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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_sweep_v2
+
+A unet model for multilabel image segmentation trained with sliding window approach.
+
+## Model Description
+
+## Wandb Parameters
+
+| Parameter | Value |
+|-----------|-------|
+| data_path | GleghornLab/Semi-Automated_LN_Segmentation_10_11_2025 |
+| img_size | 128 |
+| downsample_factor | 1 |
+| num_channels | 3 |
+| batch_size | 16 |
+| lr | 1.7122348637490954e-05 |
+| epochs | 100 |
+| patience | 10 |
+| weight_decay | 8.29726636990404e-05 |
+| model_type | unet |
+| n_filts | 32 |
+| t | 3 |
+| k | 3 |
+| augment | False |
+| norm | True |
+| keep | 0.06990272917761037 |
+| pruning_factor | 0.019243240405735405 |
+| output_dir | pooled_metrics_hev_settings |
+| device | None |
+| num_workers | 4 |
+| prefetch_factor | 2 |
+| wandb_project | segmentation-sweep |
+| wandb_run_name | hev-only-repro-pooled |
+| wandb_mode | online |
+| push_to_hub | True |
+| hub_model_id | aholk/LN_segmentation_sweep_v2 |
+| skip_report | False |
+| sweep_mode | False |
+| num_params | 34527236 |
+| num_classes | 4 |
+
+## Model Parameters
+
+| Parameter | Value |
+|-----------|-------|
+| num_channels | 3 |
+| num_classes | 4 |
+| n_filts | 32 |
+| t | 3 |
+| k | 3 |
+| img_size | 128 |
+| norm | True |
+| model_arch | unet |
+| transformers_version | 5.9.0 |
+| architectures | ["UNetForSegmentation"] |
+| output_hidden_states | False |
+| return_dict | True |
+| dtype | float32 |
+| chunk_size_feed_forward | 0 |
+| is_encoder_decoder | False |
+| id2label | {"0": "LABEL_0", "1": "LABEL_1"} |
+| label2id | {"LABEL_0": 0, "LABEL_1": 1} |
+| problem_type | None |
+| _name_or_path |  |
+| batch_size | 16 |
+| downsample_factor | 1.0 |
+| model_type | segmentation |
+| output_attentions | False |
+
+## Performance Metrics
+
+| Metric | Mean | Class 0 | Class 1 | Class 2 | Class 3 |
+|--------|------|--------|--------|--------|--------|
+| Dice | 0.8169 | 0.7188 | 0.8196 | 0.8181 | 0.9112 |
+| IoU | 0.6961 | 0.5610 | 0.6943 | 0.6923 | 0.8369 |
+| F1 | 0.8169 | 0.7188 | 0.8196 | 0.8181 | 0.9112 |
+| MCC | 0.8124 | 0.7261 | 0.8171 | 0.8134 | 0.8928 |
+| ROC AUC | 0.9768 | 0.9726 | 0.9923 | 0.9535 | 0.9888 |
+| PR AUC | 0.8821 | 0.8046 | 0.8960 | 0.8652 | 0.9627 |
+
+
+## Usage
+
+```python
+import numpy as np
+from model import MODEL_REGISTRY, SegmentationConfig
+
+# Load model
+config = SegmentationConfig.from_pretrained("aholk/LN_segmentation_sweep_v2")
+model = MODEL_REGISTRY["unet"].from_pretrained("aholk/LN_segmentation_sweep_v2")
+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=128,
+    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)
+![IoU Curves](iou_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"
+  ],
+  "batch_size": 16,
+  "downsample_factor": 1.0,
+  "dtype": "float32",
+  "img_size": 128,
+  "k": 3,
+  "model_arch": "unet",
+  "model_type": "segmentation",
+  "n_filts": 32,
+  "norm": true,
+  "num_channels": 3,
+  "num_classes": 4,
+  "t": 3,
+  "transformers_version": "5.9.0"
+}

model.safetensors

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