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README.md

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+# 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
+
+```python
+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](https://github.com/qimingfan10/Sam3/blob/main/VESSEL_SEGMENTATION_GUIDE.md) for training details.
+
+## Citation
+
+Please cite SAM3 if you use this model.