
### **AutoModelForObjectDetection**
- **Class Definition:**
  `class transformers.AutoModelForObjectDetection(*args, **kwargs)`

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

  This class **cannot be instantiated directly** using `__init__()` (doing so will raise an error).

#### **from_config**
`from_config(**kwargs)`

- **Parameters:**
  - `config (PretrainedConfig)` — The model class to instantiate is determined based on the configuration class:
    - `DetrConfig` for **DETR** (DEtection TRansformer) model.
  
  This method loads a model from a configuration file, but it does **not** load the model weights. It only affects the model’s configuration. To load the model weights, you must use `from_pretrained()`.

- **Examples:**
  ```python
  from transformers import AutoConfig, AutoModelForObjectDetection
  # Download configuration from huggingface.co and cache.
  config = AutoConfig.from_pretrained('bert-base-cased')
  model = AutoModelForObjectDetection.from_config(config)
  ```

#### **from_pretrained**
`from_pretrained(*model_args, **kwargs)`

- **Parameters:**
  - `pretrained_model_name_or_path (str or os.PathLike)` — Can be either:
    - A string model ID of a pretrained model hosted inside a model repository on `huggingface.co`. For example, `bert-base-uncased` or `dbmdz/bert-base-german-cased`.
    - A path to a directory containing model weights saved using `save_pretrained()`, e.g., `./my_model_directory/`.
    - A path or URL to a TensorFlow checkpoint file (e.g., `./tf_model/model.ckpt.index`). If this option is used, `from_tf` should be set to `True` and a configuration object must also be provided.
  
  - `model_args (additional positional arguments, optional)` — Will be passed along to the model's `__init__()` method.
  
  - `config (PretrainedConfig, optional)` — Allows specifying a custom configuration instead of automatically loading the configuration. For instance:
    - If the model is from the library (using model ID).
    - If the model was saved using `save_pretrained()` and reloaded from the saved directory.
    - If the model is loaded from a local directory containing the `config.json`.
  
  - `state_dict (Dict[str, torch.Tensor], optional)` — Allows passing a state dictionary instead of loading from the saved model file.
  
  - `cache_dir (str or os.PathLike, optional)` — Path to cache downloaded configurations.
  
  - `from_tf (bool, optional, defaults to False)` — If set to `True`, the model weights are loaded from a TensorFlow checkpoint file.
  
  - `force_download (bool, optional, defaults to False)` — Forces the download of model weights and configuration files, overriding any cached versions.
  
  - `resume_download (bool, optional, defaults to False)` — Resumes the download if the model file was incompletely received.
  
  - `proxies (Dict[str, str], optional)` — A dictionary of proxy servers to use during the download process.

  - `output_loading_info (bool, optional, defaults to False)` — Whether to return extra loading information, such as missing or unexpected keys in the model weights.

  - `local_files_only (bool, optional, defaults to False)` — Restricts the model loading to local files only (i.e., doesn't try downloading the model).

  - `revision (str, optional, defaults to "main")` — Specifies the model version (branch name, tag name, or commit id).

  - `trust_remote_code (bool, optional, defaults to False)` — Allows executing custom code from the model repository on the Hugging Face hub, but should only be enabled for trusted models.

  - `kwargs (additional keyword arguments, optional)` — These can be used to update the configuration object and modify model parameters during initialization (e.g., `output_attentions=True`).

- **Example:**
  ```python
  from transformers import AutoConfig, AutoModelForObjectDetection
  
  # Download model and configuration from huggingface.co and cache.
  model = AutoModelForObjectDetection.from_pretrained('bert-base-cased')
  
  # Update configuration during loading
  model = AutoModelForObjectDetection.from_pretrained('bert-base-cased', output_attentions=True)
  print(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 = AutoModelForObjectDetection.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
  ```

#### **Model Selection Based on Config:**
The class will instantiate one of the following based on the configuration:
  - **DETR** (DEtection TRansformer) model (`DetrForObjectDetection`).
  
  By default, the model is set in evaluation mode using `model.eval()` (which disables modules like dropout). To use it for training, you need to call `model.train()`.

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