import{S as gf,i as $f,s as vf,e as r,k as p,w as u,t as o,M as Ef,c as a,d as s,m as c,a as i,x as h,h as l,b as d,G as t,g as f,y as g,q as $,o as v,B as E,v as wf}from"../chunks/vendor-hf-doc-builder.js";import{T as Ra}from"../chunks/Tip-hf-doc-builder.js";import{I as bt}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as k}from"../chunks/CodeBlock-hf-doc-builder.js";function yf(oe){let _,C;return{c(){_=r("p"),C=o("\uB77C\uC774\uBE0C\uB7EC\uB9AC\uC5D0 \uC874\uC7AC\uD558\uB294 \uBAA8\uB378\uACFC \uAD49\uC7A5\uD788 \uC720\uC0AC\uD558\uB2E4\uBA74, \uBAA8\uB378\uC744 \uC0DD\uC131\uD560 \uB54C \uAD6C\uC131\uC744 \uCC38\uC870\uD574 \uC7AC\uC0AC\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.")},l(m){_=a(m,"P",{});var w=i(_);C=l(w,"\uB77C\uC774\uBE0C\uB7EC\uB9AC\uC5D0 \uC874\uC7AC\uD558\uB294 \uBAA8\uB378\uACFC \uAD49\uC7A5\uD788 \uC720\uC0AC\uD558\uB2E4\uBA74, \uBAA8\uB378\uC744 \uC0DD\uC131\uD560 \uB54C \uAD6C\uC131\uC744 \uCC38\uC870\uD574 \uC7AC\uC0AC\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),w.forEach(s)},m(m,w){f(m,_,w),t(_,C)},d(m){m&&s(_)}}}function bf(oe){let _,C;return{c(){_=r("p"),C=o("\uC774 API\uB294 \uC2E4\uD5D8\uC801\uC774\uBA70 \uB2E4\uC74C \uB9B4\uB9AC\uC2A4\uC5D0\uC11C \uC57D\uAC04\uC758 \uBCC0\uACBD \uC0AC\uD56D\uC774 \uC788\uC744 \uC218 \uC788\uC2B5\uB2C8\uB2E4.")},l(m){_=a(m,"P",{});var w=i(_);C=l(w,"\uC774 API\uB294 \uC2E4\uD5D8\uC801\uC774\uBA70 \uB2E4\uC74C \uB9B4\uB9AC\uC2A4\uC5D0\uC11C \uC57D\uAC04\uC758 \uBCC0\uACBD \uC0AC\uD56D\uC774 \uC788\uC744 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),w.forEach(s)},m(m,w){f(m,_,w),t(_,C)},d(m){m&&s(_)}}}function kf(oe){let _,C,m,w,Y;return{c(){_=r("p"),C=o(`\uB77C\uC774\uBE0C\uB7EC\uB9AC\uC5D0\uC11C \uBAA8\uB378\uB9C1 \uD30C\uC77C\uC744 \uBCF5\uC0AC\uD558\uB294 \uACBD\uC6B0,
\uBAA8\uB4E0 \uD30C\uC77C \uC0C1\uB2E8\uC5D0 \uC788\uB294 \uC0C1\uB300 \uACBD\uB85C \uC784\uD3EC\uD2B8(relative import) \uBD80\uBD84\uC744 `),m=r("code"),w=o("transformers"),Y=o(" \uD328\uD0A4\uC9C0\uC5D0\uC11C \uC784\uD3EC\uD2B8 \uD558\uB3C4\uB85D \uBCC0\uACBD\uD574\uC57C \uD569\uB2C8\uB2E4.")},l(M){_=a(M,"P",{});var I=i(_);C=l(I,`\uB77C\uC774\uBE0C\uB7EC\uB9AC\uC5D0\uC11C \uBAA8\uB378\uB9C1 \uD30C\uC77C\uC744 \uBCF5\uC0AC\uD558\uB294 \uACBD\uC6B0,
\uBAA8\uB4E0 \uD30C\uC77C \uC0C1\uB2E8\uC5D0 \uC788\uB294 \uC0C1\uB300 \uACBD\uB85C \uC784\uD3EC\uD2B8(relative import) \uBD80\uBD84\uC744 `),m=a(I,"CODE",{});var le=i(m);w=l(le,"transformers"),le.forEach(s),Y=l(I," \uD328\uD0A4\uC9C0\uC5D0\uC11C \uC784\uD3EC\uD2B8 \uD558\uB3C4\uB85D \uBCC0\uACBD\uD574\uC57C \uD569\uB2C8\uB2E4."),I.forEach(s)},m(M,I){f(M,_,I),t(_,C),t(_,m),t(m,w),t(_,Y)},d(M){M&&s(_)}}}function Cf(oe){let _,C,m,w,Y,M,I,le,cl,Vs,ze,dl,Us,Qe,_l,Ys,J,ye,ml,ul,kt,hl,gl,Js,z,ne,Ct,be,$l,jt,vl,zs,T,El,Pt,wl,yl,Rt,bl,kl,Qs,We,Cl,Ws,ke,Xs,re,jl,Mt,Pl,Rl,Zs,F,Xe,Ot,Ml,Ol,ql,ae,qt,Al,Dl,At,Nl,Il,Tl,Q,Fl,Dt,Hl,Bl,Nt,xl,Sl,eo,H,Ll,It,Gl,Kl,Tt,Vl,Ul,to,O,Yl,Ft,Jl,zl,Ht,Ql,Wl,Bt,Xl,Zl,so,Ze,en,oo,Ce,lo,q,tn,xt,sn,on,St,ln,nn,Lt,rn,an,no,je,ro,B,fn,Gt,pn,cn,Kt,dn,_n,ao,W,ie,Vt,Pe,mn,Ut,un,io,x,hn,Yt,gn,$n,Jt,vn,En,fo,S,wn,zt,yn,bn,Qt,kn,Cn,po,Re,co,et,jn,_o,Me,mo,P,Pn,Wt,Rn,Mn,Xt,On,qn,Zt,An,Dn,es,Nn,In,uo,fe,ho,L,Tn,ts,Fn,Hn,ss,Bn,xn,go,tt,Sn,$o,Oe,vo,A,Ln,os,Gn,Kn,ls,Vn,Un,ns,Yn,Jn,Eo,st,zn,wo,qe,yo,G,Qn,rs,Wn,Xn,as,Zn,er,bo,X,pe,is,Ae,tr,fs,sr,ko,ce,Co,y,or,ps,lr,nr,cs,rr,ar,ds,ir,fr,_s,pr,cr,ms,dr,_r,us,mr,ur,hs,hr,gr,jo,De,Po,K,$r,gs,vr,Er,$s,wr,yr,Ro,de,Mo,ot,br,Oo,lt,kr,qo,Ne,Ao,_e,Cr,vs,jr,Pr,Do,Ie,No,Z,Es,Rr,Mr,ws,Or,qr,Io,nt,Ar,To,Te,Fo,rt,Dr,Ho,Fe,Bo,at,Nr,xo,He,So,it,Ir,Lo,Be,Go,b,Tr,ys,Fr,Hr,bs,Br,xr,xe,Sr,Lr,ks,Gr,Kr,Cs,Vr,Ur,Se,Yr,Jr,Ko,ee,ft,zr,Qr,js,Wr,Xr,Vo,te,me,Ps,Le,Zr,Rs,ea,Uo,D,ta,Ms,sa,oa,Ge,la,na,Os,ra,aa,Yo,Ke,Jo,ue,ia,qs,fa,pa,zo,Ve,Qo,pt,ca,Wo,se,he,As,Ue,da,Ds,_a,Xo,ct,ma,Zo,V,ua,Ns,ha,ga,Is,$a,va,el,Ye,tl,N,Ea,Ts,wa,ya,Fs,ba,ka,Hs,Ca,ja,sl;return M=new bt({}),be=new bt({}),ke=new k({props:{code:`from transformers import PretrainedConfig
from typing import List


class ResnetConfig(PretrainedConfig):
    model_type = "resnet"

    def __init__(
        self,
        block_type="bottleneck",
        layers: List[int] = [3, 4, 6, 3],
        num_classes: int = 1000,
        input_channels: int = 3,
        cardinality: int = 1,
        base_width: int = 64,
        stem_width: int = 64,
        stem_type: str = "",
        avg_down: bool = False,
        **kwargs,
    ):
        if block_type not in ["basic", "bottleneck"]:
            raise ValueError(f"\`block_type\` must be 'basic' or bottleneck', got {block_type}.")
        if stem_type not in ["", "deep", "deep-tiered"]:
            raise ValueError(f"\`stem_type\` must be '', 'deep' or 'deep-tiered', got {stem_type}.")

        self.block_type = block_type
        self.layers = layers
        self.num_classes = num_classes
        self.input_channels = input_channels
        self.cardinality = cardinality
        self.base_width = base_width
        self.stem_width = stem_width
        self.stem_type = stem_type
        self.avg_down = avg_down
        super().__init__(**kwargs)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> PretrainedConfig
<span class="hljs-keyword">from</span> typing <span class="hljs-keyword">import</span> <span class="hljs-type">List</span>


<span class="hljs-keyword">class</span> <span class="hljs-title class_">ResnetConfig</span>(<span class="hljs-title class_ inherited__">PretrainedConfig</span>):
    model_type = <span class="hljs-string">&quot;resnet&quot;</span>

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">
        self,
        block_type=<span class="hljs-string">&quot;bottleneck&quot;</span>,
        layers: <span class="hljs-type">List</span>[<span class="hljs-built_in">int</span>] = [<span class="hljs-number">3</span>, <span class="hljs-number">4</span>, <span class="hljs-number">6</span>, <span class="hljs-number">3</span>],
        num_classes: <span class="hljs-built_in">int</span> = <span class="hljs-number">1000</span>,
        input_channels: <span class="hljs-built_in">int</span> = <span class="hljs-number">3</span>,
        cardinality: <span class="hljs-built_in">int</span> = <span class="hljs-number">1</span>,
        base_width: <span class="hljs-built_in">int</span> = <span class="hljs-number">64</span>,
        stem_width: <span class="hljs-built_in">int</span> = <span class="hljs-number">64</span>,
        stem_type: <span class="hljs-built_in">str</span> = <span class="hljs-string">&quot;&quot;</span>,
        avg_down: <span class="hljs-built_in">bool</span> = <span class="hljs-literal">False</span>,
        **kwargs,
    </span>):
        <span class="hljs-keyword">if</span> block_type <span class="hljs-keyword">not</span> <span class="hljs-keyword">in</span> [<span class="hljs-string">&quot;basic&quot;</span>, <span class="hljs-string">&quot;bottleneck&quot;</span>]:
            <span class="hljs-keyword">raise</span> ValueError(<span class="hljs-string">f&quot;\`block_type\` must be &#x27;basic&#x27; or bottleneck&#x27;, got <span class="hljs-subst">{block_type}</span>.&quot;</span>)
        <span class="hljs-keyword">if</span> stem_type <span class="hljs-keyword">not</span> <span class="hljs-keyword">in</span> [<span class="hljs-string">&quot;&quot;</span>, <span class="hljs-string">&quot;deep&quot;</span>, <span class="hljs-string">&quot;deep-tiered&quot;</span>]:
            <span class="hljs-keyword">raise</span> ValueError(<span class="hljs-string">f&quot;\`stem_type\` must be &#x27;&#x27;, &#x27;deep&#x27; or &#x27;deep-tiered&#x27;, got <span class="hljs-subst">{stem_type}</span>.&quot;</span>)

        self.block_type = block_type
        self.layers = layers
        self.num_classes = num_classes
        self.input_channels = input_channels
        self.cardinality = cardinality
        self.base_width = base_width
        self.stem_width = stem_width
        self.stem_type = stem_type
        self.avg_down = avg_down
        <span class="hljs-built_in">super</span>().__init__(**kwargs)`}}),Ce=new k({props:{code:`resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
resnet50d_config.save_pretrained("custom-resnet")`,highlighted:`resnet50d_config = ResnetConfig(block_type=<span class="hljs-string">&quot;bottleneck&quot;</span>, stem_width=<span class="hljs-number">32</span>, stem_type=<span class="hljs-string">&quot;deep&quot;</span>, avg_down=<span class="hljs-literal">True</span>)
resnet50d_config.save_pretrained(<span class="hljs-string">&quot;custom-resnet&quot;</span>)`}}),je=new k({props:{code:'resnet50d_config = ResnetConfig.from_pretrained("custom-resnet")',highlighted:'resnet50d_config = ResnetConfig.from_pretrained(<span class="hljs-string">&quot;custom-resnet&quot;</span>)'}}),Pe=new bt({}),Re=new k({props:{code:`from transformers import PreTrainedModel
from timm.models.resnet import BasicBlock, Bottleneck, ResNet
from .configuration_resnet import ResnetConfig


BLOCK_MAPPING = {"basic": BasicBlock, "bottleneck": Bottleneck}


class ResnetModel(PreTrainedModel):
    config_class = ResnetConfig

    def __init__(self, config):
        super().__init__(config)
        block_layer = BLOCK_MAPPING[config.block_type]
        self.model = ResNet(
            block_layer,
            config.layers,
            num_classes=config.num_classes,
            in_chans=config.input_channels,
            cardinality=config.cardinality,
            base_width=config.base_width,
            stem_width=config.stem_width,
            stem_type=config.stem_type,
            avg_down=config.avg_down,
        )

    def forward(self, tensor):
        return self.model.forward_features(tensor)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> PreTrainedModel
<span class="hljs-keyword">from</span> timm.models.resnet <span class="hljs-keyword">import</span> BasicBlock, Bottleneck, ResNet
<span class="hljs-keyword">from</span> .configuration_resnet <span class="hljs-keyword">import</span> ResnetConfig


BLOCK_MAPPING = {<span class="hljs-string">&quot;basic&quot;</span>: BasicBlock, <span class="hljs-string">&quot;bottleneck&quot;</span>: Bottleneck}


<span class="hljs-keyword">class</span> <span class="hljs-title class_">ResnetModel</span>(<span class="hljs-title class_ inherited__">PreTrainedModel</span>):
    config_class = ResnetConfig

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, config</span>):
        <span class="hljs-built_in">super</span>().__init__(config)
        block_layer = BLOCK_MAPPING[config.block_type]
        self.model = ResNet(
            block_layer,
            config.layers,
            num_classes=config.num_classes,
            in_chans=config.input_channels,
            cardinality=config.cardinality,
            base_width=config.base_width,
            stem_width=config.stem_width,
            stem_type=config.stem_type,
            avg_down=config.avg_down,
        )

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, tensor</span>):
        <span class="hljs-keyword">return</span> self.model.forward_features(tensor)`}}),Me=new k({props:{code:`import torch


class ResnetModelForImageClassification(PreTrainedModel):
    config_class = ResnetConfig

    def __init__(self, config):
        super().__init__(config)
        block_layer = BLOCK_MAPPING[config.block_type]
        self.model = ResNet(
            block_layer,
            config.layers,
            num_classes=config.num_classes,
            in_chans=config.input_channels,
            cardinality=config.cardinality,
            base_width=config.base_width,
            stem_width=config.stem_width,
            stem_type=config.stem_type,
            avg_down=config.avg_down,
        )

    def forward(self, tensor, labels=None):
        logits = self.model(tensor)
        if labels is not None:
            loss = torch.nn.cross_entropy(logits, labels)
            return {"loss": loss, "logits": logits}
        return {"logits": logits}`,highlighted:`<span class="hljs-keyword">import</span> torch


<span class="hljs-keyword">class</span> <span class="hljs-title class_">ResnetModelForImageClassification</span>(<span class="hljs-title class_ inherited__">PreTrainedModel</span>):
    config_class = ResnetConfig

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, config</span>):
        <span class="hljs-built_in">super</span>().__init__(config)
        block_layer = BLOCK_MAPPING[config.block_type]
        self.model = ResNet(
            block_layer,
            config.layers,
            num_classes=config.num_classes,
            in_chans=config.input_channels,
            cardinality=config.cardinality,
            base_width=config.base_width,
            stem_width=config.stem_width,
            stem_type=config.stem_type,
            avg_down=config.avg_down,
        )

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, tensor, labels=<span class="hljs-literal">None</span></span>):
        logits = self.model(tensor)
        <span class="hljs-keyword">if</span> labels <span class="hljs-keyword">is</span> <span class="hljs-keyword">not</span> <span class="hljs-literal">None</span>:
            loss = torch.nn.cross_entropy(logits, labels)
            <span class="hljs-keyword">return</span> {<span class="hljs-string">&quot;loss&quot;</span>: loss, <span class="hljs-string">&quot;logits&quot;</span>: logits}
        <span class="hljs-keyword">return</span> {<span class="hljs-string">&quot;logits&quot;</span>: logits}`}}),fe=new Ra({props:{$$slots:{default:[yf]},$$scope:{ctx:oe}}}),Oe=new k({props:{code:"resnet50d = ResnetModelForImageClassification(resnet50d_config)",highlighted:"resnet50d = ResnetModelForImageClassification(resnet50d_config)"}}),qe=new k({props:{code:`import timm

pretrained_model = timm.create_model("resnet50d", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict())`,highlighted:`<span class="hljs-keyword">import</span> timm

pretrained_model = timm.create_model(<span class="hljs-string">&quot;resnet50d&quot;</span>, pretrained=<span class="hljs-literal">True</span>)
resnet50d.model.load_state_dict(pretrained_model.state_dict())`}}),Ae=new bt({}),ce=new Ra({props:{warning:!0,$$slots:{default:[bf]},$$scope:{ctx:oe}}}),De=new k({props:{code:`.
\u2514\u2500\u2500 resnet_model
    \u251C\u2500\u2500 __init__.py
    \u251C\u2500\u2500 configuration_resnet.py
    \u2514\u2500\u2500 modeling_resnet.py`,highlighted:`.
\u2514\u2500\u2500 resnet_model
    \u251C\u2500\u2500 __init__.<span class="hljs-keyword">py</span>
    \u251C\u2500\u2500 configuration_resnet.<span class="hljs-keyword">py</span>
    \u2514\u2500\u2500 modeling_resnet.<span class="hljs-keyword">py</span>`}}),de=new Ra({props:{warning:!0,$$slots:{default:[kf]},$$scope:{ctx:oe}}}),Ne=new k({props:{code:`from resnet_model.configuration_resnet import ResnetConfig
from resnet_model.modeling_resnet import ResnetModel, ResnetModelForImageClassification`,highlighted:`<span class="hljs-keyword">from</span> resnet_model.configuration_resnet <span class="hljs-keyword">import</span> ResnetConfig
<span class="hljs-keyword">from</span> resnet_model.modeling_resnet <span class="hljs-keyword">import</span> ResnetModel, ResnetModelForImageClassification`}}),Ie=new k({props:{code:`ResnetConfig.register_for_auto_class()
ResnetModel.register_for_auto_class("AutoModel")
ResnetModelForImageClassification.register_for_auto_class("AutoModelForImageClassification")`,highlighted:`ResnetConfig.register_for_auto_class()
ResnetModel.register_for_auto_class(<span class="hljs-string">&quot;AutoModel&quot;</span>)
ResnetModelForImageClassification.register_for_auto_class(<span class="hljs-string">&quot;AutoModelForImageClassification&quot;</span>)`}}),Te=new k({props:{code:`resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
resnet50d = ResnetModelForImageClassification(resnet50d_config)

pretrained_model = timm.create_model("resnet50d", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict())`,highlighted:`resnet50d_config = ResnetConfig(block_type=<span class="hljs-string">&quot;bottleneck&quot;</span>, stem_width=<span class="hljs-number">32</span>, stem_type=<span class="hljs-string">&quot;deep&quot;</span>, avg_down=<span class="hljs-literal">True</span>)
resnet50d = ResnetModelForImageClassification(resnet50d_config)

pretrained_model = timm.create_model(<span class="hljs-string">&quot;resnet50d&quot;</span>, pretrained=<span class="hljs-literal">True</span>)
resnet50d.model.load_state_dict(pretrained_model.state_dict())`}}),Fe=new k({props:{code:"huggingface-cli login",highlighted:"huggingface-cli login"}}),He=new k({props:{code:`from huggingface_hub import notebook_login

notebook_login()`,highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

notebook_login()`}}),Be=new k({props:{code:'resnet50d.push_to_hub("custom-resnet50d")',highlighted:'resnet50d.push_to_hub(<span class="hljs-string">&quot;custom-resnet50d&quot;</span>)'}}),Le=new bt({}),Ke=new k({props:{code:`from transformers import AutoModelForImageClassification

model = AutoModelForImageClassification.from_pretrained("sgugger/custom-resnet50d", trust_remote_code=True)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForImageClassification

model = AutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;sgugger/custom-resnet50d&quot;</span>, trust_remote_code=<span class="hljs-literal">True</span>)`}}),Ve=new k({props:{code:`commit_hash = "ed94a7c6247d8aedce4647f00f20de6875b5b292"
model = AutoModelForImageClassification.from_pretrained(
    "sgugger/custom-resnet50d", trust_remote_code=True, revision=commit_hash
)`,highlighted:`commit_hash = <span class="hljs-string">&quot;ed94a7c6247d8aedce4647f00f20de6875b5b292&quot;</span>
model = AutoModelForImageClassification.from_pretrained(
    <span class="hljs-string">&quot;sgugger/custom-resnet50d&quot;</span>, trust_remote_code=<span class="hljs-literal">True</span>, revision=commit_hash
)`}}),Ue=new bt({}),Ye=new k({props:{code:`from transformers import AutoConfig, AutoModel, AutoModelForImageClassification

AutoConfig.register("resnet", ResnetConfig)
AutoModel.register(ResnetConfig, ResnetModel)
AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification)`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModel, AutoModelForImageClassification

AutoConfig.register(<span class="hljs-string">&quot;resnet&quot;</span>, ResnetConfig)
AutoModel.register(ResnetConfig, ResnetModel)
AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification)`}}),{c(){_=r("meta"),C=p(),m=r("h1"),w=r("a"),Y=r("span"),u(M.$$.fragment),I=p(),le=r("span"),cl=o("\uC0AC\uC6A9\uC790 \uC815\uC758 \uBAA8\uB378 \uACF5\uC720\uD558\uAE30"),Vs=p(),ze=r("p"),dl=o(`\u{1F917} Transformers \uB77C\uC774\uBE0C\uB7EC\uB9AC\uB294 \uC27D\uAC8C \uD655\uC7A5\uD560 \uC218 \uC788\uB3C4\uB85D \uC124\uACC4\uB418\uC5C8\uC2B5\uB2C8\uB2E4.
\uBAA8\uB4E0 \uBAA8\uB378\uC740 \uCD94\uC0C1\uD654 \uC5C6\uC774 \uC800\uC7A5\uC18C\uC758 \uC9C0\uC815\uB41C \uD558\uC704 \uD3F4\uB354\uC5D0 \uC644\uC804\uD788 \uCF54\uB529\uB418\uC5B4 \uC788\uC73C\uBBC0\uB85C, \uC190\uC27D\uAC8C \uBAA8\uB378\uB9C1 \uD30C\uC77C\uC744 \uBCF5\uC0AC\uD558\uACE0 \uD544\uC694\uC5D0 \uB530\uB77C \uC870\uC815\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.`),Us=p(),Qe=r("p"),_l=o(`\uC644\uC804\uD788 \uC0C8\uB85C\uC6B4 \uBAA8\uB378\uC744 \uB9CC\uB4DC\uB294 \uACBD\uC6B0\uC5D0\uB294 \uCC98\uC74C\uBD80\uD130 \uC2DC\uC791\uD558\uB294 \uAC83\uC774 \uB354 \uC26C\uC6B8 \uC218 \uC788\uC2B5\uB2C8\uB2E4.
\uC774 \uD29C\uD1A0\uB9AC\uC5BC\uC5D0\uC11C\uB294 Transformers \uB0B4\uC5D0\uC11C \uC0AC\uC6A9\uD560 \uC218 \uC788\uB3C4\uB85D \uC0AC\uC6A9\uC790 \uC815\uC758 \uBAA8\uB378\uACFC \uAD6C\uC131\uC744 \uC791\uC131\uD558\uB294 \uBC29\uBC95\uACFC
\u{1F917} Transformers \uB77C\uC774\uBE0C\uB7EC\uB9AC\uC5D0 \uC5C6\uB294 \uACBD\uC6B0\uC5D0\uB3C4 \uB204\uAD6C\uB098 \uC0AC\uC6A9\uD560 \uC218 \uC788\uB3C4\uB85D (\uC758\uC874\uC131\uACFC \uD568\uAED8) \uCEE4\uBBA4\uB2C8\uD2F0\uC5D0 \uACF5\uC720\uD558\uB294 \uBC29\uBC95\uC744 \uBC30\uC6B8 \uC218 \uC788\uC2B5\uB2C8\uB2E4.`),Ys=p(),J=r("p"),ye=r("a"),ml=o("timm \uB77C\uC774\uBE0C\uB7EC\uB9AC"),ul=o("\uC758 ResNet \uD074\uB798\uC2A4\uB97C "),kt=r("code"),hl=o("PreTrainedModel"),gl=o("\uB85C \uB798\uD551\uD55C ResNet \uBAA8\uB378\uC744 \uC608\uB85C \uBAA8\uB4E0 \uAC83\uC744 \uC124\uBA85\uD569\uB2C8\uB2E4."),Js=p(),z=r("h2"),ne=r("a"),Ct=r("span"),u(be.$$.fragment),$l=p(),jt=r("span"),vl=o("\uC0AC\uC6A9\uC790 \uC815\uC758 \uAD6C\uC131 \uC791\uC131\uD558\uAE30"),zs=p(),T=r("p"),El=o(`\uBAA8\uB378\uC5D0 \uB4E4\uC5B4\uAC00\uAE30 \uC804\uC5D0 \uBA3C\uC800 \uAD6C\uC131\uC744 \uC791\uC131\uD574\uBCF4\uB3C4\uB85D \uD558\uACA0\uC2B5\uB2C8\uB2E4.
\uBAA8\uB378\uC758 `),Pt=r("code"),wl=o("configuration"),yl=o(`\uC740 \uBAA8\uB378\uC744 \uB9CC\uB4E4\uAE30 \uC704\uD574 \uD544\uC694\uD55C \uBAA8\uB4E0 \uC911\uC694\uD55C \uAC83\uB4E4\uC744 \uD3EC\uD568\uD558\uACE0 \uC788\uB294 \uAC1D\uCCB4\uC785\uB2C8\uB2E4.
\uB2E4\uC74C \uC139\uC158\uC5D0\uC11C \uBCFC \uC218 \uC788\uB4EF\uC774, \uBAA8\uB378\uC740 `),Rt=r("code"),bl=o("config"),kl=o("\uB97C \uC0AC\uC6A9\uD574\uC11C\uB9CC \uCD08\uAE30\uD654\uD560 \uC218 \uC788\uAE30 \uB54C\uBB38\uC5D0 \uC644\uBCBD\uD55C \uAD6C\uC131\uC774 \uD544\uC694\uD569\uB2C8\uB2E4."),Qs=p(),We=r("p"),Cl=o(`\uC544\uB798 \uC608\uC2DC\uC5D0\uC11C\uB294 ResNet \uD074\uB798\uC2A4\uC758 \uC778\uC218(argument)\uB97C \uC870\uC815\uD574\uBCF4\uACA0\uC2B5\uB2C8\uB2E4.
\uB2E4\uB978 \uAD6C\uC131\uC740 \uAC00\uB2A5\uD55C ResNet \uC911 \uB2E4\uB978 \uC720\uD615\uC744 \uC81C\uACF5\uD569\uB2C8\uB2E4.
\uADF8\uB7F0 \uB2E4\uC74C \uBA87 \uAC00\uC9C0 \uC720\uD6A8\uC131\uC744 \uD655\uC778\uD55C \uD6C4 \uD574\uB2F9 \uC778\uC218\uB97C \uC800\uC7A5\uD569\uB2C8\uB2E4.`),Ws=p(),u(ke.$$.fragment),Xs=p(),re=r("p"),jl=o("\uC0AC\uC6A9\uC790 \uC815\uC758 "),Mt=r("code"),Pl=o("configuration"),Rl=o("\uC744 \uC791\uC131\uD560 \uB54C \uAE30\uC5B5\uD574\uC57C \uD560 \uC138 \uAC00\uC9C0 \uC911\uC694\uD55C \uC0AC\uD56D\uC740 \uB2E4\uC74C\uACFC \uAC19\uC2B5\uB2C8\uB2E4:"),Zs=p(),F=r("ul"),Xe=r("li"),Ot=r("code"),Ml=o("PretrainedConfig"),Ol=o("\uC744 \uC0C1\uC18D\uD574\uC57C \uD569\uB2C8\uB2E4."),ql=p(),ae=r("li"),qt=r("code"),Al=o("PretrainedConfig"),Dl=o("\uC758 "),At=r("code"),Nl=o("__init__"),Il=o("\uC740 \uBAA8\uB4E0 kwargs\uB97C \uD5C8\uC6A9\uD574\uC57C \uD558\uACE0,"),Tl=p(),Q=r("li"),Fl=o("\uC774\uB7EC\uD55C "),Dt=r("code"),Hl=o("kwargs"),Bl=o("\uB294 \uC0C1\uC704 \uD074\uB798\uC2A4 "),Nt=r("code"),xl=o("__init__"),Sl=o("\uC5D0 \uC804\uB2EC\uB418\uC5B4\uC57C \uD569\uB2C8\uB2E4."),eo=p(),H=r("p"),Ll=o(`\uC0C1\uC18D\uC740 \u{1F917} Transformers \uB77C\uC774\uBE0C\uB7EC\uB9AC\uC5D0\uC11C \uBAA8\uB4E0 \uAE30\uB2A5\uC744 \uAC00\uC838\uC624\uB294 \uAC83\uC785\uB2C8\uB2E4.
\uC774\uB7EC\uD55C \uC810\uC73C\uB85C\uBD80\uD130 \uBE44\uB86F\uB418\uB294 \uB450 \uAC00\uC9C0 \uC81C\uC57D \uC870\uAC74\uC740 `),It=r("code"),Gl=o("PretrainedConfig"),Kl=o(`\uC5D0 \uC124\uC815\uD558\uB294 \uAC83\uBCF4\uB2E4 \uB354 \uB9CE\uC740 \uD544\uB4DC\uAC00 \uC788\uC2B5\uB2C8\uB2E4.
`),Tt=r("code"),Vl=o("from_pretrained"),Ul=o(" \uBA54\uC11C\uB4DC\uB85C \uAD6C\uC131\uC744 \uB2E4\uC2DC \uB85C\uB4DC\uD560 \uB54C \uD574\uB2F9 \uD544\uB4DC\uB294 \uAD6C\uC131\uC5D0\uC11C \uC218\uB77D\uD55C \uD6C4 \uC0C1\uC704 \uD074\uB798\uC2A4\uB85C \uBCF4\uB0B4\uC57C \uD569\uB2C8\uB2E4."),to=p(),O=r("p"),Yl=o("\uBAA8\uB378\uC744 auto \uD074\uB798\uC2A4\uC5D0 \uB4F1\uB85D\uD558\uC9C0 \uC54A\uB294 \uD55C, "),Ft=r("code"),Jl=o("configuration"),zl=o("\uC5D0\uC11C "),Ht=r("code"),Ql=o("model_type"),Wl=o("\uC744 \uC815\uC758(\uC5EC\uAE30\uC11C "),Bt=r("code"),Xl=o('model_type="resnet"'),Zl=o(")\uD558\uB294 \uAC83\uC740 \uD544\uC218 \uC0AC\uD56D\uC774 \uC544\uB2D9\uB2C8\uB2E4 (\uB9C8\uC9C0\uB9C9 \uC139\uC158 \uCC38\uC870)."),so=p(),Ze=r("p"),en=o(`\uC774\uB807\uAC8C \uD558\uBA74 \uB77C\uC774\uBE0C\uB7EC\uB9AC\uC758 \uB2E4\uB978 \uBAA8\uB378 \uAD6C\uC131\uACFC \uB9C8\uCC2C\uAC00\uC9C0\uB85C \uAD6C\uC131\uC744 \uC27D\uAC8C \uB9CC\uB4E4\uACE0 \uC800\uC7A5\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.
\uB2E4\uC74C\uC740 resnet50d \uAD6C\uC131\uC744 \uC0DD\uC131\uD558\uACE0 \uC800\uC7A5\uD558\uB294 \uBC29\uBC95\uC785\uB2C8\uB2E4:`),oo=p(),u(Ce.$$.fragment),lo=p(),q=r("p"),tn=o("\uC774\uB807\uAC8C \uD558\uBA74 "),xt=r("code"),sn=o("custom-resnet"),on=o(" \uD3F4\uB354 \uC548\uC5D0 "),St=r("code"),ln=o("config.json"),nn=o(`\uC774\uB77C\uB294 \uD30C\uC77C\uC774 \uC800\uC7A5\uB429\uB2C8\uB2E4.
\uADF8\uB7F0 \uB2E4\uC74C `),Lt=r("code"),rn=o("from_pretrained"),an=o(" \uBA54\uC11C\uB4DC\uB97C \uC0AC\uC6A9\uD558\uC5EC \uAD6C\uC131\uC744 \uB2E4\uC2DC \uB85C\uB4DC\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),no=p(),u(je.$$.fragment),ro=p(),B=r("p"),fn=o("\uAD6C\uC131\uC744 Hub\uC5D0 \uC9C1\uC811 \uC5C5\uB85C\uB4DC\uD558\uAE30 \uC704\uD574 "),Gt=r("code"),pn=o("PretrainedConfig"),cn=o(" \uD074\uB798\uC2A4\uC758 "),Kt=r("code"),dn=o("push_to_hub()"),_n=o("\uC640 \uAC19\uC740 \uB2E4\uB978 \uBA54\uC11C\uB4DC\uB97C \uC0AC\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),ao=p(),W=r("h2"),ie=r("a"),Vt=r("span"),u(Pe.$$.fragment),mn=p(),Ut=r("span"),un=o("\uC0AC\uC6A9\uC790 \uC815\uC758 \uBAA8\uB378 \uC791\uC131\uD558\uAE30"),io=p(),x=r("p"),hn=o(`\uC774\uC81C ResNet \uAD6C\uC131\uC774 \uC788\uC73C\uBBC0\uB85C \uBAA8\uB378\uC744 \uC791\uC131\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.
\uC2E4\uC81C\uB85C\uB294 \uB450 \uAC1C\uB97C \uC791\uC131\uD560 \uAC83\uC785\uB2C8\uB2E4. \uD558\uB098\uB294 \uC774\uBBF8\uC9C0 \uBC30\uCE58\uC5D0\uC11C hidden features\uB97C \uCD94\uCD9C\uD558\uB294 \uAC83(`),Yt=r("code"),gn=o("BertModel"),$n=o("\uACFC \uAC19\uC774), \uB2E4\uB978 \uD558\uB098\uB294 \uC774\uBBF8\uC9C0 \uBD84\uB958\uC5D0 \uC801\uD569\uD55C \uAC83\uC785\uB2C8\uB2E4("),Jt=r("code"),vn=o("BertForSequenceClassification"),En=o("\uACFC \uAC19\uC774)."),fo=p(),S=r("p"),wn=o(`\uC774\uC804\uC5D0 \uC5B8\uAE09\uD588\uB4EF\uC774 \uC774 \uC608\uC81C\uC5D0\uC11C\uB294 \uB2E8\uC21C\uD558\uAC8C \uD558\uAE30 \uC704\uD574 \uBAA8\uB378\uC758 \uB290\uC2A8\uD55C \uB798\uD37C(loose wrapper)\uB9CC \uC791\uC131\uD560 \uAC83\uC785\uB2C8\uB2E4.
\uC774 \uD074\uB798\uC2A4\uB97C \uC791\uC131\uD558\uAE30 \uC804\uC5D0 \uBE14\uB85D \uC720\uD615\uACFC \uC2E4\uC81C \uBE14\uB85D \uD074\uB798\uC2A4 \uAC04\uC758 \uB9E4\uD551 \uC791\uC5C5\uB9CC \uD558\uBA74 \uB429\uB2C8\uB2E4.
\uADF8\uB7F0 \uB2E4\uC74C `),zt=r("code"),yn=o("ResNet"),bn=o(" \uD074\uB798\uC2A4\uB85C \uC804\uB2EC\uB418\uC5B4 "),Qt=r("code"),kn=o("configuration"),Cn=o("\uC744 \uD1B5\uD574 \uBAA8\uB378\uC774 \uC120\uC5B8\uB429\uB2C8\uB2E4:"),po=p(),u(Re.$$.fragment),co=p(),et=r("p"),jn=o("\uC774\uBBF8\uC9C0 \uBD84\uB958 \uBAA8\uB378\uC744 \uB9CC\uB4E4\uAE30 \uC704\uD574\uC11C\uB294 forward \uBA54\uC18C\uB4DC\uB9CC \uBCC0\uACBD\uD558\uBA74 \uB429\uB2C8\uB2E4:"),_o=p(),u(Me.$$.fragment),mo=p(),P=r("p"),Pn=o("\uB450 \uACBD\uC6B0 \uBAA8\uB450 "),Wt=r("code"),Rn=o("PreTrainedModel"),Mn=o("\uB97C \uC0C1\uC18D\uBC1B\uACE0, "),Xt=r("code"),On=o("config"),qn=o("\uB97C \uD1B5\uD574 \uC0C1\uC704 \uD074\uB798\uC2A4 \uCD08\uAE30\uD654\uB97C \uD638\uCD9C\uD558\uB2E4\uB294 \uC810\uC744 \uAE30\uC5B5\uD558\uC138\uC694 (\uC77C\uBC18\uC801\uC778 "),Zt=r("code"),An=o("torch.nn.Module"),Dn=o(`\uC744 \uC791\uC131\uD560 \uB54C\uC640 \uBE44\uC2B7\uD568).
\uBAA8\uB378\uC744 auto \uD074\uB798\uC2A4\uC5D0 \uB4F1\uB85D\uD558\uACE0 \uC2F6\uC740 \uACBD\uC6B0\uC5D0\uB294 `),es=r("code"),Nn=o("config_class"),In=o("\uB97C \uC124\uC815\uD558\uB294 \uBD80\uBD84\uC774 \uD544\uC218\uC785\uB2C8\uB2E4 (\uB9C8\uC9C0\uB9C9 \uC139\uC158 \uCC38\uC870)."),uo=p(),u(fe.$$.fragment),ho=p(),L=r("p"),Tn=o("\uC6D0\uD558\uB294 \uAC83\uC744 \uBAA8\uB378\uC774 \uBC18\uD658\uD558\uB3C4\uB85D \uD560 \uC218 \uC788\uC9C0\uB9CC, "),ts=r("code"),Fn=o("ResnetModelForImageClassification"),Hn=o(`\uC5D0\uC11C \uD588\uB358 \uAC83 \uCC98\uB7FC
\uB808\uC774\uBE14\uC744 \uD1B5\uACFC\uC2DC\uCF30\uC744 \uB54C \uC190\uC2E4\uACFC \uD568\uAED8 \uC0AC\uC804 \uD615\uD0DC\uB85C \uBC18\uD658\uD558\uB294 \uAC83\uC774 `),ss=r("code"),Bn=o("Trainer"),xn=o(` \uD074\uB798\uC2A4 \uB0B4\uC5D0\uC11C \uC9C1\uC811 \uBAA8\uB378\uC744 \uC0AC\uC6A9\uD558\uAE30\uC5D0 \uC720\uC6A9\uD569\uB2C8\uB2E4.
\uC790\uC2E0\uB9CC\uC758 \uD559\uC2B5 \uB8E8\uD504 \uB610\uB294 \uB2E4\uB978 \uD559\uC2B5 \uB77C\uC774\uBE0C\uB7EC\uB9AC\uB97C \uC0AC\uC6A9\uD560 \uACC4\uD68D\uC774\uB77C\uBA74 \uB2E4\uB978 \uCD9C\uB825 \uD615\uC2DD\uC744 \uC0AC\uC6A9\uD574\uB3C4 \uC88B\uC2B5\uB2C8\uB2E4.`),go=p(),tt=r("p"),Sn=o("\uC774\uC81C \uBAA8\uB378 \uD074\uB798\uC2A4\uAC00 \uC788\uC73C\uBBC0\uB85C \uD558\uB098 \uC0DD\uC131\uD574 \uBCF4\uACA0\uC2B5\uB2C8\uB2E4:"),$o=p(),u(Oe.$$.fragment),vo=p(),A=r("p"),Ln=o("\uB2E4\uC2DC \uB9D0\uD558\uC9C0\uB9CC, "),os=r("code"),Gn=o("save_pretrained()"),Kn=o("\uB610\uB294 "),ls=r("code"),Vn=o("push_to_hub()"),Un=o("\uCC98\uB7FC "),ns=r("code"),Yn=o("PreTrainedModel"),Jn=o(`\uC5D0 \uC18D\uD558\uB294 \uBAA8\uB4E0 \uBA54\uC18C\uB4DC\uB97C \uC0AC\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.
\uB2E4\uC74C \uC139\uC158\uC5D0\uC11C \uB450 \uBC88\uC9F8 \uBA54\uC18C\uB4DC\uB97C \uC0AC\uC6A9\uD574 \uBAA8\uB378 \uCF54\uB4DC\uC640 \uBAA8\uB378 \uAC00\uC911\uCE58\uB97C \uC5C5\uB85C\uB4DC\uD558\uB294 \uBC29\uBC95\uC744 \uC0B4\uD3B4\uBCF4\uACA0\uC2B5\uB2C8\uB2E4.
\uBA3C\uC800, \uBAA8\uB378 \uB0B4\uBD80\uC5D0 \uC0AC\uC804 \uD6C8\uB828\uB41C \uAC00\uC911\uCE58\uB97C \uB85C\uB4DC\uD574 \uBCF4\uACA0\uC2B5\uB2C8\uB2E4.`),Eo=p(),st=r("p"),zn=o(`\uC774 \uC608\uC81C\uB97C \uD65C\uC6A9\uD560 \uB54C\uB294, \uC0AC\uC6A9\uC790 \uC815\uC758 \uBAA8\uB378\uC744 \uC790\uC2E0\uB9CC\uC758 \uB370\uC774\uD130\uB85C \uD559\uC2B5\uC2DC\uD0AC \uAC83\uC785\uB2C8\uB2E4.
\uC774 \uD29C\uD1A0\uB9AC\uC5BC\uC5D0\uC11C\uB294 \uBE60\uB974\uAC8C \uC9C4\uD589\uD558\uAE30 \uC704\uD574 \uC0AC\uC804 \uD6C8\uB828\uB41C resnet50d\uB97C \uC0AC\uC6A9\uD558\uACA0\uC2B5\uB2C8\uB2E4.
\uC544\uB798 \uBAA8\uB378\uC740 resnet50d\uC758 \uB798\uD37C\uC774\uAE30 \uB54C\uBB38\uC5D0, \uAC00\uC911\uCE58\uB97C \uC27D\uAC8C \uB85C\uB4DC\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.`),wo=p(),u(qe.$$.fragment),yo=p(),G=r("p"),Qn=o("\uC774\uC81C "),rs=r("code"),Wn=o("save_pretrained()"),Xn=o(" \uB610\uB294 "),as=r("code"),Zn=o("push_to_hub()"),er=o("\uB97C \uC0AC\uC6A9\uD560 \uB54C \uBAA8\uB378 \uCF54\uB4DC\uAC00 \uC800\uC7A5\uB418\uB294\uC9C0 \uD655\uC778\uD574\uBD05\uC2DC\uB2E4."),bo=p(),X=r("h2"),pe=r("a"),is=r("span"),u(Ae.$$.fragment),tr=p(),fs=r("span"),sr=o("Hub\uB85C \uCF54\uB4DC \uC5C5\uB85C\uB4DC\uD558\uAE30"),ko=p(),u(ce.$$.fragment),Co=p(),y=r("p"),or=o("\uBA3C\uC800 \uBAA8\uB378\uC774 "),ps=r("code"),lr=o(".py"),nr=o(` \uD30C\uC77C\uC5D0 \uC644\uC804\uD788 \uC815\uC758\uB418\uC5B4 \uC788\uB294\uC9C0 \uD655\uC778\uD558\uC138\uC694.
\uBAA8\uB4E0 \uD30C\uC77C\uC774 \uB3D9\uC77C\uD55C \uC791\uC5C5 \uACBD\uB85C\uC5D0 \uC788\uAE30 \uB54C\uBB38\uC5D0 \uC0C1\uB300\uACBD\uB85C \uC784\uD3EC\uD2B8(relative import)\uC5D0 \uC758\uC874\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4 (transformers\uC5D0\uC11C\uB294 \uC774 \uAE30\uB2A5\uC5D0 \uB300\uD55C \uD558\uC704 \uBAA8\uB4C8\uC744 \uC9C0\uC6D0\uD558\uC9C0 \uC54A\uC2B5\uB2C8\uB2E4).
\uC774 \uC608\uC2DC\uC5D0\uC11C\uB294 \uC791\uC5C5 \uACBD\uB85C \uC548\uC758 `),cs=r("code"),rr=o("resnet_model"),ar=o("\uC5D0\uC11C "),ds=r("code"),ir=o("modeling_resnet.py"),fr=o(" \uD30C\uC77C\uACFC "),_s=r("code"),pr=o("configuration_resnet.py"),cr=o(` \uD30C\uC77C\uC744 \uC815\uC758\uD569\uB2C8\uB2E4.
\uAD6C\uC131 \uD30C\uC77C\uC5D0\uB294 `),ms=r("code"),dr=o("ResnetConfig"),_r=o("\uC5D0 \uB300\uD55C \uCF54\uB4DC\uAC00 \uC788\uACE0 \uBAA8\uB378\uB9C1 \uD30C\uC77C\uC5D0\uB294 "),us=r("code"),mr=o("ResnetModel"),ur=o(" \uBC0F "),hs=r("code"),hr=o("ResnetModelForImageClassification"),gr=o("\uC5D0 \uB300\uD55C \uCF54\uB4DC\uAC00 \uC788\uC2B5\uB2C8\uB2E4."),jo=p(),u(De.$$.fragment),Po=p(),K=r("p"),$r=o("Python\uC774 "),gs=r("code"),vr=o("resnet_model"),Er=o("\uC744 \uBAA8\uB4C8\uB85C \uC0AC\uC6A9\uD560 \uC218 \uC788\uB3C4\uB85D \uAC10\uC9C0\uD558\uB294 \uBAA9\uC801\uC774\uAE30 \uB54C\uBB38\uC5D0 "),$s=r("code"),wr=o("__init__.py"),yr=o("\uB294 \uBE44\uC5B4 \uC788\uC744 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),Ro=p(),u(de.$$.fragment),Mo=p(),ot=r("p"),br=o("\uAE30\uC874 \uAD6C\uC131\uC774\uB098 \uBAA8\uB378\uC744 \uC7AC\uC0AC\uC6A9(\uB610\uB294 \uC11C\uBE0C \uD074\uB798\uC2A4\uD654)\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),Oo=p(),lt=r("p"),kr=o(`\uCEE4\uBBA4\uB2C8\uD2F0\uC5D0 \uBAA8\uB378\uC744 \uACF5\uC720\uD558\uAE30 \uC704\uD574\uC11C\uB294 \uB2E4\uC74C \uB2E8\uACC4\uB97C \uB530\uB77C\uC57C \uD569\uB2C8\uB2E4:
\uBA3C\uC800, \uC0C8\uB85C \uB9CC\uB4E0 \uD30C\uC77C\uC5D0 ResNet \uBAA8\uB378\uACFC \uAD6C\uC131\uC744 \uC784\uD3EC\uD2B8\uD569\uB2C8\uB2E4:`),qo=p(),u(Ne.$$.fragment),Ao=p(),_e=r("p"),Cr=o("\uB2E4\uC74C\uC73C\uB85C "),vs=r("code"),jr=o("save_pretrained"),Pr=o(` \uBA54\uC18C\uB4DC\uB97C \uC0AC\uC6A9\uD574 \uD574\uB2F9 \uAC1D\uCCB4\uC758 \uCF54\uB4DC \uD30C\uC77C\uC744 \uBCF5\uC0AC\uD558\uACE0,
\uBCF5\uC0AC\uD55C \uD30C\uC77C\uC744 Auto \uD074\uB798\uC2A4\uB85C \uB4F1\uB85D\uD558\uACE0(\uBAA8\uB378\uC778 \uACBD\uC6B0) \uC2E4\uD589\uD569\uB2C8\uB2E4:`),Do=p(),u(Ie.$$.fragment),No=p(),Z=r("p"),Es=r("code"),Rr=o("configuration"),Mr=o("\uC5D0 \uB300\uD55C auto \uD074\uB798\uC2A4\uB97C \uC9C0\uC815\uD560 \uD544\uC694\uB294 \uC5C6\uC9C0\uB9CC("),ws=r("code"),Or=o("configuration"),qr=o(` \uAD00\uB828 auto \uD074\uB798\uC2A4\uB294 AutoConfig \uD074\uB798\uC2A4 \uD558\uB098\uB9CC \uC788\uC74C), \uBAA8\uB378\uC758 \uACBD\uC6B0\uC5D0\uB294 \uC9C0\uC815\uD574\uC57C \uD569\uB2C8\uB2E4.
\uC0AC\uC6A9\uC790 \uC9C0\uC815 \uBAA8\uB378\uC740 \uB2E4\uC591\uD55C \uC791\uC5C5\uC5D0 \uC801\uD569\uD560 \uC218 \uC788\uC73C\uBBC0\uB85C, \uBAA8\uB378\uC5D0 \uB9DE\uB294 auto \uD074\uB798\uC2A4\uB97C \uC9C0\uC815\uD574\uC57C \uD569\uB2C8\uB2E4.`),Io=p(),nt=r("p"),Ar=o("\uB2E4\uC74C\uC73C\uB85C, \uC774\uC804\uC5D0 \uC791\uC5C5\uD588\uB358 \uAC83\uACFC \uB9C8\uCC2C\uAC00\uC9C0\uB85C \uAD6C\uC131\uACFC \uBAA8\uB378\uC744 \uC791\uC131\uD569\uB2C8\uB2E4:"),To=p(),u(Te.$$.fragment),Fo=p(),rt=r("p"),Dr=o(`\uC774\uC81C \uBAA8\uB378\uC744 Hub\uB85C \uC5C5\uB85C\uB4DC\uD558\uAE30 \uC704\uD574 \uB85C\uADF8\uC778 \uC0C1\uD0DC\uC778\uC9C0 \uD655\uC778\uD558\uC138\uC694.
\uD130\uBBF8\uB110\uC5D0\uC11C \uB2E4\uC74C \uCF54\uB4DC\uB97C \uC2E4\uD589\uD574 \uD655\uC778\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4:`),Ho=p(),u(Fe.$$.fragment),Bo=p(),at=r("p"),Nr=o("\uC8FC\uD53C\uD130 \uB178\uD2B8\uBD81\uC758 \uACBD\uC6B0\uC5D0\uB294 \uB2E4\uC74C\uACFC \uAC19\uC2B5\uB2C8\uB2E4:"),xo=p(),u(He.$$.fragment),So=p(),it=r("p"),Ir=o("\uADF8\uB7F0 \uB2E4\uC74C \uC774\uB807\uAC8C \uC790\uC2E0\uC758 \uB124\uC784\uC2A4\uD398\uC774\uC2A4(\uB610\uB294 \uC790\uC2E0\uC774 \uC18D\uD55C \uC870\uC9C1)\uC5D0 \uC5C5\uB85C\uB4DC\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4:"),Lo=p(),u(Be.$$.fragment),Go=p(),b=r("p"),Tr=o(`On top of the modeling weights and the configuration in json format, this also copied the modeling and
configuration `),ys=r("code"),Fr=o(".py"),Hr=o(" files in the folder "),bs=r("code"),Br=o("custom-resnet50d"),xr=o(` and uploaded the result to the Hub. You can check the result
in this `),xe=r("a"),Sr=o("model repo"),Lr=o(`.
json \uD615\uC2DD\uC758 \uBAA8\uB378\uB9C1 \uAC00\uC911\uCE58\uC640 \uAD6C\uC131 \uC678\uC5D0\uB3C4 `),ks=r("code"),Gr=o("custom-resnet50d"),Kr=o(" \uD3F4\uB354 \uC548\uC758 \uBAA8\uB378\uB9C1\uACFC \uAD6C\uC131 "),Cs=r("code"),Vr=o(".py"),Ur=o(` \uD30C\uC77C\uC744 \uBCF5\uC0AC\uD558\uD574 Hub\uC5D0 \uC5C5\uB85C\uB4DC\uD569\uB2C8\uB2E4.
`),Se=r("a"),Yr=o("\uBAA8\uB378 \uC800\uC7A5\uC18C"),Jr=o("\uC5D0\uC11C \uACB0\uACFC\uB97C \uD655\uC778\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),Ko=p(),ee=r("p"),ft=r("a"),zr=o("sharing tutorial"),Qr=o(" \uBB38\uC11C\uC758 "),js=r("code"),Wr=o("push_to_hub"),Xr=o(" \uBA54\uC18C\uB4DC\uC5D0\uC11C \uC790\uC138\uD55C \uB0B4\uC6A9\uC744 \uD655\uC778\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),Vo=p(),te=r("h2"),me=r("a"),Ps=r("span"),u(Le.$$.fragment),Zr=p(),Rs=r("span"),ea=o("\uC0AC\uC6A9\uC790 \uC815\uC758 \uCF54\uB4DC\uB85C \uBAA8\uB378 \uC0AC\uC6A9\uD558\uAE30"),Uo=p(),D=r("p"),ta=o("auto \uD074\uB798\uC2A4\uC640 "),Ms=r("code"),sa=o("from_pretrained"),oa=o(` \uBA54\uC18C\uB4DC\uB97C \uC0AC\uC6A9\uD558\uC5EC \uC0AC\uC6A9\uC790 \uC9C0\uC815 \uCF54\uB4DC \uD30C\uC77C\uACFC \uD568\uAED8 \uBAA8\uB4E0 \uAD6C\uC131, \uBAA8\uB378, \uD1A0\uD06C\uB098\uC774\uC800\uB97C \uC0AC\uC6A9\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.
Hub\uC5D0 \uC5C5\uB85C\uB4DC\uB41C \uBAA8\uB4E0 \uD30C\uC77C \uBC0F \uCF54\uB4DC\uB294 \uBA5C\uC6E8\uC5B4\uAC00 \uC788\uB294\uC9C0 \uAC80\uC0AC\uB418\uC9C0\uB9CC (\uC790\uC138\uD55C \uB0B4\uC6A9\uC740 `),Ge=r("a"),la=o("Hub \uBCF4\uC548"),na=o(` \uC124\uBA85 \uCC38\uC870),
\uC790\uC2E0\uC758 \uCEF4\uD4E8\uD130\uC5D0\uC11C \uBAA8\uB378 \uCF54\uB4DC\uC640 \uC791\uC131\uC790\uAC00 \uC545\uC131 \uCF54\uB4DC\uB97C \uC2E4\uD589\uD558\uC9C0 \uC54A\uB294\uC9C0 \uD655\uC778\uD574\uC57C \uD569\uB2C8\uB2E4.
\uC0AC\uC6A9\uC790 \uC815\uC758 \uCF54\uB4DC\uB85C \uBAA8\uB378\uC744 \uC0AC\uC6A9\uD558\uB824\uBA74 `),Os=r("code"),ra=o("trust_remote_code=True"),aa=o("\uB85C \uC124\uC815\uD558\uC138\uC694:"),Yo=p(),u(Ke.$$.fragment),Jo=p(),ue=r("p"),ia=o("\uBAA8\uB378 \uC791\uC131\uC790\uAC00 \uC545\uC758\uC801\uC73C\uB85C \uCF54\uB4DC\uB97C \uC5C5\uB370\uC774\uD2B8\uD558\uC9C0 \uC54A\uC558\uB2E4\uB294 \uC810\uC744 \uD655\uC778\uD558\uAE30 \uC704\uD574, \uCEE4\uBC0B \uD574\uC2DC(commit hash)\uB97C "),qs=r("code"),fa=o("revision"),pa=o("\uC73C\uB85C \uC804\uB2EC\uD558\uB294 \uAC83\uB3C4 \uAC15\uB825\uD788 \uAD8C\uC7A5\uB429\uB2C8\uB2E4 (\uBAA8\uB378 \uC791\uC131\uC790\uB97C \uC644\uC804\uD788 \uC2E0\uB8B0\uD558\uC9C0 \uC54A\uB294 \uACBD\uC6B0)."),zo=p(),u(Ve.$$.fragment),Qo=p(),pt=r("p"),ca=o("Hub\uC5D0\uC11C \uBAA8\uB378 \uC800\uC7A5\uC18C\uC758 \uCEE4\uBC0B \uAE30\uB85D\uC744 \uCC3E\uC544\uBCFC \uB54C, \uBAA8\uB4E0 \uCEE4\uBC0B\uC758 \uCEE4\uBC0B \uD574\uC2DC\uB97C \uC27D\uAC8C \uBCF5\uC0AC\uD560 \uC218 \uC788\uB294 \uBC84\uD2BC\uC774 \uC788\uC2B5\uB2C8\uB2E4."),Wo=p(),se=r("h2"),he=r("a"),As=r("span"),u(Ue.$$.fragment),da=p(),Ds=r("span"),_a=o("\uC0AC\uC6A9\uC790 \uC815\uC758 \uCF54\uB4DC\uB85C \uB9CC\uB4E0 \uBAA8\uB378\uC744 auto \uD074\uB798\uC2A4\uB85C \uB4F1\uB85D\uD558\uAE30"),Xo=p(),ct=r("p"),ma=o(`\u{1F917} Transformers\uB97C \uC0C1\uC18D\uD558\uB294 \uB77C\uC774\uBE0C\uB7EC\uB9AC\uB97C \uC791\uC131\uD558\uB294 \uACBD\uC6B0 \uC0AC\uC6A9\uC790 \uC815\uC758 \uBAA8\uB378\uC744 auto \uD074\uB798\uC2A4\uC5D0 \uCD94\uAC00\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.
\uC0AC\uC6A9\uC790 \uC815\uC758 \uBAA8\uB378\uC744 \uC0AC\uC6A9\uD558\uAE30 \uC704\uD574 \uD574\uB2F9 \uB77C\uC774\uBE0C\uB7EC\uB9AC\uB97C \uC784\uD3EC\uD2B8\uD574\uC57C \uD558\uAE30 \uB54C\uBB38\uC5D0, \uC774\uB294 Hub\uB85C \uCF54\uB4DC\uB97C \uC5C5\uB85C\uB4DC\uD558\uB294 \uAC83\uACFC \uB2E4\uB985\uB2C8\uB2E4 (Hub\uC5D0\uC11C \uC790\uB3D9\uC801\uC73C\uB85C \uBAA8\uB378 \uCF54\uB4DC\uB97C \uB2E4\uC6B4\uB85C\uB4DC \uD558\uB294 \uAC83\uACFC \uBC18\uB300).`),Zo=p(),V=r("p"),ua=o("\uAD6C\uC131\uC5D0 \uAE30\uC874 \uBAA8\uB378 \uC720\uD615\uACFC \uB2E4\uB978 "),Ns=r("code"),ha=o("model_type"),ga=o(" \uC18D\uC131\uC774 \uC788\uACE0 \uBAA8\uB378 \uD074\uB798\uC2A4\uC5D0 \uC62C\uBC14\uB978 "),Is=r("code"),$a=o("config_class"),va=o(` \uC18D\uC131\uC774 \uC788\uB294 \uD55C,
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