import{s as Lo,o as Ao,n as ot}from"../chunks/scheduler.9bc65507.js";import{S as So,i as qo,g as o,s as l,u as _,A as Go,h as n,f as t,c,j as f,v as b,y as g,k as m,o as a,a as i,w as v,d as y,t as w,x as C}from"../chunks/index.78c82d43.js";import{T as Zo}from"../chunks/Tip.87d55b76.js";import{D as Be}from"../chunks/Docstring.4e7352e2.js";import{C as oo}from"../chunks/CodeBlock.73e038be.js";import{I as xe}from"../chunks/IconCopyLink.bedaa44d.js";import{E as to}from"../chunks/ExampleCodeBlock.872b014d.js";import{P as Ho}from"../chunks/PipelineTag.75b0f707.js";function Xo(x){let d,$="Example:",p,h,u;return h=new oo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BitConfig, BitModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a BiT bit-50 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = BitConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the bit-50 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BitModel(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`,wrap:!1}}),{c(){d=o("p"),d.textContent=$,p=l(),_(h.$$.fragment)},l(r){d=n(r,"P",{"data-svelte-h":!0}),g(d)!=="svelte-11lpom8"&&(d.textContent=$),p=c(r),b(h.$$.fragment,r)},m(r,T){i(r,d,T),i(r,p,T),v(h,r,T),u=!0},p:ot,i(r){u||(y(h.$$.fragment,r),u=!0)},o(r){w(h.$$.fragment,r),u=!1},d(r){r&&(t(d),t(p)),C(h,r)}}}function Vo(x){let d,$=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){d=o("p"),d.innerHTML=$},l(p){d=n(p,"P",{"data-svelte-h":!0}),g(d)!=="svelte-fincs2"&&(d.innerHTML=$)},m(p,h){i(p,d,h)},p:ot,d(p){p&&t(d)}}}function Qo(x){let d,$="Example:",p,h,u;return h=new oo({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9JbWFnZVByb2Nlc3NvciUyQyUyMEJpdE1vZGVsJTBBaW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGF0YXNldHMlMjBpbXBvcnQlMjBsb2FkX2RhdGFzZXQlMEElMEFkYXRhc2V0JTIwJTNEJTIwbG9hZF9kYXRhc2V0KCUyMmh1Z2dpbmdmYWNlJTJGY2F0cy1pbWFnZSUyMiklMEFpbWFnZSUyMCUzRCUyMGRhdGFzZXQlNUIlMjJ0ZXN0JTIyJTVEJTVCJTIyaW1hZ2UlMjIlNUQlNUIwJTVEJTBBJTBBaW1hZ2VfcHJvY2Vzc29yJTIwJTNEJTIwQXV0b0ltYWdlUHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZiaXQtNTAlMjIpJTBBbW9kZWwlMjAlM0QlMjBCaXRNb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGYml0LTUwJTIyKSUwQSUwQWlucHV0cyUyMCUzRCUyMGltYWdlX3Byb2Nlc3NvcihpbWFnZSUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBJTBBd2l0aCUyMHRvcmNoLm5vX2dyYWQoKSUzQSUwQSUyMCUyMCUyMCUyMG91dHB1dHMlMjAlM0QlMjBtb2RlbCgqKmlucHV0cyklMEElMEFsYXN0X2hpZGRlbl9zdGF0ZXMlMjAlM0QlMjBvdXRwdXRzLmxhc3RfaGlkZGVuX3N0YXRlJTBBbGlzdChsYXN0X2hpZGRlbl9zdGF0ZXMuc2hhcGUp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, BitModel
<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/bit-50&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BitModel.from_pretrained(<span class="hljs-string">&quot;google/bit-50&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">2048</span>, <span class="hljs-number">7</span>, <span class="hljs-number">7</span>]`,wrap:!1}}),{c(){d=o("p"),d.textContent=$,p=l(),_(h.$$.fragment)},l(r){d=n(r,"P",{"data-svelte-h":!0}),g(d)!=="svelte-11lpom8"&&(d.textContent=$),p=c(r),b(h.$$.fragment,r)},m(r,T){i(r,d,T),i(r,p,T),v(h,r,T),u=!0},p:ot,i(r){u||(y(h.$$.fragment,r),u=!0)},o(r){w(h.$$.fragment,r),u=!1},d(r){r&&(t(d),t(p)),C(h,r)}}}function Yo(x){let d,$=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){d=o("p"),d.innerHTML=$},l(p){d=n(p,"P",{"data-svelte-h":!0}),g(d)!=="svelte-fincs2"&&(d.innerHTML=$)},m(p,h){i(p,d,h)},p:ot,d(p){p&&t(d)}}}function Do(x){let d,$="Example:",p,h,u;return h=new oo({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, BitForImageClassification
<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/bit-50&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BitForImageClassification.from_pretrained(<span class="hljs-string">&quot;google/bit-50&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])
tiger cat`,wrap:!1}}),{c(){d=o("p"),d.textContent=$,p=l(),_(h.$$.fragment)},l(r){d=n(r,"P",{"data-svelte-h":!0}),g(d)!=="svelte-11lpom8"&&(d.textContent=$),p=c(r),b(h.$$.fragment,r)},m(r,T){i(r,d,T),i(r,p,T),v(h,r,T),u=!0},p:ot,i(r){u||(y(h.$$.fragment,r),u=!0)},o(r){w(h.$$.fragment,r),u=!1},d(r){r&&(t(d),t(p)),C(h,r)}}}function Oo(x){let d,$,p,h,u,r,T,ee,Pt,Ie,no="Big Transfer (BiT)",nt,U,E,He,te,Ut,je,so="Overview",st,oe,ao=`The BiT model was proposed in <a href="https://arxiv.org/abs/1912.11370" rel="nofollow">Big Transfer (BiT): General Visual Representation Learning</a> by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
BiT is a simple recipe for scaling up pre-training of <a href="resnet">ResNet</a>-like architectures (specifically, ResNetv2). The method results in significant improvements for transfer learning.`,at,ne,ro="The abstract from the paper is the following:",rt,se,io="<em>Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT). By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes — from 1 example per class to 1M total examples. BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class. We conduct detailed analysis of the main components that lead to high transfer performance.</em>",it,ae,lo="Tips:",lt,re,co=`<li>BiT models are equivalent to ResNetv2 in terms of architecture, except that: 1) all batch normalization layers are replaced by <a href="https://arxiv.org/abs/1803.08494" rel="nofollow">group normalization</a>,
2) <a href="https://arxiv.org/abs/1903.10520" rel="nofollow">weight standardization</a> is used for convolutional layers. The authors show that the combination of both is useful for training with large batch sizes, and has a significant
impact on transfer learning.</li>`,ct,ie,mo=`This model was contributed by <a href="https://huggingface.co/nielsr" rel="nofollow">nielsr</a>.
The original code can be found <a href="https://github.com/google-research/big_transfer" rel="nofollow">here</a>.`,dt,N,W,Xe,le,Nt,ze,po="Resources",mt,ce,ho="A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BiT.",pt,de,ht,me,fo='<li><a href="/docs/transformers/v4.34.0/en/model_doc/bit#transformers.BitForImageClassification">BitForImageClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb" rel="nofollow">notebook</a>.</li> <li>See also: <a href="../tasks/image_classification">Image classification task guide</a></li>',ft,pe,go="If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.",gt,k,Z,Ve,he,kt,Pe,uo="BitConfig",ut,M,fe,Ft,Ue,_o=`This is the configuration class to store the configuration of a <a href="/docs/transformers/v4.34.0/en/model_doc/bit#transformers.BitModel">BitModel</a>. It is used to instantiate an BiT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the BiT
<a href="https://huggingface.co/google/bit-50" rel="nofollow">google/bit-50</a> architecture.`,Rt,Ne,bo=`Configuration objects inherit from <a href="/docs/transformers/v4.34.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/v4.34.0/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Jt,L,_t,F,A,Qe,ge,Et,ke,vo="BitImageProcessor",bt,z,ue,Wt,Fe,yo="Constructs a BiT image processor.",Zt,S,_e,Lt,Re,wo="Preprocess an image or batch of images.",vt,R,q,Ye,be,At,Je,Co="BitModel",yt,P,ve,St,Ee,$o=`The bare BiT model outputting raw features without any specific head on top.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,qt,I,ye,Gt,We,To='The <a href="/docs/transformers/v4.34.0/en/model_doc/bit#transformers.BitModel">BitModel</a> forward method, overrides the <code>__call__</code> special method.',Ht,G,Xt,H,wt,J,X,De,we,Vt,Ze,Mo="BitForImageClassification",Ct,B,Ce,Qt,Le,Bo=`BiT Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.`,Yt,Ae,xo=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,Dt,j,$e,Ot,Se,Io='The <a href="/docs/transformers/v4.34.0/en/model_doc/bit#transformers.BitForImageClassification">BitForImageClassification</a> forward method, overrides the <code>__call__</code> special method.',Kt,V,eo,Q,$t,Oe,Tt;return ee=new xe({}),te=new xe({}),le=new xe({}),de=new Ho({props:{pipeline:"image-classification"}}),he=new xe({}),fe=new Be({props:{name:"class transformers.BitConfig",anchor:"transformers.BitConfig",parameters:[{name:"num_channels",val:" = 3"},{name:"embedding_size",val:" = 64"},{name:"hidden_sizes",val:" = [256, 512, 1024, 2048]"},{name:"depths",val:" = [3, 4, 6, 3]"},{name:"layer_type",val:" = 'preactivation'"},{name:"hidden_act",val:" = 'relu'"},{name:"global_padding",val:" = None"},{name:"num_groups",val:" = 32"},{name:"drop_path_rate",val:" = 0.0"},{name:"embedding_dynamic_padding",val:" = False"},{name:"output_stride",val:" = 32"},{name:"width_factor",val:" = 1"},{name:"out_features",val:" = None"},{name:"out_indices",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BitConfig.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.BitConfig.embedding_size",description:`<strong>embedding_size</strong> (<code>int</code>, <em>optional</em>, defaults to 64) &#x2014;
Dimensionality (hidden size) for the embedding layer.`,name:"embedding_size"},{anchor:"transformers.BitConfig.hidden_sizes",description:`<strong>hidden_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[256, 512, 1024, 2048]</code>) &#x2014;
Dimensionality (hidden size) at each stage.`,name:"hidden_sizes"},{anchor:"transformers.BitConfig.depths",description:`<strong>depths</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[3, 4, 6, 3]</code>) &#x2014;
Depth (number of layers) for each stage.`,name:"depths"},{anchor:"transformers.BitConfig.layer_type",description:`<strong>layer_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;preactivation&quot;</code>) &#x2014;
The layer to use, it can be either <code>&quot;preactivation&quot;</code> or <code>&quot;bottleneck&quot;</code>.`,name:"layer_type"},{anchor:"transformers.BitConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;relu&quot;</code>) &#x2014;
The non-linear activation function 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.BitConfig.global_padding",description:`<strong>global_padding</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Padding strategy to use for the convolutional layers. Can be either <code>&quot;valid&quot;</code>, <code>&quot;same&quot;</code>, or <code>None</code>.`,name:"global_padding"},{anchor:"transformers.BitConfig.num_groups",description:`<strong>num_groups</strong> (<code>int</code>, <em>optional</em>, defaults to <code>32</code>) &#x2014;
Number of groups used for the <code>BitGroupNormActivation</code> layers.`,name:"num_groups"},{anchor:"transformers.BitConfig.drop_path_rate",description:`<strong>drop_path_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The drop path rate for the stochastic depth.`,name:"drop_path_rate"},{anchor:"transformers.BitConfig.embedding_dynamic_padding",description:`<strong>embedding_dynamic_padding</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to make use of dynamic padding for the embedding layer.`,name:"embedding_dynamic_padding"},{anchor:"transformers.BitConfig.output_stride",description:`<strong>output_stride</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The output stride of the model.`,name:"output_stride"},{anchor:"transformers.BitConfig.width_factor",description:`<strong>width_factor</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The width factor for the model.`,name:"width_factor"},{anchor:"transformers.BitConfig.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). If unset and <code>out_indices</code> is set, will default to the
corresponding stages. If unset and <code>out_indices</code> is unset, will default to the last stage.`,name:"out_features"},{anchor:"transformers.BitConfig.out_indices",description:`<strong>out_indices</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and <code>out_features</code> is set, will default to the corresponding stages.
If unset and <code>out_features</code> is unset, will default to the last stage.`,name:"out_indices"}],source:"https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/models/bit/configuration_bit.py#L29"}}),L=new to({props:{anchor:"transformers.BitConfig.example",$$slots:{default:[Xo]},$$scope:{ctx:x}}}),ge=new xe({}),ue=new Be({props:{name:"class transformers.BitImageProcessor",anchor:"transformers.BitImageProcessor",parameters:[{name:"do_resize",val:": bool = True"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"resample",val:": Resampling = <Resampling.BICUBIC: 3>"},{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:"do_convert_rgb",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BitImageProcessor.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
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Size of the image after resizing. The shortest edge of the image is resized to size[&#x201C;shortest_edge&#x201D;], with
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Resampling filter to use if resizing the image. Can be overridden by <code>resample</code> in the <code>preprocess</code> method.`,name:"resample"},{anchor:"transformers.BitImageProcessor.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 to the specified <code>crop_size</code>. Can be overridden by <code>do_center_crop</code> in the
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Size of the output image after applying <code>center_crop</code>. Can be overridden by <code>crop_size</code> in the <code>preprocess</code>
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Whether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overridden by <code>do_rescale</code> in
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Scale factor to use if rescaling the image. Can be overridden by <code>rescale_factor</code> in the <code>preprocess</code>
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Whether to normalize the image. Can be overridden by <code>do_normalize</code> in the <code>preprocess</code> method.`,name:"rescale_factor"},{anchor:"transformers.BitImageProcessor.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>OPENAI_CLIP_MEAN</code>) &#x2014;
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. Expects a single or batch of images with pixel values ranging from 0 to 255. If
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Whether to resize the image.`,name:"do_resize"},{anchor:"transformers.BitImageProcessor.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
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Resampling filter to use if resizing the image. This can be one of the enum <code>PILImageResampling</code>. Only
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Whether to center crop the image.`,name:"do_center_crop"},{anchor:"transformers.BitImageProcessor.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.BitImageProcessor.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.`,name:"do_rescale"},{anchor:"transformers.BitImageProcessor.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.BitImageProcessor.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.BitImageProcessor.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 for normalization. Only has an effect if <code>do_normalize</code> is set to <code>True</code>.`,name:"image_mean"},{anchor:"transformers.BitImageProcessor.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 for normalization. Only has an effect if <code>do_normalize</code> is set to
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Whether to convert the image to RGB.`,name:"do_convert_rgb"},{anchor:"transformers.BitImageProcessor.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>
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<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>
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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"},{anchor:"transformers.BitImageProcessor.preprocess.input_data_format",description:`<strong>input_data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>) &#x2014;
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input 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><code>&quot;none&quot;</code> or <code>ChannelDimension.NONE</code>: image in (height, width) format.</li>
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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/v4.34.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.34.0/en/model_doc/deit#transformers.DeiTFeatureExtractor.__call__">BitImageProcessor.<strong>call</strong>()</a>
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>BitConfig</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>) — 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>) — 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>) — 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:`
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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/v4.34.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.34.0/en/model_doc/deit#transformers.DeiTFeatureExtractor.__call__">BitImageProcessor.<strong>call</strong>()</a>
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<p>A <a
  href="/docs/transformers/v4.34.0/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/v4.34.0/en/model_doc/bit#transformers.BitConfig"
>BitConfig</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) — 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>) — 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>) — 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/v4.34.0/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or <code>tuple(torch.FloatTensor)</code></p>
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