| --- |
| license: mit |
| tags: |
| - image-segmentation |
| - multilabel |
| - unet |
| - pytorch |
| - medical-imaging |
| library_name: transformers |
| pipeline_tag: image-segmentation |
| --- |
| |
| # LN_segmentation_sweep |
|
|
| A unet model for multilabel image segmentation trained with sliding window approach. |
|
|
| ## Model Description |
|
|
| - **Architecture:** unet |
| - **Input Channels:** 3 |
| - **Output Classes:** 4 |
| - **Base Filters:** 128 |
| - **Window Size:** 128 |
| - **Downsample Factor:** 1.0 |
|
|
| ### Model-Specific Parameters |
|
|
|
|
| ## Training Configuration |
|
|
| | Parameter | Value | |
| |-----------|-------| |
| | Batch Size | 8 | |
| | Learning Rate | 7.77451918775676e-06 | |
| | Weight Decay | 0.00164040349077736 | |
| | Epochs | 100 | |
| | Patience | 10 | |
| | Dataset | GleghornLab/Semi-Automated_LN_Segmentation_10_11_2025 | |
| |
| ## Performance Metrics |
| |
| No test metrics available. |
| |
| ## Usage |
| |
| ```python |
| import numpy as np |
| from model import MODEL_REGISTRY, SegmentationConfig |
|
|
| # Load model |
| config = SegmentationConfig.from_pretrained("aholk/LN_segmentation_sweep") |
| model = MODEL_REGISTRY["unet"].from_pretrained("aholk/LN_segmentation_sweep") |
| 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) |
| ``` |
| |
| |
| ## 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} |
| } |
| ``` |
| |
