MaskFeat

MaskFeat#

class stable_pretraining.methods.MaskFeat(encoder_name: str | Module = 'vit_small_patch16_224', patch_size: int = 16, mask_ratio: float = 0.4, n_hog_bins: int = 9, image_size: int = 224, in_channels: int = 3, pretrained: bool = False)[source]#

Bases: Module

MaskFeat: predict per-patch HOG at masked positions.

Parameters:

  • encoder_name – timm ViT name (default "vit_small_patch16_224").

  • patch_size – Patch size (default 16, must match encoder).

  • mask_ratio – Fraction of patches masked (default 0.4).

  • n_hog_bins – HOG orientation bins (default 9).

  • image_size – Input size (default 224).

  • in_channels – Image channels (default 3).

  • pretrained – Load pretrained timm weights.

forward(images: Tensor) → MaskFeatOutput[source]#

Same as torch.nn.Module.forward().

Parameters:

  • *args – Whatever you decide to pass into the forward method.

  • **kwargs – Keyword arguments are also possible.

Returns:

Your model’s output