import{S as ma,i as ha,s as fa,e as n,k as r,w as f,t as c,M as ga,c as o,d as l,m as i,a as p,x as g,h as m,b as h,N as ca,G as t,g as e,y as u,L as ua,q as b,o as y,B as j,v as ba}from"../../chunks/vendor-hf-doc-builder.js";import{I as Zl}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as T}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as ya}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function ja(zl){let d,Ms,w,U,ps,k,Os,rs,sl,ds,Z,ws,V,ll,Js,A,al,vs,P,tl,_s,D,el,Us,L,nl,xs,x,is,ol,pl,cs,rl,$s,K,il,Es,z,Ts,J,$,ms,R,cl,hs,ml,ks,v,fs,hl,fl,I,gl,ul,Zs,G,zs,O,bl,Rs,W,Is,C,ss,Rl,Gs,M,yl,gs,jl,Ml,us,dl,wl,Ws,B,Cs,_,E,bs,N,Jl,ys,vl,Bs,ls,_l,Ns,Y,H,Ul,xl,Ys,F,Hs,as,$l,Fs,S,Ss,q,ts,Il,qs,es,El,Qs,Q,Xs,ns,Tl,Vs,X,As;return k=new Zl({}),Z=new ya({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_image_classification.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/zero_shot_image_classification.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/zero_shot_image_classification.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/zero_shot_image_classification.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/zero_shot_image_classification.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/zero_shot_image_classification.ipynb"}]}}),z=new T({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1xJTIwdHJhbnNmb3JtZXJz",highlighted:"pip install -q transformers"}}),R=new Zl({}),G=new T({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBY2hlY2twb2ludCUyMCUzRCUyMCUyMm9wZW5haSUyRmNsaXAtdml0LWxhcmdlLXBhdGNoMTQlMjIlMEFkZXRlY3RvciUyMCUzRCUyMHBpcGVsaW5lKG1vZGVsJTNEY2hlY2twb2ludCUyQyUyMHRhc2slM0QlMjJ6ZXJvLXNob3QtaW1hZ2UtY2xhc3NpZmljYXRpb24lMjIp",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;openai/clip-vit-large-patch14&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>detector = pipeline(model=checkpoint, task=<span class="hljs-string">&quot;zero-shot-image-classification&quot;</span>)`}}),W=new T({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEElMEF1cmwlMjAlM0QlMjAlMjJodHRwcyUzQSUyRiUyRnVuc3BsYXNoLmNvbSUyRnBob3RvcyUyRmc4b1M4LTgyRHhJJTJGZG93bmxvYWQlM0ZpeGlkJTNETW53eE1qQTNmREI4TVh4MGIzQnBZM3g4U25Cbk5rdHBaR3d0U0d0OGZIeDhmREo4ZkRFMk56Z3hNRFl3T0RjJTI2Zm9yY2UlM0R0cnVlJTI2dyUzRDY0MCUyMiUwQWltYWdlJTIwJTNEJTIwSW1hZ2Uub3BlbihyZXF1ZXN0cy5nZXQodXJsJTJDJTIwc3RyZWFtJTNEVHJ1ZSkucmF3KSUwQSUwQWltYWdl",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://unsplash.com/photos/g8oS8-82DxI/download?ixid=MnwxMjA3fDB8MXx0b3BpY3x8SnBnNktpZGwtSGt8fHx8fDJ8fDE2NzgxMDYwODc&amp;force=true&amp;w=640&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

<span class="hljs-meta">&gt;&gt;&gt; </span>image`}}),B=new T({props:{code:"cHJlZGljdGlvbnMlMjAlM0QlMjBjbGFzc2lmaWVyKGltYWdlJTJDJTIwY2FuZGlkYXRlX2xhYmVscyUzRCU1QiUyMmZveCUyMiUyQyUyMCUyMmJlYXIlMjIlMkMlMjAlMjJzZWFndWxsJTIyJTJDJTIwJTIyb3dsJTIyJTVEKSUwQXByZWRpY3Rpb25z",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>predictions = classifier(image, candidate_labels=[<span class="hljs-string">&quot;fox&quot;</span>, <span class="hljs-string">&quot;bear&quot;</span>, <span class="hljs-string">&quot;seagull&quot;</span>, <span class="hljs-string">&quot;owl&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>predictions
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9996670484542847</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;owl&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.000199399160919711</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;seagull&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">7.392891711788252e-05</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;fox&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">5.96074532950297e-05</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;bear&#x27;</span>}]`}}),N=new Zl({}),F=new T({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Qcm9jZXNzb3IlMkMlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdEltYWdlQ2xhc3NpZmljYXRpb24lMEElMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclplcm9TaG90SW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoY2hlY2twb2ludCklMEFwcm9jZXNzb3IlMjAlM0QlMjBBdXRvUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZChjaGVja3BvaW50KQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor, AutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_pretrained(checkpoint)
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(checkpoint)`}}),S=new T({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwcmVxdWVzdHMlMEElMEF1cmwlMjAlM0QlMjAlMjJodHRwcyUzQSUyRiUyRnVuc3BsYXNoLmNvbSUyRnBob3RvcyUyRnhCUlFmUjJicU5JJTJGZG93bmxvYWQlM0ZpeGlkJTNETW53eE1qQTNmREI4TVh4aGJHeDhmSHg4Zkh4OGZId3hOamM0TXpnNE9ERXglMjZmb3JjZSUzRHRydWUlMjZ3JTNENjQwJTIyJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKHJlcXVlc3RzLmdldCh1cmwlMkMlMjBzdHJlYW0lM0RUcnVlKS5yYXcpJTBBJTBBaW1hZ2U=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://unsplash.com/photos/xBRQfR2bqNI/download?ixid=MnwxMjA3fDB8MXxhbGx8fHx8fHx8fHwxNjc4Mzg4ODEx&amp;force=true&amp;w=640&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

<span class="hljs-meta">&gt;&gt;&gt; </span>image`}}),Q=new T({props:{code:"Y2FuZGlkYXRlX2xhYmVscyUyMCUzRCUyMCU1QiUyMnRyZWUlMjIlMkMlMjAlMjJjYXIlMjIlMkMlMjAlMjJiaWtlJTIyJTJDJTIwJTIyY2F0JTIyJTVEJTBBaW5wdXRzJTIwJTNEJTIwcHJvY2Vzc29yKGltYWdlcyUzRGltYWdlJTJDJTIwdGV4dCUzRGNhbmRpZGF0ZV9sYWJlbHMlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyJTJDJTIwcGFkZGluZyUzRFRydWUp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>candidate_labels = [<span class="hljs-string">&quot;tree&quot;</span>, <span class="hljs-string">&quot;car&quot;</span>, <span class="hljs-string">&quot;bike&quot;</span>, <span class="hljs-string">&quot;cat&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(images=image, text=candidate_labels, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, padding=<span class="hljs-literal">True</span>)`}}),X=new T({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">&gt;&gt;&gt; </span>logits = outputs.logits_per_image[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>probs = logits.softmax(dim=-<span class="hljs-number">1</span>).numpy()
<span class="hljs-meta">&gt;&gt;&gt; </span>scores = probs.tolist()

<span class="hljs-meta">&gt;&gt;&gt; </span>result = [
<span class="hljs-meta">... </span>    {<span class="hljs-string">&quot;score&quot;</span>: score, <span class="hljs-string">&quot;label&quot;</span>: candidate_label}
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> score, candidate_label <span class="hljs-keyword">in</span> <span class="hljs-built_in">sorted</span>(<span class="hljs-built_in">zip</span>(probs, candidate_labels), key=<span class="hljs-keyword">lambda</span> x: -x[<span class="hljs-number">0</span>])
<span class="hljs-meta">... </span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>result
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.998572</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;car&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.0010570387</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;bike&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.0003393686</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;tree&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">3.1572064e-05</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;cat&#x27;</span>}]`}}),{c(){d=n("meta"),Ms=r(),w=n("h1"),U=n("a"),ps=n("span"),f(k.$$.fragment),Os=r(),rs=n("span"),sl=c("\uC81C\uB85C\uC0F7(zero-shot) \uC774\uBBF8\uC9C0 \uBD84\uB958"),ds=r(),f(Z.$$.fragment),ws=r(),V=n("p"),ll=c("\uC81C\uB85C\uC0F7(zero-shot) \uC774\uBBF8\uC9C0 \uBD84\uB958\uB294 \uD2B9\uC815 \uCE74\uD14C\uACE0\uB9AC\uC758 \uC608\uC2DC\uAC00 \uD3EC\uD568\uB41C \uB370\uC774\uD130\uB97C \uD559\uC2B5\uB418\uC9C0 \uC54A\uC740 \uBAA8\uB378\uC744 \uC0AC\uC6A9\uD574 \uC774\uBBF8\uC9C0 \uBD84\uB958\uB97C \uC218\uD589\uD558\uB294 \uC791\uC5C5\uC785\uB2C8\uB2E4."),Js=r(),A=n("p"),al=c(`\uC77C\uBC18\uC801\uC73C\uB85C \uC774\uBBF8\uC9C0 \uBD84\uB958\uB97C \uC704\uD574\uC11C\uB294 \uB808\uC774\uBE14\uC774 \uB2EC\uB9B0 \uD2B9\uC815 \uC774\uBBF8\uC9C0 \uB370\uC774\uD130\uB85C \uBAA8\uB378 \uD559\uC2B5\uC774 \uD544\uC694\uD558\uBA70, \uC774 \uBAA8\uB378\uC740 \uD2B9\uC815 \uC774\uBBF8\uC9C0\uC758 \uD2B9\uC9D5\uC744 \uB808\uC774\uBE14\uC5D0 \u201C\uB9E4\uD551\u201D\uD558\uB294 \uBC29\uBC95\uC744 \uD559\uC2B5\uD569\uB2C8\uB2E4.
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