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# raft_large

torchvision.models.optical_flow.raft_large(*, weights: Optional[Raft_Large_Weights] = None, progress=True, **kwargs) → RAFT

  RAFT model from RAFT: Recurrent All Pairs Field Transforms for Optical Flow.

  Please see the example below for a tutorial on how to use this model.

  Parameters:

    - weights (`Raft_Large_Weights`, optional) – The pretrained weights to use. See `Raft_Large_Weights` below for more details, and possible values. By default, no pre-trained weights are used.

    - progress (bool) – If True, displays a progress bar of the download to stderr. Default is True.

    - **kwargs – parameters passed to the `torchvision.models.optical_flow.RAFT` base class. Please refer to the source code for more details about this class.

  class torchvision.models.optical_flow.Raft_Large_Weights(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

    The metrics reported here are as follows.

    `epe` is the “end-point-error” and indicates how far (in pixels) the predicted flow is from its true value. This is averaged over all pixels of all images. `per_image_epe` is similar, but the average is different: the epe is first computed on each image independently, and then averaged over all images. This corresponds to “Fl-epe” (sometimes written “F1-epe”) in the original paper, and it’s only used on Kitti. `fl-all` is also a Kitti-specific metric, defined by the author of the dataset and used for the Kitti leaderboard. It corresponds to the average of pixels whose epe is either <3px, or <5% of flow’s 2-norm.

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