import{S as oi,i as ti,s as ri,e as r,k as m,w as v,t as n,M as ai,c as a,d as t,m as p,a as s,x as y,h as i,b as l,G as e,g as f,y as w,q as N,o as M,B as $,v as si,L as Fs}from"../../chunks/vendor-hf-doc-builder.js";import{T as ei}from"../../chunks/Tip-hf-doc-builder.js";import{D as ee}from"../../chunks/Docstring-hf-doc-builder.js";import{C as ks}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Me}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Cs}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";import{P as ni}from"../../chunks/PipelineTag-hf-doc-builder.js";function ii(P){let h,T,_,g,b;return g=new ks({props:{code:`from transformers import MobileNetV1Config, MobileNetV1Model

# Initializing a "mobilenet_v1_1.0_224" style configuration
configuration = MobileNetV1Config()

# Initializing a model from the "mobilenet_v1_1.0_224" style configuration
model = MobileNetV1Model(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> MobileNetV1Config, MobileNetV1Model

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a &quot;mobilenet_v1_1.0_224&quot; style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = MobileNetV1Config()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the &quot;mobilenet_v1_1.0_224&quot; style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileNetV1Model(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(){h=r("p"),T=n("Example:"),_=m(),v(g.$$.fragment)},l(c){h=a(c,"P",{});var u=s(h);T=i(u,"Example:"),u.forEach(t),_=p(c),y(g.$$.fragment,c)},m(c,u){f(c,h,u),e(h,T),f(c,_,u),w(g,c,u),b=!0},p:Fs,i(c){b||(N(g.$$.fragment,c),b=!0)},o(c){M(g.$$.fragment,c),b=!1},d(c){c&&t(h),c&&t(_),$(g,c)}}}function li(P){let h,T,_,g,b;return{c(){h=r("p"),T=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r("code"),g=n("Module"),b=n(`
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(c){h=a(c,"P",{});var u=s(h);T=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(u,"CODE",{});var k=s(_);g=i(k,"Module"),k.forEach(t),b=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(c,u){f(c,h,u),e(h,T),e(h,_),e(_,g),e(h,b)},d(c){c&&t(h)}}}function ci(P){let h,T,_,g,b;return g=new ks({props:{code:`from transformers import AutoImageProcessor, MobileNetV1Model
import torch
from datasets import load_dataset

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

image_processor = AutoImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224")
model = MobileNetV1Model.from_pretrained("google/mobilenet_v1_1.0_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, MobileNetV1Model
<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;google/mobilenet_v1_1.0_224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileNetV1Model.from_pretrained(<span class="hljs-string">&quot;google/mobilenet_v1_1.0_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">1024</span>, <span class="hljs-number">7</span>, <span class="hljs-number">7</span>]`}}),{c(){h=r("p"),T=n("Example:"),_=m(),v(g.$$.fragment)},l(c){h=a(c,"P",{});var u=s(h);T=i(u,"Example:"),u.forEach(t),_=p(c),y(g.$$.fragment,c)},m(c,u){f(c,h,u),e(h,T),f(c,_,u),w(g,c,u),b=!0},p:Fs,i(c){b||(N(g.$$.fragment,c),b=!0)},o(c){M(g.$$.fragment,c),b=!1},d(c){c&&t(h),c&&t(_),$(g,c)}}}function di(P){let h,T,_,g,b;return{c(){h=r("p"),T=n("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r("code"),g=n("Module"),b=n(`
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(c){h=a(c,"P",{});var u=s(h);T=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=a(u,"CODE",{});var k=s(_);g=i(k,"Module"),k.forEach(t),b=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(t)},m(c,u){f(c,h,u),e(h,T),e(h,_),e(_,g),e(h,b)},d(c){c&&t(h)}}}function mi(P){let h,T,_,g,b;return g=new ks({props:{code:`from transformers import AutoImageProcessor, MobileNetV1ForImageClassification
import torch
from datasets import load_dataset

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

image_processor = AutoImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224")
model = MobileNetV1ForImageClassification.from_pretrained("google/mobilenet_v1_1.0_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, MobileNetV1ForImageClassification
<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;google/mobilenet_v1_1.0_224&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileNetV1ForImageClassification.from_pretrained(<span class="hljs-string">&quot;google/mobilenet_v1_1.0_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(){h=r("p"),T=n("Example:"),_=m(),v(g.$$.fragment)},l(c){h=a(c,"P",{});var u=s(h);T=i(u,"Example:"),u.forEach(t),_=p(c),y(g.$$.fragment,c)},m(c,u){f(c,h,u),e(h,T),f(c,_,u),w(g,c,u),b=!0},p:Fs,i(c){b||(N(g.$$.fragment,c),b=!0)},o(c){M(g.$$.fragment,c),b=!1},d(c){c&&t(h),c&&t(_),$(g,c)}}}function pi(P){let h,T,_,g,b,c,u,k,mr,Nt,q,oe,Co,$e,pr,Fo,fr,Mt,te,hr,Te,gr,ur,$t,no,_r,Tt,io,ko,br,It,lo,vr,Et,I,Ao,E,yr,re,wr,jo,Nr,Mr,Lo,$r,Tr,Do,Ir,Er,Ro,Vr,xr,So,Pr,zr,Cr,qo,Oo,Fr,kr,Wo,Ie,Ar,co,jr,Lr,Dr,Uo,Ee,Rr,Ve,Sr,qr,Or,Ho,O,Wr,mo,Ur,Hr,Bo,Br,Gr,Vt,po,Yr,xt,z,Go,xe,Jr,fo,Kr,Qr,Xr,Yo,Pe,Zr,Jo,ea,oa,ta,Ko,Qo,ra,aa,Xo,ze,sa,Zo,na,ia,Pt,L,la,Ce,ca,da,Fe,ma,pa,zt,W,ae,et,ke,fa,ot,ha,Ct,ho,ga,Ft,Ae,kt,se,D,go,ua,_a,je,ba,va,Le,ya,wa,Na,uo,Ma,_o,$a,At,bo,Ta,jt,U,ne,tt,De,Ia,rt,Ea,Lt,V,Re,Va,H,xa,vo,Pa,za,Se,Ca,Fa,ka,B,Aa,yo,ja,La,wo,Da,Ra,Sa,ie,Dt,G,le,at,qe,qa,st,Oa,Rt,Y,Oe,Wa,ce,We,Ua,nt,Ha,St,J,de,it,Ue,Ba,lt,Ga,qt,A,He,Ya,ct,Ja,Ka,me,Be,Qa,dt,Xa,Ot,K,pe,mt,Ge,Za,pt,es,Wt,j,Ye,os,Je,ts,Ke,rs,as,ss,C,Qe,ns,Q,is,No,ls,cs,ft,ds,ms,ps,fe,fs,he,Ut,X,ge,ht,Xe,hs,gt,gs,Ht,x,Ze,us,ut,_s,bs,eo,vs,oo,ys,ws,Ns,F,to,Ms,Z,$s,Mo,Ts,Is,_t,Es,Vs,xs,ue,Ps,_e,Bt;return c=new Me({}),$e=new Me({}),ke=new Me({}),Ae=new ni({props:{pipeline:"image-classification"}}),De=new Me({}),Re=new ee({props:{name:"class transformers.MobileNetV1Config",anchor:"transformers.MobileNetV1Config",parameters:[{name:"num_channels",val:" = 3"},{name:"image_size",val:" = 224"},{name:"depth_multiplier",val:" = 1.0"},{name:"min_depth",val:" = 8"},{name:"hidden_act",val:" = 'relu6'"},{name:"tf_padding",val:" = True"},{name:"classifier_dropout_prob",val:" = 0.999"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 0.001"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MobileNetV1Config.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.MobileNetV1Config.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 224) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.MobileNetV1Config.depth_multiplier",description:`<strong>depth_multiplier</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Shrinks or expands the number of channels in each layer. Default is 1.0, which starts the network with 32
channels. This is sometimes also called &#x201C;alpha&#x201D; or &#x201C;width multiplier&#x201D;.`,name:"depth_multiplier"},{anchor:"transformers.MobileNetV1Config.min_depth",description:`<strong>min_depth</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
All layers will have at least this many channels.`,name:"min_depth"},{anchor:"transformers.MobileNetV1Config.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;relu6&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the Transformer encoder and convolution layers.`,name:"hidden_act"},{anchor:"transformers.MobileNetV1Config.tf_padding",description:`<strong>tf_padding</strong> (<code>bool</code>, <code>optional</code>, defaults to <code>True</code>) &#x2014;
Whether to use TensorFlow padding rules on the convolution layers.`,name:"tf_padding"},{anchor:"transformers.MobileNetV1Config.classifier_dropout_prob",description:`<strong>classifier_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.999) &#x2014;
The dropout ratio for attached classifiers.`,name:"classifier_dropout_prob"},{anchor:"transformers.MobileNetV1Config.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.MobileNetV1Config.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 0.001) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py#L36"}}),ie=new Cs({props:{anchor:"transformers.MobileNetV1Config.example",$$slots:{default:[ii]},$$scope:{ctx:P}}}),qe=new Me({}),Oe=new ee({props:{name:"class transformers.MobileNetV1FeatureExtractor",anchor:"transformers.MobileNetV1FeatureExtractor",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mobilenet_v1/feature_extraction_mobilenet_v1.py#L26"}}),We=new ee({props:{name:"preprocess",anchor:"transformers.MobileNetV1FeatureExtractor.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:": typing.Optional[bool] = None"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"resample",val:": Resampling = None"},{name:"do_center_crop",val:": bool = None"},{name:"crop_size",val:": typing.Dict[str, int] = None"},{name:"do_rescale",val:": typing.Optional[bool] = None"},{name:"rescale_factor",val:": typing.Optional[float] = None"},{name:"do_normalize",val:": typing.Optional[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:": typing.Union[str, transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'>"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MobileNetV1FeatureExtractor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
Image to preprocess.`,name:"images"},{anchor:"transformers.MobileNetV1FeatureExtractor.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.MobileNetV1FeatureExtractor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.size</code>) &#x2014;
Size of the image after resizing. Shortest edge of the image is resized to size[&#x201C;shortest_edge&#x201D;], with
the longest edge resized to keep the input aspect ratio.`,name:"size"},{anchor:"transformers.MobileNetV1FeatureExtractor.preprocess.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code> filter, <em>optional</em>, defaults to <code>self.resample</code>) &#x2014;
<code>PILImageResampling</code> filter to use if resizing the image e.g. <code>PILImageResampling.BILINEAR</code>. Only has
an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"resample"},{anchor:"transformers.MobileNetV1FeatureExtractor.preprocess.do_center_crop",description:`<strong>do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_center_crop</code>) &#x2014;
Whether to center crop the image.`,name:"do_center_crop"},{anchor:"transformers.MobileNetV1FeatureExtractor.preprocess.crop_size",description:`<strong>crop_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.crop_size</code>) &#x2014;
Size of the center crop. Only has an effect if <code>do_center_crop</code> is set to <code>True</code>.`,name:"crop_size"},{anchor:"transformers.MobileNetV1FeatureExtractor.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.MobileNetV1FeatureExtractor.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.MobileNetV1FeatureExtractor.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.MobileNetV1FeatureExtractor.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 to use if <code>do_normalize</code> is set to <code>True</code>.`,name:"image_mean"},{anchor:"transformers.MobileNetV1FeatureExtractor.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 to use if <code>do_normalize</code> is set to <code>True</code>.`,name:"image_std"},{anchor:"transformers.MobileNetV1FeatureExtractor.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.MobileNetV1FeatureExtractor.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>&quot;channels_first&quot;</code> or <code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>&quot;channels_last&quot;</code> or <code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
<li>Unset: Use the channel dimension format of the input image.</li>
</ul>`,name:"data_format"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py#L218"}}),Ue=new Me({}),He=new ee({props:{name:"class transformers.MobileNetV1ImageProcessor",anchor:"transformers.MobileNetV1ImageProcessor",parameters:[{name:"do_resize",val:": bool = True"},{name:"size",val:": typing.Union[typing.Dict[str, int], NoneType] = None"},{name:"resample",val:": Resampling = <Resampling.BILINEAR: 2>"},{name:"do_center_crop",val:": bool = True"},{name:"crop_size",val:": typing.Dict[str, int] = None"},{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.MobileNetV1ImageProcessor.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to resize the image&#x2019;s (height, width) dimensions to the specified <code>size</code>. Can be overridden by
<code>do_resize</code> in the <code>preprocess</code> method.`,name:"do_resize"},{anchor:"transformers.MobileNetV1ImageProcessor.size",description:`<strong>size</strong> (<code>Dict[str, int]</code> <em>optional</em>, defaults to <code>{&quot;shortest_edge&quot; -- 256}</code>):
Size of the image after resizing. The shortest edge of the image is resized to size[&#x201C;shortest_edge&#x201D;], with
the longest edge resized to keep the input aspect ratio. Can be overridden by <code>size</code> in the <code>preprocess</code>
method.`,name:"size"},{anchor:"transformers.MobileNetV1ImageProcessor.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 overridden by the <code>resample</code> parameter in the
<code>preprocess</code> method.`,name:"resample"},{anchor:"transformers.MobileNetV1ImageProcessor.do_center_crop",description:`<strong>do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to center crop the image. If the input size is smaller than <code>crop_size</code> along any edge, the image
is padded with 0&#x2019;s and then center cropped. Can be overridden by the <code>do_center_crop</code> parameter in the
<code>preprocess</code> method.`,name:"do_center_crop"},{anchor:"transformers.MobileNetV1ImageProcessor.crop_size",description:`<strong>crop_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>{&quot;height&quot; -- 224, &quot;width&quot;: 224}</code>):
Desired output size when applying center-cropping. Only has an effect if <code>do_center_crop</code> is set to <code>True</code>.
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Whether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overridden by the <code>do_rescale</code>
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Scale factor to use if rescaling the image. Can be overridden by the <code>rescale_factor</code> parameter in the
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do_normalize &#x2014;
Whether to normalize the image. Can be overridden by the <code>do_normalize</code> parameter in the <code>preprocess</code>
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Mean to use if normalizing the image. This is a float or list of floats the length of the number of
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Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
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Image to preprocess.`,name:"images"},{anchor:"transformers.MobileNetV1ImageProcessor.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.MobileNetV1ImageProcessor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.size</code>) &#x2014;
Size of the image after resizing. Shortest edge of the image is resized to size[&#x201C;shortest_edge&#x201D;], with
the longest edge resized to keep the input aspect ratio.`,name:"size"},{anchor:"transformers.MobileNetV1ImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code> filter, <em>optional</em>, defaults to <code>self.resample</code>) &#x2014;
<code>PILImageResampling</code> filter to use if resizing the image e.g. <code>PILImageResampling.BILINEAR</code>. Only has
an effect if <code>do_resize</code> is set to <code>True</code>.`,name:"resample"},{anchor:"transformers.MobileNetV1ImageProcessor.preprocess.do_center_crop",description:`<strong>do_center_crop</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>self.do_center_crop</code>) &#x2014;
Whether to center crop the image.`,name:"do_center_crop"},{anchor:"transformers.MobileNetV1ImageProcessor.preprocess.crop_size",description:`<strong>crop_size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to <code>self.crop_size</code>) &#x2014;
Size of the center crop. Only has an effect if <code>do_center_crop</code> is set to <code>True</code>.`,name:"crop_size"},{anchor:"transformers.MobileNetV1ImageProcessor.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.MobileNetV1ImageProcessor.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.MobileNetV1ImageProcessor.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.MobileNetV1ImageProcessor.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 to use if <code>do_normalize</code> is set to <code>True</code>.`,name:"image_mean"},{anchor:"transformers.MobileNetV1ImageProcessor.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 to use if <code>do_normalize</code> is set to <code>True</code>.`,name:"image_std"},{anchor:"transformers.MobileNetV1ImageProcessor.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>
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<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.MobileNetV1ImageProcessor.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>&quot;channels_first&quot;</code> or <code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>&quot;channels_last&quot;</code> or <code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
<li>Unset: Use the channel dimension format of the input image.</li>
</ul>`,name:"data_format"}],source:"https://github.com/huggingface/transformers/blob/v4.27.1/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py#L218"}}),Ge=new Me({}),Ye=new ee({props:{name:"class transformers.MobileNetV1Model",anchor:"transformers.MobileNetV1Model",parameters:[{name:"config",val:": MobileNetV1Config"},{name:"add_pooling_layer",val:": bool = True"}],parametersDescription:[{anchor:"transformers.MobileNetV1Model.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</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/mobilenet_v1/modeling_mobilenet_v1.py#L301"}}),Qe=new ee({props:{name:"forward",anchor:"transformers.MobileNetV1Model.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MobileNetV1Model.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
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more detail.`,name:"output_hidden_states"},{anchor:"transformers.MobileNetV1Model.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
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<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/mobilenet_v1#transformers.MobileNetV1Config"
>MobileNetV1Config</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>
`}}),fe=new ei({props:{$$slots:{default:[li]},$$scope:{ctx:P}}}),he=new Cs({props:{anchor:"transformers.MobileNetV1Model.forward.example",$$slots:{default:[ci]},$$scope:{ctx:P}}}),Xe=new Me({}),Ze=new ee({props:{name:"class transformers.MobileNetV1ForImageClassification",anchor:"transformers.MobileNetV1ForImageClassification",parameters:[{name:"config",val:": MobileNetV1Config"}],parametersDescription:[{anchor:"transformers.MobileNetV1ForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.27.1/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</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/mobilenet_v1/modeling_mobilenet_v1.py#L411"}}),to=new ee({props:{name:"forward",anchor:"transformers.MobileNetV1ForImageClassification.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MobileNetV1ForImageClassification.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__">MobileNetV1ImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.MobileNetV1ForImageClassification.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.MobileNetV1ForImageClassification.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.MobileNetV1ForImageClassification.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/mobilenet_v1/modeling_mobilenet_v1.py#L427",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/mobilenet_v1#transformers.MobileNetV1Config"
>MobileNetV1Config</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"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ue=new ei({props:{$$slots:{default:[di]},$$scope:{ctx:P}}}),_e=new Cs({props:{anchor:"transformers.MobileNetV1ForImageClassification.forward.example",$$slots:{default:[mi]},$$scope:{ctx:P}}}),{c(){h=r("meta"),T=m(),_=r("h1"),g=r("a"),b=r("span"),v(c.$$.fragment),u=m(),k=r("span"),mr=n("MobileNet V1"),Nt=m(),q=r("h2"),oe=r("a"),Co=r("span"),v($e.$$.fragment),pr=m(),Fo=r("span"),fr=n("Overview"),Mt=m(),te=r("p"),hr=n("The MobileNet model was proposed in "),Te=r("a"),gr=n("MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"),ur=n(" by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam."),$t=m(),no=r("p"),_r=n("The abstract from the paper is the following:"),Tt=m(),io=r("p"),ko=r("em"),br=n("We present a class of efficient models called MobileNets for mobile and embedded vision applications. 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