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facebook/sam3 for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX.SAM3InstanceSegment / SAM3Detect / SAM3SemanticSegment): pass a text noun phrase (backbone ViT-L/14).1import os
2os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
3
4from zeromodels.models.sam3 import SAM3InstanceSegment
5
6segmenter = SAM3InstanceSegment.from_weights("zeromodels/sam3")
7result = segmenter.predict(
8 images="your_image.jpg", text="person", threshold=0.3
9)[0]
10print(len(result["scores"]), result["masks"].shape)from_weights("zeromodels/<variant>") (use SAM3InstanceSegment for this repo):| Variant | Hub | Family |
|---|---|---|
sam_vit_base | zeromodels/sam_vit_base | SAM |
sam_vit_large | zeromodels/sam_vit_large | SAM |
sam_vit_huge | zeromodels/sam_vit_huge | SAM |
sam2_hiera_small | zeromodels/sam2_hiera_small | SAM2 |
sam2_hiera_base_plus | zeromodels/sam2_hiera_base_plus | SAM2 |
sam2_hiera_large | zeromodels/sam2_hiera_large | SAM2 |
sam3 | zeromodels/sam3 | SAM3 |
KERAS_BACKEND before importing Keras / zeromodels.enable_boxes=True / include_box_input=True when building the graph.SAM3InstanceSegment.predict(...) for text prompts; upstream facebook/sam3 is gated.hf: prefix, e.g. SAM3Model.from_weights("hf:facebook/sam3").