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sumitsawanttce/sam3
sam3 is a mask generation model from sumitsawanttce. Use it for the mask generation task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
This repository mirrors the official Segment Anything Model 3 (SAM 3) weights released by Meta Superintelligence Labs. SAM 3 is a unified foundation model for prompt-driven segmentation in images and videos. It suppor…
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Updated Apr 7, 2026
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From the Hugging Face model README
This repository mirrors the official Segment Anything Model 3 (SAM 3) weights released by Meta Superintelligence Labs. SAM 3 is a unified foundation model for prompt-driven segmentation in images and videos. It supports open-vocabulary text prompts and visual prompts (points/boxes/masks). Compared to SAM 2, SAM 3 exhaustively segments each instance of a requested concept and reaches ~75–80% of human-level performance on the SA-CO benchmark (270K unique concepts).
Original paper: SAM 3: Segment Anything with Concepts (Meta AI, 2024).
Resources: Project Page · Demo
sam3.safetensors — detector and tracker weights for image + video segmentation.facebookresearch/sam3 repository; this mirror only repackages the safetensors weights for self-hosting.pip install torch==2.7.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
pip install git+https://github.com/facebookresearch/sam3.git
python - <<'PY'
from sam3 import build_sam3_image_model
from sam3.model.sam3_image_processor import Sam3Processor
model = build_sam3_image_model(
bpe_path="sam3/assets/bpe_simple_vocab_16e6.txt.gz",
device="cuda",
eval_mode=True,
checkpoint_path="sam3.safetensors",
load_from_HF=False,
)
processor = Sam3Processor(model, device="cuda")
state = processor.set_image("your_image.jpg")
state = processor.set_text_prompt("white bicycle", state)
print(state["masks"].shape)
PY
These mirrored weights are used in the AILab SAM3 ComfyUI node (RMBG edition) to enable promptable segmentation workflows directly inside ComfyUI. The node loads sam3.safetensors, tokenizer assets, and the SAM3 processors locally, so the entire pipeline stays compatible even when offline.