ly17/sam3-vessel-segmentation

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"]

Training

See VESSEL_SEGMENTATION_GUIDE.md for training details.

Citation

Please cite SAM3 if you use this model.