Image Segmentation
Transformers
Safetensors
cond_unet
ultrasound
medical-image-segmentation
attention-unet
custom-pipeline
custom_code
Instructions to use AImageLab-Zip/US_Cond-UNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AImageLab-Zip/US_Cond-UNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="AImageLab-Zip/US_Cond-UNet", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("AImageLab-Zip/US_Cond-UNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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To reproduce the original evaluation pipeline for a Fetal HC image, use
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`organ_id=4`.
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## Output
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Returns a binary PIL foreground mask after sigmoid thresholding at `0.7`.
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```python
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result = segmenter("ultrasound.png", threshold=0.7)
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```
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## Preprocessing
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RGB images are aspect-ratio resized, black-padded to 512 x 512, then z-score
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normalized using non-black pixels.
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## Limitations
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Research only; not a clinical diagnostic device.
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To reproduce the original evaluation pipeline for a Fetal HC image, use
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`organ_id=4`.
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