
### `AutoModelForImageClassification` 

This class is a generic model class that will be instantiated as one of the model classes of the library (with an image classification head) when created with the `from_pretrained()` or `from_config()` class methods.

It cannot be instantiated directly using `__init__()` (it throws an error).

#### `from_config` method
```python
AutoModelForImageClassification.from_config(**kwargs)
```

- **Parameters**:
  - `config` (PretrainedConfig): The model class to instantiate is selected based on the configuration class. Below are the possible configurations and corresponding models:
    - `BeitConfig`: `BeitForImageClassification` (BEiT model)
    - `DeiTConfig`: `DeiTForImageClassification` or `DeiTForImageClassificationWithTeacher` (DeiT model)
    - `ImageGPTConfig`: `ImageGPTForImageClassification` (ImageGPT model)
    - `PerceiverConfig`: `PerceiverForImageClassificationLearned`, `PerceiverForImageClassificationFourier`, or `PerceiverForImageClassificationConvProcessing` (Perceiver model)
    - `SegformerConfig`: `SegformerForImageClassification` (SegFormer model)
    - `ViTConfig`: `ViTForImageClassification` (ViT model)

- **Note**: Loading a model from its configuration file does not load the model weights. It only affects the model’s configuration. Use `from_pretrained()` to load the model weights.

- **Example**:
  ```python
  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` method
```python
AutoModelForImageClassification.from_pretrained(*model_args, **kwargs)
```

- **Parameters**:
  - `pretrained_model_name_or_path` (str or os.PathLike): This can either be:
    - A string referring to the model ID of a pretrained model hosted inside a model repository on huggingface.co. Example: `'bert-base-uncased'`.
    - A path to a directory containing model weights saved using `save_pretrained()`. Example: `./my_model_directory/`.
    - A path or URL to a TensorFlow checkpoint file (e.g., `./tf_model/model.ckpt.index`). If you are loading a TensorFlow model, set `from_tf=True` and provide a configuration object.
  - `model_args` (optional): Additional positional arguments passed to the underlying model's `__init__()` method.
  - `config` (PretrainedConfig, optional): Configuration for the model to use. If not provided, it will be automatically loaded based on the model identifier.
  - `state_dict` (optional): A state dictionary to use instead of the default weights from the pretrained model. This is useful when you want to load custom weights.
  - `cache_dir` (optional): Path to a directory to cache downloaded configurations if the standard cache directory should not be used.
  - `from_tf` (optional, default=False): If set to True, the model weights are loaded from a TensorFlow checkpoint.
  - `force_download` (optional, default=False): If True, forces a download of the model weights even if they are already cached.
  - `resume_download` (optional, default=False): If True, attempts to resume an incomplete download.
  - `proxies` (optional): Dictionary of proxy servers for HTTP and HTTPS.
  - `output_loading_info` (optional, default=False): Whether or not to also return loading info such as missing or unexpected keys.
  - `local_files_only` (optional, default=False): If True, only local files will be used (no remote download).
  - `revision` (optional, default="main"): The specific model version to use.
  - `trust_remote_code` (optional, default=False): If True, allows for custom models defined on the Hugging Face Hub.
  - `kwargs` (optional): Additional keyword arguments that will be passed to the model’s `__init__` method or the configuration.

- **Example**:
  ```python
  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)
  print(model.config.output_attentions)  # True

  # Loading from a TF checkpoint file (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)
  ```

#### Notes:
- The model class to instantiate is selected based on the `model_type` property of the configuration object. If this is missing, it falls back to pattern matching on the `pretrained_model_name_or_path`.
- Supported models are:
  - `beit` -> `BeitForImageClassification` (BEiT model)
  - `deit` -> `DeiTForImageClassification` or `DeiTForImageClassificationWithTeacher` (DeiT model)
  - `imagegpt` -> `ImageGPTForImageClassification` (ImageGPT model)
  - `perceiver` -> `PerceiverForImageClassificationLearned`, `PerceiverForImageClassificationFourier`, `PerceiverForImageClassificationConvProcessing` (Perceiver model)
  - `segformer` -> `SegformerForImageClassification` (SegFormer model)
  - `vit` -> `ViTForImageClassification` (ViT model)

The model is set to evaluation mode by default, using `model.eval()`, to deactivate dropout layers during inference. To train the model, you should use `model.train()` first.