# TFAutoModelForSemanticSegmentation

#### TFAutoModelForSemanticSegmentation

#### class transformers.TFAutoModelForSemanticSegmentation

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/auto/modeling_tf_auto.py#L594)

( \*args\*\*kwargs )

This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation 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:
  * [Data2VecVisionConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/data2vec#transformers.Data2VecVisionConfig) configuration class: [TFData2VecVisionForSemanticSegmentation](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/data2vec#transformers.TFData2VecVisionForSemanticSegmentation) (Data2VecVision model)
  * [MobileViTConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilevit#transformers.MobileViTConfig) configuration class: [TFMobileViTForSemanticSegmentation](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilevit#transformers.TFMobileViTForSemanticSegmentation) (MobileViT model)
  * [SegformerConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/segformer#transformers.SegformerConfig) configuration class: [TFSegformerForSemanticSegmentation](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/segformer#transformers.TFSegformerForSemanticSegmentation) (SegFormer model)

Instantiates one of the model classes of the library (with a semantic segmentation 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, TFAutoModelForSemanticSegmentation

>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained("bert-base-cased")
>>> model = TFAutoModelForSemanticSegmentation.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 _PyTorch state\_dict save file_ (e.g, `./pt_model/pytorch_model.bin`). In this case, `from_pt` should be set to `True` and a configuration object should be provided as `config` argument. This loading path is slower than converting the PyTorch model in a TensorFlow model using the provided conversion scripts and loading the TensorFlow 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.
* **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\_pt** (`bool`, _optional_, defaults to `False`) — Load the model weights from a PyTorch 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 semantic segmentation 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`:

* **data2vec-vision** — [TFData2VecVisionForSemanticSegmentation](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/data2vec#transformers.TFData2VecVisionForSemanticSegmentation) (Data2VecVision model)
* **mobilevit** — [TFMobileViTForSemanticSegmentation](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/mobilevit#transformers.TFMobileViTForSemanticSegmentation) (MobileViT model)
* **segformer** — [TFSegformerForSemanticSegmentation](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/segformer#transformers.TFSegformerForSemanticSegmentation) (SegFormer model)



Examples:

Copied

```
>>> from transformers import AutoConfig, TFAutoModelForSemanticSegmentation

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

>>> # Update configuration during loading
>>> model = TFAutoModelForSemanticSegmentation.from_pretrained("bert-base-cased", output_attentions=True)
>>> model.config.output_attentions
True

>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/bert_pt_model_config.json")
>>> model = TFAutoModelForSemanticSegmentation.from_pretrained(
...     "./pt_model/bert_pytorch_model.bin", from_pt=True, config=config
... )
```
