---
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}
}
```