sam3-vessel-segmentation

ly17/sam3-vessel-segmentation

sam3-vessel-segmentation is a machine learning model from ly17. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

Fine-tuned SAM3 model for blood vessel angiography segmentation.

Downloads · 30 days

Access

Public

Updated Dec 8, 2025

Repo size

23.6 GB

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Public

Hugging Face

Repo makeup

Click a slice to open those files.

.pt23.6 GB · 100%

At a glance

Access: Public
Created: Dec 8, 2025
Updated: Dec 8, 2025
SHA: eda526e4

Model card

From the Hugging Face model README

SAM3 Blood Vessel Segmentation

Fine-tuned SAM3 model for blood vessel angiography segmentation.

Model Performance

Model	Dice	IoU	Recall
Original SAM3	0.00	0.00	0.00
Baseline (5 epochs)	0.79	0.66	0.73
Dice Optimized (10 epochs)	0.82	0.69	0.77
Dice Optimized + Post-processing	0.83	0.70	0.78

Files

checkpoint_dice_optimized.pt - Recommended - Dice optimized model
checkpoint_baseline.pt - Baseline fine-tuned model
sam3_original.pt - Original SAM3 weights

Usage

from huggingface_hub import hf_hub_download
import torch
from sam3.model_builder import build_sam3_image_model
from sam3.model.sam3_image_processor import Sam3Processor

# Download weights
checkpoint = hf_hub_download(
    repo_id="qimingfan10/sam3-vessel-segmentation",
    filename="checkpoint_dice_optimized.pt"
)

# Load model
model = build_sam3_image_model(
    checkpoint_path="path/to/sam3_original.pt",
    enable_segmentation=True,
    device="cuda"
)

# Load fine-tuned weights
ckpt = torch.load(checkpoint, map_location="cuda")
state_dict = {k.replace('module.', ''): v for k, v in ckpt['model'].items()}
model.load_state_dict(state_dict, strict=False)
model.eval()

# Inference
processor = Sam3Processor(model)
state = processor.set_image(image)
output = processor.set_text_prompt(state=state, prompt="blood vessel")
masks = output["masks"]