import{S as DM,i as zM,s as SM,e as t,k as j,w as i,t as n,M as HM,c as p,d as a,m as M,a as c,x as h,h as e,b as m,N as $M,G as l,g as r,y,q as u,o as J,B as b,v as OM}from"../../chunks/vendor-hf-doc-builder.js";import{T as PM}from"../../chunks/Tip-hf-doc-builder.js";import{I as pa}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as d}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as KM}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function LM(qn){let _,T,g,k,es,v,Rs,ts,z,is,x,q,S,Qs,F,H,Vs,$,R;return{c(){_=n(`\uC774 \uD29C\uD1A0\uB9AC\uC5BC\uC758 \uD0DC\uC2A4\uD06C\uB294 \uB2E4\uC74C \uBAA8\uB378 \uC544\uD0A4\uD14D\uCC98\uC5D0\uC11C \uC9C0\uC6D0\uB429\uB2C8\uB2E4:

`),T=t("p"),g=t("a"),k=n("Conditional DETR"),es=n(", "),v=t("a"),Rs=n("Deformable DETR"),ts=n(", "),z=t("a"),is=n("DETA"),x=n(", "),q=t("a"),S=n("DETR"),Qs=n(", "),F=t("a"),H=n("Table Transformer"),Vs=n(", "),$=t("a"),R=n("YOLOS"),this.h()},l(I){_=e(I,`\uC774 \uD29C\uD1A0\uB9AC\uC5BC\uC758 \uD0DC\uC2A4\uD06C\uB294 \uB2E4\uC74C \uBAA8\uB378 \uC544\uD0A4\uD14D\uCC98\uC5D0\uC11C \uC9C0\uC6D0\uB429\uB2C8\uB2E4:

`),T=p(I,"P",{});var w=c(T);g=p(w,"A",{href:!0});var O=c(g);k=e(O,"Conditional DETR"),O.forEach(a),es=e(w,", "),v=p(w,"A",{href:!0});var Il=c(v);Rs=e(Il,"Deformable DETR"),Il.forEach(a),ts=e(w,", "),z=p(w,"A",{href:!0});var Cl=c(z);is=e(Cl,"DETA"),Cl.forEach(a),x=e(w,", "),q=p(w,"A",{href:!0});var P=c(q);S=e(P,"DETR"),P.forEach(a),Qs=e(w,", "),F=p(w,"A",{href:!0});var gl=c(F);H=e(gl,"Table Transformer"),gl.forEach(a),Vs=e(w,", "),$=p(w,"A",{href:!0});var El=c($);R=e(El,"YOLOS"),El.forEach(a),w.forEach(a),this.h()},h(){m(g,"href","../model_doc/conditional_detr"),m(v,"href","../model_doc/deformable_detr"),m(z,"href","../model_doc/deta"),m(q,"href","../model_doc/detr"),m(F,"href","../model_doc/table-transformer"),m($,"href","../model_doc/yolos")},m(I,w){r(I,_,w),r(I,T,w),l(T,g),l(g,k),l(T,es),l(T,v),l(v,Rs),l(T,ts),l(T,z),l(z,is),l(T,x),l(T,q),l(q,S),l(T,Qs),l(T,F),l(F,H),l(T,Vs),l(T,$),l($,R)},d(I){I&&a(_),I&&a(T)}}}function sm(qn){let _,T,g,k,es,v,Rs,ts,z,is,x,q,S,Qs,F,H,Vs,$,R,I,w,O,Il,Cl,P,gl,El,Ct,ca,gt,Fn,hs,$n,_l,Et,Dn,Xs,zn,K,_t,oa,vt,At,ra,Zt,Bt,Sn,vl,Gt,Hn,Ns,On,ps,ys,ja,Ys,Wt,Ma,kt,Pn,xs,qs,Rt,Qt,Kn,Al,Vt,Ln,Fs,se,Zl,Xt,le,Bl,Nt,ae,$s,ne,Gl,Yt,ee,A,Wl,ma,xt,qt,Ft,us,ia,$t,Dt,ha,zt,St,Ht,kl,ya,Ot,Pt,Kt,Rl,ua,Lt,sp,lp,Ds,Ja,ap,np,D,Ql,ba,ep,tp,pp,Vl,Ua,cp,op,rp,Js,da,jp,Mp,zs,mp,ip,hp,E,Ta,yp,up,wa,Jp,bp,fa,Up,dp,Ia,Tp,wp,Ca,fp,Ip,ga,Cp,gp,te,cs,Ea,Ep,_p,_a,vp,Ap,pe,Xl,Zp,ce,Ss,oe,Hs,Nl,Ar,re,Q,Bp,va,Gp,Wp,Aa,kp,Rp,Za,Qp,Vp,je,Yl,Xp,Me,Os,me,os,bs,Ba,Ps,Np,Ga,Yp,ie,Z,xp,Wa,qp,Fp,ka,$p,Dp,Ra,zp,Sp,Qa,Hp,Op,he,Us,Va,Xa,Pp,Kp,Na,Ya,Lp,ye,xl,sc,ue,ql,lc,Je,Ks,be,Ls,xa,ac,nc,Ue,ds,qa,ec,tc,Fa,pc,de,L,cc,sl,oc,rc,ll,jc,Mc,Te,al,we,Ts,mc,$a,ic,hc,fe,nl,Ie,Fl,yc,Ce,el,ge,ws,uc,Da,Jc,bc,Ee,V,Uc,za,dc,Tc,Sa,wc,fc,Ha,Ic,Cc,_e,tl,ve,X,gc,Oa,Ec,_c,Pa,vc,Ac,Ka,Zc,Bc,Ae,pl,Ze,rs,fs,La,cl,Gc,sn,Wc,Be,$l,kc,Ge,Dl,Rc,We,N,zl,ln,Qc,Vc,Xc,Sl,an,Nc,Yc,xc,ol,qc,nn,Fc,$c,Dc,Hl,en,zc,Sc,ke,Y,Hc,tn,Oc,Pc,pn,Kc,Lc,cn,so,lo,Re,rl,Qe,U,on,ao,no,rn,eo,to,jn,po,co,Mn,oo,ro,mn,jo,Mo,hn,mo,io,yn,ho,yo,un,uo,Jo,Jn,bo,Uo,Ve,jl,Xe,C,To,bn,wo,fo,Un,Io,Co,dn,go,Eo,Tn,_o,vo,wn,Ao,Zo,fn,Bo,Go,Ne,Ml,Ye,G,In,Wo,ko,Cn,Ro,Qo,gn,Vo,Xo,En,No,Yo,xe,ml,qe,js,Is,_n,il,xo,vn,qo,Fe,ss,Fo,Ol,$o,Do,An,zo,So,$e,hl,Zn,Ho,Oo,De,Cs,Po,Bn,Ko,Lo,ze,yl,Se,ls,sr,Gn,lr,ar,Wn,nr,er,He,ul,Oe,Pl,tr,Pe,Jl,Ke,gs,pr,kn,cr,or,Le,Ms,Es,Rn,bl,rr,Qn,jr,st,Kl,Mr,lt,_s,mr,Vn,ir,hr,at,Ul,nt,vs,yr,Xn,ur,Jr,et,dl,tt,Ll,br,pt,Tl,ct,wl,sa,Zr,ot;return v=new pa({}),x=new KM({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/object_detection.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/object_detection.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/object_detection.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/object_detection.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/object_detection.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/object_detection.ipynb"}]}}),hs=new PM({props:{$$slots:{default:[LM]},$$scope:{ctx:qn}}}),Xs=new d({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1xJTIwZGF0YXNldHMlMjB0cmFuc2Zvcm1lcnMlMjBldmFsdWF0ZSUyMHRpbW0lMjBhbGJ1bWVudGF0aW9ucw==",highlighted:"pip install -q datasets transformers evaluate timm albumentations"}}),Ns=new d({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

<span class="hljs-meta">&gt;&gt;&gt; </span>notebook_login()`}}),Ys=new pa({}),Fs=new d({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBY3BwZTUlMjAlM0QlMjBsb2FkX2RhdGFzZXQoJTIyY3BwZS01JTIyKSUwQWNwcGU1",highlighted:`<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>cppe5 = load_dataset(<span class="hljs-string">&quot;cppe-5&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>cppe5
DatasetDict({
    train: Dataset({
        features: [<span class="hljs-string">&#x27;image_id&#x27;</span>, <span class="hljs-string">&#x27;image&#x27;</span>, <span class="hljs-string">&#x27;width&#x27;</span>, <span class="hljs-string">&#x27;height&#x27;</span>, <span class="hljs-string">&#x27;objects&#x27;</span>],
        num_rows: <span class="hljs-number">1000</span>
    })
    test: Dataset({
        features: [<span class="hljs-string">&#x27;image_id&#x27;</span>, <span class="hljs-string">&#x27;image&#x27;</span>, <span class="hljs-string">&#x27;width&#x27;</span>, <span class="hljs-string">&#x27;height&#x27;</span>, <span class="hljs-string">&#x27;objects&#x27;</span>],
        num_rows: <span class="hljs-number">29</span>
    })
})`}}),$s=new d({props:{code:"Y3BwZTUlNUIlMjJ0cmFpbiUyMiU1RCU1QjAlNUQ=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>cppe5[<span class="hljs-string">&quot;train&quot;</span>][<span class="hljs-number">0</span>]
{<span class="hljs-string">&#x27;image_id&#x27;</span>: <span class="hljs-number">15</span>,
 <span class="hljs-string">&#x27;image&#x27;</span>: &lt;PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=943x663 at <span class="hljs-number">0x7F9EC9E77C10</span>&gt;,
 <span class="hljs-string">&#x27;width&#x27;</span>: <span class="hljs-number">943</span>,
 <span class="hljs-string">&#x27;height&#x27;</span>: <span class="hljs-number">663</span>,
 <span class="hljs-string">&#x27;objects&#x27;</span>: {<span class="hljs-string">&#x27;id&#x27;</span>: [<span class="hljs-number">114</span>, <span class="hljs-number">115</span>, <span class="hljs-number">116</span>, <span class="hljs-number">117</span>],
  <span class="hljs-string">&#x27;area&#x27;</span>: [<span class="hljs-number">3796</span>, <span class="hljs-number">1596</span>, <span class="hljs-number">152768</span>, <span class="hljs-number">81002</span>],
  <span class="hljs-string">&#x27;bbox&#x27;</span>: [[<span class="hljs-number">302.0</span>, <span class="hljs-number">109.0</span>, <span class="hljs-number">73.0</span>, <span class="hljs-number">52.0</span>],
   [<span class="hljs-number">810.0</span>, <span class="hljs-number">100.0</span>, <span class="hljs-number">57.0</span>, <span class="hljs-number">28.0</span>],
   [<span class="hljs-number">160.0</span>, <span class="hljs-number">31.0</span>, <span class="hljs-number">248.0</span>, <span class="hljs-number">616.0</span>],
   [<span class="hljs-number">741.0</span>, <span class="hljs-number">68.0</span>, <span class="hljs-number">202.0</span>, <span class="hljs-number">401.0</span>]],
  <span class="hljs-string">&#x27;category&#x27;</span>: [<span class="hljs-number">4</span>, <span class="hljs-number">4</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>]}}`}}),Ss=new d({props:{code:"aW1wb3J0JTIwbnVtcHklMjBhcyUyMG5wJTBBaW1wb3J0JTIwb3MlMEFmcm9tJTIwUElMJTIwaW1wb3J0JTIwSW1hZ2UlMkMlMjBJbWFnZURyYXclMEElMEFpbWFnZSUyMCUzRCUyMGNwcGU1JTVCJTIydHJhaW4lMjIlNUQlNUIwJTVEJTVCJTIyaW1hZ2UlMjIlNUQlMEFhbm5vdGF0aW9ucyUyMCUzRCUyMGNwcGU1JTVCJTIydHJhaW4lMjIlNUQlNUIwJTVEJTVCJTIyb2JqZWN0cyUyMiU1RCUwQWRyYXclMjAlM0QlMjBJbWFnZURyYXcuRHJhdyhpbWFnZSklMEElMEFjYXRlZ29yaWVzJTIwJTNEJTIwY3BwZTUlNUIlMjJ0cmFpbiUyMiU1RC5mZWF0dXJlcyU1QiUyMm9iamVjdHMlMjIlNUQuZmVhdHVyZSU1QiUyMmNhdGVnb3J5JTIyJTVELm5hbWVzJTBBJTBBaWQybGFiZWwlMjAlM0QlMjAlN0JpbmRleCUzQSUyMHglMjBmb3IlMjBpbmRleCUyQyUyMHglMjBpbiUyMGVudW1lcmF0ZShjYXRlZ29yaWVzJTJDJTIwc3RhcnQlM0QwKSU3RCUwQWxhYmVsMmlkJTIwJTNEJTIwJTdCdiUzQSUyMGslMjBmb3IlMjBrJTJDJTIwdiUyMGluJTIwaWQybGFiZWwuaXRlbXMoKSU3RCUwQSUwQWZvciUyMGklMjBpbiUyMHJhbmdlKGxlbihhbm5vdGF0aW9ucyU1QiUyMmlkJTIyJTVEKSklM0ElMEElMjAlMjAlMjAlMjBib3glMjAlM0QlMjBhbm5vdGF0aW9ucyU1QiUyMmJib3glMjIlNUQlNUJpJTIwLSUyMDElNUQlMEElMjAlMjAlMjAlMjBjbGFzc19pZHglMjAlM0QlMjBhbm5vdGF0aW9ucyU1QiUyMmNhdGVnb3J5JTIyJTVEJTVCaSUyMC0lMjAxJTVEJTBBJTIwJTIwJTIwJTIweCUyQyUyMHklMkMlMjB3JTJDJTIwaCUyMCUzRCUyMHR1cGxlKGJveCklMEElMjAlMjAlMjAlMjBkcmF3LnJlY3RhbmdsZSgoeCUyQyUyMHklMkMlMjB4JTIwJTJCJTIwdyUyQyUyMHklMjAlMkIlMjBoKSUyQyUyMG91dGxpbmUlM0QlMjJyZWQlMjIlMkMlMjB3aWR0aCUzRDEpJTBBJTIwJTIwJTIwJTIwZHJhdy50ZXh0KCh4JTJDJTIweSklMkMlMjBpZDJsYWJlbCU1QmNsYXNzX2lkeCU1RCUyQyUyMGZpbGwlM0QlMjJ3aGl0ZSUyMiklMEElMEFpbWFnZQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> os
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image, ImageDraw

<span class="hljs-meta">&gt;&gt;&gt; </span>image = cppe5[<span class="hljs-string">&quot;train&quot;</span>][<span class="hljs-number">0</span>][<span class="hljs-string">&quot;image&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>annotations = cppe5[<span class="hljs-string">&quot;train&quot;</span>][<span class="hljs-number">0</span>][<span class="hljs-string">&quot;objects&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>draw = ImageDraw.Draw(image)

<span class="hljs-meta">&gt;&gt;&gt; </span>categories = cppe5[<span class="hljs-string">&quot;train&quot;</span>].features[<span class="hljs-string">&quot;objects&quot;</span>].feature[<span class="hljs-string">&quot;category&quot;</span>].names

<span class="hljs-meta">&gt;&gt;&gt; </span>id2label = {index: x <span class="hljs-keyword">for</span> index, x <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(categories, start=<span class="hljs-number">0</span>)}
<span class="hljs-meta">&gt;&gt;&gt; </span>label2id = {v: k <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> id2label.items()}

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(annotations[<span class="hljs-string">&quot;id&quot;</span>])):
<span class="hljs-meta">... </span>    box = annotations[<span class="hljs-string">&quot;bbox&quot;</span>][i - <span class="hljs-number">1</span>]
<span class="hljs-meta">... </span>    class_idx = annotations[<span class="hljs-string">&quot;category&quot;</span>][i - <span class="hljs-number">1</span>]
<span class="hljs-meta">... </span>    x, y, w, h = <span class="hljs-built_in">tuple</span>(box)
<span class="hljs-meta">... </span>    draw.rectangle((x, y, x + w, y + h), outline=<span class="hljs-string">&quot;red&quot;</span>, width=<span class="hljs-number">1</span>)
<span class="hljs-meta">... </span>    draw.text((x, y), id2label[class_idx], fill=<span class="hljs-string">&quot;white&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>image`}}),Os=new d({props:{code:"cmVtb3ZlX2lkeCUyMCUzRCUyMCU1QjU5MCUyQyUyMDgyMSUyQyUyMDgyMiUyQyUyMDg3NSUyQyUyMDg3NiUyQyUyMDg3OCUyQyUyMDg3OSU1RCUwQWtlZXAlMjAlM0QlMjAlNUJpJTIwZm9yJTIwaSUyMGluJTIwcmFuZ2UobGVuKGNwcGU1JTVCJTIydHJhaW4lMjIlNUQpKSUyMGlmJTIwaSUyMG5vdCUyMGluJTIwcmVtb3ZlX2lkeCU1RCUwQWNwcGU1JTVCJTIydHJhaW4lMjIlNUQlMjAlM0QlMjBjcHBlNSU1QiUyMnRyYWluJTIyJTVELnNlbGVjdChrZWVwKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>remove_idx = [<span class="hljs-number">590</span>, <span class="hljs-number">821</span>, <span class="hljs-number">822</span>, <span class="hljs-number">875</span>, <span class="hljs-number">876</span>, <span class="hljs-number">878</span>, <span class="hljs-number">879</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>keep = [i <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-built_in">len</span>(cppe5[<span class="hljs-string">&quot;train&quot;</span>])) <span class="hljs-keyword">if</span> i <span class="hljs-keyword">not</span> <span class="hljs-keyword">in</span> remove_idx]
<span class="hljs-meta">&gt;&gt;&gt; </span>cppe5[<span class="hljs-string">&quot;train&quot;</span>] = cppe5[<span class="hljs-string">&quot;train&quot;</span>].select(keep)`}}),Ps=new pa({}),Ks=new d({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9JbWFnZVByb2Nlc3NvciUwQSUwQWNoZWNrcG9pbnQlMjAlM0QlMjAlMjJmYWNlYm9vayUyRmRldHItcmVzbmV0LTUwJTIyJTBBaW1hZ2VfcHJvY2Vzc29yJTIwJTNEJTIwQXV0b0ltYWdlUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZChjaGVja3BvaW50KQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>checkpoint = <span class="hljs-string">&quot;facebook/detr-resnet-50&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(checkpoint)`}}),al=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> albumentations
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>transform = albumentations.Compose(
<span class="hljs-meta">... </span>    [
<span class="hljs-meta">... </span>        albumentations.Resize(<span class="hljs-number">480</span>, <span class="hljs-number">480</span>),
<span class="hljs-meta">... </span>        albumentations.HorizontalFlip(p=<span class="hljs-number">1.0</span>),
<span class="hljs-meta">... </span>        albumentations.RandomBrightnessContrast(p=<span class="hljs-number">1.0</span>),
<span class="hljs-meta">... </span>    ],
<span class="hljs-meta">... </span>    bbox_params=albumentations.BboxParams(<span class="hljs-built_in">format</span>=<span class="hljs-string">&quot;coco&quot;</span>, label_fields=[<span class="hljs-string">&quot;category&quot;</span>]),
<span class="hljs-meta">... </span>)`}}),nl=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">formatted_anns</span>(<span class="hljs-params">image_id, category, area, bbox</span>):
<span class="hljs-meta">... </span>    annotations = []
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">0</span>, <span class="hljs-built_in">len</span>(category)):
<span class="hljs-meta">... </span>        new_ann = {
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;image_id&quot;</span>: image_id,
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;category_id&quot;</span>: category[i],
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;isCrowd&quot;</span>: <span class="hljs-number">0</span>,
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;area&quot;</span>: area[i],
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;bbox&quot;</span>: <span class="hljs-built_in">list</span>(bbox[i]),
<span class="hljs-meta">... </span>        }
<span class="hljs-meta">... </span>        annotations.append(new_ann)

<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> annotations`}}),el=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># transforming a batch</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">transform_aug_ann</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span>    image_ids = examples[<span class="hljs-string">&quot;image_id&quot;</span>]
<span class="hljs-meta">... </span>    images, bboxes, area, categories = [], [], [], []
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> image, objects <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(examples[<span class="hljs-string">&quot;image&quot;</span>], examples[<span class="hljs-string">&quot;objects&quot;</span>]):
<span class="hljs-meta">... </span>        image = np.array(image.convert(<span class="hljs-string">&quot;RGB&quot;</span>))[:, :, ::-<span class="hljs-number">1</span>]
<span class="hljs-meta">... </span>        out = transform(image=image, bboxes=objects[<span class="hljs-string">&quot;bbox&quot;</span>], category=objects[<span class="hljs-string">&quot;category&quot;</span>])

<span class="hljs-meta">... </span>        area.append(objects[<span class="hljs-string">&quot;area&quot;</span>])
<span class="hljs-meta">... </span>        images.append(out[<span class="hljs-string">&quot;image&quot;</span>])
<span class="hljs-meta">... </span>        bboxes.append(out[<span class="hljs-string">&quot;bboxes&quot;</span>])
<span class="hljs-meta">... </span>        categories.append(out[<span class="hljs-string">&quot;category&quot;</span>])

<span class="hljs-meta">... </span>    targets = [
<span class="hljs-meta">... </span>        {<span class="hljs-string">&quot;image_id&quot;</span>: id_, <span class="hljs-string">&quot;annotations&quot;</span>: formatted_anns(id_, cat_, ar_, box_)}
<span class="hljs-meta">... </span>        <span class="hljs-keyword">for</span> id_, cat_, ar_, box_ <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(image_ids, categories, area, bboxes)
<span class="hljs-meta">... </span>    ]

<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> image_processor(images=images, annotations=targets, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)`}}),tl=new d({props:{code:"Y3BwZTUlNUIlMjJ0cmFpbiUyMiU1RCUyMCUzRCUyMGNwcGU1JTVCJTIydHJhaW4lMjIlNUQud2l0aF90cmFuc2Zvcm0odHJhbnNmb3JtX2F1Z19hbm4pJTBBY3BwZTUlNUIlMjJ0cmFpbiUyMiU1RCU1QjE1JTVEJTBBJTBB",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>cppe5[<span class="hljs-string">&quot;train&quot;</span>] = cppe5[<span class="hljs-string">&quot;train&quot;</span>].with_transform(transform_aug_ann)
<span class="hljs-meta">&gt;&gt;&gt; </span>cppe5[<span class="hljs-string">&quot;train&quot;</span>][<span class="hljs-number">15</span>]
{<span class="hljs-string">&#x27;pixel_values&#x27;</span>: tensor([[[ <span class="hljs-number">0.9132</span>,  <span class="hljs-number">0.9132</span>,  <span class="hljs-number">0.9132</span>,  ..., -<span class="hljs-number">1.9809</span>, -<span class="hljs-number">1.9809</span>, -<span class="hljs-number">1.9809</span>],
          [ <span class="hljs-number">0.9132</span>,  <span class="hljs-number">0.9132</span>,  <span class="hljs-number">0.9132</span>,  ..., -<span class="hljs-number">1.9809</span>, -<span class="hljs-number">1.9809</span>, -<span class="hljs-number">1.9809</span>],
          [ <span class="hljs-number">0.9132</span>,  <span class="hljs-number">0.9132</span>,  <span class="hljs-number">0.9132</span>,  ..., -<span class="hljs-number">1.9638</span>, -<span class="hljs-number">1.9638</span>, -<span class="hljs-number">1.9638</span>],
          ...,
          [-<span class="hljs-number">1.5699</span>, -<span class="hljs-number">1.5699</span>, -<span class="hljs-number">1.5699</span>,  ..., -<span class="hljs-number">1.9980</span>, -<span class="hljs-number">1.9980</span>, -<span class="hljs-number">1.9980</span>],
          [-<span class="hljs-number">1.5528</span>, -<span class="hljs-number">1.5528</span>, -<span class="hljs-number">1.5528</span>,  ..., -<span class="hljs-number">1.9980</span>, -<span class="hljs-number">1.9809</span>, -<span class="hljs-number">1.9809</span>],
          [-<span class="hljs-number">1.5528</span>, -<span class="hljs-number">1.5528</span>, -<span class="hljs-number">1.5528</span>,  ..., -<span class="hljs-number">1.9980</span>, -<span class="hljs-number">1.9809</span>, -<span class="hljs-number">1.9809</span>]],

         [[ <span class="hljs-number">1.3081</span>,  <span class="hljs-number">1.3081</span>,  <span class="hljs-number">1.3081</span>,  ..., -<span class="hljs-number">1.8431</span>, -<span class="hljs-number">1.8431</span>, -<span class="hljs-number">1.8431</span>],
          [ <span class="hljs-number">1.3081</span>,  <span class="hljs-number">1.3081</span>,  <span class="hljs-number">1.3081</span>,  ..., -<span class="hljs-number">1.8431</span>, -<span class="hljs-number">1.8431</span>, -<span class="hljs-number">1.8431</span>],
          [ <span class="hljs-number">1.3081</span>,  <span class="hljs-number">1.3081</span>,  <span class="hljs-number">1.3081</span>,  ..., -<span class="hljs-number">1.8256</span>, -<span class="hljs-number">1.8256</span>, -<span class="hljs-number">1.8256</span>],
          ...,
          [-<span class="hljs-number">1.3179</span>, -<span class="hljs-number">1.3179</span>, -<span class="hljs-number">1.3179</span>,  ..., -<span class="hljs-number">1.8606</span>, -<span class="hljs-number">1.8606</span>, -<span class="hljs-number">1.8606</span>],
          [-<span class="hljs-number">1.3004</span>, -<span class="hljs-number">1.3004</span>, -<span class="hljs-number">1.3004</span>,  ..., -<span class="hljs-number">1.8606</span>, -<span class="hljs-number">1.8431</span>, -<span class="hljs-number">1.8431</span>],
          [-<span class="hljs-number">1.3004</span>, -<span class="hljs-number">1.3004</span>, -<span class="hljs-number">1.3004</span>,  ..., -<span class="hljs-number">1.8606</span>, -<span class="hljs-number">1.8431</span>, -<span class="hljs-number">1.8431</span>]],

         [[ <span class="hljs-number">1.4200</span>,  <span class="hljs-number">1.4200</span>,  <span class="hljs-number">1.4200</span>,  ..., -<span class="hljs-number">1.6476</span>, -<span class="hljs-number">1.6476</span>, -<span class="hljs-number">1.6476</span>],
          [ <span class="hljs-number">1.4200</span>,  <span class="hljs-number">1.4200</span>,  <span class="hljs-number">1.4200</span>,  ..., -<span class="hljs-number">1.6476</span>, -<span class="hljs-number">1.6476</span>, -<span class="hljs-number">1.6476</span>],
          [ <span class="hljs-number">1.4200</span>,  <span class="hljs-number">1.4200</span>,  <span class="hljs-number">1.4200</span>,  ..., -<span class="hljs-number">1.6302</span>, -<span class="hljs-number">1.6302</span>, -<span class="hljs-number">1.6302</span>],
          ...,
          [-<span class="hljs-number">1.0201</span>, -<span class="hljs-number">1.0201</span>, -<span class="hljs-number">1.0201</span>,  ..., -<span class="hljs-number">1.5604</span>, -<span class="hljs-number">1.5604</span>, -<span class="hljs-number">1.5604</span>],
          [-<span class="hljs-number">1.0027</span>, -<span class="hljs-number">1.0027</span>, -<span class="hljs-number">1.0027</span>,  ..., -<span class="hljs-number">1.5604</span>, -<span class="hljs-number">1.5430</span>, -<span class="hljs-number">1.5430</span>],
          [-<span class="hljs-number">1.0027</span>, -<span class="hljs-number">1.0027</span>, -<span class="hljs-number">1.0027</span>,  ..., -<span class="hljs-number">1.5604</span>, -<span class="hljs-number">1.5430</span>, -<span class="hljs-number">1.5430</span>]]]),
 <span class="hljs-string">&#x27;pixel_mask&#x27;</span>: tensor([[<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>,  ..., <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
         [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>,  ..., <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
         [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>,  ..., <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
         ...,
         [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>,  ..., <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
         [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>,  ..., <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>],
         [<span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>,  ..., <span class="hljs-number">1</span>, <span class="hljs-number">1</span>, <span class="hljs-number">1</span>]]),
 <span class="hljs-string">&#x27;labels&#x27;</span>: {<span class="hljs-string">&#x27;size&#x27;</span>: tensor([<span class="hljs-number">800</span>, <span class="hljs-number">800</span>]), <span class="hljs-string">&#x27;image_id&#x27;</span>: tensor([<span class="hljs-number">756</span>]), <span class="hljs-string">&#x27;class_labels&#x27;</span>: tensor([<span class="hljs-number">4</span>]), <span class="hljs-string">&#x27;boxes&#x27;</span>: tensor([[<span class="hljs-number">0.7340</span>, <span class="hljs-number">0.6986</span>, <span class="hljs-number">0.3414</span>, <span class="hljs-number">0.5944</span>]]), <span class="hljs-string">&#x27;area&#x27;</span>: tensor([<span class="hljs-number">519544.4375</span>]), <span class="hljs-string">&#x27;iscrowd&#x27;</span>: tensor([<span class="hljs-number">0</span>]), <span class="hljs-string">&#x27;orig_size&#x27;</span>: tensor([<span class="hljs-number">480</span>, <span class="hljs-number">480</span>])}}`}}),pl=new d({props:{code:"ZGVmJTIwY29sbGF0ZV9mbihiYXRjaCklM0ElMEElMjAlMjAlMjAlMjBwaXhlbF92YWx1ZXMlMjAlM0QlMjAlNUJpdGVtJTVCJTIycGl4ZWxfdmFsdWVzJTIyJTVEJTIwZm9yJTIwaXRlbSUyMGluJTIwYmF0Y2glNUQlMEElMjAlMjAlMjAlMjBlbmNvZGluZyUyMCUzRCUyMGltYWdlX3Byb2Nlc3Nvci5wYWQocGl4ZWxfdmFsdWVzJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiklMEElMjAlMjAlMjAlMjBsYWJlbHMlMjAlM0QlMjAlNUJpdGVtJTVCJTIybGFiZWxzJTIyJTVEJTIwZm9yJTIwaXRlbSUyMGluJTIwYmF0Y2glNUQlMEElMjAlMjAlMjAlMjBiYXRjaCUyMCUzRCUyMCU3QiU3RCUwQSUyMCUyMCUyMCUyMGJhdGNoJTVCJTIycGl4ZWxfdmFsdWVzJTIyJTVEJTIwJTNEJTIwZW5jb2RpbmclNUIlMjJwaXhlbF92YWx1ZXMlMjIlNUQlMEElMjAlMjAlMjAlMjBiYXRjaCU1QiUyMnBpeGVsX21hc2slMjIlNUQlMjAlM0QlMjBlbmNvZGluZyU1QiUyMnBpeGVsX21hc2slMjIlNUQlMEElMjAlMjAlMjAlMjBiYXRjaCU1QiUyMmxhYmVscyUyMiU1RCUyMCUzRCUyMGxhYmVscyUwQSUyMCUyMCUyMCUyMHJldHVybiUyMGJhdGNo",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">collate_fn</span>(<span class="hljs-params">batch</span>):
<span class="hljs-meta">... </span>    pixel_values = [item[<span class="hljs-string">&quot;pixel_values&quot;</span>] <span class="hljs-keyword">for</span> item <span class="hljs-keyword">in</span> batch]
<span class="hljs-meta">... </span>    encoding = image_processor.pad(pixel_values, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">... </span>    labels = [item[<span class="hljs-string">&quot;labels&quot;</span>] <span class="hljs-keyword">for</span> item <span class="hljs-keyword">in</span> batch]
<span class="hljs-meta">... </span>    batch = {}
<span class="hljs-meta">... </span>    batch[<span class="hljs-string">&quot;pixel_values&quot;</span>] = encoding[<span class="hljs-string">&quot;pixel_values&quot;</span>]
<span class="hljs-meta">... </span>    batch[<span class="hljs-string">&quot;pixel_mask&quot;</span>] = encoding[<span class="hljs-string">&quot;pixel_mask&quot;</span>]
<span class="hljs-meta">... </span>    batch[<span class="hljs-string">&quot;labels&quot;</span>] = labels
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> batch`}}),cl=new pa({}),rl=new d({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvck9iamVjdERldGVjdGlvbiUwQSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yT2JqZWN0RGV0ZWN0aW9uLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjBjaGVja3BvaW50JTJDJTBBJTIwJTIwJTIwJTIwaWQybGFiZWwlM0RpZDJsYWJlbCUyQyUwQSUyMCUyMCUyMCUyMGxhYmVsMmlkJTNEbGFiZWwyaWQlMkMlMEElMjAlMjAlMjAlMjBpZ25vcmVfbWlzbWF0Y2hlZF9zaXplcyUzRFRydWUlMkMlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(
<span class="hljs-meta">... </span>    checkpoint,
<span class="hljs-meta">... </span>    id2label=id2label,
<span class="hljs-meta">... </span>    label2id=label2id,
<span class="hljs-meta">... </span>    ignore_mismatched_sizes=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)`}}),jl=new d({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> TrainingArguments

<span class="hljs-meta">&gt;&gt;&gt; </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span>    output_dir=<span class="hljs-string">&quot;detr-resnet-50_finetuned_cppe5&quot;</span>,
<span class="hljs-meta">... </span>    per_device_train_batch_size=<span class="hljs-number">8</span>,
<span class="hljs-meta">... </span>    num_train_epochs=<span class="hljs-number">10</span>,
<span class="hljs-meta">... </span>    fp16=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    save_steps=<span class="hljs-number">200</span>,
<span class="hljs-meta">... </span>    logging_steps=<span class="hljs-number">50</span>,
<span class="hljs-meta">... </span>    learning_rate=<span class="hljs-number">1e-5</span>,
<span class="hljs-meta">... </span>    weight_decay=<span class="hljs-number">1e-4</span>,
<span class="hljs-meta">... </span>    save_total_limit=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>    remove_unused_columns=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>    push_to_hub=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)`}}),Ml=new d({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRyYWluZXIlMEElMEF0cmFpbmVyJTIwJTNEJTIwVHJhaW5lciglMEElMjAlMjAlMjAlMjBtb2RlbCUzRG1vZGVsJTJDJTBBJTIwJTIwJTIwJTIwYXJncyUzRHRyYWluaW5nX2FyZ3MlMkMlMEElMjAlMjAlMjAlMjBkYXRhX2NvbGxhdG9yJTNEY29sbGF0ZV9mbiUyQyUwQSUyMCUyMCUyMCUyMHRyYWluX2RhdGFzZXQlM0RjcHBlNSU1QiUyMnRyYWluJTIyJTVEJTJDJTBBJTIwJTIwJTIwJTIwdG9rZW5pemVyJTNEaW1hZ2VfcHJvY2Vzc29yJTJDJTBBKSUwQSUwQXRyYWluZXIudHJhaW4oKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Trainer

<span class="hljs-meta">&gt;&gt;&gt; </span>trainer = Trainer(
<span class="hljs-meta">... </span>    model=model,
<span class="hljs-meta">... </span>    args=training_args,
<span class="hljs-meta">... </span>    data_collator=collate_fn,
<span class="hljs-meta">... </span>    train_dataset=cppe5[<span class="hljs-string">&quot;train&quot;</span>],
<span class="hljs-meta">... </span>    tokenizer=image_processor,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>trainer.train()`}}),ml=new d({props:{code:"dHJhaW5lci5wdXNoX3RvX2h1Yigp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>trainer.push_to_hub()'}}),il=new pa({}),yl=new d({props:{code:" ",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> json


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># format annotations the same as for training, no need for data augmentation</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">val_formatted_anns</span>(<span class="hljs-params">image_id, objects</span>):
<span class="hljs-meta">... </span>    annotations = []
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">0</span>, <span class="hljs-built_in">len</span>(objects[<span class="hljs-string">&quot;id&quot;</span>])):
<span class="hljs-meta">... </span>        new_ann = {
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;id&quot;</span>: objects[<span class="hljs-string">&quot;id&quot;</span>][i],
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;category_id&quot;</span>: objects[<span class="hljs-string">&quot;category&quot;</span>][i],
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;iscrowd&quot;</span>: <span class="hljs-number">0</span>,
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;image_id&quot;</span>: image_id,
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;area&quot;</span>: objects[<span class="hljs-string">&quot;area&quot;</span>][i],
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;bbox&quot;</span>: objects[<span class="hljs-string">&quot;bbox&quot;</span>][i],
<span class="hljs-meta">... </span>        }
<span class="hljs-meta">... </span>        annotations.append(new_ann)

<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> annotations


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Save images and annotations into the files torchvision.datasets.CocoDetection expects</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">save_cppe5_annotation_file_images</span>(<span class="hljs-params">cppe5</span>):
<span class="hljs-meta">... </span>    output_json = {}
<span class="hljs-meta">... </span>    path_output_cppe5 = <span class="hljs-string">f&quot;<span class="hljs-subst">{os.getcwd()}</span>/cppe5/&quot;</span>

<span class="hljs-meta">... </span>    <span class="hljs-keyword">if</span> <span class="hljs-keyword">not</span> os.path.exists(path_output_cppe5):
<span class="hljs-meta">... </span>        os.makedirs(path_output_cppe5)

<span class="hljs-meta">... </span>    path_anno = os.path.join(path_output_cppe5, <span class="hljs-string">&quot;cppe5_ann.json&quot;</span>)
<span class="hljs-meta">... </span>    categories_json = [{<span class="hljs-string">&quot;supercategory&quot;</span>: <span class="hljs-string">&quot;none&quot;</span>, <span class="hljs-string">&quot;id&quot;</span>: <span class="hljs-built_in">id</span>, <span class="hljs-string">&quot;name&quot;</span>: id2label[<span class="hljs-built_in">id</span>]} <span class="hljs-keyword">for</span> <span class="hljs-built_in">id</span> <span class="hljs-keyword">in</span> id2label]
<span class="hljs-meta">... </span>    output_json[<span class="hljs-string">&quot;images&quot;</span>] = []
<span class="hljs-meta">... </span>    output_json[<span class="hljs-string">&quot;annotations&quot;</span>] = []
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> example <span class="hljs-keyword">in</span> cppe5:
<span class="hljs-meta">... </span>        ann = val_formatted_anns(example[<span class="hljs-string">&quot;image_id&quot;</span>], example[<span class="hljs-string">&quot;objects&quot;</span>])
<span class="hljs-meta">... </span>        output_json[<span class="hljs-string">&quot;images&quot;</span>].append(
<span class="hljs-meta">... </span>            {
<span class="hljs-meta">... </span>                <span class="hljs-string">&quot;id&quot;</span>: example[<span class="hljs-string">&quot;image_id&quot;</span>],
<span class="hljs-meta">... </span>                <span class="hljs-string">&quot;width&quot;</span>: example[<span class="hljs-string">&quot;image&quot;</span>].width,
<span class="hljs-meta">... </span>                <span class="hljs-string">&quot;height&quot;</span>: example[<span class="hljs-string">&quot;image&quot;</span>].height,
<span class="hljs-meta">... </span>                <span class="hljs-string">&quot;file_name&quot;</span>: <span class="hljs-string">f&quot;<span class="hljs-subst">{example[<span class="hljs-string">&#x27;image_id&#x27;</span>]}</span>.png&quot;</span>,
<span class="hljs-meta">... </span>            }
<span class="hljs-meta">... </span>        )
<span class="hljs-meta">... </span>        output_json[<span class="hljs-string">&quot;annotations&quot;</span>].extend(ann)
<span class="hljs-meta">... </span>    output_json[<span class="hljs-string">&quot;categories&quot;</span>] = categories_json

<span class="hljs-meta">... </span>    <span class="hljs-keyword">with</span> <span class="hljs-built_in">open</span>(path_anno, <span class="hljs-string">&quot;w&quot;</span>) <span class="hljs-keyword">as</span> file:
<span class="hljs-meta">... </span>        json.dump(output_json, file, ensure_ascii=<span class="hljs-literal">False</span>, indent=<span class="hljs-number">4</span>)

<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> im, img_id <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(cppe5[<span class="hljs-string">&quot;image&quot;</span>], cppe5[<span class="hljs-string">&quot;image_id&quot;</span>]):
<span class="hljs-meta">... </span>        path_img = os.path.join(path_output_cppe5, <span class="hljs-string">f&quot;<span class="hljs-subst">{img_id}</span>.png&quot;</span>)
<span class="hljs-meta">... </span>        im.save(path_img)

<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> path_output_cppe5, path_anno`}}),ul=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torchvision


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">CocoDetection</span>(torchvision.datasets.CocoDetection):
<span class="hljs-meta">... </span>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, img_folder, image_processor, ann_file</span>):
<span class="hljs-meta">... </span>        <span class="hljs-built_in">super</span>().__init__(img_folder, ann_file)
<span class="hljs-meta">... </span>        self.image_processor = image_processor

<span class="hljs-meta">... </span>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__getitem__</span>(<span class="hljs-params">self, idx</span>):
<span class="hljs-meta">... </span>        <span class="hljs-comment"># read in PIL image and target in COCO format</span>
<span class="hljs-meta">... </span>        img, target = <span class="hljs-built_in">super</span>(CocoDetection, self).__getitem__(idx)

<span class="hljs-meta">... </span>        <span class="hljs-comment"># preprocess image and target: converting target to DETR format,</span>
<span class="hljs-meta">... </span>        <span class="hljs-comment"># resizing + normalization of both image and target)</span>
<span class="hljs-meta">... </span>        image_id = self.ids[idx]
<span class="hljs-meta">... </span>        target = {<span class="hljs-string">&quot;image_id&quot;</span>: image_id, <span class="hljs-string">&quot;annotations&quot;</span>: target}
<span class="hljs-meta">... </span>        encoding = self.image_processor(images=img, annotations=target, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">... </span>        pixel_values = encoding[<span class="hljs-string">&quot;pixel_values&quot;</span>].squeeze()  <span class="hljs-comment"># remove batch dimension</span>
<span class="hljs-meta">... </span>        target = encoding[<span class="hljs-string">&quot;labels&quot;</span>][<span class="hljs-number">0</span>]  <span class="hljs-comment"># remove batch dimension</span>

<span class="hljs-meta">... </span>        <span class="hljs-keyword">return</span> {<span class="hljs-string">&quot;pixel_values&quot;</span>: pixel_values, <span class="hljs-string">&quot;labels&quot;</span>: target}


<span class="hljs-meta">&gt;&gt;&gt; </span>im_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;devonho/detr-resnet-50_finetuned_cppe5&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>path_output_cppe5, path_anno = save_cppe5_annotation_file_images(cppe5[<span class="hljs-string">&quot;test&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>test_ds_coco_format = CocoDetection(path_output_cppe5, im_processor, path_anno)`}}),Jl=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> evaluate
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> tqdm <span class="hljs-keyword">import</span> tqdm

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(<span class="hljs-string">&quot;devonho/detr-resnet-50_finetuned_cppe5&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>module = evaluate.load(<span class="hljs-string">&quot;ybelkada/cocoevaluate&quot;</span>, coco=test_ds_coco_format.coco)
<span class="hljs-meta">&gt;&gt;&gt; </span>val_dataloader = torch.utils.data.DataLoader(
<span class="hljs-meta">... </span>    test_ds_coco_format, batch_size=<span class="hljs-number">8</span>, shuffle=<span class="hljs-literal">False</span>, num_workers=<span class="hljs-number">4</span>, collate_fn=collate_fn
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> idx, batch <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(tqdm(val_dataloader)):
<span class="hljs-meta">... </span>        pixel_values = batch[<span class="hljs-string">&quot;pixel_values&quot;</span>]
<span class="hljs-meta">... </span>        pixel_mask = batch[<span class="hljs-string">&quot;pixel_mask&quot;</span>]

<span class="hljs-meta">... </span>        labels = [
<span class="hljs-meta">... </span>            {k: v <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> t.items()} <span class="hljs-keyword">for</span> t <span class="hljs-keyword">in</span> batch[<span class="hljs-string">&quot;labels&quot;</span>]
<span class="hljs-meta">... </span>        ]  <span class="hljs-comment"># these are in DETR format, resized + normalized</span>

<span class="hljs-meta">... </span>        <span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">... </span>        outputs = model(pixel_values=pixel_values, pixel_mask=pixel_mask)

<span class="hljs-meta">... </span>        orig_target_sizes = torch.stack([target[<span class="hljs-string">&quot;orig_size&quot;</span>] <span class="hljs-keyword">for</span> target <span class="hljs-keyword">in</span> labels], dim=<span class="hljs-number">0</span>)
<span class="hljs-meta">... </span>        results = im_processor.post_process(outputs, orig_target_sizes)  <span class="hljs-comment"># convert outputs of model to COCO api</span>

<span class="hljs-meta">... </span>        module.add(prediction=results, reference=labels)
<span class="hljs-meta">... </span>        <span class="hljs-keyword">del</span> batch

<span class="hljs-meta">&gt;&gt;&gt; </span>results = module.compute()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(results)
Accumulating evaluation results...
DONE (t=<span class="hljs-number">0.08</span>s).
IoU metric: bbox
 Average Precision  (AP) @[ IoU=<span class="hljs-number">0.50</span>:<span class="hljs-number">0.95</span> | area=   <span class="hljs-built_in">all</span> | maxDets=<span class="hljs-number">100</span> ] = <span class="hljs-number">0.352</span>
 Average Precision  (AP) @[ IoU=<span class="hljs-number">0.50</span>      | area=   <span class="hljs-built_in">all</span> | maxDets=<span class="hljs-number">100</span> ] = <span class="hljs-number">0.681</span>
 Average Precision  (AP) @[ IoU=<span class="hljs-number">0.75</span>      | area=   <span class="hljs-built_in">all</span> | maxDets=<span class="hljs-number">100</span> ] = <span class="hljs-number">0.292</span>
 Average Precision  (AP) @[ IoU=<span class="hljs-number">0.50</span>:<span class="hljs-number">0.95</span> | area= small | maxDets=<span class="hljs-number">100</span> ] = <span class="hljs-number">0.168</span>
 Average Precision  (AP) @[ IoU=<span class="hljs-number">0.50</span>:<span class="hljs-number">0.95</span> | area=medium | maxDets=<span class="hljs-number">100</span> ] = <span class="hljs-number">0.208</span>
 Average Precision  (AP) @[ IoU=<span class="hljs-number">0.50</span>:<span class="hljs-number">0.95</span> | area= large | maxDets=<span class="hljs-number">100</span> ] = <span class="hljs-number">0.429</span>
 Average Recall     (AR) @[ IoU=<span class="hljs-number">0.50</span>:<span class="hljs-number">0.95</span> | area=   <span class="hljs-built_in">all</span> | maxDets=  <span class="hljs-number">1</span> ] = <span class="hljs-number">0.274</span>
 Average Recall     (AR) @[ IoU=<span class="hljs-number">0.50</span>:<span class="hljs-number">0.95</span> | area=   <span class="hljs-built_in">all</span> | maxDets= <span class="hljs-number">10</span> ] = <span class="hljs-number">0.484</span>
 Average Recall     (AR) @[ IoU=<span class="hljs-number">0.50</span>:<span class="hljs-number">0.95</span> | area=   <span class="hljs-built_in">all</span> | maxDets=<span class="hljs-number">100</span> ] = <span class="hljs-number">0.501</span>
 Average Recall     (AR) @[ IoU=<span class="hljs-number">0.50</span>:<span class="hljs-number">0.95</span> | area= small | maxDets=<span class="hljs-number">100</span> ] = <span class="hljs-number">0.191</span>
 Average Recall     (AR) @[ IoU=<span class="hljs-number">0.50</span>:<span class="hljs-number">0.95</span> | area=medium | maxDets=<span class="hljs-number">100</span> ] = <span class="hljs-number">0.323</span>
 Average Recall     (AR) @[ IoU=<span class="hljs-number">0.50</span>:<span class="hljs-number">0.95</span> | area= large | maxDets=<span class="hljs-number">100</span> ] = <span class="hljs-number">0.590</span>`}}),bl=new pa({}),Ul=new d({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEElMEF1cmwlMjAlM0QlMjAlMjJodHRwcyUzQSUyRiUyRmkuaW1ndXIuY29tJTJGMmxuV29seS5qcGclMjIlMEFpbWFnZSUyMCUzRCUyMEltYWdlLm9wZW4ocmVxdWVzdHMuZ2V0KHVybCUyQyUyMHN0cmVhbSUzRFRydWUpLnJhdyklMEElMEFvYmpfZGV0ZWN0b3IlMjAlM0QlMjBwaXBlbGluZSglMjJvYmplY3QtZGV0ZWN0aW9uJTIyJTJDJTIwbW9kZWwlM0QlMjJkZXZvbmhvJTJGZGV0ci1yZXNuZXQtNTBfZmluZXR1bmVkX2NwcGU1JTIyKSUwQW9ial9kZXRlY3RvcihpbWFnZSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline
<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;https://i.imgur.com/2lnWoly.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>obj_detector = pipeline(<span class="hljs-string">&quot;object-detection&quot;</span>, model=<span class="hljs-string">&quot;devonho/detr-resnet-50_finetuned_cppe5&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>obj_detector(image)`}}),dl=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;devonho/detr-resnet-50_finetuned_cppe5&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(<span class="hljs-string">&quot;devonho/detr-resnet-50_finetuned_cppe5&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    inputs = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">... </span>    outputs = model(**inputs)
<span class="hljs-meta">... </span>    target_sizes = torch.tensor([image.size[::-<span class="hljs-number">1</span>]])
<span class="hljs-meta">... </span>    results = image_processor.post_process_object_detection(outputs, threshold=<span class="hljs-number">0.5</span>, target_sizes=target_sizes)[<span class="hljs-number">0</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 Coverall <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.566</span> at location [<span class="hljs-number">1215.32</span>, <span class="hljs-number">147.38</span>, <span class="hljs-number">4401.81</span>, <span class="hljs-number">3227.08</span>]
Detected Mask <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.584</span> at location [<span class="hljs-number">2449.06</span>, <span class="hljs-number">823.19</span>, <span class="hljs-number">3256.43</span>, <span class="hljs-number">1413.9</span>]`}}),Tl=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>draw = ImageDraw.Draw(image)

<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>    x, y, x2, y2 = <span class="hljs-built_in">tuple</span>(box)
<span class="hljs-meta">... </span>    draw.rectangle((x, y, x2, y2), outline=<span class="hljs-string">&quot;red&quot;</span>, width=<span class="hljs-number">1</span>)
<span class="hljs-meta">... </span>    draw.text((x, y), model.config.id2label[label.item()], fill=<span class="hljs-string">&quot;white&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>image`}}),{c(){_=t("meta"),T=j(),g=t("h1"),k=t("a"),es=t("span"),i(v.$$.fragment),Rs=j(),ts=t("span"),z=n("\uAC1D\uCCB4 \uD0D0\uC9C0"),is=j(),i(x.$$.fragment),q=j(),S=t("p"),Qs=n(`\uAC1D\uCCB4 \uD0D0\uC9C0\uB294 \uC774\uBBF8\uC9C0\uC5D0\uC11C \uC778\uC2A4\uD134\uC2A4(\uC608: \uC0AC\uB78C, \uAC74\uBB3C \uB610\uB294 \uC790\uB3D9\uCC28)\uB97C \uAC10\uC9C0\uD558\uB294 \uCEF4\uD4E8\uD130 \uBE44\uC804 \uC791\uC5C5\uC785\uB2C8\uB2E4. \uAC1D\uCCB4 \uD0D0\uC9C0 \uBAA8\uB378\uC740 \uC774\uBBF8\uC9C0\uB97C \uC785\uB825\uC73C\uB85C \uBC1B\uACE0 \uD0D0\uC9C0\uB41C \uBC14\uC6B4\uB529 \uBC15\uC2A4\uC758 \uC88C\uD45C\uC640 \uAD00\uB828\uB41C \uB808\uC774\uBE14\uC744 \uCD9C\uB825\uD569\uB2C8\uB2E4.
\uD558\uB098\uC758 \uC774\uBBF8\uC9C0\uC5D0\uB294 \uC5EC\uB7EC \uAC1D\uCCB4\uAC00 \uC788\uC744 \uC218 \uC788\uC73C\uBA70 \uAC01\uAC01\uC740 \uC790\uCCB4\uC801\uC778 \uBC14\uC6B4\uB529 \uBC15\uC2A4\uC640 \uB808\uC774\uBE14\uC744 \uAC00\uC9C8 \uC218 \uC788\uC2B5\uB2C8\uB2E4(\uC608: \uCC28\uC640 \uAC74\uBB3C\uC774 \uC788\uB294 \uC774\uBBF8\uC9C0).
\uB610\uD55C \uAC01 \uAC1D\uCCB4\uB294 \uC774\uBBF8\uC9C0\uC758 \uB2E4\uB978 \uBD80\uBD84\uC5D0 \uC874\uC7AC\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4(\uC608: \uC774\uBBF8\uC9C0\uC5D0 \uC5EC\uB7EC \uB300\uC758 \uCC28\uAC00 \uC788\uC744 \uC218 \uC788\uC74C).
\uC774 \uC791\uC5C5\uC740 \uBCF4\uD589\uC790, \uB3C4\uB85C \uD45C\uC9C0\uD310, \uC2E0\uD638\uB4F1\uACFC \uAC19\uC740 \uAC83\uB4E4\uC744 \uAC10\uC9C0\uD558\uB294 \uC790\uC728 \uC8FC\uD589\uC5D0 \uC77C\uBC18\uC801\uC73C\uB85C \uC0AC\uC6A9\uB429\uB2C8\uB2E4.
\uB2E4\uB978 \uC751\uC6A9 \uBD84\uC57C\uB85C\uB294 \uC774\uBBF8\uC9C0 \uB0B4 \uAC1D\uCCB4 \uC218 \uACC4\uC0B0 \uBC0F \uC774\uBBF8\uC9C0 \uAC80\uC0C9 \uB4F1\uC774 \uC788\uC2B5\uB2C8\uB2E4.`),F=j(),H=t("p"),Vs=n("\uC774 \uAC00\uC774\uB4DC\uC5D0\uC11C \uB2E4\uC74C\uC744 \uBC30\uC6B8 \uAC83\uC785\uB2C8\uB2E4:"),$=j(),R=t("ol"),I=t("li"),w=n("\uD569\uC131\uACF1 \uBC31\uBCF8(\uC778\uD48B \uB370\uC774\uD130\uC758 \uD2B9\uC131\uC744 \uCD94\uCD9C\uD558\uB294 \uD569\uC131\uACF1 \uB124\uD2B8\uC6CC\uD06C)\uACFC \uC778\uCF54\uB354-\uB514\uCF54\uB354 \uD2B8\uB79C\uC2A4\uD3EC\uBA38 \uBAA8\uB378\uC744 \uACB0\uD569\uD55C "),O=t("a"),Il=n("DETR"),Cl=n(" \uBAA8\uB378\uC744 "),P=t("a"),gl=n("CPPE-5"),El=n(" \uB370\uC774\uD130 \uC138\uD2B8\uC5D0 \uB300\uD574 \uBBF8\uC138\uC870\uC815 \uD558\uAE30"),Ct=j(),ca=t("li"),gt=n("\uBBF8\uC138\uC870\uC815 \uD55C \uBAA8\uB378\uC744 \uCD94\uB860\uC5D0 \uC0AC\uC6A9\uD558\uAE30."),Fn=j(),i(hs.$$.fragment),$n=j(),_l=t("p"),Et=n("\uC2DC\uC791\uD558\uAE30 \uC804\uC5D0 \uD544\uC694\uD55C \uBAA8\uB4E0 \uB77C\uC774\uBE0C\uB7EC\uB9AC\uAC00 \uC124\uCE58\uB418\uC5B4 \uC788\uB294\uC9C0 \uD655\uC778\uD558\uC138\uC694:"),Dn=j(),i(Xs.$$.fragment),zn=j(),K=t("p"),_t=n("\uD5C8\uAE45\uD398\uC774\uC2A4 \uD5C8\uBE0C\uC5D0\uC11C \uB370\uC774\uD130 \uC138\uD2B8\uB97C \uAC00\uC838\uC624\uAE30 \uC704\uD55C \u{1F917} Datasets\uACFC \uBAA8\uB378\uC744 \uD559\uC2B5\uD558\uAE30 \uC704\uD55C \u{1F917} Transformers, \uB370\uC774\uD130\uB97C \uC99D\uAC15\uD558\uAE30 \uC704\uD55C "),oa=t("code"),vt=n("albumentations"),At=n(`\uB97C \uC0AC\uC6A9\uD569\uB2C8\uB2E4.
DETR \uBAA8\uB378\uC758 \uD569\uC131\uACF1 \uBC31\uBCF8\uC744 \uAC00\uC838\uC624\uAE30 \uC704\uD574\uC11C\uB294 \uD604\uC7AC `),ra=t("code"),Zt=n("timm"),Bt=n("\uC774 \uD544\uC694\uD569\uB2C8\uB2E4."),Sn=j(),vl=t("p"),Gt=n("\uCEE4\uBBA4\uB2C8\uD2F0\uC5D0 \uBAA8\uB378\uC744 \uC5C5\uB85C\uB4DC\uD558\uACE0 \uACF5\uC720\uD560 \uC218 \uC788\uB3C4\uB85D Hugging Face \uACC4\uC815\uC5D0 \uB85C\uADF8\uC778\uD558\uB294 \uAC83\uC744 \uAD8C\uC7A5\uD569\uB2C8\uB2E4. \uD504\uB86C\uD504\uD2B8\uAC00 \uB098\uD0C0\uB098\uBA74 \uD1A0\uD070\uC744 \uC785\uB825\uD558\uC5EC \uB85C\uADF8\uC778\uD558\uC138\uC694:"),Hn=j(),i(Ns.$$.fragment),On=j(),ps=t("h2"),ys=t("a"),ja=t("span"),i(Ys.$$.fragment),Wt=j(),Ma=t("span"),kt=n("CPPE-5 \uB370\uC774\uD130 \uC138\uD2B8 \uAC00\uC838\uC624\uAE30"),Pn=j(),xs=t("p"),qs=t("a"),Rt=n("CPPE-5"),Qt=n(" \uB370\uC774\uD130 \uC138\uD2B8\uB294 COVID-19 \uB300\uC720\uD589 \uC0C1\uD669\uC5D0\uC11C \uC758\uB8CC \uC804\uBB38\uC778\uB825 \uBCF4\uD638 \uC7A5\uBE44(PPE)\uB97C \uC2DD\uBCC4\uD558\uB294 \uC5B4\uB178\uD14C\uC774\uC158\uC774 \uD3EC\uD568\uB41C \uC774\uBBF8\uC9C0\uB97C \uB2F4\uACE0 \uC788\uC2B5\uB2C8\uB2E4."),Kn=j(),Al=t("p"),Vt=n("\uB370\uC774\uD130 \uC138\uD2B8\uB97C \uAC00\uC838\uC624\uC138\uC694:"),Ln=j(),i(Fs.$$.fragment),se=j(),Zl=t("p"),Xt=n("\uC774 \uB370\uC774\uD130 \uC138\uD2B8\uB294 \uD559\uC2B5 \uC138\uD2B8 \uC774\uBBF8\uC9C0 1,000\uAC1C\uC640 \uD14C\uC2A4\uD2B8 \uC138\uD2B8 \uC774\uBBF8\uC9C0 29\uAC1C\uB97C \uAC16\uACE0 \uC788\uC2B5\uB2C8\uB2E4."),le=j(),Bl=t("p"),Nt=n("\uB370\uC774\uD130\uC5D0 \uC775\uC219\uD574\uC9C0\uAE30 \uC704\uD574, \uC608\uC2DC\uAC00 \uC5B4\uB5BB\uAC8C \uAD6C\uC131\uB418\uC5B4 \uC788\uB294\uC9C0 \uC0B4\uD3B4\uBCF4\uC138\uC694."),ae=j(),i($s.$$.fragment),ne=j(),Gl=t("p"),Yt=n("\uB370\uC774\uD130 \uC138\uD2B8\uC5D0 \uC788\uB294 \uC608\uC2DC\uB294 \uB2E4\uC74C\uC758 \uC601\uC5ED\uC744 \uAC00\uC9C0\uACE0 \uC788\uC2B5\uB2C8\uB2E4:"),ee=j(),A=t("ul"),Wl=t("li"),ma=t("code"),xt=n("image_id"),qt=n(": \uC608\uC2DC \uC774\uBBF8\uC9C0 id"),Ft=j(),us=t("li"),ia=t("code"),$t=n("image"),Dt=n(": \uC774\uBBF8\uC9C0\uB97C \uD3EC\uD568\uD558\uB294 "),ha=t("code"),zt=n("PIL.Image.Image"),St=n(" \uAC1D\uCCB4"),Ht=j(),kl=t("li"),ya=t("code"),Ot=n("width"),Pt=n(": \uC774\uBBF8\uC9C0\uC758 \uB108\uBE44"),Kt=j(),Rl=t("li"),ua=t("code"),Lt=n("height"),sp=n(": \uC774\uBBF8\uC9C0\uC758 \uB192\uC774"),lp=j(),Ds=t("li"),Ja=t("code"),ap=n("objects"),np=n(": \uC774\uBBF8\uC9C0 \uC548\uC758 \uAC1D\uCCB4\uB4E4\uC758 \uBC14\uC6B4\uB529 \uBC15\uC2A4 \uBA54\uD0C0\uB370\uC774\uD130\uB97C \uD3EC\uD568\uD558\uB294 \uB515\uC154\uB108\uB9AC:"),D=t("ul"),Ql=t("li"),ba=t("code"),ep=n("id"),tp=n(": \uC5B4\uB178\uD14C\uC774\uC158 id"),pp=j(),Vl=t("li"),Ua=t("code"),cp=n("area"),op=n(": \uBC14\uC6B4\uB529 \uBC15\uC2A4\uC758 \uBA74\uC801"),rp=j(),Js=t("li"),da=t("code"),jp=n("bbox"),Mp=n(": \uAC1D\uCCB4\uC758 \uBC14\uC6B4\uB529 \uBC15\uC2A4 ("),zs=t("a"),mp=n("COCO \uD3EC\uB9F7"),ip=n("\uC73C\uB85C)"),hp=j(),E=t("li"),Ta=t("code"),yp=n("category"),up=n(": \uAC1D\uCCB4\uC758 \uCE74\uD14C\uACE0\uB9AC, \uAC00\uB2A5\uD55C \uAC12\uC73C\uB85C\uB294 "),wa=t("code"),Jp=n("Coverall (0)"),bp=n(", "),fa=t("code"),Up=n("Face_Shield (1)"),dp=n(", "),Ia=t("code"),Tp=n("Gloves (2)"),wp=n(", "),Ca=t("code"),fp=n("Goggles (3)"),Ip=n(" \uBC0F "),ga=t("code"),Cp=n("Mask (4)"),gp=n(" \uAC00 \uD3EC\uD568\uB429\uB2C8\uB2E4."),te=j(),cs=t("p"),Ea=t("code"),Ep=n("bbox"),_p=n(` \uD544\uB4DC\uAC00 DETR \uBAA8\uB378\uC774 \uC694\uAD6C\uD558\uB294 COCO \uD615\uC2DD\uC744 \uB530\uB978\uB2E4\uB294 \uAC83\uC744 \uC54C \uC218 \uC788\uC2B5\uB2C8\uB2E4.
\uADF8\uB7EC\uB098 `),_a=t("code"),vp=n("objects"),Ap=n(" \uB0B4\uBD80\uC758 \uD544\uB4DC \uADF8\uB8F9\uC740 DETR\uC774 \uC694\uAD6C\uD558\uB294 \uC5B4\uB178\uD14C\uC774\uC158 \uD615\uC2DD\uACFC \uB2E4\uB985\uB2C8\uB2E4. \uB530\uB77C\uC11C \uC774 \uB370\uC774\uD130\uB97C \uD559\uC2B5\uC5D0 \uC0AC\uC6A9\uD558\uAE30 \uC804\uC5D0 \uC804\uCC98\uB9AC\uB97C \uC801\uC6A9\uD574\uC57C \uD569\uB2C8\uB2E4."),pe=j(),Xl=t("p"),Zp=n("\uB370\uC774\uD130\uB97C \uB354 \uC798 \uC774\uD574\uD558\uAE30 \uC704\uD574\uC11C \uB370\uC774\uD130 \uC138\uD2B8\uC5D0\uC11C \uD55C \uAC00\uC9C0 \uC608\uC2DC\uB97C \uC2DC\uAC01\uD654\uD558\uC138\uC694."),ce=j(),i(Ss.$$.fragment),oe=j(),Hs=t("div"),Nl=t("img"),re=j(),Q=t("p"),Bp=n("\uBC14\uC6B4\uB529 \uBC15\uC2A4\uC640 \uC5F0\uACB0\uB41C \uB808\uC774\uBE14\uC744 \uC2DC\uAC01\uD654\uD558\uB824\uBA74 \uB370\uC774\uD130 \uC138\uD2B8\uC758 \uBA54\uD0C0 \uB370\uC774\uD130, \uD2B9\uD788 "),va=t("code"),Gp=n("category"),Wp=n(` \uD544\uB4DC\uC5D0\uC11C \uB808\uC774\uBE14\uC744 \uAC00\uC838\uC640\uC57C \uD569\uB2C8\uB2E4.
\uB610\uD55C \uB808\uC774\uBE14 ID\uB97C \uB808\uC774\uBE14 \uD074\uB798\uC2A4\uC5D0 \uB9E4\uD551\uD558\uB294 `),Aa=t("code"),kp=n("id2label"),Rp=n("\uACFC \uBC18\uB300\uB85C \uB9E4\uD551\uD558\uB294 "),Za=t("code"),Qp=n("label2id"),Vp=n(` \uB515\uC154\uB108\uB9AC\uB97C \uB9CC\uB4E4\uC5B4\uC57C \uD569\uB2C8\uB2E4.
\uBAA8\uB378\uC744 \uC124\uC815\uD560 \uB54C \uC774\uB7EC\uD55C \uB9E4\uD551\uC744 \uC0AC\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4. \uC774\uB7EC\uD55C \uB9E4\uD551\uC740 \uD5C8\uAE45\uD398\uC774\uC2A4 \uD5C8\uBE0C\uC5D0\uC11C \uBAA8\uB378\uC744 \uACF5\uC720\uD588\uC744 \uB54C \uB2E4\uB978 \uC0AC\uB78C\uB4E4\uC774 \uC7AC\uC0AC\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.`),je=j(),Yl=t("p"),Xp=n(`\uB370\uC774\uD130\uB97C \uB354 \uC798 \uC774\uD574\uD558\uAE30 \uC704\uD55C \uCD5C\uC885 \uB2E8\uACC4\uB85C, \uC7A0\uC7AC\uC801\uC778 \uBB38\uC81C\uB97C \uCC3E\uC544\uBCF4\uC138\uC694.
\uAC1D\uCCB4 \uAC10\uC9C0\uB97C \uC704\uD55C \uB370\uC774\uD130 \uC138\uD2B8\uC5D0\uC11C \uC790\uC8FC \uBC1C\uC0DD\uD558\uB294 \uBB38\uC81C \uC911 \uD558\uB098\uB294 \uBC14\uC6B4\uB529 \uBC15\uC2A4\uAC00 \uC774\uBBF8\uC9C0\uC758 \uAC00\uC7A5\uC790\uB9AC\uB97C \uB118\uC5B4\uAC00\uB294 \uAC83\uC785\uB2C8\uB2E4.
\uC774\uB7EC\uD55C \uBC14\uC6B4\uB529 \uBC15\uC2A4\uB97C \u201C\uB118\uC5B4\uAC00\uB294 \uAC83(run away)\u201C\uC740 \uD6C8\uB828 \uC911\uC5D0 \uC624\uB958\uB97C \uBC1C\uC0DD\uC2DC\uD0AC \uC218 \uC788\uAE30\uC5D0 \uC774 \uB2E8\uACC4\uC5D0\uC11C \uCC98\uB9AC\uD574\uC57C \uD569\uB2C8\uB2E4.
\uC774 \uB370\uC774\uD130 \uC138\uD2B8\uC5D0\uB3C4 \uAC19\uC740 \uBB38\uC81C\uAC00 \uC788\uB294 \uBA87 \uAC00\uC9C0 \uC608\uAC00 \uC788\uC2B5\uB2C8\uB2E4. \uC774 \uAC00\uC774\uB4DC\uC5D0\uC11C\uB294 \uAC04\uB2E8\uD558\uAC8C\uD558\uAE30 \uC704\uD574 \uB370\uC774\uD130\uC5D0\uC11C \uC774\uB7EC\uD55C \uC774\uBBF8\uC9C0\uB97C \uC81C\uAC70\uD569\uB2C8\uB2E4.`),Me=j(),i(Os.$$.fragment),me=j(),os=t("h2"),bs=t("a"),Ba=t("span"),i(Ps.$$.fragment),Np=j(),Ga=t("span"),Yp=n("\uB370\uC774\uD130 \uC804\uCC98\uB9AC\uD558\uAE30"),ie=j(),Z=t("p"),xp=n(`\uBAA8\uB378\uC744 \uBBF8\uC138 \uC870\uC815 \uD558\uB824\uBA74, \uBBF8\uB9AC \uD559\uC2B5\uB41C \uBAA8\uB378\uC5D0\uC11C \uC0AC\uC6A9\uD55C \uC804\uCC98\uB9AC \uBC29\uC2DD\uACFC \uC815\uD655\uD558\uAC8C \uC77C\uCE58\uD558\uB3C4\uB85D \uC0AC\uC6A9\uD560 \uB370\uC774\uD130\uB97C \uC804\uCC98\uB9AC\uD574\uC57C \uD569\uB2C8\uB2E4.
`),Wa=t("code"),qp=n("AutoImageProcessor"),Fp=n("\uB294 \uC774\uBBF8\uC9C0 \uB370\uC774\uD130\uB97C \uCC98\uB9AC\uD558\uC5EC DETR \uBAA8\uB378\uC774 \uD559\uC2B5\uC5D0 \uC0AC\uC6A9\uD560 \uC218 \uC788\uB294 "),ka=t("code"),$p=n("pixel_values"),Dp=n(", "),Ra=t("code"),zp=n("pixel_mask"),Sp=n(", \uADF8\uB9AC\uACE0 "),Qa=t("code"),Hp=n("labels"),Op=n(`\uB97C \uC0DD\uC131\uD558\uB294 \uC791\uC5C5\uC744 \uB2F4\uB2F9\uD569\uB2C8\uB2E4.
\uC774 \uC774\uBBF8\uC9C0 \uD504\uB85C\uC138\uC11C\uC5D0\uB294 \uAC71\uC815\uD558\uC9C0 \uC54A\uC544\uB3C4 \uB418\uB294 \uBA87 \uAC00\uC9C0 \uC18D\uC131\uC774 \uC788\uC2B5\uB2C8\uB2E4:`),he=j(),Us=t("ul"),Va=t("li"),Xa=t("code"),Pp=n("image_mean = [0.485, 0.456, 0.406 ]"),Kp=j(),Na=t("li"),Ya=t("code"),Lp=n("image_std = [0.229, 0.224, 0.225]"),ye=j(),xl=t("p"),sc=n(`\uC774 \uAC12\uB4E4\uC740 \uBAA8\uB378 \uC0AC\uC804 \uD6C8\uB828 \uC911 \uC774\uBBF8\uC9C0\uB97C \uC815\uADDC\uD654\uD558\uB294 \uB370 \uC0AC\uC6A9\uB418\uB294 \uD3C9\uADE0\uACFC \uD45C\uC900 \uD3B8\uCC28\uC785\uB2C8\uB2E4.
\uC774 \uAC12\uB4E4\uC740 \uCD94\uB860 \uB610\uB294 \uC0AC\uC804 \uD6C8\uB828\uB41C \uC774\uBBF8\uC9C0 \uBAA8\uB378\uC744 \uC138\uBC00\uD558\uAC8C \uC870\uC815\uD560 \uB54C \uBCF5\uC81C\uD574\uC57C \uD558\uB294 \uC911\uC694\uD55C \uAC12\uC785\uB2C8\uB2E4.`),ue=j(),ql=t("p"),lc=n("\uC0AC\uC804 \uD6C8\uB828\uB41C \uBAA8\uB378\uACFC \uB3D9\uC77C\uD55C \uCCB4\uD06C\uD3EC\uC778\uD2B8\uC5D0\uC11C \uC774\uBBF8\uC9C0 \uD504\uB85C\uC138\uC11C\uB97C \uC778\uC2A4\uD134\uC2A4\uD654\uD569\uB2C8\uB2E4."),Je=j(),i(Ks.$$.fragment),be=j(),Ls=t("p"),xa=t("code"),ac=n("image_processor"),nc=n("\uC5D0 \uC774\uBBF8\uC9C0\uB97C \uC804\uB2EC\uD558\uAE30 \uC804\uC5D0, \uB370\uC774\uD130 \uC138\uD2B8\uC5D0 \uB450 \uAC00\uC9C0 \uC804\uCC98\uB9AC\uB97C \uC801\uC6A9\uD574\uC57C \uD569\uB2C8\uB2E4:"),Ue=j(),ds=t("ul"),qa=t("li"),ec=n("\uC774\uBBF8\uC9C0 \uC99D\uAC15"),tc=j(),Fa=t("li"),pc=n("DETR \uBAA8\uB378\uC758 \uC694\uAD6C\uC5D0 \uB9DE\uAC8C \uC5B4\uB178\uD14C\uC774\uC158\uC744 \uB2E4\uC2DC \uD3EC\uB9F7\uD305"),de=j(),L=t("p"),cc=n("\uCCAB\uC9F8\uB85C, \uBAA8\uB378\uC774 \uD559\uC2B5 \uB370\uC774\uD130\uC5D0 \uACFC\uC801\uD569 \uB418\uC9C0 \uC54A\uB3C4\uB85D \uB370\uC774\uD130 \uC99D\uAC15 \uB77C\uC774\uBE0C\uB7EC\uB9AC \uC911 \uC544\uBB34\uAC70\uB098 \uC0AC\uC6A9\uD558\uC5EC \uBCC0\uD658\uC744 \uC801\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4. \uC5EC\uAE30\uC5D0\uC11C\uB294 "),sl=t("a"),oc=n("Albumentations"),rc=n(` \uB77C\uC774\uBE0C\uB7EC\uB9AC\uB97C \uC0AC\uC6A9\uD569\uB2C8\uB2E4\u2026
\uC774 \uB77C\uC774\uBE0C\uB7EC\uB9AC\uB294 \uBCC0\uD658\uC744 \uC774\uBBF8\uC9C0\uC5D0 \uC801\uC6A9\uD558\uACE0 \uBC14\uC6B4\uB529 \uBC15\uC2A4\uB97C \uC801\uC808\uD558\uAC8C \uC5C5\uB370\uC774\uD2B8\uD558\uB3C4\uB85D \uBCF4\uC7A5\uD569\uB2C8\uB2E4.
\u{1F917} Datasets \uB77C\uC774\uBE0C\uB7EC\uB9AC \uBB38\uC11C\uC5D0\uB294 `),ll=t("a"),jc=n("\uAC1D\uCCB4 \uD0D0\uC9C0\uB97C \uC704\uD574 \uC774\uBBF8\uC9C0\uB97C \uBCF4\uAC15\uD558\uB294 \uBC29\uBC95\uC5D0 \uB300\uD55C \uC790\uC138\uD55C \uAC00\uC774\uB4DC"),Mc=n(`\uAC00 \uC788\uC73C\uBA70,
\uC774 \uC608\uC81C\uC640 \uC815\uD655\uD788 \uB3D9\uC77C\uD55C \uB370\uC774\uD130 \uC138\uD2B8\uB97C \uC0AC\uC6A9\uD569\uB2C8\uB2E4. \uC5EC\uAE30\uC11C\uB294 \uAC01 \uC774\uBBF8\uC9C0\uB97C (480, 480) \uD06C\uAE30\uB85C \uC870\uC815\uD558\uACE0, \uC88C\uC6B0\uB85C \uB4A4\uC9D1\uACE0, \uBC1D\uAE30\uB97C \uB192\uC774\uB294 \uB3D9\uC77C\uD55C \uC811\uADFC\uBC95\uC744 \uC801\uC6A9\uD569\uB2C8\uB2E4:`),Te=j(),i(al.$$.fragment),we=j(),Ts=t("p"),mc=n("\uC774\uBBF8\uC9C0 \uD504\uB85C\uC138\uC11C\uB294 \uC5B4\uB178\uD14C\uC774\uC158\uC774 \uB2E4\uC74C\uACFC \uAC19\uC740 \uD615\uC2DD\uC77C \uAC83\uC73C\uB85C \uC608\uC0C1\uD569\uB2C8\uB2E4: "),$a=t("code"),ic=n("{'image_id': int, 'annotations': List[Dict]}"),hc=n(", \uC5EC\uAE30\uC11C \uAC01 \uB515\uC154\uB108\uB9AC\uB294 COCO \uAC1D\uCCB4 \uC5B4\uB178\uD14C\uC774\uC158\uC785\uB2C8\uB2E4. \uB2E8\uC77C \uC608\uC81C\uC5D0 \uB300\uD574 \uC5B4\uB178\uD14C\uC774\uC158\uC758 \uD615\uC2DD\uC744 \uB2E4\uC2DC \uC9C0\uC815\uD558\uB294 \uD568\uC218\uB97C \uCD94\uAC00\uD574 \uBCF4\uACA0\uC2B5\uB2C8\uB2E4:"),fe=j(),i(nl.$$.fragment),Ie=j(),Fl=t("p"),yc=n("\uC774\uC81C \uC774\uBBF8\uC9C0\uC640 \uC5B4\uB178\uD14C\uC774\uC158 \uC804\uCC98\uB9AC \uBCC0\uD658\uC744 \uACB0\uD569\uD558\uC5EC \uC608\uC81C \uBC30\uCE58\uC5D0 \uC0AC\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4:"),Ce=j(),i(el.$$.fragment),ge=j(),ws=t("p"),uc=n("\uC774\uC804 \uB2E8\uACC4\uC5D0\uC11C \uB9CC\uB4E0 \uC804\uCC98\uB9AC \uD568\uC218\uB97C \u{1F917} Datasets\uC758 "),Da=t("code"),Jc=n("with_transform"),bc=n(` \uBA54\uC18C\uB4DC\uB97C \uC0AC\uC6A9\uD558\uC5EC \uB370\uC774\uD130 \uC138\uD2B8 \uC804\uCCB4\uC5D0 \uC801\uC6A9\uD569\uB2C8\uB2E4.
\uC774 \uBA54\uC18C\uB4DC\uB294 \uB370\uC774\uD130 \uC138\uD2B8\uC758 \uC694\uC18C\uB97C \uAC00\uC838\uC62C \uB54C\uB9C8\uB2E4 \uC804\uCC98\uB9AC \uD568\uC218\uB97C \uC801\uC6A9\uD569\uB2C8\uB2E4.`),Ee=j(),V=t("p"),Uc=n(`\uC774 \uC2DC\uC810\uC5D0\uC11C\uB294 \uC804\uCC98\uB9AC \uD6C4 \uB370\uC774\uD130 \uC138\uD2B8\uC5D0\uC11C \uC608\uC2DC \uD558\uB098\uB97C \uAC00\uC838\uC640\uC11C \uBCC0\uD658 \uD6C4 \uBAA8\uC591\uC774 \uC5B4\uB5BB\uAC8C \uB418\uB294\uC9C0 \uD655\uC778\uD574 \uBCFC \uC218 \uC788\uC2B5\uB2C8\uB2E4.
\uC774\uB54C, `),za=t("code"),dc=n("pixel_values"),Tc=n(" \uD150\uC11C, "),Sa=t("code"),wc=n("pixel_mask"),fc=n(" \uD150\uC11C, \uADF8\uB9AC\uACE0 "),Ha=t("code"),Ic=n("labels"),Cc=n("\uB85C \uAD6C\uC131\uB41C \uD150\uC11C\uAC00 \uC788\uC5B4\uC57C \uD569\uB2C8\uB2E4."),_e=j(),i(tl.$$.fragment),ve=j(),X=t("p"),gc=n(`\uAC01\uAC01\uC758 \uC774\uBBF8\uC9C0\uB97C \uC131\uACF5\uC801\uC73C\uB85C \uC99D\uAC15\uD558\uACE0 \uC774\uBBF8\uC9C0\uC758 \uC5B4\uB178\uD14C\uC774\uC158\uC744 \uC900\uBE44\uD588\uC2B5\uB2C8\uB2E4.
\uADF8\uB7EC\uB098 \uC804\uCC98\uB9AC\uB294 \uC544\uC9C1 \uB05D\uB098\uC9C0 \uC54A\uC558\uC2B5\uB2C8\uB2E4. \uB9C8\uC9C0\uB9C9 \uB2E8\uACC4\uB85C, \uC774\uBBF8\uC9C0\uB97C \uBC30\uCE58\uB85C \uB9CC\uB4E4 \uC0AC\uC6A9\uC790 \uC815\uC758 `),Oa=t("code"),Ec=n("collate_fn"),_c=n(`\uC744 \uC0DD\uC131\uD569\uB2C8\uB2E4.
\uD574\uB2F9 \uBC30\uCE58\uC5D0\uC11C \uAC00\uC7A5 \uD070 \uC774\uBBF8\uC9C0\uC5D0 \uC774\uBBF8\uC9C0(\uD604\uC7AC `),Pa=t("code"),vc=n("pixel_values"),Ac=n(" \uC778)\uB97C \uD328\uB4DC\uD558\uACE0, \uC2E4\uC81C \uD53D\uC140(1)\uACFC \uD328\uB529(0)\uC744 \uB098\uD0C0\uB0B4\uAE30 \uC704\uD574 \uADF8\uC5D0 \uD574\uB2F9\uD558\uB294 \uC0C8\uB85C\uC6B4 "),Ka=t("code"),Zc=n("pixel_mask"),Bc=n("\uB97C \uC0DD\uC131\uD574\uC57C \uD569\uB2C8\uB2E4."),Ae=j(),i(pl.$$.fragment),Ze=j(),rs=t("h2"),fs=t("a"),La=t("span"),i(cl.$$.fragment),Gc=j(),sn=t("span"),Wc=n("DETR \uBAA8\uB378 \uD559\uC2B5\uC2DC\uD0A4\uAE30"),Be=j(),$l=t("p"),kc=n(`\uC774\uC804 \uC139\uC158\uC5D0\uC11C \uB300\uBD80\uBD84\uC758 \uC791\uC5C5\uC744 \uC218\uD589\uD558\uC5EC \uC774\uC81C \uBAA8\uB378\uC744 \uD559\uC2B5\uD560 \uC900\uBE44\uAC00 \uB418\uC5C8\uC2B5\uB2C8\uB2E4!
\uC774 \uB370\uC774\uD130 \uC138\uD2B8\uC758 \uC774\uBBF8\uC9C0\uB294 \uB9AC\uC0AC\uC774\uC988 \uD6C4\uC5D0\uB3C4 \uC5EC\uC804\uD788 \uC6A9\uB7C9\uC774 \uD06C\uAE30 \uB54C\uBB38\uC5D0, \uC774 \uBAA8\uB378\uC744 \uBBF8\uC138 \uC870\uC815 \uD558\uB824\uBA74 \uC801\uC5B4\uB3C4 \uD558\uB098\uC758 GPU\uAC00 \uD544\uC694\uD569\uB2C8\uB2E4.`),Ge=j(),Dl=t("p"),Rc=n("\uD559\uC2B5\uC740 \uB2E4\uC74C\uC758 \uB2E8\uACC4\uB97C \uC218\uD589\uD569\uB2C8\uB2E4:"),We=j(),N=t("ol"),zl=t("li"),ln=t("code"),Qc=n("AutoModelForObjectDetection"),Vc=n("\uC744 \uC0AC\uC6A9\uD558\uC5EC \uC804\uCC98\uB9AC\uC640 \uB3D9\uC77C\uD55C \uCCB4\uD06C\uD3EC\uC778\uD2B8\uB97C \uC0AC\uC6A9\uD558\uC5EC \uBAA8\uB378\uC744 \uAC00\uC838\uC635\uB2C8\uB2E4."),Xc=j(),Sl=t("li"),an=t("code"),Nc=n("TrainingArguments"),Yc=n("\uC5D0\uC11C \uD559\uC2B5 \uD558\uC774\uD37C\uD30C\uB77C\uBBF8\uD130\uB97C \uC815\uC758\uD569\uB2C8\uB2E4."),xc=j(),ol=t("li"),qc=n("\uBAA8\uB378, \uB370\uC774\uD130 \uC138\uD2B8, \uC774\uBBF8\uC9C0 \uD504\uB85C\uC138\uC11C \uBC0F \uB370\uC774\uD130 \uCF5C\uB808\uC774\uD130\uC640 \uD568\uAED8 "),nn=t("code"),Fc=n("Trainer"),$c=n("\uC5D0 \uD6C8\uB828 \uC778\uC218\uB97C \uC804\uB2EC\uD569\uB2C8\uB2E4."),Dc=j(),Hl=t("li"),en=t("code"),zc=n("train()"),Sc=n("\uB97C \uD638\uCD9C\uD558\uC5EC \uBAA8\uB378\uC744 \uBBF8\uC138 \uC870\uC815 \uD569\uB2C8\uB2E4."),ke=j(),Y=t("p"),Hc=n("\uC804\uCC98\uB9AC\uC5D0 \uC0AC\uC6A9\uD55C \uCCB4\uD06C\uD3EC\uC778\uD2B8\uC640 \uB3D9\uC77C\uD55C \uCCB4\uD06C\uD3EC\uC778\uD2B8\uC5D0\uC11C \uBAA8\uB378\uC744 \uAC00\uC838\uC62C \uB54C, \uB370\uC774\uD130 \uC138\uD2B8\uC758 \uBA54\uD0C0\uB370\uC774\uD130\uC5D0\uC11C \uB9CC\uB4E0 "),tn=t("code"),Oc=n("label2id"),Pc=n("\uC640 "),pn=t("code"),Kc=n("id2label"),Lc=n(` \uB9E4\uD551\uC744 \uC804\uB2EC\uD574\uC57C \uD569\uB2C8\uB2E4.
\uB610\uD55C, `),cn=t("code"),so=n("ignore_mismatched_sizes=True"),lo=n("\uB97C \uC9C0\uC815\uD558\uC5EC \uAE30\uC874 \uBD84\uB958 \uD5E4\uB4DC(\uBAA8\uB378\uC5D0\uC11C \uBD84\uB958\uC5D0 \uC0AC\uC6A9\uB418\uB294 \uB9C8\uC9C0\uB9C9 \uB808\uC774\uC5B4)\uB97C \uC0C8 \uBD84\uB958 \uD5E4\uB4DC\uB85C \uB300\uCCB4\uD569\uB2C8\uB2E4."),Re=j(),i(rl.$$.fragment),Qe=j(),U=t("p"),on=t("code"),ao=n("TrainingArguments"),no=n("\uC5D0\uC11C "),rn=t("code"),eo=n("output_dir"),to=n(`\uC744 \uC0AC\uC6A9\uD558\uC5EC \uBAA8\uB378\uC744 \uC800\uC7A5\uD560 \uC704\uCE58\uB97C \uC9C0\uC815\uD55C \uB2E4\uC74C, \uD544\uC694\uC5D0 \uB530\uB77C \uD558\uC774\uD37C\uD30C\uB77C\uBBF8\uD130\uB97C \uAD6C\uC131\uD558\uC138\uC694.
\uC0AC\uC6A9\uD558\uC9C0 \uC54A\uB294 \uC5F4\uC744 \uC81C\uAC70\uD558\uC9C0 \uC54A\uB3C4\uB85D \uC8FC\uC758\uD574\uC57C \uD569\uB2C8\uB2E4. \uB9CC\uC57D `),jn=t("code"),po=n("remove_unused_columns"),co=n("\uAC00 "),Mn=t("code"),oo=n("True"),ro=n(`\uC77C \uACBD\uC6B0 \uC774\uBBF8\uC9C0 \uC5F4\uC774 \uC0AD\uC81C\uB429\uB2C8\uB2E4.
\uC774\uBBF8\uC9C0 \uC5F4\uC774 \uC5C6\uB294 \uACBD\uC6B0 `),mn=t("code"),jo=n("pixel_values"),Mo=n("\uB97C \uC0DD\uC131\uD560 \uC218 \uC5C6\uAE30 \uB54C\uBB38\uC5D0 "),hn=t("code"),mo=n("remove_unused_columns"),io=n("\uB97C "),yn=t("code"),ho=n("False"),yo=n(`\uB85C \uC124\uC815\uD574\uC57C \uD569\uB2C8\uB2E4.
\uBAA8\uB378\uC744 Hub\uC5D0 \uC5C5\uB85C\uB4DC\uD558\uC5EC \uACF5\uC720\uD558\uB824\uBA74 `),un=t("code"),uo=n("push_to_hub"),Jo=n("\uB97C "),Jn=t("code"),bo=n("True"),Uo=n("\uB85C \uC124\uC815\uD558\uC2ED\uC2DC\uC624(\uD5C8\uAE45\uD398\uC774\uC2A4\uC5D0 \uB85C\uADF8\uC778\uD558\uC5EC \uBAA8\uB378\uC744 \uC5C5\uB85C\uB4DC\uD574\uC57C \uD569\uB2C8\uB2E4)."),Ve=j(),i(jl.$$.fragment),Xe=j(),C=t("p"),To=n("\uB9C8\uC9C0\uB9C9\uC73C\uB85C "),bn=t("code"),wo=n("model"),fo=n(", "),Un=t("code"),Io=n("training_args"),Co=n(", "),dn=t("code"),go=n("collate_fn"),Eo=n(", "),Tn=t("code"),_o=n("image_processor"),vo=n("\uC640 \uB370\uC774\uD130 \uC138\uD2B8("),wn=t("code"),Ao=n("cppe5"),Zo=n(")\uB97C \uBAA8\uB450 \uAC00\uC838\uC628 \uD6C4, "),fn=t("code"),Bo=n("train()"),Go=n("\uB97C \uD638\uCD9C\uD569\uB2C8\uB2E4."),Ne=j(),i(Ml.$$.fragment),Ye=j(),G=t("p"),In=t("code"),Wo=n("training_args"),ko=n("\uC5D0\uC11C "),Cn=t("code"),Ro=n("push_to_hub"),Qo=n("\uB97C "),gn=t("code"),Vo=n("True"),Xo=n(`\uB85C \uC124\uC815\uD55C \uACBD\uC6B0, \uD559\uC2B5 \uCCB4\uD06C\uD3EC\uC778\uD2B8\uB294 \uD5C8\uAE45\uD398\uC774\uC2A4 \uD5C8\uBE0C\uC5D0 \uC5C5\uB85C\uB4DC\uB429\uB2C8\uB2E4.
\uD559\uC2B5 \uC644\uB8CC \uD6C4, `),En=t("code"),No=n("push_to_hub()"),Yo=n(" \uBA54\uC18C\uB4DC\uB97C \uD638\uCD9C\uD558\uC5EC \uCD5C\uC885 \uBAA8\uB378\uC744 \uD5C8\uAE45\uD398\uC774\uC2A4 \uD5C8\uBE0C\uC5D0 \uC5C5\uB85C\uB4DC\uD569\uB2C8\uB2E4."),xe=j(),i(ml.$$.fragment),qe=j(),js=t("h2"),Is=t("a"),_n=t("span"),i(il.$$.fragment),xo=j(),vn=t("span"),qo=n("\uD3C9\uAC00\uD558\uAE30"),Fe=j(),ss=t("p"),Fo=n("\uAC1D\uCCB4 \uD0D0\uC9C0 \uBAA8\uB378\uC740 \uC77C\uBC18\uC801\uC73C\uB85C \uC77C\uB828\uC758 "),Ol=t("a"),$o=n("COCO-\uC2A4\uD0C0\uC77C \uC9C0\uD45C"),Do=n(`\uB85C \uD3C9\uAC00\uB429\uB2C8\uB2E4.
\uAE30\uC874\uC5D0 \uAD6C\uD604\uB41C \uD3C9\uAC00 \uC9C0\uD45C \uC911 \uD558\uB098\uB97C \uC0AC\uC6A9\uD560 \uC218\uB3C4 \uC788\uC9C0\uB9CC, \uC5EC\uAE30\uC5D0\uC11C\uB294 \uD5C8\uAE45\uD398\uC774\uC2A4 \uD5C8\uBE0C\uC5D0 \uD478\uC2DC\uD55C \uCD5C\uC885 \uBAA8\uB378\uC744 \uD3C9\uAC00\uD558\uB294 \uB370 `),An=t("code"),zo=n("torchvision"),So=n("\uC5D0\uC11C \uC81C\uACF5\uD558\uB294 \uD3C9\uAC00 \uC9C0\uD45C\uB97C \uC0AC\uC6A9\uD569\uB2C8\uB2E4."),$e=j(),hl=t("p"),Zn=t("code"),Ho=n("torchvision"),Oo=n(` \uD3C9\uAC00\uC790(evaluator)\uB97C \uC0AC\uC6A9\uD558\uB824\uBA74 \uC2E4\uCE21\uAC12\uC778 COCO \uB370\uC774\uD130 \uC138\uD2B8\uB97C \uC900\uBE44\uD574\uC57C \uD569\uB2C8\uB2E4.
COCO \uB370\uC774\uD130 \uC138\uD2B8\uB97C \uBE4C\uB4DC\uD558\uB294 API\uB294 \uB370\uC774\uD130\uB97C \uD2B9\uC815 \uD615\uC2DD\uC73C\uB85C \uC800\uC7A5\uD574\uC57C \uD558\uBBC0\uB85C, \uBA3C\uC800 \uC774\uBBF8\uC9C0\uC640 \uC5B4\uB178\uD14C\uC774\uC158\uC744 \uB514\uC2A4\uD06C\uC5D0 \uC800\uC7A5\uD574\uC57C \uD569\uB2C8\uB2E4.
\uD559\uC2B5\uC744 \uC704\uD574 \uB370\uC774\uD130\uB97C \uC900\uBE44\uD560 \uB54C\uC640 \uB9C8\uCC2C\uAC00\uC9C0\uB85C, cppe5[\u201Ctest\u201D]\uC5D0\uC11C\uC758 \uC5B4\uB178\uD14C\uC774\uC158\uC740 \uD3EC\uB9F7\uC744 \uB9DE\uCDB0\uC57C \uD569\uB2C8\uB2E4. \uADF8\uB7EC\uB098 \uC774\uBBF8\uC9C0\uB294 \uADF8\uB300\uB85C \uC720\uC9C0\uD574\uC57C \uD569\uB2C8\uB2E4.`),De=j(),Cs=t("p"),Po=n(`\uD3C9\uAC00 \uB2E8\uACC4\uB294 \uC57D\uAC04\uC758 \uC791\uC5C5\uC774 \uD544\uC694\uD558\uC9C0\uB9CC, \uD06C\uAC8C \uC138 \uAC00\uC9C0 \uC8FC\uC694 \uB2E8\uACC4\uB85C \uB098\uB20C \uC218 \uC788\uC2B5\uB2C8\uB2E4.
\uBA3C\uC800, `),Bn=t("code"),Ko=n('cppe5["test"]'),Lo=n(" \uC138\uD2B8\uB97C \uC900\uBE44\uD569\uB2C8\uB2E4: \uC5B4\uB178\uD14C\uC774\uC158\uC744 \uD3EC\uB9F7\uC5D0 \uB9DE\uAC8C \uB9CC\uB4E4\uACE0 \uB370\uC774\uD130\uB97C \uB514\uC2A4\uD06C\uC5D0 \uC800\uC7A5\uD569\uB2C8\uB2E4."),ze=j(),i(yl.$$.fragment),Se=j(),ls=t("p"),sr=n("\uB2E4\uC74C\uC73C\uB85C, "),Gn=t("code"),lr=n("cocoevaluator"),ar=n("\uC640 \uD568\uAED8 \uC0AC\uC6A9\uD560 \uC218 \uC788\uB294 "),Wn=t("code"),nr=n("CocoDetection"),er=n(" \uD074\uB798\uC2A4\uC758 \uC778\uC2A4\uD134\uC2A4\uB97C \uC900\uBE44\uD569\uB2C8\uB2E4."),He=j(),i(ul.$$.fragment),Oe=j(),Pl=t("p"),tr=n("\uB9C8\uC9C0\uB9C9\uC73C\uB85C, \uD3C9\uAC00 \uC9C0\uD45C\uB97C \uAC00\uC838\uC640\uC11C \uD3C9\uAC00\uB97C \uC2E4\uD589\uD569\uB2C8\uB2E4."),Pe=j(),i(Jl.$$.fragment),Ke=j(),gs=t("p"),pr=n("\uC774\uB7EC\uD55C \uACB0\uACFC\uB294 "),kn=t("code"),cr=n("TrainingArguments"),or=n("\uC758 \uD558\uC774\uD37C\uD30C\uB77C\uBBF8\uD130\uB97C \uC870\uC815\uD558\uC5EC \uB354\uC6B1 \uAC1C\uC120\uB420 \uC218 \uC788\uC2B5\uB2C8\uB2E4. \uD55C\uBC88 \uC2DC\uB3C4\uD574 \uBCF4\uC138\uC694!"),Le=j(),Ms=t("h2"),Es=t("a"),Rn=t("span"),i(bl.$$.fragment),rr=j(),Qn=t("span"),jr=n("\uCD94\uB860\uD558\uAE30"),st=j(),Kl=t("p"),Mr=n("DETR \uBAA8\uB378\uC744 \uBBF8\uC138 \uC870\uC815 \uBC0F \uD3C9\uAC00\uD558\uACE0, \uD5C8\uAE45\uD398\uC774\uC2A4 \uD5C8\uBE0C\uC5D0 \uC5C5\uB85C\uB4DC \uD588\uC73C\uBBC0\uB85C \uCD94\uB860\uC5D0 \uC0AC\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),lt=j(),_s=t("p"),mr=n("\uBBF8\uC138 \uC870\uC815\uB41C \uBAA8\uB378\uC744 \uCD94\uB860\uC5D0 \uC0AC\uC6A9\uD558\uB294 \uAC00\uC7A5 \uAC04\uB2E8\uD55C \uBC29\uBC95\uC740 "),Vn=t("code"),ir=n("pipeline()"),hr=n(`\uC5D0\uC11C \uBAA8\uB378\uC744 \uC0AC\uC6A9\uD558\uB294 \uAC83\uC785\uB2C8\uB2E4.
\uBAA8\uB378\uACFC \uD568\uAED8 \uAC1D\uCCB4 \uD0D0\uC9C0\uB97C \uC704\uD55C \uD30C\uC774\uD504\uB77C\uC778\uC744 \uC778\uC2A4\uD134\uC2A4\uD654\uD558\uACE0, \uC774\uBBF8\uC9C0\uB97C \uC804\uB2EC\uD558\uC138\uC694:`),at=j(),i(Ul.$$.fragment),nt=j(),vs=t("p"),yr=n("\uB9CC\uC57D \uC6D0\uD55C\uB2E4\uBA74 \uC218\uB3D9\uC73C\uB85C "),Xn=t("code"),ur=n("pipeline"),Jr=n("\uC758 \uACB0\uACFC\uB97C \uC7AC\uD604\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4:"),et=j(),i(dl.$$.fragment),tt=j(),Ll=t("p"),br=n("\uACB0\uACFC\uB97C \uC2DC\uAC01\uD654\uD558\uACA0\uC2B5\uB2C8\uB2E4:"),pt=j(),i(Tl.$$.fragment),ct=j(),wl=t("div"),sa=t("img"),this.h()},l(s){const o=HM('[data-svelte="svelte-1phssyn"]',document.head);_=p(o,"META",{name:!0,content:!0}),o.forEach(a),T=M(s),g=p(s,"H1",{class:!0});var fl=c(g);k=p(fl,"A",{id:!0,class:!0,href:!0});var Br=c(k);es=p(Br,"SPAN",{});var Gr=c(es);h(v.$$.fragment,Gr),Gr.forEach(a),Br.forEach(a),Rs=M(fl),ts=p(fl,"SPAN",{});var Wr=c(ts);z=e(Wr,"\uAC1D\uCCB4 \uD0D0\uC9C0"),Wr.forEach(a),fl.forEach(a),is=M(s),h(x.$$.fragment,s),q=M(s),S=p(s,"P",{});var kr=c(S);Qs=e(kr,`\uAC1D\uCCB4 \uD0D0\uC9C0\uB294 \uC774\uBBF8\uC9C0\uC5D0\uC11C \uC778\uC2A4\uD134\uC2A4(\uC608: \uC0AC\uB78C, \uAC74\uBB3C \uB610\uB294 \uC790\uB3D9\uCC28)\uB97C \uAC10\uC9C0\uD558\uB294 \uCEF4\uD4E8\uD130 \uBE44\uC804 \uC791\uC5C5\uC785\uB2C8\uB2E4. \uAC1D\uCCB4 \uD0D0\uC9C0 \uBAA8\uB378\uC740 \uC774\uBBF8\uC9C0\uB97C \uC785\uB825\uC73C\uB85C \uBC1B\uACE0 \uD0D0\uC9C0\uB41C \uBC14\uC6B4\uB529 \uBC15\uC2A4\uC758 \uC88C\uD45C\uC640 \uAD00\uB828\uB41C \uB808\uC774\uBE14\uC744 \uCD9C\uB825\uD569\uB2C8\uB2E4.
\uD558\uB098\uC758 \uC774\uBBF8\uC9C0\uC5D0\uB294 \uC5EC\uB7EC \uAC1D\uCCB4\uAC00 \uC788\uC744 \uC218 \uC788\uC73C\uBA70 \uAC01\uAC01\uC740 \uC790\uCCB4\uC801\uC778 \uBC14\uC6B4\uB529 \uBC15\uC2A4\uC640 \uB808\uC774\uBE14\uC744 \uAC00\uC9C8 \uC218 \uC788\uC2B5\uB2C8\uB2E4(\uC608: \uCC28\uC640 \uAC74\uBB3C\uC774 \uC788\uB294 \uC774\uBBF8\uC9C0).
\uB610\uD55C \uAC01 \uAC1D\uCCB4\uB294 \uC774\uBBF8\uC9C0\uC758 \uB2E4\uB978 \uBD80\uBD84\uC5D0 \uC874\uC7AC\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4(\uC608: \uC774\uBBF8\uC9C0\uC5D0 \uC5EC\uB7EC \uB300\uC758 \uCC28\uAC00 \uC788\uC744 \uC218 \uC788\uC74C).
\uC774 \uC791\uC5C5\uC740 \uBCF4\uD589\uC790, \uB3C4\uB85C \uD45C\uC9C0\uD310, \uC2E0\uD638\uB4F1\uACFC \uAC19\uC740 \uAC83\uB4E4\uC744 \uAC10\uC9C0\uD558\uB294 \uC790\uC728 \uC8FC\uD589\uC5D0 \uC77C\uBC18\uC801\uC73C\uB85C \uC0AC\uC6A9\uB429\uB2C8\uB2E4.
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