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.data import Sentence from flair.models import SequenceTagger tagger = SequenceTagger.load("Bawil/wmh_leverage_normal_abnormal_segmentation") sentence = Sentence("George Washington went to Washington.") tagger.predict(sentence) print(sentence) - Keras
How to use Bawil/wmh_leverage_normal_abnormal_segmentation with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Bawil/wmh_leverage_normal_abnormal_segmentation") - Notebooks
- Google Colab
- Kaggle
Download unet/figures/comparison_visualization.pdf from Bawil/wmh_leverage_normal_abnormal_segmentation: direct link, hf CLI and curl.
- Browser
- Download file 3.44 MB
-
https://huggingface.co/Bawil/wmh_leverage_normal_abnormal_segmentation/resolve/main/unet/figures/comparison_visualization.pdf
- Command line
-
hf download hf://Bawil/wmh_leverage_normal_abnormal_segmentation/unet/figures/comparison_visualization.pdf
-
curl -L -o comparison_visualization.pdf https://huggingface.co/Bawil/wmh_leverage_normal_abnormal_segmentation/resolve/main/unet/figures/comparison_visualization.pdf
3.44 MB
- Xet hash:
- c55d1782aeb15d887cb51863ef5f69b1ebb231ee891ea27f6ff19efde4cc1581
- Size of remote file:
- 3.44 MB
- SHA256:
- f862d272fb899e9ec2bae3ca94e0510c74ed050cad7b36ef79cd80bc2d8ebc57
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