",{href:!0});var ba=a(lt);Un=i(ba,"DetaForObjectDetection"),ba.forEach(o),Hn=i(xo,` into final bounding boxes in (top_left_x, top_left_y,
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