import{S as Ao,i as xo,s as Co,e as o,k as h,w as M,t as s,N as $o,c as r,d as l,m as d,a as n,x as w,h as a,b as i,P as Fe,G as t,g as m,y as J,L as So,q as b,o as g,B as T,v as Ho}from"../chunks/vendor-hf-doc-builder.js";import{I as Xe}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as Ze}from"../chunks/CodeBlock-hf-doc-builder.js";function Vo(Ia){let E,_t,Z,V,De,ee,Il,Pe,jl,Et,B,Le,_l,El,te,Zl,Bl,Zt,f,Wl,qe,kl,Gl,Oe,Al,xl,Ke,Cl,$l,le,Sl,Hl,Bt,j,Vl,et,Nl,Yl,se,Ql,zl,Wt,Be,Rl,kt,U,ae,Fl,We,Xl,Dl,Pl,oe,Ll,tt,ql,Ol,Kl,re,es,ke,ts,ls,ss,ne,as,Ge,os,rs,Gt,W,N,lt,ie,ns,st,is,At,u,cs,at,ms,hs,ot,ds,ps,rt,us,fs,nt,ys,Ms,it,ws,Js,xt,ce,Ct,Y,bs,ct,gs,Ts,$t,k,Ae,ja,Us,xe,_a,St,G,Ce,Ea,vs,$e,Za,Ht,A,Q,mt,me,Is,ht,js,Vt,z,_s,dt,Es,Zs,Nt,he,Yt,Se,Bs,Qt,de,zt,x,R,pt,pe,Ws,ut,ks,Rt,F,Gs,ft,As,xs,Ft,He,Cs,Xt,ue,Dt,X,$s,yt,Ss,Hs,Pt,fe,Lt,D,Vs,Mt,Ns,Ys,qt,ye,Ot,C,P,wt,Me,Qs,Jt,zs,Kt,p,Rs,bt,Fs,Xs,Ve,Ds,Ps,we,Ls,qs,gt,Os,Ks,Ne,ea,ta,Tt,la,sa,el,$,Ye,Ba,aa,Qe,Wa,tl,Je,ll,S,L,Ut,be,oa,vt,ra,sl,v,ge,na,Te,ia,ca,ma,Ue,ha,ve,da,pa,ua,H,fa,Ie,ya,Ma,je,wa,Ja,ba,q,_e,ga,Ta,Ee,Ua,va,al;return ee=new Xe({}),ie=new Xe({}),ce=new Ze({props:{code:"aW1wb3J0JTIwdGltbSUwQSUwQSUyMyUyMExvYWRpbmclMjBodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGdGltbSUyRmVjYV9uZm5ldF9sMCUwQW1vZGVsJTIwJTNEJTIwdGltbS5jcmVhdGVfbW9kZWwoJTIyaGYtaHViJTNBdGltbSUyRmVjYV9uZm5ldF9sMCUyMiUyQyUyMHByZXRyYWluZWQlM0RUcnVlKQ==",highlighted:`<span class="hljs-keyword">import</span> timm

<span class="hljs-comment"># Loading https://huggingface.co/timm/eca_nfnet_l0</span>
model = timm.create_model(<span class="hljs-string">&quot;hf-hub:timm/eca_nfnet_l0&quot;</span>, pretrained=<span class="hljs-literal">True</span>)`}}),me=new Xe({}),he=new Ze({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> timm
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">from</span> timm.data <span class="hljs-keyword">import</span> resolve_data_config
<span class="hljs-keyword">from</span> timm.data.transforms_factory <span class="hljs-keyword">import</span> create_transform

<span class="hljs-comment"># Load from Hub \u{1F525}</span>
model = timm.create_model(
    <span class="hljs-string">&#x27;hf-hub:nateraw/resnet50-oxford-iiit-pet&#x27;</span>,
    pretrained=<span class="hljs-literal">True</span>
)

<span class="hljs-comment"># Set model to eval mode for inference</span>
model.<span class="hljs-built_in">eval</span>()

<span class="hljs-comment"># Create Transform</span>
transform = create_transform(**resolve_data_config(model.pretrained_cfg, model=model))

<span class="hljs-comment"># Get the labels from the model config</span>
labels = model.pretrained_cfg[<span class="hljs-string">&#x27;labels&#x27;</span>]
top_k = <span class="hljs-built_in">min</span>(<span class="hljs-built_in">len</span>(labels), <span class="hljs-number">5</span>)

<span class="hljs-comment"># Use your own image file here...</span>
image = Image.<span class="hljs-built_in">open</span>(<span class="hljs-string">&#x27;boxer.jpg&#x27;</span>).convert(<span class="hljs-string">&#x27;RGB&#x27;</span>)

<span class="hljs-comment"># Process PIL image with transforms and add a batch dimension</span>
x = transform(image).unsqueeze(<span class="hljs-number">0</span>)

<span class="hljs-comment"># Pass inputs to model forward function to get outputs</span>
out = model(x)

<span class="hljs-comment"># Apply softmax to get predicted probabilities for each class</span>
probabilities = torch.nn.functional.softmax(out[<span class="hljs-number">0</span>], dim=<span class="hljs-number">0</span>)

<span class="hljs-comment"># Grab the values and indices of top 5 predicted classes</span>
values, indices = torch.topk(probabilities, top_k)

<span class="hljs-comment"># Prepare a nice dict of top k predictions</span>
predictions = [
    {<span class="hljs-string">&quot;label&quot;</span>: labels[i], <span class="hljs-string">&quot;score&quot;</span>: v.item()}
    <span class="hljs-keyword">for</span> i, v <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(indices, values)
]
<span class="hljs-built_in">print</span>(predictions)`}}),de=new Ze({props:{code:"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",highlighted:`[
    {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;american_pit_bull_terrier&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9999998807907104</span>},
    {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;staffordshire_bull_terrier&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">1.0000000149011612e-07</span>},
    {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;miniature_pinscher&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">1.0000000149011612e-07</span>},
    {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;chihuahua&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">1.0000000149011612e-07</span>},
    {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;beagle&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">1.0000000149011612e-07</span>}
]`}}),pe=new Xe({}),ue=new Ze({props:{code:"cHl0aG9uJTIwLW0lMjBwaXAlMjBpbnN0YWxsJTIwaHVnZ2luZ2ZhY2VfaHViJTBBaHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4=",highlighted:`python -m pip install huggingface_hub
huggingface-cli login`}}),fe=new Ze({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login
notebook_login()`}}),ye=new Ze({props:{code:"aW1wb3J0JTIwdGltbSUwQSUwQSUyMyUyMEJ1aWxkJTIwb3IlMjBsb2FkJTIwYSUyMG1vZGVsJTJDJTIwZS5nLiUyMHRpbW0ncyUyMHByZXRyYWluZWQlMjByZXNuZXQxOCUwQW1vZGVsJTIwJTNEJTIwdGltbS5jcmVhdGVfbW9kZWwoJ3Jlc25ldDE4JyUyQyUyMHByZXRyYWluZWQlM0RUcnVlJTJDJTIwbnVtX2NsYXNzZXMlM0Q0KSUwQSUwQSUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUyMyUwQSUyMyUyMCU1QkZpbmUlMjB0dW5lJTIweW91ciUyMG1vZGVsLi4uJTVEJTBBJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTIzJTBBJTBBJTIzJTIwUHVzaCUyMGl0JTIwdG8lMjB0aGUlMjAlRjAlOUYlQTQlOTclMjBIdWIlMEF0aW1tLm1vZGVscy5odWIucHVzaF90b19oZl9odWIoJTBBJTIwJTIwJTIwJTIwbW9kZWwlMkMlMEElMjAlMjAlMjAlMjAncmVzbmV0MTgtcmFuZG9tLWNsYXNzaWZpZXInJTJDJTBBJTIwJTIwJTIwJTIwbW9kZWxfY29uZmlnJTNEJTdCJ2xhYmVscyclM0ElMjAlNUInYSclMkMlMjAnYiclMkMlMjAnYyclMkMlMjAnZCclNUQlN0QlMEEpJTBBJTBBJTIzJTIwTG9hZCUyMHlvdXIlMjBtb2RlbCUyMGZyb20lMjB0aGUlMjBIdWIlMEFtb2RlbF9yZWxvYWRlZCUyMCUzRCUyMHRpbW0uY3JlYXRlX21vZGVsKCUwQSUyMCUyMCUyMCUyMCdoZi1odWIlM0ElM0N5b3VyLXVzZXJuYW1lJTNFJTJGcmVzbmV0MTgtcmFuZG9tLWNsYXNzaWZpZXInJTJDJTBBJTIwJTIwJTIwJTIwcHJldHJhaW5lZCUzRFRydWUlMEEp",highlighted:`<span class="hljs-keyword">import</span> timm

<span class="hljs-comment"># Build or load a model, e.g. timm&#x27;s pretrained resnet18</span>
model = timm.create_model(<span class="hljs-string">&#x27;resnet18&#x27;</span>, pretrained=<span class="hljs-literal">True</span>, num_classes=<span class="hljs-number">4</span>)

<span class="hljs-comment">###########################</span>
<span class="hljs-comment"># [Fine tune your model...]</span>
<span class="hljs-comment">###########################</span>

<span class="hljs-comment"># Push it to the \u{1F917} Hub</span>
timm.models.hub.push_to_hf_hub(
    model,
    <span class="hljs-string">&#x27;resnet18-random-classifier&#x27;</span>,
    model_config={<span class="hljs-string">&#x27;labels&#x27;</span>: [<span class="hljs-string">&#x27;a&#x27;</span>, <span class="hljs-string">&#x27;b&#x27;</span>, <span class="hljs-string">&#x27;c&#x27;</span>, <span class="hljs-string">&#x27;d&#x27;</span>]}
)

<span class="hljs-comment"># Load your model from the Hub</span>
model_reloaded = timm.create_model(
    <span class="hljs-string">&#x27;hf-hub:&lt;your-username&gt;/resnet18-random-classifier&#x27;</span>,
    pretrained=<span class="hljs-literal">True</span>
)`}}),Me=new Xe({}),Je=new Ze({props:{code:"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",highlighted:`curl https://api-inference.huggingface.co/models/nateraw/timm-resnet50-beans \\
        -X POST \\
        --data-binary <span class="hljs-string">&#x27;@beans.jpeg&#x27;</span> \\
        -H <span class="hljs-string">&quot;Authorization: Bearer {<span class="hljs-variable">$HF_API_TOKEN</span>}&quot;</span>
<span class="hljs-comment"># [{&quot;label&quot;:&quot;angular_leaf_spot&quot;,&quot;score&quot;:0.9845947027206421},{&quot;label&quot;:&quot;bean_rust&quot;,&quot;score&quot;:0.01368315052241087},{&quot;label&quot;:&quot;healthy&quot;,&quot;score&quot;:0.001722085871733725}]</span>`}}),be=new Xe({}),{c(){E=o("meta"),_t=h(),Z=o("h1"),V=o("a"),De=o("span"),M(ee.$$.fragment),Il=h(),Pe=o("span"),jl=s("Using timm at Hugging Face"),Et=h(),B=o("p"),Le=o("code"),_l=s("timm"),El=s(", also known as "),te=o("a"),Zl=s("pytorch-image-models"),Bl=s(", is an open-source collection of state-of-the-art PyTorch image models, pretrained weights, and utility scripts for training, inference, and validation."),Zt=h(),f=o("p"),Wl=s("This documentation focuses on "),qe=o("code"),kl=s("timm"),Gl=s(" functionality in the Hugging Face Hub instead of the "),Oe=o("code"),Al=s("timm"),xl=s(" library itself. 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