You can deploy this model and start earning money today!

kerasformers/
sam_vit_huge

Name: sam_vit_huge
Availability: InStock

facebook/sam-vit-huge

finetune

apache-2.0

Views

No views yet

See our collection for all versions of SAM.

Run SAM with Keras 3: JAX, PyTorch, or TensorFlow

GitHub

zeromodels/sam_vit_huge

Paper: Segment Anything (arXiv:2304.02643) · HF Papers

SAM (Segment Anything Model) segments whatever you point at. It has no class vocabulary: you give it a prompt (a click or a box) and it returns a mask. A heavy ViT image encoder runs once per image; the prompt encoder and mask decoder are light enough for interactive use.

For more details on the model, please go to Meta's original model card.

Pure-Keras 3 conversion of facebook/sam-vit-huge for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX.

This is a promptable segmentation checkpoint (SAMPromptableSegment): point (and optional box) prompts, backbone ViT-H.

✨ Quick start

python

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

import numpy as np
from PIL import Image
from zeromodels.models.sam import (
    SAMPromptableSegment,
    SAMProcessor,
)

model = SAMPromptableSegment.from_weights("zeromodels/sam_vit_huge")
processor = SAMProcessor.from_weights("zeromodels/sam_vit_huge")

image = Image.open("your_image.jpg").convert("RGB")
inputs = processor(
    image,
    input_points=np.array([[[[450, 200]]]], dtype="float32"),
    input_labels=np.array([[[1]]], dtype="int32"),
)
META = ("original_size", "reshaped_size")
output = model({k: v for k, v in inputs.items() if k not in META})
masks = processor.post_process_masks(
    output["pred_masks"], original_size=inputs["original_size"]
)
print(output["iou_scores"].shape, masks.shape)

Load any SAM / SAM2 / SAM3 variant the same way with from_weights("zeromodels/<variant>") (use SAMPromptableSegment 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

Tips

Set KERAS_BACKEND before importing Keras / zeromodels.
SAM / SAM2: point coordinates are in original pixel space; box prompts need enable_boxes=True / include_box_input=True when building the graph.
SAM2 in this port is image-only (no video memory bank).
SAM3: prefer SAM3InstanceSegment.predict(...) for text prompts; upstream facebook/sam3 is gated.
See SAM docs and Loading Weights.
Community / upstream safetensors still work via the hf: prefix, e.g. SAMPromptableSegment.from_weights("hf:facebook/sam-vit-huge").

Special Thanks

A huge thank you to the Meta Segment Anything authors for creating and releasing these models.

License: Apache 2.0.

kerasformers/sam_vit_huge