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ultralytics 8.2.70 Segment Anything Model 2 (SAM 2) (#14813)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Laughing 2024-07-30 22:06:49 +08:00 • committed by GitHub
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36 changed files with 3276 additions and 77 deletions
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12
ultralytics/models/sam/modules/sam.py
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@ -15,15 +15,14 @@ from .decoders import MaskDecoder
from .encoders import ImageEncoderViT, PromptEncoder
class Sam(nn.Module):
class SAMModel(nn.Module):
"""
Sam (Segment Anything Model) is designed for object segmentation tasks. It uses image encoders to generate image
embeddings, and prompt encoders to encode various types of input prompts. These embeddings are then used by the mask
decoder to predict object masks.
SAMModel (Segment Anything Model) is designed for object segmentation tasks. It uses image encoders to generate
image embeddings, and prompt encoders to encode various types of input prompts. These embeddings are then used by
the mask decoder to predict object masks.
Attributes:
mask_threshold (float): Threshold value for mask prediction.
image_format (str): Format of the input image, default is 'RGB'.
image_encoder (ImageEncoderViT): The backbone used to encode the image into embeddings.
prompt_encoder (PromptEncoder): Encodes various types of input prompts.
mask_decoder (MaskDecoder): Predicts object masks from the image and prompt embeddings.
@ -32,7 +31,6 @@ class Sam(nn.Module):
"""
mask_threshold: float = 0.0
image_format: str = "RGB"
def __init__(
self,
@ -43,7 +41,7 @@ class Sam(nn.Module):
pixel_std: List[float] = (58.395, 57.12, 57.375),
) -> None:
"""
Initialize the Sam class to predict object masks from an image and input prompts.
Initialize the SAMModel class to predict object masks from an image and input prompts.
Note:
All forward() operations moved to SAMPredictor.