# AutoModelForImageClassification #### AutoModelForImageClassification #### class transformers.AutoModelForImageClassification [\](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/auto/modeling_auto.py#L1341) ( \*args\*\*kwargs ) This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created with the [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.FlaxAutoModelForVision2Seq.from_pretrained) class method or the [from\_config()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.FlaxAutoModelForVision2Seq.from_config) class method. This class cannot be instantiated directly using `__init__()` (throws an error). **from\_config** [\](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/auto/auto_factory.py#L417) ( \*\*kwargs ) Parameters * **config** ([PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig)) — The model class to instantiate is selected based on the configuration class: * [BeitConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/beit#transformers.BeitConfig) configuration class: [BeitForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/beit#transformers.BeitForImageClassification) (BEiT model) * [BitConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bit#transformers.BitConfig) configuration class: [BitForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bit#transformers.BitForImageClassification) (BiT model) * [ConvNextConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/convnext#transformers.ConvNextConfig) configuration class: [ConvNextForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/convnext#transformers.ConvNextForImageClassification) (ConvNeXT model) * [ConvNextV2Config](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/convnextv2#transformers.ConvNextV2Config) configuration class: [ConvNextV2ForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/convnextv2#transformers.ConvNextV2ForImageClassification) (ConvNeXTV2 model) * [CvtConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/cvt#transformers.CvtConfig) configuration class: [CvtForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/cvt#transformers.CvtForImageClassification) (CvT model) * [Data2VecVisionConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig) configuration class: [Data2VecVisionForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/data2vec#transformers.Data2VecVisionForImageClassification) (Data2VecVision model) * [DeiTConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/deit#transformers.DeiTConfig) configuration class: [DeiTForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/deit#transformers.DeiTForImageClassification) or [DeiTForImageClassificationWithTeacher](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher) (DeiT model) * [DinatConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/dinat#transformers.DinatConfig) configuration class: [DinatForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/dinat#transformers.DinatForImageClassification) (DiNAT model) * [Dinov2Config](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/dinov2#transformers.Dinov2Config) configuration class: [Dinov2ForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/dinov2#transformers.Dinov2ForImageClassification) (DINOv2 model) * [EfficientFormerConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/efficientformer#transformers.EfficientFormerConfig) configuration class: [EfficientFormerForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassification) or [EfficientFormerForImageClassificationWithTeacher](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassificationWithTeacher) (EfficientFormer model) * [EfficientNetConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/efficientnet#transformers.EfficientNetConfig) configuration class: [EfficientNetForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/efficientnet#transformers.EfficientNetForImageClassification) (EfficientNet model) * [FocalNetConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/focalnet#transformers.FocalNetConfig) configuration class: [FocalNetForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/focalnet#transformers.FocalNetForImageClassification) (FocalNet model) * [ImageGPTConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/imagegpt#transformers.ImageGPTConfig) configuration class: [ImageGPTForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification) (ImageGPT model) * [LevitConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/levit#transformers.LevitConfig) configuration class: [LevitForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/levit#transformers.LevitForImageClassification) or [LevitForImageClassificationWithTeacher](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/levit#transformers.LevitForImageClassificationWithTeacher) (LeViT model) * [MobileNetV1Config](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config) configuration class: [MobileNetV1ForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilenet_v1#transformers.MobileNetV1ForImageClassification) (MobileNetV1 model) * [MobileNetV2Config](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config) configuration class: [MobileNetV2ForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForImageClassification) (MobileNetV2 model) * [MobileViTConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilevit#transformers.MobileViTConfig) configuration class: [MobileViTForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilevit#transformers.MobileViTForImageClassification) (MobileViT model) * [MobileViTV2Config](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilevitv2#transformers.MobileViTV2Config) configuration class: [MobileViTV2ForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilevitv2#transformers.MobileViTV2ForImageClassification) (MobileViTV2 model) * [NatConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/nat#transformers.NatConfig) configuration class: [NatForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/nat#transformers.NatForImageClassification) (NAT model) * [PerceiverConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/perceiver#transformers.PerceiverConfig) configuration class: [PerceiverForImageClassificationLearned](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned) or [PerceiverForImageClassificationFourier](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier) or [PerceiverForImageClassificationConvProcessing](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing) (Perceiver model) * [PoolFormerConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/poolformer#transformers.PoolFormerConfig) configuration class: [PoolFormerForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/poolformer#transformers.PoolFormerForImageClassification) (PoolFormer model) * [PvtConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/pvt#transformers.PvtConfig) configuration class: [PvtForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/pvt#transformers.PvtForImageClassification) (PVT model) * [RegNetConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/regnet#transformers.RegNetConfig) configuration class: [RegNetForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/regnet#transformers.RegNetForImageClassification) (RegNet model) * [ResNetConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/resnet#transformers.ResNetConfig) configuration class: [ResNetForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/resnet#transformers.ResNetForImageClassification) (ResNet model) * [SegformerConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/segformer#transformers.SegformerConfig) configuration class: [SegformerForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/segformer#transformers.SegformerForImageClassification) (SegFormer model) * [SwiftFormerConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/swiftformer#transformers.SwiftFormerConfig) configuration class: [SwiftFormerForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/swiftformer#transformers.SwiftFormerForImageClassification) (SwiftFormer model) * [SwinConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/swin#transformers.SwinConfig) configuration class: [SwinForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/swin#transformers.SwinForImageClassification) (Swin Transformer model) * [Swinv2Config](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/swinv2#transformers.Swinv2Config) configuration class: [Swinv2ForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/swinv2#transformers.Swinv2ForImageClassification) (Swin Transformer V2 model) * [VanConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/van#transformers.VanConfig) configuration class: [VanForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/van#transformers.VanForImageClassification) (VAN model) * [ViTConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vit#transformers.ViTConfig) configuration class: [ViTForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vit#transformers.ViTForImageClassification) (ViT model) * [ViTHybridConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vit_hybrid#transformers.ViTHybridConfig) configuration class: [ViTHybridForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vit_hybrid#transformers.ViTHybridForImageClassification) (ViT Hybrid model) * [ViTMSNConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vit_msn#transformers.ViTMSNConfig) configuration class: [ViTMSNForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification) (ViTMSN model) Instantiates one of the model classes of the library (with a image classification head) from a configuration. Note: Loading a model from its configuration file does **not** load the model weights. It only affects the model’s configuration. Use [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.FlaxAutoModelForVision2Seq.from_pretrained) to load the model weights. Examples: Copied ``` >>> from transformers import AutoConfig, AutoModelForImageClassification >>> # Download configuration from huggingface.co and cache. >>> config = AutoConfig.from_pretrained("bert-base-cased") >>> model = AutoModelForImageClassification.from_config(config) ``` **from\_pretrained** [\](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/auto/auto_factory.py#L448) ( \*model\_args\*\*kwargs ) Parameters * **pretrained\_model\_name\_or\_path** (`str` or `os.PathLike`) — Can be either: * A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. * A path to a *directory* containing model weights saved using [save\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained), e.g., `./my_model_directory/`. * A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In this case, `from_tf` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards. * **model\_args** (additional positional arguments, *optional*) — Will be passed along to the underlying model `__init__()` method. * **config** ([PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig), *optional*) — Configuration for the model to use instead of an automatically loaded configuration. Configuration can be automatically loaded when: * The model is a model provided by the library (loaded with the *model id* string of a pretrained model). * The model was saved using [save\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained) and is reloaded by supplying the save directory. * The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a configuration JSON file named *config.json* is found in the directory. * **state\_dict** (*Dict\[str, torch.Tensor]*, *optional*) — A state dictionary to use instead of a state dictionary loaded from saved weights file. This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using [save\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained) and [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) is not a simpler option. * **cache\_dir** (`str` or `os.PathLike`, *optional*) — Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. * **from\_tf** (`bool`, *optional*, defaults to `False`) — Load the model weights from a TensorFlow checkpoint save file (see docstring of `pretrained_model_name_or_path` argument). * **force\_download** (`bool`, *optional*, defaults to `False`) — Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. * **resume\_download** (`bool`, *optional*, defaults to `False`) — Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. * **proxies** (`Dict[str, str]`, *optional*) — A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. * **output\_loading\_info(`bool`,** *optional*, defaults to `False`) — Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages. * **local\_files\_only(`bool`,** *optional*, defaults to `False`) — Whether or not to only look at local files (e.g., not try downloading the model). * **revision** (`str`, *optional*, defaults to `"main"`) — The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. * **trust\_remote\_code** (`bool`, *optional*, defaults to `False`) — Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. * **code\_revision** (`str`, *optional*, defaults to `"main"`) — The specific revision to use for the code on the Hub, if the code leaves in a different repository than the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. * **kwargs** (additional keyword arguments, *optional*) — Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). Behaves differently depending on whether a `config` is provided or automatically loaded: * If a configuration is provided with `config`, `**kwargs` will be directly passed to the underlying model’s `__init__` method (we assume all relevant updates to the configuration have already been done) * If a configuration is not provided, `kwargs` will be first passed to the configuration class initialization function ([from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained)). Each key of `kwargs` that corresponds to a configuration attribute will be used to override said attribute with the supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model’s `__init__` function. Instantiate one of the model classes of the library (with a image classification head) from a pretrained model. The model class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it’s missing, by falling back to using pattern matching on `pretrained_model_name_or_path`: * **beit** — [BeitForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/beit#transformers.BeitForImageClassification) (BEiT model) * **bit** — [BitForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bit#transformers.BitForImageClassification) (BiT model) * **convnext** — [ConvNextForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/convnext#transformers.ConvNextForImageClassification) (ConvNeXT model) * **convnextv2** — [ConvNextV2ForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/convnextv2#transformers.ConvNextV2ForImageClassification) (ConvNeXTV2 model) * **cvt** — [CvtForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/cvt#transformers.CvtForImageClassification) (CvT model) * **data2vec-vision** — [Data2VecVisionForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/data2vec#transformers.Data2VecVisionForImageClassification) (Data2VecVision model) * **deit** — [DeiTForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/deit#transformers.DeiTForImageClassification) or [DeiTForImageClassificationWithTeacher](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher) (DeiT model) * **dinat** — [DinatForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/dinat#transformers.DinatForImageClassification) (DiNAT model) * **dinov2** — [Dinov2ForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/dinov2#transformers.Dinov2ForImageClassification) (DINOv2 model) * **efficientformer** — [EfficientFormerForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassification) or [EfficientFormerForImageClassificationWithTeacher](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassificationWithTeacher) (EfficientFormer model) * **efficientnet** — [EfficientNetForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/efficientnet#transformers.EfficientNetForImageClassification) (EfficientNet model) * **focalnet** — [FocalNetForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/focalnet#transformers.FocalNetForImageClassification) (FocalNet model) * **imagegpt** — [ImageGPTForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification) (ImageGPT model) * **levit** — [LevitForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/levit#transformers.LevitForImageClassification) or [LevitForImageClassificationWithTeacher](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/levit#transformers.LevitForImageClassificationWithTeacher) (LeViT model) * **mobilenet\_v1** — [MobileNetV1ForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilenet_v1#transformers.MobileNetV1ForImageClassification) (MobileNetV1 model) * **mobilenet\_v2** — [MobileNetV2ForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForImageClassification) (MobileNetV2 model) * **mobilevit** — [MobileViTForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilevit#transformers.MobileViTForImageClassification) (MobileViT model) * **mobilevitv2** — [MobileViTV2ForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilevitv2#transformers.MobileViTV2ForImageClassification) (MobileViTV2 model) * **nat** — [NatForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/nat#transformers.NatForImageClassification) (NAT model) * **perceiver** — [PerceiverForImageClassificationLearned](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned) or [PerceiverForImageClassificationFourier](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier) or [PerceiverForImageClassificationConvProcessing](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing) (Perceiver model) * **poolformer** — [PoolFormerForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/poolformer#transformers.PoolFormerForImageClassification) (PoolFormer model) * **pvt** — [PvtForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/pvt#transformers.PvtForImageClassification) (PVT model) * **regnet** — [RegNetForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/regnet#transformers.RegNetForImageClassification) (RegNet model) * **resnet** — [ResNetForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/resnet#transformers.ResNetForImageClassification) (ResNet model) * **segformer** — [SegformerForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/segformer#transformers.SegformerForImageClassification) (SegFormer model) * **swiftformer** — [SwiftFormerForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/swiftformer#transformers.SwiftFormerForImageClassification) (SwiftFormer model) * **swin** — [SwinForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/swin#transformers.SwinForImageClassification) (Swin Transformer model) * **swinv2** — [Swinv2ForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/swinv2#transformers.Swinv2ForImageClassification) (Swin Transformer V2 model) * **van** — [VanForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/van#transformers.VanForImageClassification) (VAN model) * **vit** — [ViTForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vit#transformers.ViTForImageClassification) (ViT model) * **vit\_hybrid** — [ViTHybridForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vit_hybrid#transformers.ViTHybridForImageClassification) (ViT Hybrid model) * **vit\_msn** — [ViTMSNForImageClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification) (ViTMSN model) The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are deactivated). To train the model, you should first set it back in training mode with `model.train()` Examples: Copied ``` >>> from transformers import AutoConfig, AutoModelForImageClassification >>> # Download model and configuration from huggingface.co and cache. >>> model = AutoModelForImageClassification.from_pretrained("bert-base-cased") >>> # Update configuration during loading >>> model = AutoModelForImageClassification.from_pretrained("bert-base-cased", output_attentions=True) >>> model.config.output_attentions True >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower) >>> config = AutoConfig.from_pretrained("./tf_model/bert_tf_model_config.json") >>> model = AutoModelForImageClassification.from_pretrained( ... "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config ... ) ```