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README.md

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+---
+license: mit
+tags:
+  - image-segmentation
+  - multilabel
+  - unet
+  - pytorch
+  - medical-imaging
+library_name: transformers
+pipeline_tag: image-segmentation
+---
+
+# LN_segmentation
+
+A unet model for multilabel image segmentation trained with sliding window approach.
+
+## Model Description
+
+- **Architecture:** unet
+- **Input Channels:** 3
+- **Output Classes:** 4
+- **Base Filters:** 32
+- **Window Size:** 256
+
+### Model-Specific Parameters
+
+
+## Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Batch Size | 64 |
+| Learning Rate | 0.0003 |
+| Weight Decay | 0.01 |
+| Epochs | 100 |
+| Patience | 10 |
+| Dataset | GleghornLab/Semi-Automated_LN_Segmentation_10_11_2025 |
+
+## Performance Metrics
+
+| Metric | Mean | Class 0 | Class 1 | Class 2 | Class 3 |
+|--------|------|--------|--------|--------|--------|
+| Dice | 0.5196 | 0.1800 | 0.2978 | 0.7189 | 0.8819 |
+| IoU | 0.4059 | 0.0989 | 0.1749 | 0.5612 | 0.7887 |
+| F1 | 0.5196 | 0.1800 | 0.2978 | 0.7189 | 0.8819 |
+| MCC | 0.5044 | 0.1730 | 0.2861 | 0.7032 | 0.8554 |
+| ROC AUC | 0.8338 | 0.6482 | 0.7772 | 0.9252 | 0.9847 |
+| PR AUC | 0.4846 | 0.0767 | 0.1807 | 0.7583 | 0.9227 |
+
+
+## Usage
+
+```python
+import numpy as np
+from model import MODEL_REGISTRY, SegmentationConfig
+
+# Load model
+config = SegmentationConfig.from_pretrained("aholk/LN_segmentation")
+model = MODEL_REGISTRY["unet"].from_pretrained("aholk/LN_segmentation")
+model.eval()
+
+# Run inference on a full image with sliding window
+image = np.random.rand(2048, 2048, 3).astype(np.float32)  # Your image here
+probs = model.predict_full_image(
+    image,
+    dim=256,
+    batch_size=16,
+    device="cuda"  # or "cpu"
+)
+# probs shape: (num_classes, H, W) with values in [0, 1]
+
+# Threshold to get binary masks
+masks = (probs > 0.5).astype(np.uint8)
+```
+
+## Training Plots
+
+![Training Loss](training_loss.png)
+![Dice Curves](dice_curves.png)
+![MCC Curves](mcc_curves.png)
+![Best Validation](best_validation_reconstruction.png)
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@software{windowz_segmentation,
+  title={Multilabel Image Segmentation with Sliding Window U-Net},
+  author={Gleghorn Lab},
+  year={2025},
+  url={https://github.com/GleghornLab/ComputerVision2}
+}
+```

config.json

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+{
+  "architectures": [
+    "UNetForSegmentation"
+  ],
+  "dtype": "float32",
+  "img_size": 256,
+  "k": 2,
+  "model_arch": "unet",
+  "model_type": "segmentation",
+  "n_filts": 32,
+  "norm": false,
+  "num_channels": 3,
+  "num_classes": 4,
+  "t": 2,
+  "transformers_version": "5.2.0"
+}

model.safetensors

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