# AutoModelForVideoClassification

#### AutoModelForVideoClassification

#### class transformers.AutoModelForVideoClassification

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/auto/modeling_auto.py#L1414)

( \*args\*\*kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a video 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**

[\<source>](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:
  * [TimesformerConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/timesformer#transformers.TimesformerConfig) configuration class: [TimesformerForVideoClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/timesformer#transformers.TimesformerForVideoClassification) (TimeSformer model)
  * [VideoMAEConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/videomae#transformers.VideoMAEConfig) configuration class: [VideoMAEForVideoClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/videomae#transformers.VideoMAEForVideoClassification) (VideoMAE model)
  * [VivitConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vivit#transformers.VivitConfig) configuration class: [VivitForVideoClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vivit#transformers.VivitForVideoClassification) (ViViT model)

Instantiates one of the model classes of the library (with a video 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, AutoModelForVideoClassification

>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained("bert-base-cased")
>>> model = AutoModelForVideoClassification.from_config(config)
```

**from\_pretrained**

[\<source>](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 video 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`:

* **timesformer** — [TimesformerForVideoClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/timesformer#transformers.TimesformerForVideoClassification) (TimeSformer model)
* **videomae** — [VideoMAEForVideoClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/videomae#transformers.VideoMAEForVideoClassification) (VideoMAE model)
* **vivit** — [VivitForVideoClassification](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vivit#transformers.VivitForVideoClassification) (ViViT 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, AutoModelForVideoClassification

>>> # Download model and configuration from huggingface.co and cache.
>>> model = AutoModelForVideoClassification.from_pretrained("bert-base-cased")

>>> # Update configuration during loading
>>> model = AutoModelForVideoClassification.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 = AutoModelForVideoClassification.from_pretrained(
...     "./tf_model/bert_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
```
