
The styles and naming conventions align closely with a prototype mechanism proposed by Philip Meier for the [Datasets V2](https://github.com/pytorch/vision/blob/main/torchvision/prototype/datasets/_api.py) API, aiming to offer a similar user experience. The model registration methods are kept private on purpose as we currently focus only on supporting the built-in models of TorchVision.

### List models

Listing all available models in TorchVision can be done with a single function call:

```python
>>> list_models()
['alexnet', 'mobilenet_v3_large', 'mobilenet_v3_small', 'quantized_mobilenet_v3_large', ...]

To list the available models of specific submodules:
>>> list_models(module=torchvision.models)
['alexnet', 'mobilenet_v3_large', 'mobilenet_v3_small', ...]
>>> list_models(module=torchvision.models.quantization)
['quantized_mobilenet_v3_large', ...]

Initialize models
Now that you know which models are available, you can easily initialize a model with pre-trained weights: