Function convnext_small

torchvision/models/convnext.py:323–351

ConvNeXt Small model architecture from the `A ConvNet for the 2020s `_ paper. Args: weights (:class:`~torchvision.models.convnext.ConvNeXt_Small_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.convnext

(
    *, weights: Optional[ConvNeXt_Small_Weights] = None, progress: bool = True, **kwargs: Any
)

@register_model()
@handle_legacy_interface(weights=("pretrained", ConvNeXt_Small_Weights.IMAGENET1K_V1))
def convnext_small(
    *, weights: Optional[ConvNeXt_Small_Weights] = None, progress: bool = True, **kwargs: Any
) -> ConvNeXt:
    """ConvNeXt Small model architecture from the
    `A ConvNet for the 2020s <https://arxiv.org/abs/2201.03545>`_ paper.

    Args:
        weights (:class:`~torchvision.models.convnext.ConvNeXt_Small_Weights`, optional): The pretrained
            weights to use. See :class:`~torchvision.models.convnext.ConvNeXt_Small_Weights`
            below for more details and possible values. By default, no pre-trained weights are used.
        progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.convnext.ConvNext``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/convnext.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.ConvNeXt_Small_Weights
        :members:
    """
    weights = ConvNeXt_Small_Weights.verify(weights)

    block_setting = [
        CNBlockConfig(96, 192, 3),
        CNBlockConfig(192, 384, 3),
        CNBlockConfig(384, 768, 27),
        CNBlockConfig(768, None, 3),
    ]
    stochastic_depth_prob = kwargs.pop("stochastic_depth_prob", 0.4)
    return _convnext(block_setting, stochastic_depth_prob, weights, progress, **kwargs)


@register_model()