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
Download preprocessor_config.json from AImageLab-Zip/US_Cond-UNet: direct link, hf CLI and curl.
- Browser
- Download file 234 Bytes
-
https://huggingface.co/AImageLab-Zip/US_Cond-UNet/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://AImageLab-Zip/US_Cond-UNet/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/AImageLab-Zip/US_Cond-UNet/resolve/main/preprocessor_config.json
234 Bytes
| { | |
| "image_processor_type": "CondUNetImageProcessor", | |
| "image_size": 512, | |
| "keep_aspect_ratio": true, | |
| "mean": [ | |
| 123.675, | |
| 116.28, | |
| 103.53 | |
| ], | |
| "self_normalize": true, | |
| "std": [ | |
| 58.395, | |
| 57.12, | |
| 57.375 | |
| ] | |
| } | |