import{S as ze,i as Ze,s as Ne,e as n,k as r,w as y,t as m,M as Be,c as p,d as l,m as o,a as i,x as M,h,b as c,N as q,G as t,g as e,y as g,L as Xe,q as j,o as u,B as b,v as Ge}from"../../chunks/vendor-hf-doc-builder.js";import{I as ml}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as f}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as qe}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function Ye($t){let d,hl,w,C,Hs,Y,va,Ss,_a,yl,H,Ml,I,Wa,ds,Ca,Ia,gl,E,Ea,ws,ka,$a,jl,k,Va,Ts,Ra,Qa,ul,xs,za,bl,vs,Za,fl,J,As,Na,Ba,Fs,Xa,Ga,Ds,qa,Jl,_s,Ya,Ul,S,dl,T,$,Ls,A,Ha,Ps,Sa,wl,x,Ks,Aa,Fa,F,Da,La,Tl,D,xl,V,Pa,L,Ka,Oa,vl,P,_l,K,Ws,Vt,Wl,Cs,st,Cl,O,Il,Is,lt,El,ss,kl,ls,Es,Rt,$l,v,R,Os,as,at,sl,tt,Vl,ks,et,Rl,ts,es,nt,pt,Ql,ns,zl,$s,it,Zl,ps,Nl,is,Vs,Qt,Bl,Q,rt,ll,ot,ct,Xl,rs,Gl,z,mt,al,ht,yt,ql,os,Yl,cs,Rs,zt,Hl,_,Z,tl,ms,Mt,el,gt,Sl,Qs,jt,Al,hs,Fl,N,ut,nl,bt,ft,Dl,ys,Ll,Ms,zs,Zt,Pl,W,B,pl,gs,Jt,il,Ut,Kl,Zs,dt,Ol,Ns,wt,sa,js,la,Bs,Tt,aa,us,ta,bs,Xs,Nt,ea,X,xt,rl,vt,_t,na,fs,pa,G,Wt,ol,Ct,It,ia,Js,ra,Us,Gs,Bt,oa,qs,Et,ca,U,Xt,ma;return Y=new ml({}),H=new qe({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/zero_shot_object_detection.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/zero_shot_object_detection.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/zero_shot_object_detection.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/zero_shot_object_detection.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/zero_shot_object_detection.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/zero_shot_object_detection.ipynb"}]}}),S=new f({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1xJTIwdHJhbnNmb3JtZXJz",highlighted:"pip install -q transformers"}}),A=new ml({}),D=new f({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBY2hlY2twb2ludCUyMCUzRCUyMCUyMmdvb2dsZSUyRm93bHZpdC1iYXNlLXBhdGNoMzIlMjIlMEFkZXRlY3RvciUyMCUzRCUyMHBpcGVsaW5lKG1vZGVsJTNEY2hlY2twb2ludCUyQyUyMHRhc2slM0QlMjJ6ZXJvLXNob3Qtb2JqZWN0LWRldGVjdGlvbiUyMik=",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>checkpoint = <span class="hljs-string">&quot;google/owlvit-base-patch32&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>detector = pipeline(model=checkpoint, task=<span class="hljs-string">&quot;zero-shot-object-detection&quot;</span>)`}}),P=new f({props:{code:"aW1wb3J0JTIwc2tpbWFnZSUwQWltcG9ydCUyMG51bXB5JTIwYXMlMjBucCUwQWZyb20lMjBQSUwlMjBpbXBvcnQlMjBJbWFnZSUwQSUwQWltYWdlJTIwJTNEJTIwc2tpbWFnZS5kYXRhLmFzdHJvbmF1dCgpJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5mcm9tYXJyYXkobnAudWludDgoaW1hZ2UpKS5jb252ZXJ0KCUyMlJHQiUyMiklMEElMEFpbWFnZQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> skimage
<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">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-meta">&gt;&gt;&gt; </span>image = skimage.data.astronaut()
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.fromarray(np.uint8(image)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>image`}}),O=new f({props:{code:"cHJlZGljdGlvbnMlMjAlM0QlMjBkZXRlY3RvciglMEElMjAlMjAlMjAlMjBpbWFnZSUyQyUwQSUyMCUyMCUyMCUyMGNhbmRpZGF0ZV9sYWJlbHMlM0QlNUIlMjJodW1hbiUyMGZhY2UlMjIlMkMlMjAlMjJyb2NrZXQlMjIlMkMlMjAlMjJuYXNhJTIwYmFkZ2UlMjIlMkMlMjAlMjJzdGFyLXNwYW5nbGVkJTIwYmFubmVyJTIyJTVEJTJDJTBBKSUwQXByZWRpY3Rpb25z",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>predictions = detector(
<span class="hljs-meta">... </span>    image,
<span class="hljs-meta">... </span>    candidate_labels=[<span class="hljs-string">&quot;human face&quot;</span>, <span class="hljs-string">&quot;rocket&quot;</span>, <span class="hljs-string">&quot;nasa badge&quot;</span>, <span class="hljs-string">&quot;star-spangled banner&quot;</span>],
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>predictions
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.3571370542049408</span>,
  <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;human face&#x27;</span>,
  <span class="hljs-string">&#x27;box&#x27;</span>: {<span class="hljs-string">&#x27;xmin&#x27;</span>: <span class="hljs-number">180</span>, <span class="hljs-string">&#x27;ymin&#x27;</span>: <span class="hljs-number">71</span>, <span class="hljs-string">&#x27;xmax&#x27;</span>: <span class="hljs-number">271</span>, <span class="hljs-string">&#x27;ymax&#x27;</span>: <span class="hljs-number">178</span>}},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.28099656105041504</span>,
  <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;nasa badge&#x27;</span>,
  <span class="hljs-string">&#x27;box&#x27;</span>: {<span class="hljs-string">&#x27;xmin&#x27;</span>: <span class="hljs-number">129</span>, <span class="hljs-string">&#x27;ymin&#x27;</span>: <span class="hljs-number">348</span>, <span class="hljs-string">&#x27;xmax&#x27;</span>: <span class="hljs-number">206</span>, <span class="hljs-string">&#x27;ymax&#x27;</span>: <span class="hljs-number">427</span>}},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.2110239565372467</span>,
  <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;rocket&#x27;</span>,
  <span class="hljs-string">&#x27;box&#x27;</span>: {<span class="hljs-string">&#x27;xmin&#x27;</span>: <span class="hljs-number">350</span>, <span class="hljs-string">&#x27;ymin&#x27;</span>: -<span class="hljs-number">1</span>, <span class="hljs-string">&#x27;xmax&#x27;</span>: <span class="hljs-number">468</span>, <span class="hljs-string">&#x27;ymax&#x27;</span>: <span class="hljs-number">288</span>}},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.13790413737297058</span>,
  <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;star-spangled banner&#x27;</span>,
  <span class="hljs-string">&#x27;box&#x27;</span>: {<span class="hljs-string">&#x27;xmin&#x27;</span>: <span class="hljs-number">1</span>, <span class="hljs-string">&#x27;ymin&#x27;</span>: <span class="hljs-number">1</span>, <span class="hljs-string">&#x27;xmax&#x27;</span>: <span class="hljs-number">105</span>, <span class="hljs-string">&#x27;ymax&#x27;</span>: <span class="hljs-number">509</span>}},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.11950037628412247</span>,
  <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;nasa badge&#x27;</span>,
  <span class="hljs-string">&#x27;box&#x27;</span>: {<span class="hljs-string">&#x27;xmin&#x27;</span>: <span class="hljs-number">277</span>, <span class="hljs-string">&#x27;ymin&#x27;</span>: <span class="hljs-number">338</span>, <span class="hljs-string">&#x27;xmax&#x27;</span>: <span class="hljs-number">327</span>, <span class="hljs-string">&#x27;ymax&#x27;</span>: <span class="hljs-number">380</span>}},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.10649408400058746</span>,
  <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;rocket&#x27;</span>,
  <span class="hljs-string">&#x27;box&#x27;</span>: {<span class="hljs-string">&#x27;xmin&#x27;</span>: <span class="hljs-number">358</span>, <span class="hljs-string">&#x27;ymin&#x27;</span>: <span class="hljs-number">64</span>, <span class="hljs-string">&#x27;xmax&#x27;</span>: <span class="hljs-number">424</span>, <span class="hljs-string">&#x27;ymax&#x27;</span>: <span class="hljs-number">280</span>}}]`}}),ss=new f({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlRHJhdyUwQSUwQWRyYXclMjAlM0QlMjBJbWFnZURyYXcuRHJhdyhpbWFnZSklMEElMEFmb3IlMjBwcmVkaWN0aW9uJTIwaW4lMjBwcmVkaWN0aW9ucyUzQSUwQSUyMCUyMCUyMCUyMGJveCUyMCUzRCUyMHByZWRpY3Rpb24lNUIlMjJib3glMjIlNUQlMEElMjAlMjAlMjAlMjBsYWJlbCUyMCUzRCUyMHByZWRpY3Rpb24lNUIlMjJsYWJlbCUyMiU1RCUwQSUyMCUyMCUyMCUyMHNjb3JlJTIwJTNEJTIwcHJlZGljdGlvbiU1QiUyMnNjb3JlJTIyJTVEJTBBJTBBJTIwJTIwJTIwJTIweG1pbiUyQyUyMHltaW4lMkMlMjB4bWF4JTJDJTIweW1heCUyMCUzRCUyMGJveC52YWx1ZXMoKSUwQSUyMCUyMCUyMCUyMGRyYXcucmVjdGFuZ2xlKCh4bWluJTJDJTIweW1pbiUyQyUyMHhtYXglMkMlMjB5bWF4KSUyQyUyMG91dGxpbmUlM0QlMjJyZWQlMjIlMkMlMjB3aWR0aCUzRDEpJTBBJTIwJTIwJTIwJTIwZHJhdy50ZXh0KCh4bWluJTJDJTIweW1pbiklMkMlMjBmJTIyJTdCbGFiZWwlN0QlM0ElMjAlN0Jyb3VuZChzY29yZSUyQzIpJTdEJTIyJTJDJTIwZmlsbCUzRCUyMndoaXRlJTIyKSUwQSUwQWltYWdl",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> ImageDraw

<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> prediction <span class="hljs-keyword">in</span> predictions:
<span class="hljs-meta">... </span>    box = prediction[<span class="hljs-string">&quot;box&quot;</span>]
<span class="hljs-meta">... </span>    label = prediction[<span class="hljs-string">&quot;label&quot;</span>]
<span class="hljs-meta">... </span>    score = prediction[<span class="hljs-string">&quot;score&quot;</span>]

<span class="hljs-meta">... </span>    xmin, ymin, xmax, ymax = box.values()
<span class="hljs-meta">... </span>    draw.rectangle((xmin, ymin, xmax, ymax), outline=<span class="hljs-string">&quot;red&quot;</span>, width=<span class="hljs-number">1</span>)
<span class="hljs-meta">... </span>    draw.text((xmin, ymin), <span class="hljs-string">f&quot;<span class="hljs-subst">{label}</span>: <span class="hljs-subst">{<span class="hljs-built_in">round</span>(score,<span class="hljs-number">2</span>)}</span>&quot;</span>, fill=<span class="hljs-string">&quot;white&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>image`}}),as=new ml({}),ns=new f({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Qcm9jZXNzb3IlMkMlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdE9iamVjdERldGVjdGlvbiUwQSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yWmVyb1Nob3RPYmplY3REZXRlY3Rpb24uZnJvbV9wcmV0cmFpbmVkKGNoZWNrcG9pbnQpJTBBcHJvY2Vzc29yJTIwJTNEJTIwQXV0b1Byb2Nlc3Nvci5mcm9tX3ByZXRyYWluZWQoY2hlY2twb2ludCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, AutoModelForZeroShotObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_pretrained(checkpoint)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(checkpoint)`}}),ps=new f({props:{code:"aW1wb3J0JTIwcmVxdWVzdHMlMEElMEF1cmwlMjAlM0QlMjAlMjJodHRwcyUzQSUyRiUyRnVuc3BsYXNoLmNvbSUyRnBob3RvcyUyRm9qMHplWTJMdGs0JTJGZG93bmxvYWQlM0ZpeGlkJTNETW53eE1qQTNmREI4TVh4elpXRnlZMmg4TVRSOGZIQnBZMjVwWTN4bGJud3dmSHg4ZkRFMk56YzBPVEUxTkRrJTI2Zm9yY2UlM0R0cnVlJTI2dyUzRDY0MCUyMiUwQWltJTIwJTNEJTIwSW1hZ2Uub3BlbihyZXF1ZXN0cy5nZXQodXJsJTJDJTIwc3RyZWFtJTNEVHJ1ZSkucmF3KSUwQWlt",highlighted:`<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://unsplash.com/photos/oj0zeY2Ltk4/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MTR8fHBpY25pY3xlbnwwfHx8fDE2Nzc0OTE1NDk&amp;force=true&amp;w=640&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>im = 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>im`}}),rs=new f({props:{code:"dGV4dF9xdWVyaWVzJTIwJTNEJTIwJTVCJTIyaGF0JTIyJTJDJTIwJTIyYm9vayUyMiUyQyUyMCUyMnN1bmdsYXNzZXMlMjIlMkMlMjAlMjJjYW1lcmElMjIlNUQlMEFpbnB1dHMlMjAlM0QlMjBwcm9jZXNzb3IodGV4dCUzRHRleHRfcXVlcmllcyUyQyUyMGltYWdlcyUzRGltJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>text_queries = [<span class="hljs-string">&quot;hat&quot;</span>, <span class="hljs-string">&quot;book&quot;</span>, <span class="hljs-string">&quot;sunglasses&quot;</span>, <span class="hljs-string">&quot;camera&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(text=text_queries, images=im, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)`}}),os=new f({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model(**inputs)
<span class="hljs-meta">... </span>    target_sizes = torch.tensor([im.size[::-<span class="hljs-number">1</span>]])
<span class="hljs-meta">... </span>    results = processor.post_process_object_detection(outputs, threshold=<span class="hljs-number">0.1</span>, target_sizes=target_sizes)[<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>draw = ImageDraw.Draw(im)

<span class="hljs-meta">&gt;&gt;&gt; </span>scores = results[<span class="hljs-string">&quot;scores&quot;</span>].tolist()
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = results[<span class="hljs-string">&quot;labels&quot;</span>].tolist()
<span class="hljs-meta">&gt;&gt;&gt; </span>boxes = results[<span class="hljs-string">&quot;boxes&quot;</span>].tolist()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> box, score, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(boxes, scores, labels):
<span class="hljs-meta">... </span>    xmin, ymin, xmax, ymax = box
<span class="hljs-meta">... </span>    draw.rectangle((xmin, ymin, xmax, ymax), outline=<span class="hljs-string">&quot;red&quot;</span>, width=<span class="hljs-number">1</span>)
<span class="hljs-meta">... </span>    draw.text((xmin, ymin), <span class="hljs-string">f&quot;<span class="hljs-subst">{text_queries[label]}</span>: <span class="hljs-subst">{<span class="hljs-built_in">round</span>(score,<span class="hljs-number">2</span>)}</span>&quot;</span>, fill=<span class="hljs-string">&quot;white&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>im`}}),ms=new ml({}),hs=new f({props:{code:"aW1hZ2VzJTIwJTNEJTIwJTVCaW1hZ2UlMkMlMjBpbSU1RCUwQXRleHRfcXVlcmllcyUyMCUzRCUyMCU1QiUwQSUyMCUyMCUyMCUyMCU1QiUyMmh1bWFuJTIwZmFjZSUyMiUyQyUyMCUyMnJvY2tldCUyMiUyQyUyMCUyMm5hc2ElMjBiYWRnZSUyMiUyQyUyMCUyMnN0YXItc3BhbmdsZWQlMjBiYW5uZXIlMjIlNUQlMkMlMEElMjAlMjAlMjAlMjAlNUIlMjJoYXQlMjIlMkMlMjAlMjJib29rJTIyJTJDJTIwJTIyc3VuZ2xhc3NlcyUyMiUyQyUyMCUyMmNhbWVyYSUyMiU1RCUyQyUwQSU1RCUwQWlucHV0cyUyMCUzRCUyMHByb2Nlc3Nvcih0ZXh0JTNEdGV4dF9xdWVyaWVzJTJDJTIwaW1hZ2VzJTNEaW1hZ2VzJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>images = [image, im]
<span class="hljs-meta">&gt;&gt;&gt; </span>text_queries = [
<span class="hljs-meta">... </span>    [<span class="hljs-string">&quot;human face&quot;</span>, <span class="hljs-string">&quot;rocket&quot;</span>, <span class="hljs-string">&quot;nasa badge&quot;</span>, <span class="hljs-string">&quot;star-spangled banner&quot;</span>],
<span class="hljs-meta">... </span>    [<span class="hljs-string">&quot;hat&quot;</span>, <span class="hljs-string">&quot;book&quot;</span>, <span class="hljs-string">&quot;sunglasses&quot;</span>, <span class="hljs-string">&quot;camera&quot;</span>],
<span class="hljs-meta">... </span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(text=text_queries, images=images, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)`}}),ys=new f({props:{code:"d2l0aCUyMHRvcmNoLm5vX2dyYWQoKSUzQSUwQSUyMCUyMCUyMCUyMG91dHB1dHMlMjAlM0QlMjBtb2RlbCgqKmlucHV0cyklMEElMjAlMjAlMjAlMjB0YXJnZXRfc2l6ZXMlMjAlM0QlMjAlNUJ4LnNpemUlNUIlM0ElM0EtMSU1RCUyMGZvciUyMHglMjBpbiUyMGltYWdlcyU1RCUwQSUyMCUyMCUyMCUyMHJlc3VsdHMlMjAlM0QlMjBwcm9jZXNzb3IucG9zdF9wcm9jZXNzX29iamVjdF9kZXRlY3Rpb24ob3V0cHV0cyUyQyUyMHRocmVzaG9sZCUzRDAuMSUyQyUyMHRhcmdldF9zaXplcyUzRHRhcmdldF9zaXplcyklMEElMEFpbWFnZV9pZHglMjAlM0QlMjAxJTBBZHJhdyUyMCUzRCUyMEltYWdlRHJhdy5EcmF3KGltYWdlcyU1QmltYWdlX2lkeCU1RCklMEElMEFzY29yZXMlMjAlM0QlMjByZXN1bHRzJTVCaW1hZ2VfaWR4JTVEJTVCJTIyc2NvcmVzJTIyJTVELnRvbGlzdCgpJTBBbGFiZWxzJTIwJTNEJTIwcmVzdWx0cyU1QmltYWdlX2lkeCU1RCU1QiUyMmxhYmVscyUyMiU1RC50b2xpc3QoKSUwQWJveGVzJTIwJTNEJTIwcmVzdWx0cyU1QmltYWdlX2lkeCU1RCU1QiUyMmJveGVzJTIyJTVELnRvbGlzdCgpJTBBJTBBZm9yJTIwYm94JTJDJTIwc2NvcmUlMkMlMjBsYWJlbCUyMGluJTIwemlwKGJveGVzJTJDJTIwc2NvcmVzJTJDJTIwbGFiZWxzKSUzQSUwQSUyMCUyMCUyMCUyMHhtaW4lMkMlMjB5bWluJTJDJTIweG1heCUyQyUyMHltYXglMjAlM0QlMjBib3glMEElMjAlMjAlMjAlMjBkcmF3LnJlY3RhbmdsZSgoeG1pbiUyQyUyMHltaW4lMkMlMjB4bWF4JTJDJTIweW1heCklMkMlMjBvdXRsaW5lJTNEJTIycmVkJTIyJTJDJTIwd2lkdGglM0QxKSUwQSUyMCUyMCUyMCUyMGRyYXcudGV4dCgoeG1pbiUyQyUyMHltaW4pJTJDJTIwZiUyMiU3QnRleHRfcXVlcmllcyU1QmltYWdlX2lkeCU1RCU1QmxhYmVsJTVEJTdEJTNBJTIwJTdCcm91bmQoc2NvcmUlMkMyKSU3RCUyMiUyQyUyMGZpbGwlM0QlMjJ3aGl0ZSUyMiklMEElMEFpbWFnZXMlNUJpbWFnZV9pZHglNUQ=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model(**inputs)
<span class="hljs-meta">... </span>    target_sizes = [x.size[::-<span class="hljs-number">1</span>] <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> images]
<span class="hljs-meta">... </span>    results = processor.post_process_object_detection(outputs, threshold=<span class="hljs-number">0.1</span>, target_sizes=target_sizes)

<span class="hljs-meta">&gt;&gt;&gt; </span>image_idx = <span class="hljs-number">1</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>draw = ImageDraw.Draw(images[image_idx])

<span class="hljs-meta">&gt;&gt;&gt; </span>scores = results[image_idx][<span class="hljs-string">&quot;scores&quot;</span>].tolist()
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = results[image_idx][<span class="hljs-string">&quot;labels&quot;</span>].tolist()
<span class="hljs-meta">&gt;&gt;&gt; </span>boxes = results[image_idx][<span class="hljs-string">&quot;boxes&quot;</span>].tolist()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> box, score, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(boxes, scores, labels):
<span class="hljs-meta">... </span>    xmin, ymin, xmax, ymax = box
<span class="hljs-meta">... </span>    draw.rectangle((xmin, ymin, xmax, ymax), outline=<span class="hljs-string">&quot;red&quot;</span>, width=<span class="hljs-number">1</span>)
<span class="hljs-meta">... </span>    draw.text((xmin, ymin), <span class="hljs-string">f&quot;<span class="hljs-subst">{text_queries[image_idx][label]}</span>: <span class="hljs-subst">{<span class="hljs-built_in">round</span>(score,<span class="hljs-number">2</span>)}</span>&quot;</span>, fill=<span class="hljs-string">&quot;white&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>images[image_idx]`}}),gs=new ml({}),js=new f({props:{code:"dXJsJTIwJTNEJTIwJTIyaHR0cCUzQSUyRiUyRmltYWdlcy5jb2NvZGF0YXNldC5vcmclMkZ2YWwyMDE3JTJGMDAwMDAwMDM5NzY5LmpwZyUyMiUwQWltYWdlX3RhcmdldCUyMCUzRCUyMEltYWdlLm9wZW4ocmVxdWVzdHMuZ2V0KHVybCUyQyUyMHN0cmVhbSUzRFRydWUpLnJhdyklMEElMEFxdWVyeV91cmwlMjAlM0QlMjAlMjJodHRwJTNBJTJGJTJGaW1hZ2VzLmNvY29kYXRhc2V0Lm9yZyUyRnZhbDIwMTclMkYwMDAwMDA1MjQyODAuanBnJTIyJTBBcXVlcnlfaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKHJlcXVlc3RzLmdldChxdWVyeV91cmwlMkMlMjBzdHJlYW0lM0RUcnVlKS5yYXcp",highlighted:`<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_target = 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>query_url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000524280.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>query_image = Image.<span class="hljs-built_in">open</span>(requests.get(query_url, stream=<span class="hljs-literal">True</span>).raw)`}}),us=new f({props:{code:"aW1wb3J0JTIwbWF0cGxvdGxpYi5weXBsb3QlMjBhcyUyMHBsdCUwQSUwQWZpZyUyQyUyMGF4JTIwJTNEJTIwcGx0LnN1YnBsb3RzKDElMkMlMjAyKSUwQWF4JTVCMCU1RC5pbXNob3coaW1hZ2VfdGFyZ2V0KSUwQWF4JTVCMSU1RC5pbXNob3cocXVlcnlfaW1hZ2Up",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt

<span class="hljs-meta">&gt;&gt;&gt; </span>fig, ax = plt.subplots(<span class="hljs-number">1</span>, <span class="hljs-number">2</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ax[<span class="hljs-number">0</span>].imshow(image_target)
<span class="hljs-meta">&gt;&gt;&gt; </span>ax[<span class="hljs-number">1</span>].imshow(query_image)`}}),fs=new f({props:{code:"aW5wdXRzJTIwJTNEJTIwcHJvY2Vzc29yKGltYWdlcyUzRGltYWdlX3RhcmdldCUyQyUyMHF1ZXJ5X2ltYWdlcyUzRHF1ZXJ5X2ltYWdlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMik=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(images=image_target, query_images=query_image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)'}}),Js=new f({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model.image_guided_detection(**inputs)
<span class="hljs-meta">... </span>    target_sizes = torch.tensor([image_target.size[::-<span class="hljs-number">1</span>]])
<span class="hljs-meta">... </span>    results = processor.post_process_image_guided_detection(outputs=outputs, target_sizes=target_sizes)[<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>draw = ImageDraw.Draw(image_target)

<span class="hljs-meta">&gt;&gt;&gt; </span>scores = results[<span class="hljs-string">&quot;scores&quot;</span>].tolist()
<span class="hljs-meta">&gt;&gt;&gt; </span>boxes = results[<span class="hljs-string">&quot;boxes&quot;</span>].tolist()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> box, score, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(boxes, scores, labels):
<span class="hljs-meta">... </span>    xmin, ymin, xmax, ymax = box
<span class="hljs-meta">... </span>    draw.rectangle((xmin, ymin, xmax, ymax), outline=<span class="hljs-string">&quot;white&quot;</span>, width=<span class="hljs-number">4</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>image_target`}}),{c(){d=n("meta"),hl=r(),w=n("h1"),C=n("a"),Hs=n("span"),y(Y.$$.fragment),va=r(),Ss=n("span"),_a=m("\uC81C\uB85C\uC0F7(zero-shot) \uAC1D\uCCB4 \uD0D0\uC9C0"),yl=r(),y(H.$$.fragment),Ml=r(),I=n("p"),Wa=m("\uC77C\uBC18\uC801\uC73C\uB85C "),ds=n("a"),Ca=m("\uAC1D\uCCB4 \uD0D0\uC9C0"),Ia=m(`\uC5D0 \uC0AC\uC6A9\uB418\uB294 \uBAA8\uB378\uC744 \uD559\uC2B5\uD558\uAE30 \uC704\uD574\uC11C\uB294 \uB808\uC774\uBE14\uC774 \uC9C0\uC815\uB41C \uC774\uBBF8\uC9C0 \uB370\uC774\uD130 \uC138\uD2B8\uAC00 \uD544\uC694\uD569\uB2C8\uB2E4.
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candidate_labels\uB294 \uC774 \uC608\uC2DC\uCC98\uB7FC \uAC04\uB2E8\uD55C \uB2E8\uC5B4\uC77C \uC218\uB3C4 \uC788\uACE0 \uC880 \uB354 \uC124\uBA85\uC801\uC778 \uB2E8\uC5B4\uC77C \uC218\uB3C4 \uC788\uC2B5\uB2C8\uB2E4.
\uB610\uD55C, \uC774\uBBF8\uC9C0\uB97C \uAC80\uC0C9(query)\uD558\uB824\uB294 \uBAA8\uB4E0 \uD56D\uBAA9\uC5D0 \uB300\uD55C \uD14D\uC2A4\uD2B8 \uC124\uBA85\uB3C4 \uC804\uB2EC\uD569\uB2C8\uB2E4.`),Cl=r(),y(O.$$.fragment),Il=r(),Is=n("p"),lt=m("\uC774\uC81C \uC608\uCE21\uAC12\uC744 \uC2DC\uAC01\uD654\uD574\uBD05\uC2DC\uB2E4:"),El=r(),y(ss.$$.fragment),kl=r(),ls=n("div"),Es=n("img"),$l=r(),v=n("h2"),R=n("a"),Os=n("span"),y(as.$$.fragment),at=r(),sl=n("span"),tt=m("\uD14D\uC2A4\uD2B8 \uD504\uB86C\uD504\uD2B8 \uAE30\uBC18 \uAC1D\uCCB4 \uD0D0\uC9C0"),Vl=r(),ks=n("p"),et=m("\uC81C\uB85C\uC0F7 \uAC1D\uCCB4 \uD0D0\uC9C0 \uD30C\uC774\uD504\uB77C\uC778 \uC0AC\uC6A9\uBC95\uC5D0 \uB300\uD574 \uC0B4\uD3B4\uBCF4\uC558\uC73C\uB2C8, \uC774\uC81C \uB3D9\uC77C\uD55C \uACB0\uACFC\uB97C \uBCF5\uC81C\uD574\uBCF4\uACA0\uC2B5\uB2C8\uB2E4."),Rl=r(),ts=n("p"),es=n("a"),nt=m("Hugging Face Hub\uC5D0 \uC5C5\uB85C\uB4DC\uB41C \uCCB4\uD06C\uD3EC\uC778\uD2B8"),pt=m(`\uC5D0\uC11C \uAD00\uB828 \uBAA8\uB378\uACFC \uD504\uB85C\uC138\uC11C\uB97C \uAC00\uC838\uC624\uB294 \uAC83\uC73C\uB85C \uC2DC\uC791\uD569\uB2C8\uB2E4.
\uC5EC\uAE30\uC11C\uB294 \uC774\uC804\uACFC \uB3D9\uC77C\uD55C \uCCB4\uD06C\uD3EC\uC778\uD2B8\uB97C \uC0AC\uC6A9\uD558\uACA0\uC2B5\uB2C8\uB2E4:`),Ql=r(),y(ns.$$.fragment),zl=r(),$s=n("p"),it=m("\uB2E4\uB978 \uC774\uBBF8\uC9C0\uB97C \uC0AC\uC6A9\uD574 \uBCF4\uACA0\uC2B5\uB2C8\uB2E4:"),Zl=r(),y(ps.$$.fragment),Nl=r(),is=n("div"),Vs=n("img"),Bl=r(),Q=n("p"),rt=m(`\uD504\uB85C\uC138\uC11C\uB97C \uC0AC\uC6A9\uD574 \uBAA8\uB378\uC758 \uC785\uB825\uC744 \uC900\uBE44\uD569\uB2C8\uB2E4.
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\uC774\uBBF8\uC9C0 \uD504\uB85C\uC138\uC11C\uAC00 \uBAA8\uB378\uC5D0 \uC774\uBBF8\uC9C0\uB97C \uC785\uB825\uD558\uAE30 \uC804\uC5D0 \uC774\uBBF8\uC9C0 \uD06C\uAE30\uB97C \uC870\uC815\uD588\uAE30 \uB54C\uBB38\uC5D0, `),al=n("code"),ht=m("post_process_object_detection()"),yt=m(` \uBA54\uC18C\uB4DC\uB97C \uC0AC\uC6A9\uD574
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\uC77C\uAD04 \uCC98\uB9AC\uB97C \uC704\uD574\uC11C \uD14D\uC2A4\uD2B8 \uCFFC\uB9AC\uB294 \uC774\uC911 \uB9AC\uC2A4\uD2B8\uB85C, \uC774\uBBF8\uC9C0\uB294 PIL \uC774\uBBF8\uC9C0, PyTorch \uD150\uC11C, \uB610\uB294 NumPy \uBC30\uC5F4\uB85C \uC774\uB8E8\uC5B4\uC9C4 \uB9AC\uC2A4\uD2B8\uB85C \uD504\uB85C\uC138\uC11C\uC5D0 \uC804\uB2EC\uD574\uC57C \uD569\uB2C8\uB2E4.`),Al=r(),y(hs.$$.fragment),Fl=r(),N=n("p"),ut=m(`\uC774\uC804\uC5D0\uB294 \uD6C4\uCC98\uB9AC\uB97C \uC704\uD574 \uB2E8\uC77C \uC774\uBBF8\uC9C0\uC758 \uD06C\uAE30\uB97C \uD150\uC11C\uB85C \uC804\uB2EC\uD588\uC9C0\uB9CC, \uD29C\uD50C\uC744 \uC804\uB2EC\uD560 \uC218 \uC788\uACE0, \uC5EC\uB7EC \uC774\uBBF8\uC9C0\uB97C \uCC98\uB9AC\uD558\uB294 \uACBD\uC6B0\uC5D0\uB294 \uD29C\uD50C\uB85C \uC774\uB8E8\uC5B4\uC9C4 \uB9AC\uC2A4\uD2B8\uB97C \uC804\uB2EC\uD560 \uC218\uB3C4 \uC788\uC2B5\uB2C8\uB2E4.
\uC544\uB798 \uB450 \uC608\uC81C\uC5D0 \uB300\uD55C \uC608\uCE21\uC744 \uC0DD\uC131\uD558\uACE0, \uB450 \uBC88\uC9F8 \uC774\uBBF8\uC9C0(`),nl=n("code"),bt=m("image_idx = 1"),ft=m(")\uB97C \uC2DC\uAC01\uD654\uD574 \uBCF4\uACA0\uC2B5\uB2C8\uB2E4."),Dl=r(),y(ys.$$.fragment),Ll=r(),Ms=n("div"),zs=n("img"),Pl=r(),W=n("h2"),B=n("a"),pl=n("span"),y(gs.$$.fragment),Jt=r(),il=n("span"),Ut=m("\uC774\uBBF8\uC9C0 \uAC00\uC774\uB4DC \uAC1D\uCCB4 \uD0D0\uC9C0"),Kl=r(),Zs=n("p"),dt=m(`\uD14D\uC2A4\uD2B8 \uCFFC\uB9AC\uB97C \uC774\uC6A9\uD55C \uC81C\uB85C\uC0F7 \uAC1D\uCCB4 \uD0D0\uC9C0 \uC678\uC5D0\uB3C4 OWL-ViT \uBAA8\uB378\uC740 \uC774\uBBF8\uC9C0 \uAC00\uC774\uB4DC \uAC1D\uCCB4 \uD0D0\uC9C0 \uAE30\uB2A5\uC744 \uC81C\uACF5\uD569\uB2C8\uB2E4.
\uC774\uBBF8\uC9C0\uB97C \uCFFC\uB9AC\uB85C \uC0AC\uC6A9\uD574 \uB300\uC0C1 \uC774\uBBF8\uC9C0\uC5D0\uC11C \uC720\uC0AC\uD55C \uAC1D\uCCB4\uB97C \uCC3E\uC744 \uC218 \uC788\uB2E4\uB294 \uC758\uBBF8\uC785\uB2C8\uB2E4.
\uD14D\uC2A4\uD2B8 \uCFFC\uB9AC\uC640 \uB2EC\uB9AC \uD558\uB098\uC758 \uC608\uC81C \uC774\uBBF8\uC9C0\uC5D0\uC11C\uB9CC \uAC00\uB2A5\uD569\uB2C8\uB2E4.`),Ol=r(),Ns=n("p"),wt=m("\uC18C\uD30C\uC5D0 \uACE0\uC591\uC774 \uB450 \uB9C8\uB9AC\uAC00 \uC788\uB294 \uC774\uBBF8\uC9C0\uB97C \uB300\uC0C1 \uC774\uBBF8\uC9C0(target image)\uB85C, \uACE0\uC591\uC774 \uD55C \uB9C8\uB9AC\uAC00 \uC788\uB294 \uC774\uBBF8\uC9C0\uB97C \uCFFC\uB9AC\uB85C \uC0AC\uC6A9\uD574\uBCF4\uACA0\uC2B5\uB2C8\uB2E4:"),sa=r(),y(js.$$.fragment),la=r(),Bs=n("p"),Tt=m("\uB2E4\uC74C \uC774\uBBF8\uC9C0\uB97C \uC0B4\uD3B4\uBCF4\uACA0\uC2B5\uB2C8\uB2E4:"),aa=r(),y(us.$$.fragment),ta=r(),bs=n("div"),Xs=n("img"),ea=r(),X=n("p"),xt=m("\uC804\uCC98\uB9AC \uB2E8\uACC4\uC5D0\uC11C \uD14D\uC2A4\uD2B8 \uCFFC\uB9AC \uB300\uC2E0\uC5D0 "),rl=n("code"),vt=m("query_images"),_t=m("\uB97C \uC0AC\uC6A9\uD569\uB2C8\uB2E4:"),na=r(),y(fs.$$.fragment),pa=r(),G=n("p"),Wt=m("\uC608\uCE21\uC758 \uACBD\uC6B0, \uBAA8\uB378\uC5D0 \uC785\uB825\uC744 \uC804\uB2EC\uD558\uB294 \uB300\uC2E0 "),ol=n("code"),Ct=m("image_guided_detection()"),It=m(`\uC5D0 \uC804\uB2EC\uD569\uB2C8\uB2E4.
\uB808\uC774\uBE14\uC774 \uC5C6\uB2E4\uB294 \uC810\uC744 \uC81C\uC678\uD558\uBA74 \uC774\uC804\uACFC \uB3D9\uC77C\uD569\uB2C8\uB2E4.
\uC774\uC804\uACFC \uB3D9\uC77C\uD558\uAC8C \uC774\uBBF8\uC9C0\uB97C \uC2DC\uAC01\uD654\uD569\uB2C8\uB2E4.`),ia=r(),y(Js.$$.fragment),ra=r(),Us=n("div"),Gs=n("img"),oa=r(),qs=n("p"),Et=m("OWL-ViT \uBAA8\uB378\uC744 \uCD94\uB860\uD558\uACE0 \uC2F6\uB2E4\uBA74 \uC544\uB798 \uB370\uBAA8\uB97C \uD655\uC778\uD558\uC138\uC694:"),ca=r(),U=n("iframe"),this.h()},l(s){const a=Be('[data-svelte="svelte-1phssyn"]',document.head);d=p(a,"META",{name:!0,content:!0}),a.forEach(l),hl=o(s),w=p(s,"H1",{class:!0});var ha=i(w);C=p(ha,"A",{id:!0,class:!0,href:!0});var Gt=i(C);Hs=p(Gt,"SPAN",{});var qt=i(Hs);M(Y.$$.fragment,qt),qt.forEach(l),Gt.forEach(l),va=o(ha),Ss=p(ha,"SPAN",{});var Yt=i(Ss);_a=h(Yt,"\uC81C\uB85C\uC0F7(zero-shot) \uAC1D\uCCB4 \uD0D0\uC9C0"),Yt.forEach(l),ha.forEach(l),yl=o(s),M(H.$$.fragment,s),Ml=o(s),I=p(s,"P",{});var ya=i(I);Wa=h(ya,"\uC77C\uBC18\uC801\uC73C\uB85C "),ds=p(ya,"A",{href:!0});var Ht=i(ds);Ca=h(Ht,"\uAC1D\uCCB4 \uD0D0\uC9C0"),Ht.forEach(l),Ia=h(ya,`\uC5D0 \uC0AC\uC6A9\uB418\uB294 \uBAA8\uB378\uC744 \uD559\uC2B5\uD558\uAE30 \uC704\uD574\uC11C\uB294 \uB808\uC774\uBE14\uC774 \uC9C0\uC815\uB41C \uC774\uBBF8\uC9C0 \uB370\uC774\uD130 \uC138\uD2B8\uAC00 \uD544\uC694\uD569\uB2C8\uB2E4.
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OWL-ViT\uB294 \uAC1C\uBC29\uD615 \uC5B4\uD718(open-vocabulary) \uAC1D\uCCB4 \uD0D0\uC9C0\uAE30\uC785\uB2C8\uB2E4.
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`),Ts=p(ga,"A",{href:!0});var At=i(Ts);Ra=h(At,"CLIP"),At.forEach(l),Qa=h(ga,` \uBAA8\uB378\uC5D0 \uACBD\uB7C9\uD654(lightweight)\uB41C \uAC1D\uCCB4 \uBD84\uB958\uC640 \uC9C0\uC5ED\uD654(localization) \uD5E4\uB4DC\uB97C \uACB0\uD569\uD569\uB2C8\uB2E4.
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