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 requirements.txt from AImageLab-Zip/US_Cond-UNet: direct link, hf CLI and curl.
- Browser
- Download file 72 Bytes
-
https://huggingface.co/AImageLab-Zip/US_Cond-UNet/resolve/main/requirements.txt
- Command line
-
hf download hf://AImageLab-Zip/US_Cond-UNet/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/AImageLab-Zip/US_Cond-UNet/resolve/main/requirements.txt
72 Bytes
| transformers>=4.40 | |
| torch | |
| torchvision | |
| safetensors | |
| einops | |
| ptwt | |
| wandb | |
| peft | |