Image Segmentation
Flair
Keras
tensorflow
medical-imaging
white-matter-hyperintensities
mri
deep-learning
neurology
multiple-sclerosis
Instructions to use Bawil/wmh_leverage_normal_abnormal_segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Flair
How to use Bawil/wmh_leverage_normal_abnormal_segmentation with Flair:
from flair.models import SequenceTagger tagger = SequenceTagger.load("Bawil/wmh_leverage_normal_abnormal_segmentation") - Keras
How to use Bawil/wmh_leverage_normal_abnormal_segmentation with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Bawil/wmh_leverage_normal_abnormal_segmentation") - Notebooks
- Google Colab
- Kaggle
Download unet/figures/training_curves.pdf from Bawil/wmh_leverage_normal_abnormal_segmentation: direct link, hf CLI and curl.
- Browser
- Download file 21.6 kB
-
https://huggingface.co/Bawil/wmh_leverage_normal_abnormal_segmentation/resolve/f4d9c79455fcf4b46331a1ac32fcc23a0646afd4/unet/figures/training_curves.pdf
- Command line
-
hf download hf://Bawil/wmh_leverage_normal_abnormal_segmentation@f4d9c79455fcf4b46331a1ac32fcc23a0646afd4/unet/figures/training_curves.pdf
-
curl -L -o training_curves.pdf https://huggingface.co/Bawil/wmh_leverage_normal_abnormal_segmentation/resolve/f4d9c79455fcf4b46331a1ac32fcc23a0646afd4/unet/figures/training_curves.pdf
21.6 kB