Reference for ultralytics/models/sam/modules/sam.py

class Sam(nn.Module):
    mask_threshold: float = 0.0
    image_format: str = 'RGB'

    def __init__(
        self,
        image_encoder: ImageEncoderViT,
        prompt_encoder: PromptEncoder,
        mask_decoder: MaskDecoder,
        pixel_mean: List[float] = (123.675, 116.28, 103.53),
        pixel_std: List[float] = (58.395, 57.12, 57.375)
    ) -> None:
        """
        SAM predicts object masks from an image and input prompts.

        Note:
            All forward() operations moved to SAMPredictor.

        Args:
          image_encoder (ImageEncoderViT): The backbone used to encode the image into image embeddings that allow for
            efficient mask prediction.
          prompt_encoder (PromptEncoder): Encodes various types of input prompts.
          mask_decoder (MaskDecoder): Predicts masks from the image embeddings and encoded prompts.
          pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
          pixel_std (list(float)): Std values for normalizing pixels in the input image.
        """
        super().__init__()
        self.image_encoder = image_encoder
        self.prompt_encoder = prompt_encoder
        self.mask_decoder = mask_decoder
        self.register_buffer('pixel_mean', torch.Tensor(pixel_mean).view(-1, 1, 1), False)
        self.register_buffer('pixel_std', torch.Tensor(pixel_std).view(-1, 1, 1), False)