import{S as bi,i as xi,s as Ci,e as s,k as p,w as x,t as r,M as $i,c as n,d as t,m,a,x as C,h as i,b as c,N as Ti,G as e,g as f,y as $,q as T,o as N,B as E,v as Ni,L as Is}from"../../chunks/vendor-hf-doc-builder.js";import{T as Yo}from"../../chunks/Tip-hf-doc-builder.js";import{D as ze}from"../../chunks/Docstring-hf-doc-builder.js";import{C as ks}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as st}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Es}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";import{P as Ei}from"../../chunks/PipelineTag-hf-doc-builder.js";function Ii(j){let d,w,v,g,y;return g=new ks({props:{code:`from transformers import ConvNextConfig, ConvNextModel

# Initializing a ConvNext convnext-tiny-224 style configuration
configuration = ConvNextConfig()

# Initializing a model (with random weights) from the convnext-tiny-224 style configuration
model = ConvNextModel(configuration)

# Accessing the model configuration
configuration = model.config`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ConvNextConfig, ConvNextModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a ConvNext convnext-tiny-224 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = ConvNextConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the convnext-tiny-224 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ConvNextModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`}}),{c(){d=s("p"),w=r("Example:"),v=p(),x(g.$$.fragment)},l(l){d=n(l,"P",{});var h=a(d);w=i(h,"Example:"),h.forEach(t),v=m(l),C(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,w),f(l,v,h),$(g,l,h),y=!0},p:Is,i(l){y||(T(g.$$.fragment,l),y=!0)},o(l){N(g.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(v),E(g,l)}}}function ki(j){let d,w,v,g,y;return{c(){d=s("p"),w=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),v=s("code"),g=r("Module"),y=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=n(l,"P",{});var h=a(d);w=i(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),v=n(h,"CODE",{});var P=a(v);g=i(P,"Module"),P.forEach(t),y=i(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(l,h){f(l,d,h),e(d,w),e(d,v),e(v,g),e(d,y)},d(l){l&&t(d)}}}function Pi(j){let d,w,v,g,y;return g=new ks({props:{code:`from transformers import AutoImageProcessor, ConvNextModel
import torch
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224")
model = ConvNextModel.from_pretrained("facebook/convnext-tiny-224")

inputs = image_processor(image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state
list(last_hidden_states.shape)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, ConvNextModel
<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">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/convnext-tiny-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ConvNextModel.from_pretrained(<span class="hljs-string">&quot;facebook/convnext-tiny-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<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>last_hidden_states = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">768</span>, <span class="hljs-number">7</span>, <span class="hljs-number">7</span>]`}}),{c(){d=s("p"),w=r("Example:"),v=p(),x(g.$$.fragment)},l(l){d=n(l,"P",{});var h=a(d);w=i(h,"Example:"),h.forEach(t),v=m(l),C(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,w),f(l,v,h),$(g,l,h),y=!0},p:Is,i(l){y||(T(g.$$.fragment,l),y=!0)},o(l){N(g.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(v),E(g,l)}}}function Fi(j){let d,w,v,g,y;return{c(){d=s("p"),w=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),v=s("code"),g=r("Module"),y=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=n(l,"P",{});var h=a(d);w=i(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),v=n(h,"CODE",{});var P=a(v);g=i(P,"Module"),P.forEach(t),y=i(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(l,h){f(l,d,h),e(d,w),e(d,v),e(v,g),e(d,y)},d(l){l&&t(d)}}}function ji(j){let d,w,v,g,y;return g=new ks({props:{code:`from transformers import AutoImageProcessor, ConvNextForImageClassification
import torch
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224")
model = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224")

inputs = image_processor(image, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, ConvNextForImageClassification
<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">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;huggingface/cats-image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/convnext-tiny-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ConvNextForImageClassification.from_pretrained(<span class="hljs-string">&quot;facebook/convnext-tiny-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># model predicts one of the 1000 ImageNet classes</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(model.config.id2label[predicted_label])
tabby, tabby cat`}}),{c(){d=s("p"),w=r("Example:"),v=p(),x(g.$$.fragment)},l(l){d=n(l,"P",{});var h=a(d);w=i(h,"Example:"),h.forEach(t),v=m(l),C(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,w),f(l,v,h),$(g,l,h),y=!0},p:Is,i(l){y||(T(g.$$.fragment,l),y=!0)},o(l){N(g.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(v),E(g,l)}}}function Ai(j){let d,w,v,g,y,l,h,P,De,be,z,Y,ee,b,Oe,X,Le,xe,L,Se,te,oe,Re,Ce,W,We,$e,U,ue,Ue,pe,F,D,Te,H,ge,He,J,Z,Qe,q,Q,se,me,Xe,ne,Ne,A,K,ae,re,Ke,V,O,Ve,M,Be,Ge;return{c(){d=s("p"),w=r("TensorFlow models and layers in "),v=s("code"),g=r("transformers"),y=r(" accept two formats as input:"),l=p(),h=s("ul"),P=s("li"),De=r("having all inputs as keyword arguments (like PyTorch models), or"),be=p(),z=s("li"),Y=r("having all inputs as a list, tuple or dict in the first positional argument."),ee=p(),b=s("p"),Oe=r(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),X=s("code"),Le=r("model.fit()"),xe=r(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=s("code"),Se=r("model.fit()"),te=r(` supports! If, however, you want to use the second
format outside of Keras methods like `),oe=s("code"),Re=r("fit()"),Ce=r(" and "),W=s("code"),We=r("predict()"),$e=r(`, such as when creating your own layers or models with
the Keras `),U=s("code"),ue=r("Functional"),Ue=r(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),pe=p(),F=s("ul"),D=s("li"),Te=r("a single Tensor with "),H=s("code"),ge=r("pixel_values"),He=r(" only and nothing else: "),J=s("code"),Z=r("model(pixel_values)"),Qe=p(),q=s("li"),Q=r(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),se=s("code"),me=r("model([pixel_values, attention_mask])"),Xe=r(" or "),ne=s("code"),Ne=r("model([pixel_values, attention_mask, token_type_ids])"),A=p(),K=s("li"),ae=r(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),re=s("code"),Ke=r('model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),V=p(),O=s("p"),Ve=r(`Note that when creating models and layers with
`),M=s("a"),Be=r("subclassing"),Ge=r(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(_){d=n(_,"P",{});var I=a(d);w=i(I,"TensorFlow models and layers in "),v=n(I,"CODE",{});var nt=a(v);g=i(nt,"transformers"),nt.forEach(t),y=i(I," accept two formats as input:"),I.forEach(t),l=m(_),h=n(_,"UL",{});var B=a(h);P=n(B,"LI",{});var at=a(P);De=i(at,"having all inputs as keyword arguments (like PyTorch models), or"),at.forEach(t),be=m(B),z=n(B,"LI",{});var rt=a(z);Y=i(rt,"having all inputs as a list, tuple or dict in the first positional argument."),rt.forEach(t),B.forEach(t),ee=m(_),b=n(_,"P",{});var k=a(b);Oe=i(k,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),X=n(k,"CODE",{});var ie=a(X);Le=i(ie,"model.fit()"),ie.forEach(t),xe=i(k,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=n(k,"CODE",{});var fe=a(L);Se=i(fe,"model.fit()"),fe.forEach(t),te=i(k,` supports! If, however, you want to use the second
format outside of Keras methods like `),oe=n(k,"CODE",{});var Ye=a(oe);Re=i(Ye,"fit()"),Ye.forEach(t),Ce=i(k," and "),W=n(k,"CODE",{});var ve=a(W);We=i(ve,"predict()"),ve.forEach(t),$e=i(k,`, such as when creating your own layers or models with
the Keras `),U=n(k,"CODE",{});var it=a(U);ue=i(it,"Functional"),it.forEach(t),Ue=i(k,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),k.forEach(t),pe=m(_),F=n(_,"UL",{});var R=a(F);D=n(R,"LI",{});var le=a(D);Te=i(le,"a single Tensor with "),H=n(le,"CODE",{});var et=a(H);ge=i(et,"pixel_values"),et.forEach(t),He=i(le," only and nothing else: "),J=n(le,"CODE",{});var Ee=a(J);Z=i(Ee,"model(pixel_values)"),Ee.forEach(t),le.forEach(t),Qe=m(R),q=n(R,"LI",{});var ce=a(q);Q=i(ce,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),se=n(ce,"CODE",{});var tt=a(se);me=i(tt,"model([pixel_values, attention_mask])"),tt.forEach(t),Xe=i(ce," or "),ne=n(ce,"CODE",{});var _e=a(ne);Ne=i(_e,"model([pixel_values, attention_mask, token_type_ids])"),_e.forEach(t),ce.forEach(t),A=m(R),K=n(R,"LI",{});var Ie=a(K);ae=i(Ie,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),re=n(Ie,"CODE",{});var he=a(re);Ke=i(he,'model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),he.forEach(t),Ie.forEach(t),R.forEach(t),V=m(_),O=n(_,"P",{});var S=a(O);Ve=i(S,`Note that when creating models and layers with
`),M=n(S,"A",{href:!0,rel:!0});var ke=a(M);Be=i(ke,"subclassing"),ke.forEach(t),Ge=i(S,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),S.forEach(t),this.h()},h(){c(M,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),c(M,"rel","nofollow")},m(_,I){f(_,d,I),e(d,w),e(d,v),e(v,g),e(d,y),f(_,l,I),f(_,h,I),e(h,P),e(P,De),e(h,be),e(h,z),e(z,Y),f(_,ee,I),f(_,b,I),e(b,Oe),e(b,X),e(X,Le),e(b,xe),e(b,L),e(L,Se),e(b,te),e(b,oe),e(oe,Re),e(b,Ce),e(b,W),e(W,We),e(b,$e),e(b,U),e(U,ue),e(b,Ue),f(_,pe,I),f(_,F,I),e(F,D),e(D,Te),e(D,H),e(H,ge),e(D,He),e(D,J),e(J,Z),e(F,Qe),e(F,q),e(q,Q),e(q,se),e(se,me),e(q,Xe),e(q,ne),e(ne,Ne),e(F,A),e(F,K),e(K,ae),e(K,re),e(re,Ke),f(_,V,I),f(_,O,I),e(O,Ve),e(O,M),e(M,Be),e(O,Ge)},d(_){_&&t(d),_&&t(l),_&&t(h),_&&t(ee),_&&t(b),_&&t(pe),_&&t(F),_&&t(V),_&&t(O)}}}function Mi(j){let d,w,v,g,y;return{c(){d=s("p"),w=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),v=s("code"),g=r("Module"),y=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=n(l,"P",{});var h=a(d);w=i(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),v=n(h,"CODE",{});var P=a(v);g=i(P,"Module"),P.forEach(t),y=i(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(l,h){f(l,d,h),e(d,w),e(d,v),e(v,g),e(d,y)},d(l){l&&t(d)}}}function qi(j){let d,w,v,g,y;return g=new ks({props:{code:`from transformers import AutoImageProcessor, TFConvNextModel
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224")
model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")

inputs = image_processor(images=image, return_tensors="tf")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, TFConvNextModel
<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;http://images.cocodataset.org/val2017/000000039769.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>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/convnext-tiny-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFConvNextModel.from_pretrained(<span class="hljs-string">&quot;facebook/convnext-tiny-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`}}),{c(){d=s("p"),w=r("Examples:"),v=p(),x(g.$$.fragment)},l(l){d=n(l,"P",{});var h=a(d);w=i(h,"Examples:"),h.forEach(t),v=m(l),C(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,w),f(l,v,h),$(g,l,h),y=!0},p:Is,i(l){y||(T(g.$$.fragment,l),y=!0)},o(l){N(g.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(v),E(g,l)}}}function zi(j){let d,w,v,g,y,l,h,P,De,be,z,Y,ee,b,Oe,X,Le,xe,L,Se,te,oe,Re,Ce,W,We,$e,U,ue,Ue,pe,F,D,Te,H,ge,He,J,Z,Qe,q,Q,se,me,Xe,ne,Ne,A,K,ae,re,Ke,V,O,Ve,M,Be,Ge;return{c(){d=s("p"),w=r("TensorFlow models and layers in "),v=s("code"),g=r("transformers"),y=r(" accept two formats as input:"),l=p(),h=s("ul"),P=s("li"),De=r("having all inputs as keyword arguments (like PyTorch models), or"),be=p(),z=s("li"),Y=r("having all inputs as a list, tuple or dict in the first positional argument."),ee=p(),b=s("p"),Oe=r(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),X=s("code"),Le=r("model.fit()"),xe=r(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=s("code"),Se=r("model.fit()"),te=r(` supports! If, however, you want to use the second
format outside of Keras methods like `),oe=s("code"),Re=r("fit()"),Ce=r(" and "),W=s("code"),We=r("predict()"),$e=r(`, such as when creating your own layers or models with
the Keras `),U=s("code"),ue=r("Functional"),Ue=r(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),pe=p(),F=s("ul"),D=s("li"),Te=r("a single Tensor with "),H=s("code"),ge=r("pixel_values"),He=r(" only and nothing else: "),J=s("code"),Z=r("model(pixel_values)"),Qe=p(),q=s("li"),Q=r(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),se=s("code"),me=r("model([pixel_values, attention_mask])"),Xe=r(" or "),ne=s("code"),Ne=r("model([pixel_values, attention_mask, token_type_ids])"),A=p(),K=s("li"),ae=r(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),re=s("code"),Ke=r('model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),V=p(),O=s("p"),Ve=r(`Note that when creating models and layers with
`),M=s("a"),Be=r("subclassing"),Ge=r(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(_){d=n(_,"P",{});var I=a(d);w=i(I,"TensorFlow models and layers in "),v=n(I,"CODE",{});var nt=a(v);g=i(nt,"transformers"),nt.forEach(t),y=i(I," accept two formats as input:"),I.forEach(t),l=m(_),h=n(_,"UL",{});var B=a(h);P=n(B,"LI",{});var at=a(P);De=i(at,"having all inputs as keyword arguments (like PyTorch models), or"),at.forEach(t),be=m(B),z=n(B,"LI",{});var rt=a(z);Y=i(rt,"having all inputs as a list, tuple or dict in the first positional argument."),rt.forEach(t),B.forEach(t),ee=m(_),b=n(_,"P",{});var k=a(b);Oe=i(k,`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like `),X=n(k,"CODE",{});var ie=a(X);Le=i(ie,"model.fit()"),ie.forEach(t),xe=i(k,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),L=n(k,"CODE",{});var fe=a(L);Se=i(fe,"model.fit()"),fe.forEach(t),te=i(k,` supports! If, however, you want to use the second
format outside of Keras methods like `),oe=n(k,"CODE",{});var Ye=a(oe);Re=i(Ye,"fit()"),Ye.forEach(t),Ce=i(k," and "),W=n(k,"CODE",{});var ve=a(W);We=i(ve,"predict()"),ve.forEach(t),$e=i(k,`, such as when creating your own layers or models with
the Keras `),U=n(k,"CODE",{});var it=a(U);ue=i(it,"Functional"),it.forEach(t),Ue=i(k,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),k.forEach(t),pe=m(_),F=n(_,"UL",{});var R=a(F);D=n(R,"LI",{});var le=a(D);Te=i(le,"a single Tensor with "),H=n(le,"CODE",{});var et=a(H);ge=i(et,"pixel_values"),et.forEach(t),He=i(le," only and nothing else: "),J=n(le,"CODE",{});var Ee=a(J);Z=i(Ee,"model(pixel_values)"),Ee.forEach(t),le.forEach(t),Qe=m(R),q=n(R,"LI",{});var ce=a(q);Q=i(ce,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),se=n(ce,"CODE",{});var tt=a(se);me=i(tt,"model([pixel_values, attention_mask])"),tt.forEach(t),Xe=i(ce," or "),ne=n(ce,"CODE",{});var _e=a(ne);Ne=i(_e,"model([pixel_values, attention_mask, token_type_ids])"),_e.forEach(t),ce.forEach(t),A=m(R),K=n(R,"LI",{});var Ie=a(K);ae=i(Ie,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),re=n(Ie,"CODE",{});var he=a(re);Ke=i(he,'model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})'),he.forEach(t),Ie.forEach(t),R.forEach(t),V=m(_),O=n(_,"P",{});var S=a(O);Ve=i(S,`Note that when creating models and layers with
`),M=n(S,"A",{href:!0,rel:!0});var ke=a(M);Be=i(ke,"subclassing"),ke.forEach(t),Ge=i(S,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),S.forEach(t),this.h()},h(){c(M,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),c(M,"rel","nofollow")},m(_,I){f(_,d,I),e(d,w),e(d,v),e(v,g),e(d,y),f(_,l,I),f(_,h,I),e(h,P),e(P,De),e(h,be),e(h,z),e(z,Y),f(_,ee,I),f(_,b,I),e(b,Oe),e(b,X),e(X,Le),e(b,xe),e(b,L),e(L,Se),e(b,te),e(b,oe),e(oe,Re),e(b,Ce),e(b,W),e(W,We),e(b,$e),e(b,U),e(U,ue),e(b,Ue),f(_,pe,I),f(_,F,I),e(F,D),e(D,Te),e(D,H),e(H,ge),e(D,He),e(D,J),e(J,Z),e(F,Qe),e(F,q),e(q,Q),e(q,se),e(se,me),e(q,Xe),e(q,ne),e(ne,Ne),e(F,A),e(F,K),e(K,ae),e(K,re),e(re,Ke),f(_,V,I),f(_,O,I),e(O,Ve),e(O,M),e(M,Be),e(O,Ge)},d(_){_&&t(d),_&&t(l),_&&t(h),_&&t(ee),_&&t(b),_&&t(pe),_&&t(F),_&&t(V),_&&t(O)}}}function Di(j){let d,w,v,g,y;return{c(){d=s("p"),w=r("Although the recipe for forward pass needs to be defined within this function, one should call the "),v=s("code"),g=r("Module"),y=r(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=n(l,"P",{});var h=a(d);w=i(h,"Although the recipe for forward pass needs to be defined within this function, one should call the "),v=n(h,"CODE",{});var P=a(v);g=i(P,"Module"),P.forEach(t),y=i(h,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),h.forEach(t)},m(l,h){f(l,d,h),e(d,w),e(d,v),e(v,g),e(d,y)},d(l){l&&t(d)}}}function Oi(j){let d,w,v,g,y;return g=new ks({props:{code:`from transformers import AutoImageProcessor, TFConvNextForImageClassification
import tensorflow as tf
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224")
model = TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224")

inputs = image_processor(images=image, return_tensors="tf")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = tf.math.argmax(logits, axis=-1)[0]
print("Predicted class:", model.config.id2label[int(predicted_class_idx)])`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, TFConvNextForImageClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<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;http://images.cocodataset.org/val2017/000000039769.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>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/convnext-tiny-224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFConvNextForImageClassification.from_pretrained(<span class="hljs-string">&quot;facebook/convnext-tiny-224&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># model predicts one of the 1000 ImageNet classes</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_idx = tf.math.argmax(logits, axis=-<span class="hljs-number">1</span>)[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Predicted class:&quot;</span>, model.config.id2label[<span class="hljs-built_in">int</span>(predicted_class_idx)])`}}),{c(){d=s("p"),w=r("Examples:"),v=p(),x(g.$$.fragment)},l(l){d=n(l,"P",{});var h=a(d);w=i(h,"Examples:"),h.forEach(t),v=m(l),C(g.$$.fragment,l)},m(l,h){f(l,d,h),e(d,w),f(l,v,h),$(g,l,h),y=!0},p:Is,i(l){y||(T(g.$$.fragment,l),y=!0)},o(l){N(g.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(v),E(g,l)}}}function Li(j){let d,w,v,g,y,l,h,P,De,be,z,Y,ee,b,Oe,X,Le,xe,L,Se,te,oe,Re,Ce,W,We,$e,U,ue,Ue,pe,F,D,Te,H,ge,He,J,Z,Qe,q,Q,se,me,Xe,ne,Ne,A,K,ae,re,Ke,V,O,Ve,M,Be,Ge,_,I,nt,B,at,rt,k,ie,fe,Ye,ve,it,R,le,et,Ee,ce,tt,_e,Ie,he,S,ke,dn,pn,Kt,mn,fn,Vt,hn,un,gn,Po,vn,Fo,_n,Ps,jo,yn,Fs,lt,bt,Jo,Bt,wn,Zo,bn,js,ye,Gt,xn,ct,Cn,Ao,$n,Tn,Yt,Nn,En,In,dt,kn,Mo,Pn,Fn,qo,jn,An,Mn,xt,As,pt,Ct,Qo,Jt,qn,es,zn,Ms,Zt,Qt,qs,mt,$t,ts,eo,Dn,os,On,zs,Je,to,Ln,ss,Sn,Rn,Tt,oo,Wn,ns,Un,Ds,ft,Nt,as,so,Hn,rs,Xn,Os,Ze,no,Kn,ao,Vn,ro,Bn,Gn,Yn,Pe,io,Jn,ht,Zn,zo,Qn,ea,is,ta,oa,sa,Et,na,It,Ls,ut,kt,ls,lo,aa,cs,ra,Ss,we,co,ia,ds,la,ca,po,da,mo,pa,ma,fa,Fe,fo,ha,gt,ua,Do,ga,va,ps,_a,ya,wa,Pt,ba,Ft,Rs,vt,jt,ms,ho,xa,fs,Ca,Ws,de,uo,$a,go,Ta,Oo,Na,Ea,Ia,vo,ka,_o,Pa,Fa,ja,At,Aa,je,yo,Ma,_t,qa,Lo,za,Da,hs,Oa,La,Sa,Mt,Ra,qt,Us,yt,zt,us,wo,Wa,gs,Ua,Hs,G,bo,Ha,vs,Xa,Ka,xo,Va,So,Ba,Ga,Ya,Co,Ja,$o,Za,Qa,er,Dt,tr,Ae,To,or,wt,sr,Ro,nr,ar,_s,rr,ir,lr,Ot,cr,Lt,Xs;return l=new st({}),b=new st({}),ve=new st({}),_e=new Ei({props:{pipeline:"image-classification"}}),Bt=new st({}),Gt=new ze({props:{name:"class transformers.ConvNextConfig",anchor:"transformers.ConvNextConfig",parameters:[{name:"num_channels",val:" = 3"},{name:"patch_size",val:" = 4"},{name:"num_stages",val:" = 4"},{name:"hidden_sizes",val:" = None"},{name:"depths",val:" = None"},{name:"hidden_act",val:" = 'gelu'"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"layer_scale_init_value",val:" = 1e-06"},{name:"drop_path_rate",val:" = 0.0"},{name:"image_size",val:" = 224"},{name:"out_features",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ConvNextConfig.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The number of input channels.`,name:"num_channels"},{anchor:"transformers.ConvNextConfig.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, optional, defaults to 4) &#x2014;
Patch size to use in the patch embedding layer.`,name:"patch_size"},{anchor:"transformers.ConvNextConfig.num_stages",description:`<strong>num_stages</strong> (<code>int</code>, optional, defaults to 4) &#x2014;
The number of stages in the model.`,name:"num_stages"},{anchor:"transformers.ConvNextConfig.hidden_sizes",description:`<strong>hidden_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to [96, 192, 384, 768]) &#x2014;
Dimensionality (hidden size) at each stage.`,name:"hidden_sizes"},{anchor:"transformers.ConvNextConfig.depths",description:`<strong>depths</strong> (<code>List[int]</code>, <em>optional</em>, defaults to [3, 3, 9, 3]) &#x2014;
Depth (number of blocks) for each stage.`,name:"depths"},{anchor:"transformers.ConvNextConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in each block. If string, <code>&quot;gelu&quot;</code>, <code>&quot;relu&quot;</code>,
<code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.ConvNextConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.ConvNextConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.ConvNextConfig.layer_scale_init_value",description:`<strong>layer_scale_init_value</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-6) &#x2014;
The initial value for the layer scale.`,name:"layer_scale_init_value"},{anchor:"transformers.ConvNextConfig.drop_path_rate",description:`<strong>drop_path_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The drop rate for stochastic depth.`,name:"drop_path_rate"},{anchor:"transformers.ConvNextConfig.out_features",description:`<strong>out_features</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
If used as backbone, list of features to output. Can be any of <code>&quot;stem&quot;</code>, <code>&quot;stage1&quot;</code>, <code>&quot;stage2&quot;</code>, etc.
(depending on how many stages the model has). Will default to the last stage if unset.`,name:"out_features"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/convnext/configuration_convnext.py#L35"}}),xt=new Es({props:{anchor:"transformers.ConvNextConfig.example",$$slots:{default:[Ii]},$$scope:{ctx:j}}}),Jt=new st({}),Qt=new ze({props:{name:"class transformers.ConvNextFeatureExtractor",anchor:"transformers.ConvNextFeatureExtractor",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/convnext/feature_extraction_convnext.py#L26"}}),eo=new st({}),to=new ze({props:{name:"class transformers.ConvNextImageProcessor",anchor:"transformers.ConvNextImageProcessor",parameters:[{name:"do_resize",val:": bool = True"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"crop_pct",val:": float = None"},{name:"resample",val:": Resampling = <Resampling.BILINEAR: 2>"},{name:"do_rescale",val:": bool = True"},{name:"rescale_factor",val:": typing.Union[int, float] = 0.00392156862745098"},{name:"do_normalize",val:": bool = True"},{name:"image_mean",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"image_std",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ConvNextImageProcessor.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Controls whether to resize the image&#x2019;s (height, width) dimensions to the specified <code>size</code>. Can be overriden
by <code>do_resize</code> in the <code>preprocess</code> method.`,name:"do_resize"},{anchor:"transformers.ConvNextImageProcessor.size",description:`<strong>size</strong> (<code>Dict[str, int]</code> <em>optional</em>, defaults to <code>{&quot;shortest_edge&quot; -- 384}</code>):
Resolution of the output image after <code>resize</code> is applied. If <code>size[&quot;shortest_edge&quot;]</code> &gt;= 384, the image is
resized to <code>(size[&quot;shortest_edge&quot;], size[&quot;shortest_edge&quot;])</code>. Otherwise, the smaller edge of the image will
be matched to <code>int(size[&quot;shortest_edge&quot;]/crop_pct)</code>, after which the image is cropped to
<code>(size[&quot;shortest_edge&quot;], size[&quot;shortest_edge&quot;])</code>. Only has an effect if <code>do_resize</code> is set to <code>True</code>. Can
be overriden by <code>size</code> in the <code>preprocess</code> method.`,name:"size"},{anchor:"transformers.ConvNextImageProcessor.crop_pct",description:`<strong>crop_pct</strong> (<code>float</code> <em>optional</em>, defaults to 224 / 256) &#x2014;
Percentage of the image to crop. Only has an effect if <code>do_resize</code> is <code>True</code> and size &lt; 384. Can be
overriden by <code>crop_pct</code> in the <code>preprocess</code> method.`,name:"crop_pct"},{anchor:"transformers.ConvNextImageProcessor.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>PILImageResampling.BILINEAR</code>) &#x2014;
Resampling filter to use if resizing the image. Can be overriden by <code>resample</code> in the <code>preprocess</code> method.`,name:"resample"},{anchor:"transformers.ConvNextImageProcessor.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overriden by <code>do_rescale</code> in
the <code>preprocess</code> method.`,name:"do_rescale"},{anchor:"transformers.ConvNextImageProcessor.rescale_factor",description:`<strong>rescale_factor</strong> (<code>int</code> or <code>float</code>, <em>optional</em>, defaults to <code>1/255</code>) &#x2014;
Scale factor to use if rescaling the image. Can be overriden by <code>rescale_factor</code> in the <code>preprocess</code>
method.`,name:"rescale_factor"},{anchor:"transformers.ConvNextImageProcessor.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to normalize the image. Can be overridden by the <code>do_normalize</code> parameter in the <code>preprocess</code>
method.`,name:"do_normalize"},{anchor:"transformers.ConvNextImageProcessor.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>IMAGENET_STANDARD_MEAN</code>) &#x2014;
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the <code>image_mean</code> parameter in the <code>preprocess</code> method.`,name:"image_mean"},{anchor:"transformers.ConvNextImageProcessor.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>IMAGENET_STANDARD_STD</code>) &#x2014;
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the <code>image_std</code> parameter in the <code>preprocess</code> method.`,name:"image_std"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/convnext/image_processing_convnext.py#L50"}}),oo=new ze({props:{name:"preprocess",anchor:"transformers.ConvNextImageProcessor.preprocess",parameters:[{name:"images",val:": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]"},{name:"do_resize",val:": bool = None"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"crop_pct",val:": float = None"},{name:"resample",val:": Resampling = None"},{name:"do_rescale",val:": bool = None"},{name:"rescale_factor",val:": float = None"},{name:"do_normalize",val:": bool = None"},{name:"image_mean",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"image_std",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"return_tensors",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"},{name:"data_format",val:": ChannelDimension = <ChannelDimension.FIRST: 'channels_first'>"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ConvNextImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
Image to preprocess.`,name:"images"},{anchor:"transformers.ConvNextImageProcessor.preprocess.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_resize</code>) &#x2014;
Whether to resize the image.`,name:"do_resize"},{anchor:"transformers.ConvNextImageProcessor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.size</code>) &#x2014;
Size of the output image after <code>resize</code> has been applied. If <code>size[&quot;shortest_edge&quot;]</code> &gt;= 384, the image
is resized to <code>(size[&quot;shortest_edge&quot;], size[&quot;shortest_edge&quot;])</code>. Otherwise, the smaller edge of the
image will be matched to <code>int(size[&quot;shortest_edge&quot;]/ crop_pct)</code>, after which the image is cropped to
<code>(size[&quot;shortest_edge&quot;], size[&quot;shortest_edge&quot;])</code>. Only has an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"size"},{anchor:"transformers.ConvNextImageProcessor.preprocess.crop_pct",description:`<strong>crop_pct</strong> (<code>float</code>, <em>optional</em>, defaults to <code>self.crop_pct</code>) &#x2014;
Percentage of the image to crop if size &lt; 384.`,name:"crop_pct"},{anchor:"transformers.ConvNextImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>int</code>, <em>optional</em>, defaults to <code>self.resample</code>) &#x2014;
Resampling filter to use if resizing the image. This can be one of <code>PILImageResampling</code>, filters. Only
has an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"resample"},{anchor:"transformers.ConvNextImageProcessor.preprocess.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_rescale</code>) &#x2014;
Whether to rescale the image values between [0 - 1].`,name:"do_rescale"},{anchor:"transformers.ConvNextImageProcessor.preprocess.rescale_factor",description:`<strong>rescale_factor</strong> (<code>float</code>, <em>optional</em>, defaults to <code>self.rescale_factor</code>) &#x2014;
Rescale factor to rescale the image by if <code>do_rescale</code> is set to <code>True</code>.`,name:"rescale_factor"},{anchor:"transformers.ConvNextImageProcessor.preprocess.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_normalize</code>) &#x2014;
Whether to normalize the image.`,name:"do_normalize"},{anchor:"transformers.ConvNextImageProcessor.preprocess.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_mean</code>) &#x2014;
Image mean.`,name:"image_mean"},{anchor:"transformers.ConvNextImageProcessor.preprocess.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>self.image_std</code>) &#x2014;
Image standard deviation.`,name:"image_std"},{anchor:"transformers.ConvNextImageProcessor.preprocess.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <code>TensorType</code>, <em>optional</em>) &#x2014;
The type of tensors to return. Can be one of:<ul>
<li>Unset: Return a list of <code>np.ndarray</code>.</li>
<li><code>TensorType.TENSORFLOW</code> or <code>&apos;tf&apos;</code>: Return a batch of type <code>tf.Tensor</code>.</li>
<li><code>TensorType.PYTORCH</code> or <code>&apos;pt&apos;</code>: Return a batch of type <code>torch.Tensor</code>.</li>
<li><code>TensorType.NUMPY</code> or <code>&apos;np&apos;</code>: Return a batch of type <code>np.ndarray</code>.</li>
<li><code>TensorType.JAX</code> or <code>&apos;jax&apos;</code>: Return a batch of type <code>jax.numpy.ndarray</code>.</li>
</ul>`,name:"return_tensors"},{anchor:"transformers.ConvNextImageProcessor.preprocess.data_format",description:`<strong>data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>, defaults to <code>ChannelDimension.FIRST</code>) &#x2014;
The channel dimension format for the output image. Can be one of:<ul>
<li><code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
</ul>`,name:"data_format"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/convnext/image_processing_convnext.py#L204"}}),so=new st({}),no=new ze({props:{name:"class transformers.ConvNextModel",anchor:"transformers.ConvNextModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.ConvNextModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/convnext/modeling_convnext.py#L332"}}),io=new ze({props:{name:"forward",anchor:"transformers.ConvNextModel.forward",parameters:[{name:"pixel_values",val:": FloatTensor = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.ConvNextModel.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor.__call__">ConvNextImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.ConvNextModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.ConvNextModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/convnext/modeling_convnext.py#L346",returnDescription:`
<p>A <code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.ConvNextConfig"
>ConvNextConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state after a pooling operation on the spatial dimensions.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, num_channels, height, width)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Et=new Yo({props:{$$slots:{default:[ki]},$$scope:{ctx:j}}}),It=new Es({props:{anchor:"transformers.ConvNextModel.forward.example",$$slots:{default:[Pi]},$$scope:{ctx:j}}}),lo=new st({}),co=new ze({props:{name:"class transformers.ConvNextForImageClassification",anchor:"transformers.ConvNextForImageClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.ConvNextForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor.__call__">ConvNextImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.ConvNextForImageClassification.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.ConvNextForImageClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.ConvNextForImageClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the image classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/convnext/modeling_convnext.py#L413",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.ConvNextConfig"
>ConvNextConfig</a>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</li>
<li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</li>
<li><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape <code>(batch_size, num_channels, height, width)</code>. Hidden-states (also
called feature maps) of the model at the output of each stage.</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
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`}}),Pt=new Yo({props:{$$slots:{default:[Fi]},$$scope:{ctx:j}}}),Ft=new Es({props:{anchor:"transformers.ConvNextForImageClassification.forward.example",$$slots:{default:[ji]},$$scope:{ctx:j}}}),ho=new st({}),uo=new ze({props:{name:"class transformers.TFConvNextModel",anchor:"transformers.TFConvNextModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFConvNextModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/convnext/modeling_tf_convnext.py#L451"}}),At=new Yo({props:{$$slots:{default:[Ai]},$$scope:{ctx:j}}}),yo=new ze({props:{name:"call",anchor:"transformers.TFConvNextModel.call",parameters:[{name:"pixel_values",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"training",val:": bool = False"}],parametersDescription:[{anchor:"transformers.TFConvNextModel.call.pixel_values",description:`<strong>pixel_values</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor.__call__">ConvNextImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFConvNextModel.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFConvNextModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/convnext/modeling_tf_convnext.py#L456",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.ConvNextConfig"
>ConvNextConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
<p>This output is usually <em>not</em> a good summary of the semantic content of the input, you\u2019re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Mt=new Yo({props:{$$slots:{default:[Mi]},$$scope:{ctx:j}}}),qt=new Es({props:{anchor:"transformers.TFConvNextModel.call.example",$$slots:{default:[qi]},$$scope:{ctx:j}}}),wo=new st({}),bo=new ze({props:{name:"class transformers.TFConvNextForImageClassification",anchor:"transformers.TFConvNextForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFConvNextForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/v4.27.1/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/convnext/modeling_tf_convnext.py#L526"}}),Dt=new Yo({props:{$$slots:{default:[zi]},$$scope:{ctx:j}}}),To=new ze({props:{name:"call",anchor:"transformers.TFConvNextForImageClassification.call",parameters:[{name:"pixel_values",val:": typing.Union[typing.List[tensorflow.python.framework.ops.Tensor], typing.List[numpy.ndarray], typing.List[keras.engine.keras_tensor.KerasTensor], typing.Dict[str, tensorflow.python.framework.ops.Tensor], typing.Dict[str, numpy.ndarray], typing.Dict[str, keras.engine.keras_tensor.KerasTensor], tensorflow.python.framework.ops.Tensor, numpy.ndarray, keras.engine.keras_tensor.KerasTensor, NoneType] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Union[numpy.ndarray, tensorflow.python.framework.ops.Tensor, NoneType] = None"},{name:"training",val:": typing.Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFConvNextForImageClassification.call.pixel_values",description:`<strong>pixel_values</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.27.1/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.27.1/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor.__call__">ConvNextImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.TFConvNextForImageClassification.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFConvNextForImageClassification.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFConvNextForImageClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> or <code>np.ndarray</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the image classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
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<p>A <a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/v4.27.1/en/model_doc/convnext#transformers.ConvNextConfig"
>ConvNextConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, )</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.27.1/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Ot=new Yo({props:{$$slots:{default:[Di]},$$scope:{ctx:j}}}),Lt=new Es({props:{anchor:"transformers.TFConvNextForImageClassification.call.example",$$slots:{default:[Oi]},$$scope:{ctx:j}}}),{c(){d=s("meta"),w=p(),v=s("h1"),g=s("a"),y=s("span"),x(l.$$.fragment),h=p(),P=s("span"),De=r("ConvNeXT"),be=p(),z=s("h2"),Y=s("a"),ee=s("span"),x(b.$$.fragment),Oe=p(),X=s("span"),Le=r("Overview"),xe=p(),L=s("p"),Se=r("The ConvNeXT model was proposed in "),te=s("a"),oe=r("A ConvNet for the 2020s"),Re=r(` by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
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(e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide
variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive
biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually \u201Cmodernize\u201D a standard ResNet toward the design
of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models
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