sam2.1-hiera-large EU AI Act Compliance Profile

Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and videos from FAIR. See the SAM 2 paper for more information.

The official code is publicly release in this repo.

Usage

For image prediction:

import torch
from sam2.sam2_image_predictor import SAM2ImagePredictor

predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2.1-hiera-large")

with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
    predictor.set_image(<your_image>)
    masks, _, _ = predictor.predict(<input_prompts>)

For video prediction:

import torch
from sam2.sam2_video_predictor import SAM2VideoPredictor

predictor = SAM2VideoPredictor.from_pretrained("facebook/sam2.1-hiera-large")

with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
    state = predictor.init_state(<your_video>)

    # add new prompts and instantly get the output on the same frame
    frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, <your_prompts>):

    # propagate the prompts to get masklets throughout the video
    for frame_idx, object_ids, masks in predictor.propagate_in_video(state):
        ...

Refer to the demo notebooks for more details.

Usage with 🤗 Transformers

Automatic Mask Generation with Pipeline

SAM2 can be used for automatic mask generation to segment all objects in an image using the mask-generation pipeline:

>>> from transformers import pipeline

>>> generator = pipeline("mask-generation", model="facebook/sam2.1-hiera-large", device=0)
>>> image_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg"
>>> outputs = generator(image_url, points_per_batch=64)

>>> len(outputs["masks"])  # Numb

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